Reviewerly/exHarmony huggingface dataset:
Object detection network based on dense dilated encoder net

Abstract In this paper, the authors apply the feature pyramid network (FPN) to the single‐stage anchor‐free object detection algorithm CenterNet, and the effectiveness of the multi‐level feature fusion of FPN for the object detection algorithm is proved by experiments. However, multi‐level feature fusion leads to an increase in computational cost. In this regard, this paper proposes an object detection algorithm, called DDE‐Net, that does not use multi‐level feature fusion and only uses single‐level feature for optimization. The key component in it: the dense dilated encoder, which encourages dense information exchange of features between different spatial scales. This paper presents extensive experiments, and DDE‐Net shows strong performance compared to that of other popular models on the PASCAL VOC and on the COCO2017 dataset. On the COCO2017 dataset, the authors’ DDE‐Net achieves comparable results with its feature pyramids counterpart RetinaNet, while applying the same backbone with smaller params and GFLOPs than RetinaNet. With an image size of 512 × 512, DDE‐Net achieves 37.3 AP running at 81 fps on 2080 Ti.
A Systematic Solution for Moving-Target Detection and Tracking While Only Using a Monocular Camera

This paper focuses on moving-target detection and tracking in a three-dimensional (3D) space, and proposes a visual target tracking system only using a two-dimensional (2D) camera. To quickly detect moving targets, an improved optical flow method with detailed modifications in the pyramid, warping, and cost volume network (PWC-Net) is applied. Meanwhile, a clustering algorithm is used to accurately extract the moving target from a noisy background. Then, the target position is estimated using a proposed geometrical pinhole imaging algorithm and cubature Kalman filter (CKF). Specifically, the camera's installation position and inner parameters are applied to calculate the azimuth, elevation angles, and depth of the target while only using 2D measurements. The proposed geometrical solution has a simple structure and fast computational speed. Different simulations and experiments verify the effectiveness of the proposed method.
Autonomous Navigation of Mobile Robot Assisted by Its Identified Neural Network Model

Autonomous navigation is one of the key tasks in the development of control systems for real autonomous mobile objects.This paper presents the developed technology for accurately determining the position of a mobile robot in an autonomous operating mode without an external positioning system.The approach involves using a high-precision model of a real robot identified by a neural network.The robot adjusts its position, determined using odometry and video camera, according to the position of the robot, obtained using an accurate model.To train the neural network, a training set is used that takes into account the features of the movement of a wheeled robot, including wheel slip.In the experimental part, the problem of autonomous movement of a mobile robot along a given trajectory is considered.
A Study on the Recommendation Method of Intelligent Media Learning Resources in the Foreign Communication and Teaching of International Communication of Chinese Central Plains Culture

Today, with the development of intelligent media, the foreign communication and teaching activities of the Chinese central plains culture should actively seek experiences that can be learned from, establish a multi-channel foreign communication mode, and then promote the Chinese central plains culture to go out of the country and into the world better. The study improves the collaborative filtering recommendation algorithm and the joint matrix decomposition algorithm based on the theory of migration learning, aiming to improve the learning to optimize the resource recommendation system by calculating the user similarity and establishing the user preference-resource feature matrix. The experimental results show that the average absolute error and root mean square error of the improved algorithms are lower than those of other algorithms, proving that the optimized algorithms improve the accuracy and efficiency of resource recommendation in the foreign communication and teaching activities of the Chinese central plains culture while operating stably and with wide applicability on the recommendation system.
The main psychological determinants of the participants’ readiness of professional skill competitions to perform successfully

