Reviewerly/exHarmony huggingface dataset:
Leveraging Feature Headers to Learn Sparse and Semantically Pertinent Linear Models

Readily available data and software tools have turned "analytics" into a game anyone can play. But genuine, serious modeling demands prudence: domain experts routinely use their knowledge to assess the relevance of various input features and to be judicious with model selection criteria. While engaged in analysis, they marshal knowledge to consider the meaning of the data involved. Seeking to automate and reproduce such aspects, the present paper proposes a framework that makes use of semantics latent within given feature headers to help produce sparse and semantically pertinent linear models, rather than exploiting mere correlations or (potentially spurious) patterns. This framework enables a model builder to employ both features’ data and certain semantic information derived from their headers to search for an optimal feature subset in order to improve generalization of a linear model being built. To do so, a characteristic called "semantic inconsistency" is formulated in order to quantify the degree of conflict between weights learned from data and the amount of relationship between a set of input features and the output being predicted in the semantic space. Using this quantity, semantic information can be incorporated into a regularization procedure in a manner that is quite general and may be computed from any form of background knowledge. The results obtained from validating the framework with four datasets indicate that taking the semantics of features into account can improve model generalization: the approach is shown to perform better than classic linear regression and regularization techniques that consider only complexity of learned models.
Mae Mai Muay Thai Layered Classification Using CNN and LSTM Models

In competitive physical sports such as boxing, analytics on a boxer's efficiency, particularly the number and kind of punches delivered, offer information and feedback commonly utilized for performance and coaching enhancement. In this paper, we look at the challenge of recognizing Mae Mai Muay Thai (MMM-Thai) actions in still imagery. By activity recognition, we mean a collection of problems that encompasses both action categorization and action recognition. Bag-of-words picture representations do a great job of classifying actions, while deformable component models do a great job of recognizing objects. Action recognition representations often employ shape cues and omit color information. This research proposes a comprehensive framework for automated MMM-Thai style classification. MMM-Thai recognition is tackled using Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) classifiers. This framework was employed to analyze MMM-Thai boxing picture sequences. Experiments were carried out using the MMM-Thai dataset with four professional boxers. The findings provide evidence that the strategy that was presented was successful. The combination of CNN and LSTM classifiers achieved an accuracy of 99%, indicating that they are appropriate for analyzing boxers' techniques during competition. Finally, we will evaluate the model's overall effectiveness using a confusion matrix. To evaluate the performance of our model, we also utilize the ROC Receiver Operating Characteristics (ROC) curve and Area Under the Curve (AUC). Accuracy, precision, recall, and the F1-score performance indicators were also used in the analysis.
Empirical Study of U-Net Based Models for 2D Face Recovery from Face-Mask Occlusions

Human face related digital technologies have been widely applied in various fields including face recognition based biometrics, facial landmarks based face deformation for gaming, facial reconstruction for those who are disfigured from an accident in the medical field and others. Such technologies typically rely on the information of a full, uncovered face and their performance would suffer varying degrees of deterioration according to the level of facial occlusion exhibited. 2D face recovery from occluded faces has therefore become an important research area as it is both crucial and desirable to attain full facial information before it is used in downstream tasks. In this paper, we address the problem of 2D face recovery from facial-mask occlusions, a pertinent issue that is widely observed in situations such as the Covid-19 pandemic. In recent trends, most researches are carried out through deep learning techniques to recover masked faces. The whole process consists of two tasks which are image segmentation and image inpainting. As U-Net is a typical deep learning model for image segmentation, but it also helpful in image inpainting and image colorization, so it has been frequently used in solving face recovery problems. To further explore the capability of U-Net and its variants for face recovery from masked faces, we propose to conduct a comparative study on several U-Net based models on a synthetic dataset that was generated based on public face datasets and mask generator. Results showed that Resnet U-Net and VGG16 U-Net had performed better in face recovery among the six different U-Net based models.
Isarn Dialect Word Segmentation using Bi-directional Gated Recurrent Unit with transfer learning approach

This paper presents an Isarn dialect word segmentation based on a recurrent neural network. In this study, the Isarn text written in Thai script is taken as input. We explored the effectiveness of the types of recurrent layers; recurrent neural networks (RNN), gated recurrent units (GRU), and long short-term memory (LSTM). The F1-scores of RNN, GRU, and LSTM are 95.36, 96.05, and 95.70, respectively. The experiment results showed that using GRU as the recurrent layer achieved the best performance. To deal with borrowed words from Thai, transfer learning was applied to improve the performance of the model by fine-tuning the pre-trained model given the limited size of the Isarn corpus. The model trained through the transfer learning approach outperformed the model trained from the Isarn dataset alone.
Thai Privacy Notice Analysis Based On Named-Entity Recognition Technique

