Reviewerly/exHarmony huggingface dataset:
Project activity of students of the college of physical culture as a means of developing natural science thinking

The purpose of study is to analyze and evaluate the impact of project activities on the formation of natural science thinking among students. The results of the project activities of the 1st year students and their level of training in the disciplines of the natural science cycle in subsequent courses are analyzed. The statistical analysis of the obtained results was carried out and the corresponding conclusions were made. The scientific novelty of this study lies in the actualization of the relationship between project activities as a means of forming and developing natural science thinking and general professional competencies of a physical culture specialist. The practical significance is confirmed by the conclusions made about the importance of implementing individual design.
Multi-Band Texture Modeling Using Finite Mixtures of Multivariate Generalized Gaussian Distributions

We present a unified statistical model for multivariate and multi-modal texture representation. This model is based on the formalism of finite mixtures of multivariate generalized Gaussians (MoMGG) which enables a compact and accurate representation of joint statistics of different sub-bands of multireslotion texture transform. This representation expresses correlation between sub-bands at different scales and orientations, and also between adjacent locations within the same subbands, providing a precise description of the texture layout. It enables also to combine different multi-scale transforms to build a richer and more representative texture signature. We successfully tested the model on traditional texture transforms such as wavelets and contourlets. Experiments on color-texture image retrieval have demonstrated the performance of our approach comparatively to state-of-art methods.
Research on Mass Data Retrieval Model Method Based on Privacy Computing

In order to protect data privacy in the context of big data, a new scheme to protect data privacy and user privacy is proposed. This study believes that blockchain technology can be used to build a massive private data retrieval and sharing platform, considering the realization of privacy protection under horizontal federated learning and vertical federated learning, combined with privacy computing technology, to verify the results of massive data retrieval, and to build a "data availability that is not available." The "visible" security reduction model involves three cryptographic algorithms, namely functional encryption, zero-knowledge proof and asymmetric encryption. By combining blockchain and cloud servers, a hybrid storage architecture with data storage on the chain and off-chain storage is realized. And use function encryption and zero-knowledge proof to achieve privacy protection and secure data sharing of verifiable results. The final experimental results prove that the security feasibility of this model is proposed in this paper, which can fully improve the level of data log security management, operation and maintenance supervision, and the quality and efficiency of data security protection, and improve the network security protection system of the power grid in all scenarios.
Encryption Image Based on Z-Fractal and Hash Function with Grigorchuk’s Group

The information and data exchanged between people over the Internet needs security to maintain the confidentiality of the data and avoid unauthorized access. Therefore, it is necessary to use more powerful encryption techniques and methods in the face of hostile attacks. In this paper, the propose an encryption method that depends primarily on the sensitivity of the key and the randomness of the encryption. The key generation was based on audio files as a basic seed, then Grigorchuk’s group was applied as a hash of data and then entered it on the hash function SHA-3 (256), which outputs a series of 256-bit data as a key. Audio files This increases the sensitivity of the key. Then encrypted with two methods XOR and the RC6 algorithm to enhance the security of the encryption. The final stage is the application of Z-Fractal to the encrypted image and this is also applied before encoding. Noted through the security analysis of the algorithm that it has strength in the face of hostile attacks based on the randomness of the encryption and the sensitivity of the key and in view of the measure’s values as the value of the scale NPCR, UACI was 100 and 35, respectively. In addition to its execution time, which not exceed a minute.
Conditional Differential Cryptanalysis on Bagua

At ASIACRYPT 2010, Knellwolf et al. proposed conditional differential cryptanalysis (CDC) on NFSR-based cryptosystems, and applied this technique to analyze the security of the eSTREAM finalist Grain v1. Bagua is a hardware-oriented stream cipher supporting key length of 128 and 256 bits proposed by Tan, Zhu and Qi at Inscrypt 2020. In this paper, we study the security of the stream cipher Bagua against CDC since the structure of Bagua is novel. First, we analyze the difference propagation of round-reduced Bagua by exhaustive search over low weight input differences and propose an input difference choosing strategy. Then, we apply CDC on 182-round and 204-round Bagua with different condition imposing strategies. For the 182-round Bagua, we can recover 8 key expressions of both the two versions with time complexities $$2^{32.5}$$ and $$2^{33}$$ respectively. For 204-round Bagua, we can recover 26 key expressions of both the two versions with time complexities $$2^{59.5}$$ and $$2^{60}$$ respectively. Furthermore, all the distinguishers obtained in the CDC are verified by practical experiments. As far as we know, this is the first third-party cryptanalysis on the stream cipher Bagua.
Single Camera based Real Time Framework for Automated Fall Detection