The article presents the results of psychological research and the primary analysis of the results obtained, which allow us to formulate the main psychological determinants of the readiness of the contestants for competitions in professional skills and possible success. The study was conducted by means of questionnaire methods, primary statistical processing (percentage ratio), comparative analysis of the data obtained. The object of the study was the participants of professional skill competitions: winners, prize-winners and contestants who performed unsuccessfully. It was revealed that the winners and prize-winners of professional skill competitions, as well as participants who performed unsuccessfully, actively use psychological and professional techniques, techniques, creativity, and so on in preparation for competitions. The personal and professional qualities of the contestants contributing to their success and possible reasons for unsuccessful performances are noted. The main psychological determinants of the contestants’ readiness for competitions in professional skills and possible success are identified and formulated – personality strength; confidence; a high level of need for achievements and victories; an adequate level of self-esteem; a high level of reflection; competent knowledge of psychological technologies of self-action; a high level of personal and professional qualities development; rational independence in preparation.
A Novel Unsupervised Video Anomaly Detection Framework Based on Optical Flow Reconstruction and Erased Frame Prediction

Reconstruction-based and prediction-based approaches are widely used for video anomaly detection (VAD) in smart city surveillance applications. However, neither of these approaches can effectively utilize the rich contextual information that exists in videos, which makes it difficult to accurately perceive anomalous activities. In this paper, we exploit the idea of a training model based on the "Cloze Test" strategy in natural language processing (NLP) and introduce a novel unsupervised learning framework to encode both motion and appearance information at an object level. Specifically, to store the normal modes of video activity reconstructions, we first design an optical stream memory network with skip connections. Secondly, we build a space-time cube (STC) for use as the basic processing unit of the model and erase a patch in the STC to form the frame to be reconstructed. This enables a so-called "incomplete event (IE)" to be completed. On this basis, a conditional autoencoder is utilized to capture the high correspondence between optical flow and STC. The model predicts erased patches in IEs based on the context of the front and back frames. Finally, we employ a generating adversarial network (GAN)-based training method to improve the performance of VAD. By distinguishing the predicted erased optical flow and erased video frame, the anomaly detection results are shown to be more reliable with our proposed method which can help reconstruct the original video in IE. Comparative experiments conducted on the benchmark UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets demonstrate AUROC scores reaching 97.7%, 89.7%, and 75.8%, respectively.
DVDR-SRGAN: Differential Value Dense Residual Super-Resolution Generative Adversarial Network

In the field of single-image super-resolution reconstruction, GAN can obtain the image texture more in line with the human eye. However, during the reconstruction process, it is easy to generate artifacts, false textures, and large deviations in details between the reconstructed image and the Ground Truth. In order to further improve the visual quality, we study the feature correlation between adjacent layers and propose a differential value dense residual network to solve this problem. We first use the deconvolution layer to enlarge the features, then extract the features through the convolution layer, and finally make a difference between the features before being magnified and the features after being extracted so that the difference can better reflect the areas that need attention. In the process of extracting the differential value, using the dense residual connection method for each layer can make the magnified features more complete, so the differential value obtained is more accurate. Next, the joint loss function is introduced to fuse high-frequency information and low-frequency information, which improves the visual effect of the reconstructed image to a certain extent. The experimental results on Set5, Set14, BSD100, and Urban datasets show that our proposed DVDR-SRGAN model is improved in terms of PSNR, SSIM, and LPIPS compared with the Bicubic, SRGAN, ESRGAN, Beby-GAN, and SPSR models.
Task Containerization and Container Placement Optimization for MEC: A Joint Communication and Computing Perspective

Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems -- graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and-container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling schemes.
MRLBot: Multi-Dimensional Representation Learning for Social Media Bot Detection

Social media bots pose potential threats to the online environment, and the continuously evolving anti-detection technologies require bot detection methods to be more reliable and general. Current detection methods encounter challenges, including limited generalization ability, susceptibility to evasion in traditional feature engineering, and insufficient exploration of user relationships. To tackle these challenges, this paper proposes MRLBot, a social media bot detection framework based on unsupervised representation learning. We design a behavior representation learning model that utilizes Transformer and a CNN encoder–decoder to simultaneously extract global and local features from behavioral information. Furthermore, a network representation learning model is proposed that introduces intra- and outer-community-oriented random walks to learn structural features and community connections from the relationship graph. Finally, the behavioral representation and relationship representation learning models are combined to generate fused representations for bot detection. The experimental results of four publicly available social network datasets demonstrate that the proposed method has certain advantages over state-of-the-art detection methods in this field.
LoyalDE: Improving the performance of Graph Neural Networks with loyal node discovery and emphasis

