Reviewerly/exHarmony huggingface dataset:
Breast Lesions Screening of Mammographic Images with 2D Spatial and 1D Convolutional Neural Network-Based Classifier

Mammography is a first-line imaging examination that employs low-dose X-rays to rapidly screen breast tumors, cysts, and calcifications. This study proposes a two-dimensional (2D) spatial and one-dimensional (1D) convolutional neural network (CNN) to early detect possible breast lesions (tumors) to reduce patients’ mortality rates and to develop a classifier for use in mammographic images on regions of interest where breast lesions (tumors) may likely occur. The 2D spatial fractional-order convolutional processes are used to strengthen and sharpen the lesions’ features, denoise, and improve the feature extraction processes. Then, an automatic extraction task is performed using a specific bounding box to sequentially pick out feature patterns from each mammographic image. The multi-round 1D kernel convolutional processes can also strengthen and denoise 1D feature signals and assist in the identification of the differentiation levels of normality and abnormality signals. In the classification layer, a gray relational analysis-based classifier is used to screen the possible lesions, including normal (Nor), benign (B), and malignant (M) classes. The classifier development for clinical applications can reduce classifier’s training time, computational complexity level, computational time, and achieve a more accurate rate for meeting clinical/medical purpose. Mammographic images were selected from the mammographic image analysis society image database for experimental tests on breast lesions screening and K-fold cross-validations were performed. The experimental results showed promising performance in quantifying the classifier’s outcome for medical purpose evaluation in terms of recall (%), precision (%), accuracy (%), and F1 score.
Research on the Strategic Management of Local Universities under the Background of Comprehensive Reform of Universities

How to promote the high-quality development of universities from the strategic level is a hot and difficult problem in theoretical research under the background of comprehensive reform of universities in my country. This paper firstly analyzes the outstanding problems and causes of the strategic management of local universities, and then analyzes the conditions for the effective formulation of the school&amp;#39;s strategic management, and puts forward the strategies for the effective implementation of the school&amp;#39;s strategy. On the other hand, it also provides methodological guidance for the practice of school strategic management.
An Extensive Literature Review on the Predominant Conceptualizations of Quality in Higher Education and Its Main Components

Given the multidimensional nature of the notion of quality and its growing relevance in higher education, it is necessary to lay the foundations for conceptualizing it in terms of its context of application, in order to provide support and consistency to the design of specific policies. This paper presents the main methodological guidelines and findings of an exhaustive literature review focused on identifying the conceptions of quality in higher education in papers published in high-impact journals between 2016-2020. Following the axial guidelines of the PRISMA-P method, 186 articles were selected out of 53,290 identified as part of the initial universe. Using open deductive coding, prevailing conceptions of quality of higher education and its valued components were identified. A crucial question arose as a consequence of the analysis of those articles: who is responsible for determining the quality of a product or service in higher education? The answer to this question gave rise to the emergence of an alternative theoretical and conceptual positioning to those that predominate in the specialized literature.
Forecast of Rice Reserves in Special Region of Yogyakarta

Food is one of the basic human needs. A priority food commodity is rice. The Special Region of Yogyakarta Province is one of the areas that has agricultural land that is able to supply the food needs in the area. However, the population in the Special Region of Yogyakarta is growing annually, having an impact on the food needs that have to be met. The study aimed to determine the food reserves, especially rice reserves, in the Special Region of Yogyakarta. This study used a descriptive analytical method and ARIMA forecasting model. The study used time series data from 2011 to 2021 obtained from the dataku application managed by BAPPEDA of the Special Region of Yogyakarta. The data consisted of rice production data, per capita consumption data, and population data. The results showed that the rice reserves in the Special Region of Yogyakarta from 2011 to 2021 decreased over years. Based on the forecasting results, the best model was ARIMA c(9,1,6). In addition, the result of the rice reserve forecast from 2022 to 2025 also showed a decreasing trend from year to year. Therefore, it is crucial for the government to make efforts to maintain food availability to achieve local food security.Keywords: ARIMA, Food Reserves Rice, Forecasting, Special Region of Yogyakarta
On Computing Probabilistic Explanations for Decision Trees

Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models. Inside formal XAI, one of the most studied cases is that of explaining the choices taken by decision trees, as they are traditionally deemed as one of the most interpretable classes of models. Recent work has focused on studying the computation of "sufficient reasons", a kind of explanation in which given a decision tree $T$ and an instance $x$, one explains the decision $T(x)$ by providing a subset $y$ of the features of $x$ such that for any other instance $z$ compatible with $y$, it holds that $T(z) = T(x)$, intuitively meaning that the features in $y$ are already enough to fully justify the classification of $x$ by $T$. It has been argued, however, that sufficient reasons constitute a restrictive notion of explanation, and thus the community has started to study their probabilistic counterpart, in which one requires that the probability of $T(z) = T(x)$ must be at least some value $\delta \in (0, 1]$, where $z$ is a random instance that is compatible with $y$. Our paper settles the computational complexity of $\delta$-sufficient-reasons over decision trees, showing that both (1) finding $\delta$-sufficient-reasons that are minimal in size, and (2) finding $\delta$-sufficient-reasons that are minimal inclusion-wise, do not admit polynomial-time algorithms (unless P=NP). This is in stark contrast with the deterministic case ($\delta = 1$) where inclusion-wise minimal sufficient-reasons are easy to compute. By doing this, we answer two open problems originally raised by Izza et al. On the positive side, we identify structural restrictions of decision trees that make the problem tractable, and show how SAT solvers might be able to tackle these problems in practical settings.
Android Based Educational Game Development to Improve Early Childhood Reading Ability

This research is motivated by the sophistication of technology to be developed in early childhood learning to improve children's reading skills. Reading is one of the skills retained by early childhood. The ability to read can facilitate children's learning at the subsequent level of education. This study aims to improve children's reading skills through android-based educational games. This study employs the type of research R D (Research and Development) with the ADDIE model, which refers to Analysis, Design, Development, Implementation, and Evaluation. Data collection techniques used a questionnaire. The results obtained from the development of children's reading skills through android-based educational games were declared valid, practical, and effective. The data collection instrument in this study was a questionnaire to test the validity, practicality, and effectiveness. The validation results of educational games were declared valid and very feasible to use with the means of Aiken's V by material experts was 0.89. The means of Aiken's V by media experts was 0.93. The Practicality of the Android-based Educational Game is highly feasible and has an average percentage result of 89%. The effectiveness is 91%. Therefore, educational games are declared effective. Hence, android-based educational games to improve children's reading skills in Raudhatul Atfhal are valid, practical, and effective.
Multimodal Image Synthesis and Editing: The Generative AI Era

As information exists in various modalities in real world, effective interaction and fusion among multimodal information plays a key role for the creation and perception of multimodal data in computer vision and deep learning research. With superb power in modeling the interaction among multimodal information, multimodal image synthesis and editing has become a hot research topic in recent years. Instead of providing explicit guidance for network training, multimodal guidance offers intuitive and flexible means for image synthesis and editing. On the other hand, this field is also facing several challenges in alignment of multimodal features, synthesis of high-resolution images, faithful evaluation metrics, etc. In this survey, we comprehensively contextualize the advance of the recent multimodal image synthesis and editing and formulate taxonomies according to data modalities and model types. We start with an introduction to different guidance modalities in image synthesis and editing, and then describe multimodal image synthesis and editing approaches extensively according to their model types. After that, we describe benchmark datasets and evaluation metrics as well as corresponding experimental results. Finally, we provide insights about the current research challenges and possible directions for future research. A project associated with this survey is available at https://github.com/fnzhan/Generative-AI.
Intelligent Optimization Algorithm of 3D Tracking Technology in Football Player Moving Image Analysis

Computer vision technology began to affect the development of football. There is increasingly high-tech in football broadcast technology, and many application tools have emerged in the field of football broadcast video analysis. The purpose of this paper is to study the improvement of target tracking algorithm for football broadcast video and to study the intelligent optimization algorithm of 3D tracking technology in football player moving image analysis. This paper proposes to select four models of YOLOv5 to perform target detection experiments in football broadcast videos and analyzes the principle of the Deep SORT multitarget tracking algorithm. At the same time, it is based on the 3D tracking and 3D pose estimation of players based on cross-view correlation matching, and to measure the comprehensive performance of the tracker in the football scene, experiments are carried out on the accuracy and speed of the tracker under the football datasets of four different scenes. The experimental results in this paper show that the MOTA values corresponding to the 3D tracking results and 2D projection results obtained in the campus dataset are only 50 and 56.2. This is much lower than the tracking performance when based on other similarity matrices. The MOTA value of the obtained tracking result (92.6) is very close and significantly outperforms other methods. CCOT performs better on datasets 28, 29, 31, and 32, ECO stands out on dataset 38, and Siamese also performs well on datasets 22 and 36.
A Novel Hyperparameter Optimization Aided Hand Gesture Recognition Framework Based on Deep Learning Algorithms

