Reviewerly/exHarmony huggingface dataset:
Information Theory With Kernel Methods

We consider the analysis of probability distributions through their associated covariance operators from reproducing kernel Hilbert spaces. We show that the von Neumann entropy and relative entropy of these operators are intimately related to the usual notions of Shannon entropy and relative entropy, and share many of their properties. They come together with efficient estimation algorithms from various oracles on the probability distributions. We also consider product spaces and show that for tensor product kernels, we can define notions of mutual information and joint entropies, which can then characterize independence perfectly, but only partially conditional independence. We finally show how these new notions of relative entropy lead to new upper-bounds on log partition functions, that can be used together with convex optimization within variational inference methods, providing a new family of probabilistic inference methods.
An Improved Multi-Label Learning Method with ELM-RBF and a Synergistic Adaptive Genetic Algorithm

Profiting from the great progress of information technology, a huge number of multi-label samples are available in our daily life. As a result, multi-label classification has aroused widespread concern. Different from traditional machine learning methods which are time-consuming during the training phase, ELM-RBF (extreme learning machine-radial basis function) is more efficient and has become a research hotspot in multi-label classification. However, because of the lack of effective optimization methods, conventional extreme learning machines are always unstable and tend to fall into local optimum, which leads to low prediction accuracy in practical applications. To this end, a modified ELM-RBF with a synergistic adaptive genetic algorithm (ELM-RBF-SAGA) is proposed in this paper. In ELM-RBF-SAGA, we present a synergistic adaptive genetic algorithm (SAGA) to optimize the performance of ELM-RBF. In addition, two optimization methods are employed collaboratively in SAGA. One is used for adjusting the range of fitness value, the other is applied to update crossover and mutation probability. Sufficient experiments show that ELM-RBF-SAGA has excellent performance in multi-label classification.
TGNN: A Joint Semi-supervised Framework for Graph-level Classification

This paper studies semi-supervised graph classification, a crucial task with a wide range of applications in social network analysis and bioinformatics. Recent works typically adopt graph neural networks to learn graph-level representations for classification, failing to explicitly leverage features derived from graph topology (e.g., paths). Moreover, when labeled data is scarce, these methods are far from satisfactory due to their insufficient topology exploration of unlabeled data. We address the challenge by proposing a novel semi-supervised framework called Twin Graph Neural Network (TGNN). To explore graph structural information from complementary views, our TGNN has a message passing module and a graph kernel module. To fully utilize unlabeled data, for each module, we calculate the similarity of each unlabeled graph to other labeled graphs in the memory bank and our consistency loss encourages consistency between two similarity distributions in different embedding spaces. The two twin modules collaborate with each other by exchanging instance similarity knowledge to fully explore the structure information of both labeled and unlabeled data. We evaluate our TGNN on various public datasets and show that it achieves strong performance.
Application of Factorial and Binomial identities in Computing and Cybersecurity

Abstract This paper focuses on the application of factorial and binomial identities in computing, communication, and cybersecurity in connection with the symmetric and asymmetric encryptions. We can use the binomial identities and theorem in factorials as an effective security algorithm to protect the computing systems, programs, and networks.
Early Stopping Effectiveness for YOLOv4

Background: YOLOv4 is one of the fastest algorithms for object detection. Its methods, i.e., bag of freebies and bag of specials, can prevent overfitting, but this can be combined with early stopping as it could also prevent overfitting. Objective: This study aims to identify the effectiveness of early stopping in preventing overfitting in the YOLOv4 training process. Methods: Four datasets were grouped based on the training data size and object class, These datasets were tested in the experiment, which was carried out using three patience hyperparameters: 2, 3, and 5. To assess the consistency, it was repeated eight times. Results: The experimental results show that early stopping is triggered more frequently in training with data below 2,000 images. Of the three patience hyperparameters used, patience 2 and 3 were able to halve the training duration without sacrificing accuracy. Patience 5 rarely triggers early stopping. There is no pattern of correlation between the number of object classes and early stopping. Conclusion: Early stopping is useful only in training with data below 2,000 images. Patience with a value of 2 or 3 are recommended. Keywords: Early Stopping, Overfitting, Training data, YOLOv4
Indonesian automatic short answer grading system

