Reviewerly/exHarmony huggingface dataset:
dm-GAN: Distributed multi-latent code inversion enhanced GAN for fast and accurate breast X-ray image automatic generation

&lt;abstract&gt; &lt;p&gt;Breast cancer seriously threatens women's physical and mental health. Mammography is one of the most effective methods for breast cancer diagnosis via artificial intelligence algorithms to identify diverse breast masses. The popular intelligent diagnosis methods require a large amount of breast images for training. However, collecting and labeling many breast images manually is extremely time consuming and inefficient. In this paper, we propose a distributed multi-latent code inversion enhanced Generative Adversarial Network (dm-GAN) for fast, accurate and automatic breast image generation. The proposed dm-GAN takes advantage of the generator and discriminator of the GAN framework to achieve automatic image generation. The new generator in dm-GAN adopts a multi-latent code inverse mapping method to simplify the data fitting process of GAN generation and improve the accuracy of image generation, while a multi-discriminator structure is used to enhance the discrimination accuracy. The experimental results show that the proposed dm-GAN can automatically generate breast images with higher accuracy, up to a higher 1.84 dB Peak Signal-to-Noise Ratio (PSNR) and lower 5.61% Fréchet Inception Distance (FID), as well as 1.38x faster generation than the state-of-the-art.&lt;/p&gt; &lt;/abstract&gt;
METHODS OF INCREASING MOTIVATION FOR CHOREOGRAPHY LESSONS IN STUDENTS OF HIGHER EDUCATION INSTITUTIONS OF CULTURE

This article touches upon the problem of motivation for the activities of students-choreographers of universities of culture and art, without which the most effective pedagogical methods and technologies do not bring the desired result. The problem is relevant for the system of higher choreographic education. The paper considers the methods of stimulating motivation for activity, which, in the author's opinion, bring the greatest benefit in the formation of motivation for choreographic activity. The revealed methods are tested during the ascertaining and forming experiment in the system of higher education of choreographers. The author gives statistical data of the changes that have taken place with the motivation of choreographer students to work, after the introduction of a set of selected methods for stimulating motivation. Analysis of the data shows that during the experiment the number of students with a positive motive for activity in the second and third year of the Samara State Institute of Culture increased significantly and the number of students with a negative motive for activity decreased (from 53% to 0%) and (from 66% to 0 %). For first-year students, the changes are not statically significant, due to the fact that a positive motive for activity was observed among choreographer students in the first year of study and amounted to (89%). This led to the conclusion that the primary task of a teacher of a modern university will be to promote the emergence of positive motives for activity, before introducing new methods and technologies for teaching students.
DIFFERENTIATION OF THE PUBLIC BY THE LEVEL OF ORIENTATIONS TO STAGE ART

At the moment, an important feature of the existence of the theater as a socio-cultural institution is its functioning in market conditions. Performances become the subject of sale and purchase, and the hedonistic function of the theater is opposed by great competition: cinema, the Internet, social networks, entertainment shows and show programs. In this regard, in order to remain in the consumer market, the theater uses innovative technologies and experimental forms, which lead to the loss of the uniqueness of the theater and a decrease in the level of the public. The effective implementation of the ideological and educational functions of the theater is directly related to the level of training of the theatrical public. The questions of his typology, the classification of the level of art education, are decisive in the perception of the performance. The genre orientation and subject orientation of theaters certainly depend on the ability of the public to perceive works of theatrical art. The article deals with the analysis of sociological studies of the interaction of the individual with art, as well as the study of sociocultural processes that form the orientation of the theater audience towards works of theatrical art. The author of the article pays special attention to the leisure activities of individuals and the formed stereotypes of the theatrical audience. The author also carried out an analysis of the cultural and leisure behavior of the public and derived the differentiation of the public according to the level of orientations and preferences for the performing arts.
Neuro-Fuzzy Enthalpy Calculations for Heat Consumption Recording

An approximation method for calculating thermal energy, based on the use of neuro-fuzzy calculations is considered. Method for applying approaches based on the concepts of artificial intelligence to improve the accuracy of energy calculations based on periodically obtained data on metering parameters is described. An assessment is made of the accuracy of the operation of a neuro-fuzzy system when approximating equations that describe the physical dependence on several parameters of the environment. The reasons that limit the use of neuro-fuzzy systems in approximation problems are analysed.
Enthusiastic and Grounded, Avoidant and Cautious: Understanding Public Receptivity to Data and Visualizations