The recognition of hand gestures in cluttered or complex environments is a vital research area in the human-computer interaction and computer vision fields due to its various potential applications, such as hand action analysis, driver hand behaviour monitoring, virtual reality, pose estimation, human action recognition, and sign language recognition. In order to create more reliable and efficient algorithms in this research field, various approaches have been suggested in recent years. However, a robust system is still elusive. For this reason, a new deep learning-based architecture for classifying hand gestures is suggested in this study; it is based on a hybrid model. The study makes two main contributions to the literature. The first is the creation of a new database for hand gesture recognition. The second is a novel hybrid architecture that combines two widely used pre-trained network models in an optimised manner, using a genetic algorithm for hyperparameter optimization. The proposed method comprises five main phases, namely, data acquisition, pre-processing, the design of the deep neural network architecture, hyperparameter optimization, the training of the proposed deep neural network architecture. The proposed method was tested on three comprehensive datasets. The experimental results reveal that the suggested method can effectively classify hand gestures with a high accuracy rate and that it outperforms the state-of-the-art methods in the literature.
Pengembangan Media Big Book Untuk Meningkatkan Kemampuan Membaca Siswa Kelas Awal MINU Ngingas Waru Sidoarjo

The purpose of this study was to develop big book media to improve the reading skills of early grade students at Madrasah Ibtidaiyah Nahdlatul Ulama (MINU) Ngingas Waru Sidoarjo. The research method used is development research developed by Borg and Gall. There are four stages of development carried out in this study, namely: initial data collection, planning, development, and validation and testing. The results showed that the students' reading skills were very good. In the initial data of 39% reading skills and when intervened using big book media in the third trial stage, data obtained 85 percent of the average reading skills of students. So it can be concluded that the effectiveness of using big books is very good with a 46 percent increase in the level of reading skills of early grade students who have reading difficulties.
Better together

Entity linking (EL) is the task of automatically identifying entity mentions in text and resolving them to a corresponding entity in a reference knowledge base like Wikipedia. Throughout the past decade, a plethora of EL systems and pipelines have become available, where performance of individual systems varies heavily across corpora, languages or domains. Linking performance varies even between different mentions in the same text corpus, where, for instance, some EL approaches are better able to deal with short surface forms while others may perform better when more context information is available. To this end, we argue that performance may be optimised by exploiting results from distinct EL systems on the same corpus, thereby leveraging their individual strengths on a per-mention basis. In this paper, we introduce a supervised approach which exploits the output of multiple ready-made EL systems by predicting the correct link on a per-mention basis. Experimental results obtained on existing ground truth datasets and exploiting three state-of-the-art EL systems show the effectiveness of our approach and its capacity to significantly outperform the individual EL systems as well as a set of baseline methods.
Investigating Speaking Difficulties of Senior High School Students: Linguistics and Psychological Problems

The purpose of this study was to gain an understanding of the problems in communication experienced by senior high school students in Jambi City. Students in their final year of secondary education in Jambi City served as participants in this study. This study was carried out at SMAN 03, SMAN 08, and SMAN 11 Jambi City, all of which are recognized educational institutions. There were a total of 130 students in the tenth grade that took part in this research. The research was a quantitative study that was designed in the form of a survey. The questionnaire was the instrument that was employed for this study. There are a total of 25 questions on the questionnaire, all of which pertain to the difficulty that students have expressing themselves verbally regarding linguistics and psychological issues. The findings of this study also suggested that the students attending the three authorized institutions had the perception that psycholog
A Novel Deep-Learning Model Compression Based on Filter-Stripe Group Pruning and Its IoT Application

Nowadays, there is a tradeoff between the deep-learning module-compression ratio and the module accuracy. In this paper, a strategy for refining the pruning quantification and weights based on neural network filters is proposed. Firstly, filters in the neural network were refined into strip-like filter strips. Then, the evaluation of the filter strips was used to refine the partial importance of the filter, cut off the unimportant filter strips and reorganize the remaining filter strips. Finally, the training of the neural network after recombination was quantified to further compress the computational amount of the neural network. The results show that the method can significantly reduce the computational effort of the neural network and compress the number of parameters in the model. Based on experimental results on ResNet56, this method can reduce the number of parameters to 1/4 and the amount of calculation to 1/5, and the loss of model accuracy is only 0.01. On VGG16, the number of parameters is reduced to 1/14, the amount of calculation is reduced to 1/3, and the accuracy loss is 0.5%.
Fractional Differential Equations in Electronic Information Models