Short answer question is one of the methods used to evaluate student cognitive abilities, including memorizing, designing, and freely expressing answers based on their thoughts. Unfortunately, grading short answers is more complicated than grading multiple choices answers. For that problem, several studies have tried to build an artificial intelligence system called automatic short answer grading (ASAG). We tried to improve the accuracy of the ASAG system at scoring student answers in Indonesian by enhancing the earlier state-of-the-art models and methods. They were the bidirectional encoder representations from transformer (BERT) with fine-tuning approach and ridge regression models utilizing advanced feature extraction. We conducted this study by doing stages of literature review, data set preparation, model development, implementation, and comparison. Using two different ASAG data sets, the best result of this study was an achievement of 0.9508 in pearson’s correlation and 0.4138 in root-mean-square error (RMSE) by the BERT-based model with the fine-tuning approach. This result outperformed the results of the previous studies using the same evaluation metrics. Thus, it proved our ASAG system using the BERT model with fine-tuning approach can improve the accuracy of grading short answers.
Multi-Modal Alignment of Visual Question Answering Based on Multi-Hop Attention Mechanism

The alignment of information between the image and the question is of great significance in the visual question answering (VQA) task. Self-attention is commonly used to generate attention weights between image and question. These attention weights can align two modalities. Through the attention weight, the model can select the relevant area of the image to align with the question. However, when using the self-attention mechanism, the attention weight between two objects is only determined by the representation of these two objects. It ignores the influence of other objects around these two objects. This contribution proposes a novel multi-hop attention alignment method that enriches surrounding information when using self-attention to align two modalities. Simultaneously, in order to utilize position information in alignment, we also propose a position embedding mechanism. The position embedding mechanism extracts the position information of each object and implements the position embedding mechanism to align the question word with the correct position in the image. According to the experiment on the VQA2.0 dataset, our model achieves validation accuracy of 65.77%, outperforming several state-of-the-art methods. The experimental result shows that our proposed methods have better performance and effectiveness.
Football Game Video Analysis Method with Deep Learning

Football is a beloved sport, and its wide audience makes football video one of the most analytically valuable types of video. Researchers have achieved certain research results in football video content analysis. How to locate interesting event clips from a complete long video is an urgent issue to be addressed in football game video analysis. The granularity of sports event detection results with traditional machine learning is relatively coarse, and the types of events that can be detected are limited. In recent years, deep learning has made good progress in the research of video single-person events and action detection, but there are few achievements in the detection of sports video events. In response to this problem, this work uses a deep learning method to build an event detection model to detect events contained in football videos. The whole model is divided into two stages, in which the first stage is utilized to generate candidate event fragments. It divides the football video to be detected into a sequence of frames of a certain length and scans using a sliding window. Multiple frame sequences within a sliding window form a segment, and each segment is a prediction unit. The frame sequence features within the segment are obtained through a three-dimensional convolutional neural network, which is used as the input of each time point of the bidirectional recurrent neural network and further integrated to generate the event prediction of the segment. The second stage is to further process the above results to remove all segments predicted as nonevents. The thresholds are set according to the detection effect of various events to filter out event fragments with higher probability values, obtain the start and end positions of the events through merging, classify and mark them, and finally output complete event fragments. This work has carried out comprehensive and systematic experiments to verify correctness of the proposed method.
When Do the Public Support Hate Speech Restrictions? Symmetries and Asymmetries across Partisans in Denmark and the United States

When should hate speech be restricted? As the prevalence of hateful political discussions increase, governments and tech companies face pressures to regulate social media platforms. Yet, hate speech is often used as a generic everyday term conflating the severity of the content with whom it targets, leading to a widespread perception that citizens cannot agree on what “hate” is. I offer a comprehensive test of when ordinary voters want to restrict hate speech across two nationally representative samples in Denmark and the United States. Despite national differences in hate speech legislation, support for hate speech restrictions are in both nations primarily shaped by the severity of the hate, while target characteristics have asymmetrical partisan effects. I discuss how ambiguous measures of support for hate speech restrictions in previous research overestimate both the magnitude of partisan differences and the effects of target characteristics.
Teachers’ Perceptions and Challenges to the Use of Technology in Teaching and Learning during Covid-19 in Malaysia