To identify fall of a person is the most significant issue for caring of elderly who is living alone in the home. In this paper, we propose a real-time automated fall detection and recognition framework using Support Vector Machine and vision techniques. We first capture the person using single camera and then apply Gaussian Mixture Model (GMM) method to detect the person from the video. After some preprocessing, we represent the actual human body by detecting the list of contours. After that, ellipse and rectangle shapes are fitted on these contours. Now, different important features are extracted that are useful for detection of fall activity and fed to Support Vector Machine (SVM) for the classification of fall event of a person. For the experiment, we take UR Fall Dataset that contains different activity of a person and it is available publicly. We compare our propose framework with other proposed methods and achieves better recognition accuracy.
Multi-Disciplinary Paths to Actor-Centric Non-Player Character Emotion Models

Video game non-player characters (NPCs) are a type of agent that often inherits emotion models and functions from ancestor virtual agents. Few emotion models have been designed for NPCs explicitly, and therefore do not approach the expressive possibilities available to live-action performing actors nor hand-crafted animated characters. With distinct perspectives on emotion generation from multiple fields within narratology and computational cognitive psychology, the architecture of NPC emotion systems can reflect the theories and practices of performing artists. This chapter argues that the deployment of virtual agent emotion models applied to NPCs can constrain the performative aesthetic properties of NPCs. An actor-centric emotion model can accommodate creative processes for actors and may reveal what features emotion model architectures should have that are most useful for contemporary game production of photorealistic NPCs that achieve cinematic acting styles and robust narrative design.
Zero-Shot Knowledge Graph Completion for Recommendation System

Knowledge graphs are structured representations of actual entities and relations. They are widely used to improve the performance of downstream tasks such as recommendation systems and semantic searching. Knowledge graph completion (KGC) is a technology for discovering the missing relations between the entities in a knowledge graph (KG). Existing methods leverage known relations on a KG to build a model to predict missing relations. Such methods implicitly require a substantial number of relations to be known in advance, which might not be available in practice. To cope with the cold-start scenario for KGC, i.e., no relation is known in advance, we propose a zero-shot approach in this paper. Our approach converts the KGC process to an optimization problem. It uses the Evolutionary Strategy (ES) algorithm to optimize a model used to complete the KG according to the performance of the recommendation system constructed based on the completed KG. Experiments on a movie dataset demonstrate that our approach can complete the KG in the cold-start scenario and improve the performance of the recommendation system built based on the completed KG.
Binarization of Color Document Image Based on Adversarial Generative Network and Discrete Wavelet Transform

Document binarization is an important task to separate the foreground text information in the document image from the background, which is generally applied to the digital archive of historical documents. This paper proposes to use the generative adversarial network for training with a small amount of data. In the first stage, discrete wavelets are used for image enhancement of four-channel images, and local binarization and global binarization are trained separately to obtain the final result in the second stage. The experimental results show that our proposed method has better performance than the classical algorithm on the DIBCO dataset.
Clustering on ‘Z’ e-learning application’s reviews using self organizing maps and K-means methods

“Z” is one of the famous online learning applications in Indonesia which has an increasing number of users during the pandemic. Based on these conditions, “Z” is required to improve their services to keep user satisfaction. Reviews are great tool to get information about the user experience. The number of reviews obtained may be very large and contain various information. Reading all the reviews will take a long time and effort, so it needs a method to summarize the information, and one of the methods is to apply clustering analysis. This study groups the reviews taken from Google Play on rating 1 until 5, which aim is to obtain more detailed information about the weaknesses and strengths of the application. This study also compares performance of clustering using Self Organizing Maps (SOM) which is neural network-based algorithm and K-Means which is centered-based algorithm. Clustering is done using the bigram model to get a clearer meaning. Clustering reviews on each rating successfully getting much information regarding the good or bad user experience, and from those information can be given some recommendations for improvement. Based on Silhoutte and Davies Bouldin Index shows that K-Means gives better result than SOM.
Salient Foreground-Aware Network for Person Search