Despite an abundance of open data initiatives aimed to inform and empower “general” audiences, we still know little about the ways people outside of traditional data analysis communities experience and engage with public data and visualizations. To investigate this gap, we present results from an in-depth qualitative interview study with 19 participants from diverse ethnic, occupational, and demographic backgrounds. Our findings characterize a set of lived experiences with open data and visualizations in the domain of energy consumption, production, and transmission. This work exposes information receptivity - an individual's transient state of willingness or openness to receive information - as a blind spot for the data visualization community, complementary to but distinct from previous notions of data visualization literacy and engagement. We observed four clusters of receptivity responses to data- and visualization-based rhetoric: Information-Avoidant, Data-Cautious, Data-Enthusiastic, and Domain-Grounded. Based on our findings, we highlight research opportunities for the visualization community. This exploratory work identifies the existence of diverse receptivity responses, highlighting the need to consider audiences with varying levels of openness to new information. Our findings also suggest new approaches for improving the accessibility and inclusivity of open data and visualization initiatives targeted at broad audiences. A free copy of this paper and all supplemental materials are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://OSF.IO/MPQ32.</uri>
HoopInSight: Analyzing and Comparing Basketball Shooting Performance Through Visualization

Data visualization has the power to revolutionize sports. For example, the rise of shot maps has changed basketball strategy by visually illustrating where “good/bad” shots are taken from. As a result, professional basketball teams today take shots from very different positions on the court than they did 20 years ago. Although the shot map has transformed many facets of the game, there is still much room for improvement to support richer and more complex analytical tasks. More specifically, we believe that the lack of sufficient interactivity to support various analytical queries and the inability to visually compare differences across situations are significant limitations of current shot maps. To address these limitations and showcase new possibilities, we designed and developed HoopInSight, an interactive visualization system that centers around a novel spatial comparison visual technique, enhancing the capabilities of shot maps in basketball analytics. This article presents the system, with a focus on our proposed visual technique and its accompanying interactions, all designed to promote comparison of two different scenarios. Furthermore, we provide reflections on and a discussion of relevant issues, including considerations for designing spatial comparison techniques, the scalability and transferability of this approach, and the benefits and pitfalls of designing as domain experts.
OW-Adapter: Human-Assisted Open-World Object Detection with a Few Examples

Open-world object detection (OWOD) is an emerging computer vision problem that involves not only the identification of predefined object classes, like what general object detectors do, but also detects new unknown objects simultaneously. Recently, several end-to-end deep learning models have been proposed to address the OWOD problem. However, these approaches face several challenges: a) significant changes in both network architecture and training procedure are required; b) they are trained from scratch, which can not leverage existing pre-trained general detectors; c) costly annotations for all unknown classes are needed. To overcome these challenges, we present a visual analytic framework called OW-Adapter. It acts as an adaptor to enable pre-trained general object detectors to handle the OWOD problem. Specifically, OW-Adapter is designed to identify, summarize, and annotate unknown examples with minimal human effort. Moreover, we introduce a lightweight classifier to learn newly annotated unknown classes and plug the classifier into pre-trained general detectors to detect unknown objects. We demonstrate the effectiveness of our framework through two case studies of different domains, including common object recognition and autonomous driving. The studies show that a simple yet powerful adaptor can extend the capability of pre-trained general detectors to detect unknown objects and improve the performance on known classes simultaneously.
Blockchain Based Intelligent Resource Management in Distributed Digital Twins Cloud

This study focuses on optimizing resource monitoring and management (RMM) of artificial intelligence (AI) systems. The study begins by discussing the fundamental concepts of Digital Twins and cloud collaboration platforms. Subsequently, a collaborative platform called the distributed Digital Twins cloud is designed, incorporating blockchain technology (BCT) to enhance its security. The utilization of cryptographic algorithms in the BCT ensures data security. The study then presents a comprehensive evaluation of the designed BCT-based distributed Digital Twins cloud. The evaluation results demonstrate that the proposed distributed Digital Twins cloud achieves job scheduling times ranging from approximately 35ms to 40ms during training, with the shortest and longest durations observed. The computed data consistency by the proposed platform ranges from 90% to 98%. In the test set, the proposed model achieves job allocation times ranging from approximately 30ms to 35ms, with the shortest and longest durations observed. The data consistency computed by the platform ranges from approximately 90% to 98%, with the lowest and highest values. The network resource management technology model developed in this study exhibits higher efficiency and intelligence, and its performance is comprehensively evaluated. The research findings indicate that the designed model enhances the data processing capability of the cloud platform and effectively improves the security of platform data management, providing substantial support for the advancement of Digital Twins platforms. Moreover, this study offers technical support for the application, design, and optimization of AI technology in network RMM, providing insights into the efficient application and innovation of AI and network technology for future societal development. It contributes to the profound development of AI technology.
Schema-Aware Hyper-Relational Knowledge Graph Embeddings for Link Prediction