The use of technology in education has been an ongoing debate among educators. Although educators are highly encouraged to integrate technology in the classroom, they are still sceptical to fully utilize them. This changed drastically during the COVID-19 pandemic in 2020 which forced educators and learners to rely heavily on the use of technology to support learning. This paper identifies teachers' technology usage in the classroom, their perceptions on the use of technology in the classroom and the challenges of integrating technology in teaching and learning. We employed a quantitative research approach with survey design and non-probability snowball sampling. To collect the data, a questionnaire was distributed to 78 primary and secondary school teachers. We found that teachers in that particular area mostly used smart phones to access internet and conduct lessons due to the limited availability of devices from the students’ end, internet connectivity, and limited instructional time. Despite the challenges presented, they believed the importance of technology in education, and it helped enhance students' digital competence and responsible use. This research, thus, present an insight to the educational authorities in Malaysia of the general perceptions of teachers on the technology used during the pandemic and in turn, will increase digital access and awareness for a better quality of technology integration in teaching and learning.
A Novel Feature Selection Algorithm using Multi-Objective Improved Honey Badger Algorithm and Strength Pareto Evolutionary Algorithm-II

An important task for classification is feature selection that removes the redundant or irrelevant features from the dataset. Multi-objective feature selection approach is mainly proposed by many researchers. However, these approaches failed to maintain the higher classification accuracy while removing redundancy in the features. In this work, a wrapper based feature selection technique is proposed with a hybrid of Multi Objective Honey Badger Algorithm (MO-HBA) and Strength Pareto Evolutionary Algorithm-II to maintain the balance between classification accuracy and removal of redundancy. Classification accuracy improvement and removal of redundant features are considered as the multi-objective optimization functions of the proposed multi-objective feature selection technique. The Levy flight algorithm is utilized to initialize the population to enhance the ability of the exploration and exploitation of MO-HBA. The regularized Extreme Learning Machine is used to classify the selected features. To evaluate the performance of the proposed feature selection technique, eighteen benchmark datasets are utilized and results are compared with the four well known multi-objective feature selection techniques in terms of accuracy, hamming loss, ranking loss, mean value, standard deviation, length of features, and training time. The proposed approach achieved maximum accuracy of 100% with the maximum value of selected features as 80. The minimum value of hamming loss, ranking loss, mean value and standard deviation value achieved by the proposed approach are 0.0092, 0.0003, 0.018 and 0.001 respectively. The experimental results show that the proposed approach can give improved classification accuracy while the removal of redundancy in large scale datasets.
Accelerating the Best Trail Search on AES-Like Ciphers

In this study, we accelerate Matsui’s search algorithm to search for the best differential and linear trails of AES-like ciphers. Our acceleration points are twofold. The first exploits the structure and branch number of an AES-like round function to apply strict pruning conditions to Matsui’s search algorithm. The second employs permutation characteristics in trail search to reduce the inputs that need to be analyzed. We demonstrate the optimization of the search algorithm by obtaining the best differential and linear trails of existing block ciphers: AES, LED, MIDORI-64, CRAFT, SKINNY, PRESENT, and GIFT. In particular, our search program finds the fullround best differential and linear trails of GIFT-64 (in approx. 1 s and 10 s) and GIFT-128 (in approx. 89 h and 452 h), respectively.For a more in-depth application, we leverage the acceleration to investigate the optimal DC/LC resistance that GIFT-variants, called BOGI-based ciphers, can achieve. To this end, we identify all the BOGI-based ciphers and reduce them into 41,472 representatives. Deriving 16-, 32-, 64-, and 128-bit BOGI-based ciphers from the representatives, we obtain their best trails until 15, 15, 13, and 11 rounds, respectively. The investigation shows that 12 rounds are the minimum threshold for a 64-bit BOGIbased cipher to prevent efficient trails for DC/LC, whereas GIFT-64 requires 14 rounds. Moreover, it is shown that GIFT can provide better resistance by only replacing the existing bit permutation. Specifically, the bit permutation variants of GIFT-64 and GIFT-128 require fewer rounds, one and two, respectively, to prevent efficient differential and linear trails.
A Novel Design of Audio CAPTCHA for Visually Impaired Users

