Reviewerly/exHarmony huggingface dataset:
Detecting Abusive Activity with Enhanced Convolutional Neural Networks

There is a growing reliance on surveillance systems for law enforcement and public safety purposes. Video surveillance scenarios, such as those seen in train stations, schools, and hospitals, must automatically detect hostile and suspicious behavior to avoid social, economic, and environmental harm. Human contact can now be seen using intelligent video surveillance systems. Crowds and the camera’s field of view reduce the system’s effectiveness as a security procedure. As a result, there is a lot of study on the many strategies for detecting violent behavior. The method of detection discussed in this study can be divided into three categories. These groups were established as a result of the classification procedures that were used. Among the categorizations is a refined CNN for using AI to identify instances of violence. Principal Component Analysis (PCA) is used to develop techniques for feature extraction and violence detection. The AIRTLab dataset was utilized to analyze models based on dataset learning for violence detection by testing the resilience of algorithms against false positives. To evaluate the proposed model’s robustness against false positives, we calculated accuracy measures and utilized them to establish a baseline on the AIRTLab dataset. The study literature claims a 99.98% success rate for the suggested models, with transfer learning-based networks boasting greater generalization abilities.
Recognition and Evaluation of Stability Movements in Gymnastics Based on Deep Learning

Research in the field of computer vision is currently an active and developing area. The evolution of technology and the increasing capabilities of computing and artificial neural networks are factors that have contributed to the advancement of this field. Computer vision systems are relied upon in many fields at present, such as the sports, agricultural, and medical fields, which gave excellent results in all areas on which these systems relied. This article presents the method of relying on computer vision systems in the sports field by distinguishing and evaluating the movements of the male gymnastics player on the ring apparatus, the evaluation process by the referees is complex, and the angle of view of the referee must be clear to ensure the assessment is correct, as the movements are distinguished. Stability in the fixed ring game in gymnastics, a data set was created to distinguish the activities and to evaluate the accuracy of their performance due to the lack of a data set available on the Internet, knowing that these movements are only for men’s gymnastics. One of the convolutional neural network models (yolov7) was adopted after dividing the data into 80% for training and the rest for testing. A set of points on the body based on which the type of movement is known, in addition to the player’s accuracy in executing the action. The time it takes for the player to perform this movement has also been calculated. This system has also been applied to a set of video clips to evaluate player movement. Adopting it to train some players to implement the actions well, the implementation results were very high, as the accuracy reached 95%.
Literature Review for Health Secure Image Steganography and Visual Encryption

The security image of medical data for transmission is considered highly important during communication issues because of the sensitive information of medical. Encryption and steganography are used to ensure that patient medical information is not accessible by unauthorized individuals. This paper presents a literature review for medical image security by encryption and steganography. This literature review discusses twenty-one publications with various applied steganography and encryption of medical image security techniques. This paper provides the research knowledge basis for researchers to have a solid background for their proposed contributions. All approaches are distributed based on two groups the encryption and steganography techniques. The main metrics in this survey to investigate and evaluate model performance was considered PSNR, NPCR, UACI, SSIM, and MSE. When compared to the methods steganography, the results have shown interesting results.
Image Generation Conditioned on Text Using Deep Learning Models: Survey

Generating an image from textual input poses a significant challenge, which has garnered substantial global interest in recent times. To address this issue, deep learning is developed especially generative models. This work aims to introduce an introductory overview and critical evaluation of various models. This study also presents some of the datasets used for image generation from text, also the evaluation metrics used to assess the results of models used to generate images from text. A summary of previous work is also presented.
Practice and Exploration of the English Teaching Model of "Promoting Teaching through Competition and Integrating Competition and Curriculum" in County Vocational Schools

Exploring and verifying the English teaching model of "promoting teaching through competitions and integrating courses" in county-level vocational schools is an effective way to promote the continuous updating of English teaching in China. This article mainly discusses the characteristics of the English teaching model of "integration of competition and curriculum", the current situation of vocational English education in counties, and the practical exploration path of the English teaching model of integration of competition and curriculum. By analyzing the current situation and existing problems of English education in vocational schools, this article believes that adopting an English teaching model that integrates competition and curriculum can help vocational schools better cultivate skilled talents and improve students' competitiveness in the workplace. At the same time, this article explores how teachers and students should transform their teaching concepts and learning attitudes, innovate teaching models and evaluation methods, position their career direction, and improve their overall quality in practice. I hope that the ideas and methods described in this article can provide some reference for the reform and development of vocational English education.
Exceptional-point-assisted entanglement, squeezing, and reset in a chain of three superconducting resonators