With the spread of global technology and the rising value of personal data, privacy has become a major problem. Many countries have enacted privacy laws to protect their citizens' privacy. Thailand has a similar law known as the Personal Data Protection Act (PDPA). According to the PDPA, the data controllers must provide data subjects with information about handling of personal data which is generally referred to as privacy notices. A privacy notice is typically a comprehensive document that uses formal language. As a result, many customers find it difficult to comprehend essential information in a short time. One of Natural Language Processing (NLP) methods, Named-Entity Recognition (NER), is a technique that can be used to capture the main contents of a document. The feasibility of NLP techniques for extracting significant privacy practices regarding the PDPA requirements for Thai privacy notices, however, has not yet been explored. Therefore, this study explores this feasibility and proposes a dataset of Thai privacy notices and a privacy annotation scheme based on PDPA requirements. The effectiveness of NLP approaches in extracting Thai privacy practice information is also evaluated in this study. The privacy notices comprehension has been formulated into the problem of Thai privacy information extraction, Thai privacy NER. The results show that the pre-trained transformer model outperforms the traditional method, and the increasing training dataset can affect higher performance value.
Rugby Goal Kick Prediction Using OpenPose Coordinates and LSTM

The goal kick is the most delicate play in rugby. To train athletes, giving accurate instructions is difficult because each player’s form is different. Furthermore, in Japan, rugby goal posts are only installed in a few areas, so athletes who do not have access to a goal post have limited possibilities to perform practical kicks, and goal kick practice is insufficient. As a result, we used Long Short-Term Memory (LSTM) to create a goal prediction model from goal-kick videos to provide the player with feedback. We also attempted to determine which parts of the body are crucial criteria for scoring. This paper addresses only goal kicks/conversion kicks and penalty goal kicks made from stationary locations, not kicks made while the ball was in play such as the drop-kick. According to the findings, the model built using domain expertise was just as precise as the model built using all joint data. This result proved that the right knee and ankle of the kicking leg, as well as the positions of the right eye and shoulder, are crucial elements in determining a successful kick.
Weakly Supervised Chinese Short Text Classification Algorithm Based on ConWea Model

The version sort algorithm based on full supervision needs to use a large amount of label data, and the labeling task of text data is time-consuming and difficult to label. Finally, use high-quality pseudo-label data to train a Chinese brief version systematics model. Experiment on the THUCNews news headlines dataset. The test outcome illustrate the performance of this algorithm is slightly stronger than the mainstream semi-supervised classification algorithm when only a small amount of label data is used, and it is not inferior to the general fully supervised classification algorithm. which provides a better solution for unlabeled data classification tasks.
Analysis of Pipa Performance Based on Image Database

Pipa is a traditional Chinese musical instrument, which is the integration of Chinese unique cultural flavor and artistic charm. Pipa has normative and elegant style, which can improve the quality of national civilization and spiritual and cultural needs. However, Pipa technique is a complex skill which needs high intensity training, which will directly affect the systematization of pipa playing technique. Pipa performance art is an extension of Chinese traditional culture, which has rich emotion and artistic effect. Therefore, we must inherit the pipa performance art, which is the precious wealth left by the traditional culture. Pipa playing skills can be divided into left-hand and right-hand techniques, which is a relatively complex playing techniques. Therefore, there are many difficulties in the inheritance of pipa playing technology. With the rapid development of computer, we can use computer-aided Pipa performance technology inheritance and innovation. First of all, this paper analyzes the importance of computer aided. Then, a computer aided method is proposed. Finally, some suggestions are put forward.
Adaptive Learning Rate For Neural Network Classification Model

Imbalanced data cause prediction inaccuracy of the classification model. Two types of techniques have been devised to address this problem: pre-processing data before training a classification model and adjusting the classification algorithm. This study, which introduced the adaptive learning rate into a backpropagation neural network algorithm, is of the latter type. The learning rate was adjusted in each iterative learning cycle: the learning rate is increased for the data class with fewer samples and decreased for the data class with more samples. K-fold cross-validation was used to test the effectiveness of the prediction model on 10 datasets. The results showed that the proposed ZMP algorithm outperformed the original backpropagation neural network on 6 datasets; the improvement ranged from 2.24% to 20.22%. Moreover, on the other 4 datasets, even though the proposed technique provided less accurate predictions, the differences were very slight.
G-AUC: An improved metric for classification model selection