Recent years have witnessed an increasing focus on graph-based semi-supervised learning with Graph Neural Networks (GNNs). Despite existing GNNs having achieved remarkable accuracy, research on the quality of graph supervision information has inadvertently been ignored. In fact, there are significant differences in the quality of supervision information provided by different labeled nodes, and treating supervision information with different qualities equally may lead to sub-optimal performance of GNNs. We refer to this as the graph supervision loyalty problem, which is a new perspective for improving the performance of GNNs. In this paper, we devise FT-Score to quantify node loyalty by considering both the local feature similarity and the local topology similarity, and nodes with higher loyalty are more likely to provide higher-quality supervision. Based on this, we propose LoyalDE (Loyal Node Discovery and Emphasis), a model-agnostic hot-plugging training strategy, which can discover potential nodes with high loyalty to expand the training set, and then emphasize nodes with high loyalty during model training to improve performance. Experiments demonstrate that the graph supervision loyalty problem will fail most existing GNNs. In contrast, LoyalDE brings about at most 9.1% performance improvement to vanilla GNNs and consistently outperforms several state-of-the-art training strategies for semi-supervised node classification.
Assistance of Internet of Things to Intelligent Business Management Model of Supply Chain Finance and Modern Logistics Enterprises

Since its birth, supply chain finance (SCF) has made contributions to the development of small and medium-sized enterprises, but it also faces many challenges in the development process. With the development and continuous progress of the internet and information technology, it has also opened up new ways for urban development and innovation. This article introduced the background of intelligent business model, conducted academic research and summary on the keywords of SCF and the internet of things (IOT), and then summarized urban analysis by combining AI and big data. Then it put forward the business model factor analysis of SCF and modern logistics enterprises. At the end of the article, the simulation experiment was carried out, and the experiment was summarized and discussed. The experimental results showed that the average transaction cost of the new business model was 3.5 lower than that of the traditional business model. With the continuous development of artificial intelligence technology and big data technology, urban planning is also facing new opportunities and challenges.
Using Catalyst Mass-Based Clustering Analysis to Identify Adverse Events during Approach

Discovering and mitigating potential risks in advance is essential for preventing aviation accidents on routine flights. Although anomaly detection-based explanation techniques have successfully uncovered potential risks for proactive flight safety management, explaining group-scale precursors using these methods is challenging due to the assumption that risky flights are significantly fewer in number than normal flights, as well as the reliance on non-domain knowledge for hyperparameter adjustment. To characterize the group-scale precursors more accurately, we propose a novel technique called Catalyst Mass-Based Clustering Analysis (CMCA), which employs a composite entropy-energy dissipation index during approach to evaluate the energy management performance. On this basis, an optimization objective is constructed to identify clusters exhibiting significant energy management differences during the approach phase. We successfully identify group-scale precursors with energy management issues by applying CMCA to a combination of minority-labeled and majority-unlabeled flights. Comparative experiments show that these precursors have energy levels that deviate from normal flights by 5.83% and 10.93%, respectively, 1000 ft above touchdown, demonstrating the effectiveness of our method. The analysis suggests that poor energy management awareness on the part of pilots could be responsible for these group-scale precursors. Notably, the results obtained using CMCA are comprehensible for Subject Matter Experts, making the method a valuable tool for proactive flight safety management.
Smart glasses for 3D multimodal composition

Extended reality technologies – mixed, augmented, and virtual reality, and future-related technologies – are rapidly expanding in many fields, with underexplored potentials for multimodal composition in digital media environments. This research generates new knowledge about the novel wearable technology – smart glasses – to support elementary students’ multimodal story authoring with 3D virtual objects or holograms. The researchers and teachers implemented learning experiences with upper elementary students from three classrooms to compose and illustrate written narratives before retelling the story with Microsoft HoloLens 2 smart glasses, selecting 3D holograms to illustrate the settings, characters, and events from the 3D Viewer software. The findings analyse how smart glasses supported students’ multimodal composition, and relatedly, the new modal resources available to students wearing smart glasses to compose 3D stories. The findings have significance for educators and researchers to understand and utilise the multimodal affordances of augmented and mixed reality environments for composing and storytelling.
Dynamics reconstruction in the presence of bistability by using reservoir computer