Knowledge Graph (KG) embeddings have become a powerful paradigm to resolve link prediction tasks for KG completion. The widely adopted triple-based representation, where each triplet <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(h,r,t)$</tex-math></inline-formula> links two entities <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$h$</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">$t$</tex-math></inline-formula> through a relation <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$r$</tex-math></inline-formula> , oversimplifies the complex nature of the data stored in a KG, in particular for hyper-relational facts, where each fact contains not only a base triplet <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(h,r,t)$</tex-math></inline-formula> , but also the associated key-value pairs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(k,v)$</tex-math></inline-formula> . Even though a few recent techniques tried to learn from such data by transforming a hyper-relational fact into an n-ary representation (i.e., a set of key-value pairs only without triplets), they result in suboptimal models as they are unaware of the triplet structure, which serves as the fundamental data structure in modern KGs and preserves the essential information for link prediction. Moreover, as the KG schema information has been shown to be useful for resolving link prediction tasks, it is thus essential to incorporate the corresponding hyper-relational schema in KG embeddings. Against this background, we propose sHINGE, a schema-aware hyper-relational KG embedding model, which learns from hyper-relational facts directly (without the transformation to the n-ary representation) and their corresponding hyper-relational schema in a KG. Our extensive evaluation shows the superiority of sHINGE on various link prediction tasks over KGs. In particular, compared to a sizeable collection of 21 baselines, sHINGE consistently outperforms the best-performing triple-based KG embedding method, hyper-relational KG embedding method, and schema-aware KG embedding method by 19.1%, 1.8%, and 12.9%, respectively.
Adaptively-accelerated Parallel Stochastic Gradient Descent for High-Dimensional and Incomplete Data Representation Learning

High-dimensional and incomplete (HDI) interactions among numerous nodes are commonly encountered in a big data-related application, like user-item interactions in a recommender system. Owing to its high efficiency and flexibility, a stochastic gradient descent (SGD) algorithm can enable efficient latent feature analysis (LFA) of HDI data for its precise representation, thereby enabling efficient solutions to knowledge acquisition issues like missing data estimation. However, LFA on HDI data involves a bilinear issue, making SGD-based LFA a sequential process, i.e., the update on a feature can impact the results on the others. Intervening the sequence of SGD-based LFA on HDI data can affect the training results. Therefore, a parallel SGD algorithm to LFA should be designed with care. Existing parallel SGD-based LFA models suffer from a) low parallelization degree, and b) slow convergence, which significantly restrict their scalability. Aiming at addressing these vital issues, this paper presents an Adaptively-accelerated Parallel Stochastic Gradient Descent (AP-SGD) algorithm to LFA by: a) establishing a novel local minimum-based data splitting and scheduling scheme to reduce the scheduling cost among threads, thereby achieving high parallelization degree; and b) incorporating the adaptive momentum method into the learning scheme, thereby accelerating the convergence rate by making the learning rate and acceleration coefficient self-adaptive. The convergence of the achieved AP-SGD-based LFA model is theoretically proved. Experimental results on three HDI matrices generated by real industrial applications demonstrate that the AP-SGD-based LFA model outperforms state-of-the-art parallel SGD-based LFA models in both estimation accuracy for missing data and parallelization degree. Hence, it has the potential for efficient representation of HDI data in industrial scenes. The future study will focus on the GPU-deployed extensions of the proposed AP-SGD algorithm and an AP-SGD-based LFA model.
Constrained Bipartite Graph Learning for Imbalanced Multi-modal Retrieval

Real-world multi-modal retrieval tasks always encounter modal imbalance scenarios. The scales of instances from different modalities are inconsistent and unaligned with each other. Though several methods alleviate the issue by establishing miscellaneous representations of multi-modal data, they still suffer from difficulties like laborious human-being annotations and complex common-space optimization. In the research, we present Constrained Bipartite Graph Learning (CBGL) for imbalanced correlations, where a size-flexible correlation graph is learned from instances' representations. To guide the graph learning, we take advantage of prior side information, including positive pairs and negative pairs, which readily express intramodality affinity and inter-modality discrepancy. Accordingly, both positive and negative correlations are propagated over instances, and a similarity graph with satisfactory neighbors is achieved. Benefiting from the probabilistic similarities, a query graph is then naturally constructed that directly achieves multimodal retrieval. To validate the effect, we build a Music-Video- Image (MVI) dataset in regard to music and images with imbalanced scales. Experimental results reported on MVI dataset and three benchmarks demonstrate our prominent superiority over ten representative competitors in multi-modal retrieval.
Fast Flight of the Flying Robot With Fuzzy Decision and Multimodal Control Tackling Uncertainties