Person search aims to simultaneously localize and identify a query person from realistic and uncropped images, which consists of person detection and re-identification. In existing methods, the extracted features come from the low-quality proposals generated by the structure like Region Proposal Network (RPN), and convolution is used to learn local features in the process of extracting features while the receptive field cannot grasp the global structural information. We propose an end-to-end network embedded with our Salient Foreground-Aware Module (SFAM). Self-attention mechanism in SFAM allows for better capture of global information. Our network incorporates our embedding method for person detection and person re-identification, which can effectively optimize the process of extracting features and improve feature expression capabilities. We merge the above modules into our Salient Foreground-Aware Network (SFAN). Extensive experiments have shown that our SFAN significantly improves the performance of end-to-end models with acceptable time-consuming and achieves state-of-the-art results.
Short question-answers assessment using lexical and semantic similarity based features

Evaluation of short answers is a challenging task. As there could be more than one way of expressing the same thing in a sentence by quite different words and phrases, evaluation through computer-based system of Short answers requires natural language understanding. Study has performed comparative analysis for short answer assessment with regression algorithms namely: Support Vector Regression, Linear Regression, Bagging Tree, Boosting Tree, Multilayer Perceptron Regressor, and Random Forest on extracted features. It proposes the combined features that take account of lexical, approximate string matching, and semantic similarity features. An empirical evaluation of feature selection is also done that further improves the results. These combined features achieved improved results as 0.71 & 0.78 for correlation and RMSE values respectively.
ARTW: a Model of Author Recognition based on Writing Style by Recognizing Style Crack

Recently, plagiarism such as deleting keywords, changing the order of words, adjusting the sentence structure has also become more sophisticated than before. However, plagiarism detection systems usually fail to detect duplication, resulting in omissions, errors, etc. For better plagiarism detection, a model called ARTW, the word level author recognition framework inspired by writing style, extracting the writing style of the text and applying the writing style to recognize the author was proposed. The style crack indicates the position where the writing style changes and the text segmentation is performed according to the style crack. The recognition of style cracks is achieved by style feature extraction. Considering the style features from words, sentences and emotions, etc. This paper designs 7 features for style crack recognition, finds the location of the style crack based on the results of the style feature extraction combined with the clustering algorithm, and performs text segmentation according to style cracks. The author recognition is performed on each part based on the recognition of style cracks. Our experiment result shows that the ARTW model accuracy rate is 4∼5% higher than the SVM and 2∼3% higher than the Naive Bayes, which outperforms the traditional model of SVM and Naive Bayes.
On Interdisciplinary Studies of a New Generation of Artificial Intelligence and Logic

A new generation of artificial intelligence (NGAI), currently based on big data and machine learning, follows a path of connectionism. Although this path achieves huge success in data-intensive applications under closed environments, there are some bottleneck problems, including a lack of explainability, the difficulty of ethical alignment, the weakness of cognitive reasoning, etc. To address these problems inevitably involves the depiction of information from an open, dynamic and real environment and the modeling of human reasoning and explanation mechanisms. Formal argumentation is a general formalism for modeling various types of knowledge representation and reasoning in a context of disagreement, and is flexible enough to incorporate other types of knowledge for decisionmaking, such as preferences, weights, and probabilities. Meanwhile, there are various approaches for efficient computation of argumentation semantics by exploiting the locality and modularity of argumentation, and for providing explanations based on arguments and dialogues. The organic combination of formal argumentation with existing big data and machine learning techniques can be expected to break through some existing technical bottlenecks and facilitate the sustainable development of NGAI.
Self-supervised Learning Based on Max-tree Representation for Medical Image Segmentation

In recent years, the convolutional neural network(CNN) based deep learning architectures have achieved great success in medical image segmentation. However, CNN usually relies on abundant labeled data for training. At the same time, collecting labeled training data is time-consuming and expensive. Therefore, in addition to the common unsupervised learning methods, a series of self-supervised learning(SSL) methods have been proposed in medical image analysis using a large amount of unlabeled data. These SSL strategies usually extract potential supervised signals by pretext tasks and help the networks learn a way of feature representation. However, the learned feature representations in the pretext tasks are not commonly directly related to downstream tasks like segmentation. We assume that the more pretext tasks help the model learn the structural features of the image, the better the model's performance on the downstream segmentation task will be. In this paper, we propose an SSL strategy based on max-tree representation to extract the image structure information, and the CNN learns this max-tree representation in the pretext task. To the best of our knowledge, we are the first to take the structure information into account in the SSL pretext task. Extensive experiments show that our SSL strategy based on max-tree representation can help the CNN to learn abundant structural information, which is significantly useful for the downstream segmentation task.
Driving Behavior Identification of Operating Vehicles Based on Real-Time Trajectory Data