The performance of classification models is often measured using the metric, area under the curve (AUC). The non-parametric estimate of this metric only considers the ranks of the test instances and fails to consider the predicted scores of the model. Consequently, not all the valuable information about the model’s output is utilized. To address this issue, the present paper introduces a new metric, called Gamma AUC (G-AUC) that can take into account both ranks as well as scores. The parameter G tackles the problem of overfitting scores into the metric. To validate the proposed metric, we tested it on 20 UCI datasets with 10 state-of-the-art models. Out of all the values of the parameter G that we tested, four of them got p-value less than 0.05 for the alternative hypothesis that, on the training sets, G-AUC has a greater correlation than AUC itself, with AUC on test sets. Furthermore, for all values of G considered, G-AUC always won majority of the times than AUC for selecting better models.
Pre-processing techniques for Tamil online handwritten character recognition

In the modern era, due to the rise of the information technology field, human-computer interfacing devices such as a tablet, pc, and smartphones are becoming more popular [1]. This technological advancement led to the advancement of online handwriting character identification system-based applications. Handwritten characters are recorded in digital form by writing on an electronic surface with a digital pen/stylus. Online handwriting recognition systems face several challenges, out of which some could be tackled in the pre-processing part and few can be resolved in the post-processing part. In this paper, the pre-processing steps for the Tamil handwritten character recognition have been addressed. Due to the high cursiveness property and similar shape of some characters in the Tamil script, it is necessary to work on these challenges at the pre-processing level. Therefore, the pre-processing techniques discussed in this study are; duplicate point removal, size-normalization, missing point interpolation, and points resampling. A total of 10 Tamil consonants samples from the hpl-Tamil-iso-char dataset have been utilized which was collected by HP Labs.
A Comparative Analysis of Deep-Learning Models with Novel Hybrid Biometric Modality Deep-Learning Network (BIOMODEN) to cognize Classification Accuracy of Fused Biometric Image

Image processing through feature identification and classification have entailed various phases of algorithmic effectuation. However, the need to scrupulously diverge into the precise stratification of feature identification has always remained a challenge. Deep-learning models in the recent times, have bolstered the accuracy of classification through the various networks and layered-models that have been established. However, this paper strives to render a bimodal approach of biometric image processing, along with establishing an elaborate indagation on the juxtaposed analysis of biometric image classification through transfer learning from the pre-trained models such as Darknet-19 and GoogleNet. The research also delves into the implementation of classification accuracy through hybrid contrived network model called the Biometric Modality Deep-Learning Network (BIOMODEN) which further augments the emphasis to comprehend and effectively procure the zenith of classification accuracy for specifically incorporated biometric fused modality images. The implementation of this study is carried out in MATLAB, and the results are successfully obtained.
Movie Recommendation System Using Director-Based

A recommendation system saves the user the time and effort of searching for information by analyzing their profile and recommending the most appropriate content. To perform recommendations, a variety of techniques have been proposed, including content-based, collaborative, and hybrid filtering. Recommendation systems are used to suggest content such as books, music, and movies. The film business, in particular, makes movie recommendations using collaborative filtering that is based on genres and is frequently utilized in film recommendation systems. When customers first come across movie suggestion services or have certain movie interests, such as preferences for directors, this method may not work as well. This inspired us to propose a director-based recommendation system that uses content-based filtering and takes into account the genres of 5,000 records of Kaggle movie data as well as information on the filmographies of the directors. The cosine similarity function is used to assess the effectiveness and performance of the recommended system, and the results are very satisfactory.
Contextual Data Modeling for Recommender System in Building and Construction Materials Business

The recommendation system is one of the most important supported technologies to e-commerce that aims for recommending products or services to increase customer’s satisfaction. In this paper, we propose the method for Contextual Data Modeling as an improved version of Hybrid Filtering to introduce the context-awareness in the building and construction materials business. The recommendation along with e-commerce system are built, deployed, and tested in the real situation. The evaluation score is up to 97.8 compared to baseline.
Comparison of Image Interpolation Methods for Image Zooming