To address the challenges of flying at high speed and maintaining safety in uncertain environments, a novel autonomous flight control approach is proposed based on fuzzy decision and multimodal control. This approach first develops an uncertainty assessment model that functionalizes the uncertainties included in target capture, environmental modeling, and flight control. On this basis, a fuzzy decision on flight control is formulated comprising two parts: the control mode decision and the speed decision. In a certain environment, the control mode decision strategy selects an optimal control mode that can minimize the uncertainties in the subsequent flight according to the aforementioned uncertainty assessment model. With the determined control mode, the speed decision continuously evaluates the maximum velocity that ensures safety regarding the lumped uncertainties. Subsequently, multimodal control was developed to facilitate fast flight using the determined control mode and flight speed. Finally, simulations and experiments demonstrate that the proposed method can effectively adapt to uncertain environments and the flying robot can fly at the appropriate decided speed while maintaining safety. In a typical environment, the proposed method can approximately reduce the time cost by 20% and increase the maximum speed by 121% while ensuring safety, achieving a fast and safe flight in a complex environment with a maximum speed of 3.2 m/s.
Vocal Cord Vibration Signal Recognition Model Based on Feature Engineering Preprocessing

The vocal cord vibration signal carries as much rich phonetic information as the speech signal. However, conventional automatic speech recognition (ASR) technology cannot effectively deal with vocal cord vibration signal. In this paper, a wearable throat speech detection sensor (TSDS) based on graphene sheets is designed. Subsequently, a large number of Chinese and English words are collected, and a dataset of vocal cord vibration for recognition tasks is established. Applying the knowledge of transfer learning, we use XGBoost applied to bearing fault detection as a source model, combined with feature engineering technology PCA, and propose an extreme gradient boosting model fused with principal component analysis for efficient identification of vocal fold vibration signals. The model reduces the data dimension, saves the operation time, and can automatically extract the characteristic parameters with a large contribution to the vocal cord vibration signal at the same time.
CroMIC-QA: The Cross-Modal Information Complementation based Question Answering

This paper proposes a new multi-modal question-answering task, named as Cross-Modal Information Complementation based Question Answering (CroMIC-QA), to promote the exploration on bridging the semantic gap between visual and linguistic signals. The proposed task is inspired by the common phenomenon that, in most user-generated QA scenarios, the information of the given textual question is incomplete, and thus it is required to merge the semantics of both the text and the accompanying image to infer the complete real question. In this work, the CroMIC-QA task is first formally defined and compared with the classic Visual Question Answering (VQA) task. On this basis, a specified dataset, CroMIC-QA-Agri, is collected from an online QA community in the agriculture domain for the proposed task. A group of experiments is conducted on this dataset, with the typical multi-modal deep architectures implemented and compared. The experimental results show that the appropriate text/image presentations and text-image semantic interaction methods are effective to improve the performance of the framework.
Audio-Visual Temporal Forgery Detection Using Embedding-Level Fusion and Multi-Dimensional Contrastive Loss

Audio-visual deepfake detection is the process of identifying and detecting deepfakes that have been generated using both audio and visual content with AI algorithms. Most existing methods primarily focus on the overall authenticity while neglecting the position of forgeries in time. This can be particularly problematic, as even a small alteration in a clip can significantly impact its meaning. Such brand new attacks are dangerous and how to tackle such attacks remains an open question. In this paper, we present a novel neural network-based model to tackle the temporal forgery detection (TFD) problem. It consists of new audio and visual encoders with cross-modal attention for embedding extraction, and an embedding-level fusion mechanism with self-attention for forgery localization. Besides, a multi-dimensional contrastive loss is proposed which helps the model not only to capture audio-visual inconsistency for deepfake detection but also to exploit temporal inconsistency by coherently constraining the extracted embeddings. Extensive experiments on the LAV-DF dataset show that the presented method outperforms several state-of-the-art temporal forgery localization methods by up to 23.4% on AP@0.5 and 13.8% on AR@100. In addition, we also show the effectiveness of the proposed model on deepfake detection.