The interplay between coherent and dissipative dynamics required in various control protocols of quantum technology has motivated studies of open-system degeneracies, referred to as exceptional points (EPs). Here, we introduce a scheme for fast quantum-state synthesis using exceptional-point engineering in a lossy chain of three superconducting resonators. We theoretically find that the rich physics of EPs can be used to identify regions in the parameter space that favor a fast and quasistable transfer of squeezing and entanglement or a fast reset of the system. For weakly interacting resonators with the coupling strength $g$, the obtained quasistabilization timescales are identified as $1/(2\sqrt{2}g)$, and reset infidelities below ${10}^{\ensuremath{-}5}$ are obtained with a waiting time of roughly $6/g$ in the case of weakly squeezed resonators. Our results shed light on the role of EPs in multimode Gaussian systems and pave the way for optimized distribution of squeezing and entanglement between different nodes of a photonic network using dissipation as a resource.
Graph Convolutional Network with Interactive Memory Fusion for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity of a specified aspect in a sentence. Graph neural networks (GNN) based on dependency trees have been shown to be effective for ABSA by explicitly modeling the connection between aspect and opinion terms and exploiting local semantic and syntactic information in the sentence. However, most previous works have overlooked the use of global dependency information. In this paper, we propose a novel Graph Convolutional Network (GCN) with an Interactive Memory Fusion (IMF) mechanism (IMF-GCN) that incorporates both global and local structural information for aspect-based sentiment classification. The IMF mechanism efficiently fuses global and local structural dependency information by assigning different weights to global and local dependency modules. Syntactic constraints are also imposed to prevent the graph convolution propagation unrelated to the target words, further improving the model’s performance. The evaluation metrics used in the paper are accuracy and macro-average F1 scores, and the proposed approach achieves optimal results on three datasets with F1 scores of 79.60%, 82.19%, and 77.75%, which outperform the baseline model.
Hierarchical Fusion Network with Enhanced Knowledge and Contrastive Learning for Multimodal Aspect-Based Sentiment Analysis on Social Media

Aspect-based sentiment analysis (ABSA) is a task of fine-grained sentiment analysis that aims to determine the sentiment of a given target. With the increased prevalence of smart devices and social media, diverse data modalities have become more abundant. This fuels interest in multimodal ABSA (MABSA). However, most existing methods for MABSA prioritize analyzing the relationship between aspect-text and aspect-image, overlooking the semantic gap between text and image representations. Moreover, they neglect the rich information in external knowledge, e.g., image captions. To address these limitations, in this paper, we propose a novel hierarchical framework for MABSA, known as HF-EKCL, which also offers perspectives on sensor development within the context of sentiment analysis. Specifically, we generate captions for images to supplement the textual and visual features. The multi-head cross-attention mechanism and graph attention neural network are utilized to capture the interactions between modalities. This enables the construction of multi-level aspect fusion features that incorporate element-level and structure-level information. Furthermore, for this paper, we integrated modality-based and label-based contrastive learning methods into our framework, making the model learn shared features that are relevant to the sentiment of corresponding words in multimodal data. The results, based on two Twitter datasets, demonstrate the effectiveness of our proposed model.
The $ b $-weight distribution for MDS codes

For a positive integer b ≥ 2, the b-symbol code is a new coding framework proposed to combat b-errors in b-symbol read channels.Especially, the 2-symbol code is called the symbol-pair code.Remarkably, a classical maximum distance separable (MDS) code is also an MDS b-symbol code.Recently, for any MDS code C, Ma and Luo determined the symbol-pair weight distribution of C. In this paper, by calculating the number of solutions for some equations and utilizing some shortened codes of C, we give the connection between the b-weight distribution and the number of codewords in shortened codes of C with special shape.Note that shortened codes of C are also MDS codes, the number of these codewords with special shape are also determined by the shorten method and the number of r-combinations of the multiset problem.From the above calculation, the b-weight distribution of C is determined.Our result generalizes the corresponding result of Ma and Luo [15].
A different kind of meantone temperament and a novel mapping from the harmonic series to Pythagorean pitch classes