The primary approach in the realm of digital image processing is zooming. Image When using zooming, a high-quality image is required in order to enlarge the image to the specified magnification factor [1]. When enlarging images, or zooming in, digital image interpolation techniques are commonly employed. They are also becoming more and more popular for creating product applications with digital still cameras. While shrinking is considered under-sampling, zooming is considered over-sampling. In this study, we upscale the image using linear methods and contrast them against one another. However, all of these techniques have problems with blocking, aliasing, blurring, and other artefacts. We'll try to get beyond the objects indicated above.
Creative Gameplay of Stealth-Action Game with Unusual Game Mechanics “Metamorph”

The presented paper is discussing the gameplay of “Metamorph” game concept. The “Metamorph” is a stealth-action game with various game mechanics, based on previous games or brand-new mechanics, such as hacking mechanic and transformation mechanic. The game also provides various types of enemies, so there will be great choice for player to transform. Paper provides the environment and items mechanics as well. This paper discusses all of the mechanics stated above, to see if it can provide the smooth flow of the gameplay. For this purpose, there are characters, items and environment mechanics written down in this paper. The flow of the gameplay will be ensured based on the one of the game level. For this reason, paper provides simple walkthrough of the game level, so it will be like simulation of the way game mechanics will work in actual game. This game flow simulation will be displayed with bubble diagram.
Quality-Aware Downlink Scalable Video Coding Multicasting using Non-Orthogonal Multiple Access in CRN Network

We suggest a fuzzy logic-based scheduling system that prioritises the transmission to various users by taking into account both the media content and the network's circumstances. Additionally, in order to enhance the quality of experience (QoE) that downstream users receive, an analytical model and statistical approach have been created for the aim of performing a quantitative study of the proposed scheduling algorithm for downlink SVC multicasting over CRN. In terms of the average weighted sum rate and the average quality of experience (QoE) of SUs, extensive simulation demonstrates that our proposed power allocation scheme for NOMA with QoS constraint outperforms the other two conventional schemes, namely NOMA fixed power allocation and NOMA without QoS constraint. Our research of how our suggested system stacks up against the two traditional methods led us to this conclusion.
An authenticated University file system using EdDSA

People have shifted to online transactions for various reasons and ease. Sending documents through e-mail is more accessible than handing over a printed document personally or through other courier services. In university, a vast amount of data is generated and transferred across the students and faculty. The authentication of these files is essential as there is a chance of modifying these files by active adversaries. A digital signature could be presented as a mathematical plan to demonstrate the authenticity of a message or file sent. A Digital sign is the checksum that depends upon the time it was produced. A significant difference between digital signatures and handwritten signatures is that a digital signature can never remain constant. It changes even if a single character of the message is changed, because of which it isn't straightforward to be forged. This paper mainly focuses on securing the university files stored in the cloud using the Edward Curve Digital signature.
An Agile Review of Machine Learning Technique

Machine learning is a widely known terminology for solve real world problems and provide automatic problem solving apprroach. Machine can learn based on the previous data and past experiences rather then explicit programing. Machine learning offer a powerful role in digital era for solving complex real life problems. In this paper, review of most popular machine learning algorithm are provided, pros and cons of machine learning algorithm also explained with their application. How to select appropriate learning algorithm for solving complex problem of real world also explained.comparision of machine learning techinques with one another also discuss for solving future generation problems.
Detecting On-The-Fly Side-Channel Key-Recovery Attacks and Measuring Security of Symmetric Encryption Schemes

A “valid padding” oracle was recently used by Vaudenay to show Side-channel attacks against common CBC mode ciphers algorithm. He demonstrated that symmetric encryption techniques are equally susceptible to side-channel attacks as asymmetric systems are by Mirrored Side Channel Attacks by Bleichenbacher and Manger. when a foe is able to differentiate between vulnerabilities cipher - texts that are both legitimate and illegitimate. Your study shows that when the integrity of cipher - texts is not ensured, such attacks are widespread. Then, by taking into account different padding methods, symmetric encryption techniques, and side channels, we expand the attack in numerous directions and provide assaults of varying potency against each. Finally, we make the case that requiring the Cipher - text Integrity [BN00]. Along with semantic safety being the “correct” data protection term for symmetric cryptography is the best method to defend against all of these assaults.
Research on Situational Awareness System Big Data Analysis

Situational awareness assessment system is a comprehensive subject, involving a large number of activities. In order to influence factors of scientific and rational assessment of the situational awareness, this paper mainly uses method to construct the optimization genetic algorithm for collaborative model, analysis the influence weight of situational awareness, influencing factors and mutual influence between them. In order to cope with the complexity of the cooperative system, this paper studies the establishment of an executable model system, mainly for system construction, system debugging, test system design, implementation and deployment.