CAPTCHAs are widely used by web applications for the purpose of security and privacy. However, traditional text-based CAPTCHAs are not suitable for sighted users much less users with visual impairments. To address the issue, this paper proposes a new mechanism for CAPTCHA called HearAct, which is a real-time audio-based CAPTCHA that enables easy access for users with visual impairments. The user listens to the sound of something (the “sound-maker”), and he/she must identify what the sound-maker is. After that, HearAct identifies a word and requires the user to analyze a word and determine whether it has the stated letter or not. If the word has the letter, the user must tap and if not, they swipe. This paper presents our HearAct pilot study conducted with thirteen blind users. The preliminary user study results suggest the new form of CAPTCHA has a lot of potential for both blind and visual users. The results also show that the HearAct CAPTCHA can be solved in a shorter time than the text-based CAPTCHAs because HearAct allows users to solve the CAPTCHA using gestures instead of typing. Thus, participants preferred HearAct over audio-based CAPTCHAs. The results of the study also show that the success rate of solving the HearAct CAPTCHA is 82.05% and 43.58% for audio CAPTCHA. A significant usability differences between the System Usability score for HearAct CAPTCHA method was 88.07 compared to audio CAPTCHA was 52.11%. Using gestures to solve the CAPTCHA challenge is the most preferable feature in the HearAct solution. To increase the security of HearAct, it is necessary to increase the number of sounds in the CAPTCHA. There is also a need to improve the CAPTCHA solution to cover wide range of users by adding corresponding image with each sound to meet deaf users’ needs; they then need to identify the spelling of the sound maker’s word.
Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images

Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues.
Saddle-point exciton signature on high-order harmonic generation in two-dimensional hexagonal nanostructures

The disclosure of basic nonlinear optical properties of graphene-like nanostructures with correlated electron-hole nonlinear dynamics over a wide range of frequencies and pump field intensities is of great importance for both graphene fundamental physics and for expected novel applications of 2D hexagonal nanostructures in extreme nonlinear optics. In the current paper, the nonlinear interaction of 2D hexagonal nanostructures with the bichromatic infrared driving field taking into account many-body Coulomb interaction is investigated. Numerical investigation in the scope of the Bloch equations within the Houston basis that take into account $e-e$ and $e-h$ interactions in the Hartree-Fock approximation reveals significant excitonic effects in the high harmonic generation process in 2D hexagonal nanostructures such as graphene and silicene. It is shown that due to the correlated electron-hole nonlinear dynamics around the van Hove singularity, spectral caustics in the high harmonic generation spectrum are induced near the saddle point excitonic resonances.
Integration-lifelong educational space in formation of competent agricultural engineer

The technology of the competency-based approach requires fundamental changes in the organization, content and methodology of the educational process and a shift of emphasis in theoretical training to theoretical and applied, professionally oriented training.This particularly applies to the fundamental disciplines, in particular physics, as the most universal basic discipline.The purpose of this study is to introduce into the educational process of integration-lifelong educational space in engineering education, aimed at the formation and development of different levels and types of professional competencies.The purpose of the research is to study the organization of an integration-lifelong educational space in the engineering education, aimed at the formation and development of various levels and types of professional competencies.A universal methodology for the integration-lifelong organization of the students' educational activities has been developed.The results of the pedagogical experiment showed an increase in the absolute indicator of the student progress by 9%, which indicates the efficiency of the proposed methodology.There is revealed the influence of the students' specially organized professionally-oriented activity in physics upon the motivation of learning and professional orientation.In addition, motivation to study physics increased by 7%, professional orientation by 11%.Increase in the students' levels of educational achievements, the growth of levels of motivation and professional orientation testify to the efficiency and pedagogical expediency of the proposed methodological principles of the integration-lifelong organization of the students' educational activities.
One-step implementation of Rydberg nonadiabatic noncyclic geometric quantum computation in decoherence-free subspaces

Quantum gates constructed by geometric phase are naturally robust to control errors due to the global nature of the geometric evolution path. Therefore, how to cope with the inevitable decoherence errors is worthy of serious attention for geometric quantum computation. Different from conventional nonadiabatic geometric quantum computation (NGQC), which needs the same evolution time for any geometric rotation angle, we have proposed and experimentally demonstrated nonadiabatic noncyclic geometric quantum computation (NNGQC) without the requirement of the cyclic evolution condition, which thus reduces decoherence errors due to shortened evolution time [J. W. Zhang et al., Phys. Rev. Lett. 127, 030502 (2021)]. In addition, the use of decoherence-free subspaces (DFS) is another effective strategy to protect quantum gates from dephasing error and has been studied in nonadiabatic holonomic quantum computation [G. F. Xu et al., Phys. Rev. Lett. 109, 170501 (2012)]. Motivated by previous studies, we propose a one-step scheme to implement single-qubit NNGQC operation in DFS constructed by two Rydberg atoms, and then generalize the scheme to the two-logical-qubit case in one step. The numerical results show that our scheme is robust against decoherence in contrast to the NGQC counterparts. The present scheme combines the features of NNGQC and DFS and can further reduce the impact of decoherence on gate infidelity, which may provide an option for the realization of Rydberg-atom-based quantum computation in the future.