AbstractThe logarithm mapping of natural numbers is a sum of products of coefficients. If these coefficients are arbitrary parameters, a new mapping of natural numbers to some subset of real numbers appears. This mapping preserves some crucial logarithm properties and constructs a new musical sound with a spectrum of inharmonic overtones. The simplest one-parameter mapping to a subset of polynomials with integer coefficients is constructed. The parameter defines a new “perfect fifth” similarly to the meantone temperament. It is an interesting case when the mapping parameter is defined from the linear relation between the new “major third” and the new “perfect fifth.” The most interesting case of a linear relation is the condition of zeroing the syntonic comma. Here, the new meantone temperament, based on the new “perfect fifth,” simultaneously coincides with Pythagorean- and five-limit-like tunings.Keywords: harmonic seriesPythagorean tuningfive-limit tuningmeantone temperamentmeantone temperament's mappingspecial tuned spectrumconformal temperamentconformal temperament's mapping2010 Mathematics Subject Classification:: 00A65 AcknowledgmentsThe author thanks Vasily V. Kabachenko for his great patience and constructive comments on previous versions of this paper. The author would also like to offer many thanks to Co-Editors-in-Chief Jason Yust and Emmanuel Amiot. Section 7 was not present in the (N−1)th version and was added later as the answer to questions by reviewers. The author thanks the reviewers for helpful information, numerous helpful comments and good questions. The author would also like to thank Editage (www.editage.com) for English language editing.Disclosure statementNo potential conflict of interest was reported by the author(s).Supplemental dataSupplemental data for this article can be accessed online at https://doi.org/10.1080/17459737.2023.2233972.
Data-driven public health in times of pandemic: towards deep spatialisation

ABSTRACTIn the COVID-19 pandemic, data-driven healthcare practices are subject to rapid growth. These practices challenge the aspects of social and spatial orders encapsulated in the term deep spatialisation. Our assumption is that this term indicates the emergence of new possibilities of relations among open data, social actors and localities. We explore this using established concepts in social theory of space and media theory. Namely, the concept of deep mediatisation – to point to the background processes of digitalisation and datafication; and the concept of translocalisation – to explain the spatial dynamics of the process. We analyse examples of data-driven public health practices developed during the COVID-19 pandemic. We define deep spatialisation as a modulation of deep mediatisation. It emerges at the global scale with effects in spatial dynamics we recognise as translocalisation. It is still a ‘sensitising concept’ and it needs further empirical verification. However, it does seem plausible in aiming to fully grasp the refigurations that occur simultaneously in social and spatial structures as a result of open data-sharing and use in data-driven practices, particularly in the domain of public health.KEYWORDS: COVID-19dataficationdeep spatialisationopen datapandemicpublic health DISCLOSURE STATEMENTNo potential conflict of interest was reported by the authors.Additional informationFundingThe authors received no financial support for the research, authorship and/or publication of this paper.
The Aesthetics of Musical Complex Systems

This article introduces the history and aesthetics of feedback-based music, from early practitioners to more advanced methods and state-of-the-art works based on adaptation. Some of the most relevant techniques developed over almost six decades of investigations in the area of recursive systems for electronic music are discussed to show the variety and richness that a single specialised domain can have, providing examples of how scientific and philosophical principles can be translated into music. The historical context is key to understanding the evolution of the field: feedback-based music arose during the same years in which cybernetics, together with other disciplines, were experiencing a profound transformation. I will provide an overview of how such disciplines changed, highlighting the connections between seemingly distant areas such as philosophy, biology and engineering, and the fact that the development of feedback-based music appears to have followed somewhat closely the evolution of systems thinking. Finally, the article explores questions of musical aesthetics and music theory related to the use of complex autonomous systems in live performance through observations on the author’s creative practice.
Blockchain application in maritime supply chain: a systematic literature review and conceptual framework

ABSTRACTThis research aims to establish the link between blockchain technology adoption in the maritime and shipping industry and its impact on maritime supply chain integration via a systematic review of both the academic and practice literature. In total 148 articles were identified and analysed. Blockchain applications identified from the literature are categorized into three domains: document management, transaction management, and cargo/vessel/terminal operations. An analysis of the benefits and challenges that influence the deployment of blockchain technology for maritime supply chain integration leads to the development of an integrated and extended Technology, Organization, and Environment (TOE) framework. This study is among the first to examine the current state of blockchain diffusion within the maritime supply chain, making a significant contribution to the field. The extended TOE framework offers guidance for future research and understanding of the relationship between blockchain adoption and maritime supply chain integration. It can be used to assist organisations in successfully adopting blockchain technology in their supply chain operations.KEYWORDS: Blockchaindistributed ledger technology, maritimeshippingsupply chain integrationsupply chainTOE framework Disclosure statementNo potential conflict of interest was reported by the author(s).
Width residual neuro fuzzy system based on random feature selection