Abstract The article first uses the fractional derivative to define a new fractional bounded variation function space. This method constructs the corresponding electronic information image model denoising mask by setting a smaller fractional integration order. The experimental results show that the image denoising algorithm based on fractional integration can not only improve the signal-to-noise ratio of the image compared with the traditional denoising method, but also can better retain the details of the edge and texture of the electronic information image.
Neural Density Estimation and Likelihood-free Inference

I consider two problems in machine learning and statistics: the problem of estimating the joint probability density of a collection of random variables, known as density estimation, and the problem of inferring model parameters when their likelihood is intractable, known as likelihood-free inference. The contribution of the thesis is a set of new methods for addressing these problems that are based on recent advances in neural networks and deep learning.
Few-shot Video-to-Video Synthesis

Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the state-of-the-art of vid2vid has advanced significantly, existing approaches share two major limitations. First, they are data-hungry. Numerous images of a target human subject or a scene are required for training. Second, a learned model has limited generalization capability. A pose-to-human vid2vid model can only synthesize poses of the single person in the training set. It does not generalize to other humans that are not in the training set. To address the limitations, we propose a few-shot vid2vid framework, which learns to synthesize videos of previously unseen subjects or scenes by leveraging few example images of the target at test time. Our model achieves this few-shot generalization capability via a novel network weight generation module utilizing an attention mechanism. We conduct extensive experimental validations with comparisons to strong baselines using several large-scale video datasets including human-dancing videos, talking-head videos, and street-scene videos. The experimental results verify the effectiveness of the proposed framework in addressing the two limitations of existing vid2vid approaches.
Automated discovery of interpretable gravitational-wave population models

We present an automatic approach to discover analytic population models for gravitational-wave (GW) events from data. As more gravitational-wave (GW) events are detected, flexible models such as Gaussian Mixture Models have become more important in fitting the distribution of GW properties due to their expressivity. However, flexible models come with many parameters that lack physical motivation, making interpreting the implication of these models challenging. In this work, we demonstrate symbolic regression can complement flexible models by distilling the posterior predictive distribution of such flexible models into interpretable analytic expressions. We recover common GW population models such as a power-law-plus-Gaussian, and find a new empirical population model which combines accuracy and simplicity. This demonstrates a strategy to automatically discover interpretable population models in the ever-growing GW catalog, which can potentially be applied to other astrophysical phenomena.
PERBANDINGAN AKURASI ALGORITMA NAÏVE BAYES, K-NN DAN SVM DALAM MEMPREDIKSI PENERIMAAN PEGAWAI

To supporting academic and non-academic activities, the Polytechnic Business Indonesian (PBI) must be supported by employees with reliable Human Resources (HRD) who have good behavior, good abilities and can complete work professionally and responsibly. Conventional techniques for analyzing existing large amounts of data cannot be handled which is the background for the emergence of a new branch of science to overcome the problem of extracting important information from data sets, which is called Data Mining. Utilizing methods to classify data by utilizing methods including: Naïve Bayes method, K-Nearest Neighbor (K-NN) and Supervise Vector Machine (SVM). From this research, in Predicting Applicants Graduation at PBI, the SVM method is better than Naïve Bayes and K-NN. With 33 test data used, SVM has 84.9% accuracy, 85.1% precision while K-NN has 81.8% accuracy, 84.1% precision and Naïve Bayes has 78.8% accuracy and 80.1% precision.
Learning Symbolic Physics with Graph Networks

We introduce an approach for imposing physically motivated inductive biases on graph networks to learn interpretable representations and improved zero-shot generalization. Our experiments show that our graph network models, which implement this inductive bias, can learn message representations equivalent to the true force vector when trained on n-body gravitational and spring-like simulations. We use symbolic regression to fit explicit algebraic equations to our trained model's message function and recover the symbolic form of Newton's law of gravitation without prior knowledge. We also show that our model generalizes better at inference time to systems with more bodies than had been experienced during training. Our approach is extensible, in principle, to any unknown interaction law learned by a graph network, and offers a valuable technique for interpreting and inferring explicit causal theories about the world from implicit knowledge captured by deep learning.