Trajectory data of operating vehicles contains abundant spatiotemporal information about driving behaviors. With the development of big data processing and mode identification techniques, these methods can be used to mine the patterns of driving behaviors from the trajectory data, and compensate the disadvantages of visual image analysis results. In this research, the trajectory data is partitioned by reactive greedy randomized adaptive search procedure into small segments, and driving behavior on the segments is identified by gated recurrent unit neural network model. The proposed method is verified by real-time trajectory data from Chongqing and Shanghai with over 370000 trajectory points, and the result shows that the accuracy of identifying operating vehicles’ driving behavior has reached 91.57%, which indicates that the proposed method improves performances in dealing with large scale trajectory data. This could be used to enhance the supervision on operating vehicles’ safety for transportation companies as well as transportation management departments, and improve operating vehicles transportation management strategies.
Cost estimates for modern e-business systems

Recently, the adoption of cloud computing for e-business has become standardized and began evolving into further execution models, such as serverless computing. One of the features of cloud computing is to provide computational resources on a pay-as-you-go model, where users pay only for what they use. These managed and serviced resources have characteristics that make them suitable for cost estimation in the early stages of business. This paper proposes a rapid cost estimation method for serverless computing using directed acyclic graph- (DAG) based formalization and matrix operations. Using a case study, we demonstrate that cost estimation under serverless computing can be effective in the early stages of business. Furthermore, we show that the cost estimation of serverless computing is more proportional to the required computational resources than the virtual machine- (VM) based cost estimation method.
Tree Density Estimation

We study the problem of estimating the density <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f({\mathbf {x}})$ </tex-math></inline-formula> of a random vector <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${ {\mathbf {X}}}$ </tex-math></inline-formula> in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb R}^{d}$ </tex-math></inline-formula> . For a spanning tree <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> defined on the vertex set <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\{1, {\dots },d\}$ </tex-math></inline-formula> , the tree density <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{T}$ </tex-math></inline-formula> is a product of bivariate conditional densities. An optimal spanning tree minimizes the Kullback-Leibler divergence between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{T}$ </tex-math></inline-formula> . From i.i.d. data we identify an optimal tree <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$T^{*}$ </tex-math></inline-formula> and efficiently construct a tree density estimate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f_{n}$ </tex-math></inline-formula> such that, without any regularity conditions on the density <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> , one has <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lim _{n\to \infty } \int | f_{n}({\mathbf {x}})-f_{T^{*}}({\mathbf {x}})|d {\mathbf {x}}=0$ </tex-math></inline-formula> a.s. For Lipschitz <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> with bounded support, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathbb E}\left \{{ \int | f_{n}({\mathbf {x}})-f_{T^{*}}({\mathbf {x}})|d {\mathbf {x}}}\right \}=O\big (n^{-1/4}\big)$ </tex-math></inline-formula> , a dimension-free rate.
Cognitive Human Gait Analysis for Neuro-Physically Challenged Patients by Bat Optimization Algorithm

Autism spectrum disorder and cerebral palsy are called developmental disorders that affect the brain development, communication, and behaviour of a child or an adult. Individuals with Cerebral palsy can also display symptoms of autism. Both conditions have varying degrees of severity, which can make it difficult to form a clear diagnosis. This research paper proposes the model-free green environment for the prediction of the above-mentioned disorders by doing gait analysis only with the camera. The new intelligent algorithm CAGLearner (cognitive analysis for gait) works on the standards of graphical extreme machines. CAGLearner uses the new powerful algorithm called bat optimized ELM for classification, which is then related with the prevailing machine learning algorithms such as artificial neural networks (ANN), support vector machines (SVM), and random forest (RF) algorithms in which the accuracy, sensitivity, and response time were analyzed. In terms of prediction time and precision, the model provided in this paper also yields more benefits.