To shorten the operating time of the high-dimensional problems on fuzzy systems, we proposed the width residual neuro fuzzy system (WRNFS) before, but the discussion on the structure of WRNFS was insufficient, especially on the divide-and-conquer strategies of the input dimensions. In previous research, the optimization methods for WRNFS were not discussed. In this paper we proposed the first optimization method for WRNFS, which is an improved scheme for grouping the input dimensions of WRNFS, using random feature selection(RFS) to find a better solution, so as to improve the overall capability of the system. We call the width residual neuro fuzzy system based on random feature-selection as RFS-WRNFS. In this paper, the exhaustive experiment analysis and practical test of WRNFS and RFS-WRNFS are carried out on the reconstructed MG dataset, and the following conclusions are obtained: ding172 The performance of WRNFS is generally consistent when the structure of the WRNFS sub-systems and the input-output pairs are fixed; ding173 When searching for the optimal solution on the WRNFS, the time cost of exhaustive search is acceptable when the system remains in a small scale; ding174 In most cases, RFS-WRNFS carries out several random tests and produces better results than WRNFS. Furthermore, assuming that the input dimension is N and the times of attempts used to random feature selection for a better solution of WRNFS is M, we found:1) when M = 1 N, there is a certain probability to get an acceptable solution, and the system takes the shortest time; 2) When M = 2 N, there is a great chance to get an acceptable solution in a limited time; 3) When M = 3 N, best solution can be obtained with the longest search time. We suggest M = 2 N for the RFS-WRNFS for the comprehensive performance. Comparing the experiment results of exhaustive search and random feature selection, WRNFS always reaches the optimal solution by exhaustive search through a finite set in a limited time, while RFS-WRNFS in most time keeps a good balance between prediction precision and time efficiency.
Movie recommendation system using taymon optimized deep learning network

The increased usage of the internet and social networks generates a large volume of information. Exploring through the large collection is time-consuming and hard to find the required one, so there is a serious need for a recommendation system. Based on this context several movie recommendation (MR) systems have been recently established. In addition, they have poor data analytics capability and cannot handle changing user preferences. As a result, there are many movies listed on the recommendation page, which provides for a poor user experience is the major issue. Therefore, in this work, a novel Taymon Optimized Deep Learning network (TODL net) for recommending top best movies based on their past choices, behaviour and movie contents. The deep neural network is a combination of Dilated CNN with Bi-directional LSTM. The DiCNN-BiLSTM model eliminates the functionality pooling operations and uses a dilated convolution layer to address the issue of information loss. The DiCNN is employed to learn the movie contents by mining user behavioral pattern attributes. The BiLSTM is applied to recommend the best movies on basis of the extracted features of the movie rating sequences of users in other social mediums. Moreover, for providing better results the DiCNN-BiLSTM is optimized with Taymon optimization algorithm to recommend best movies for the users. The proposed TODL net obtains the overall accuracy of 97.24% for best movies recommendation by using TMDB and MovieLens datasets.
Two-Person Stochastic Duel with Energy Fuel Constraint Ammo

This paper deals with a novel variation of the versatile stochastic duel game that incorporates an energy fuel constraint into a two-player duel game. The energy fuel not only measures the vitality of players but also determines the power of the shooting projectile. The game requires players to carefully balance their energy usage, while trying to outmaneuver their opponent. This unique theoretical framework for the stochastic game model provides a valuable method for understanding strategic behavior in competitive environments, particularly in decision-making scenarios with fluctuating processes. The proposed game provides players with the challenge of optimizing their energy fuel usage, while managing the risk of losing the game. This novel model has potential for implementation across diverse fields, as it allows for a versatile conception of energy fuel. These energy fuels may encompass conventional forms, such as natural gas, petroleum, and electrical power, and even financial budgets, human capital, and temporal resources. The unique rules and constraints of the game in this research are expected to contribute insights into the decision-making strategies and behaviors of players in a wide range of practical applications. This research primarily focuses on deriving compact closed-form solutions, utilizing transformation and flexible analysis techniques adapted to varying the concept of the energy fuel level. By presenting a comprehensive description of our novel analytical approach and its application to the proposed model, this study aims to elucidate the fundamental principles underlying the energy fuel constraint stochastic duel game model.