Coupled chaotic oscillators usually display rich dynamics and undergo complicated bifurcations when the coupling strength changes. Dynamics reconstruction of the coupled system without relying on a model is a difficult problem in the field of nonlinear dynamics, especially in the presence of bistability. Following previous works (PRE 104 (2021) 014205 and 024205), we design a reservoir computer with parameter input channel consisting of the coupling strength and an indicator parameter. The indicator parameter is used to distinguish the possible coexisting dynamical states. Using a ring of coupled Rössler oscillators, we demonstrate the power of the reservoir computer in dynamics reconstruction and predicting the transitions between different dynamical states. We find that a single one reservoir computer is enough to reconstruct the complete bifurcation diagrams of the original coupled system.
Automated sentimental analysis using heuristic-based CNN-BiLSTM for E-commerce dataset

The sentimental analysis is processed according to structural detection, extraction, quantification, and evaluation of impacts and information present in natural language processing, biometric data, computer-language society, and text analysis. Advanced implementations in data analytics occur, which allows for identifying the underlining trends based on certain efficient computational models. Some of the social and e-commerce platforms consist of numerous reviews regarding the online products that are posted by the end-users. This helps the developers to understand priceless insight during the process of product design. The most significant aim of this work is to develop a Heuristic-based sentimental analysis for E-Commerce data. Here, the pre-processing of input E-Commerce data is performed by blank space removal, stop word removal, and stemming. Here, the statistical features are extracted using Cross Similarity Score (CSS) and Joint Similarity Score (JSS) regarding “positive, negative, and neutral keywords”. In addition, the word2vec features are also extracted. From the two sets of features, “Convolutional Neural Network (CNN) with Bidirectional Long Short Term Memory (BiLSTM)” is separately used that is named as Optimal Hybrid CNN-BiLSTM (OH-CNN-BiLSTM), and performed the hybridization of two models for the sentimental analysis. The designed CNN-BiLSTM technique is ensured better outcomes when processing long text as it provides advantages through the ability of CNN to extract the features and superiority of Bi-LSTM in learning the bidirectional dependencies of the long-term text. The main novelty of this work is to develop an improved Galactic Swarm Optimization (IGSO) algorithm for improving the hybridized model, as it provides faster convergence when getting the accurate solution on a wide range of dimensional space and is also able to solve the optimization issues with multimodal benchmark data. The evaluation of the suggested algorithm is done with various E-Commerce data and secures high accuracy on sentimental analysis and prediction while comparing with the traditional models.
Mobile phone coverage and infant mortality in sub-Saharan Africa

Infant mortality is still high in Sub-Saharan Africa. Mobile phone technology has the potential to reduce mortality by facilitating the exchange of information. To test for effects, we combine georeferenced information on mobile phone signal coverage with infant mortality data on 1,268,041 children born in 30 Sub-Saharan African countries between 1999–2016. Our results reveal that infant mortality risk drops substantially as mobile phone coverage expands. Infants are 0.9 percentage points less likely to die within the first year after birth compared to their sibling(s) when mobile phone signal is available. In line with this result, we also find that fertility rates decline with the rollout of mobile coverage. Suggestive evidence indicates that improved health knowledge is relevant in explaining our findings. Mobile-phone related changes in access to in-person healthcare services or improvements in income opportunities, on the other hand, are unlikely to play an important role.
Examining the Psychosocial and Behavioral Factors Associated with Adolescent Engagement in Multiple Types of Cyberdeviance: Results from an Australian Study

Abstract Research has shown that psychosocial and behavioral factors are associated with engagement in a range of deviant behaviors across offline settings. To date, however, very little research has explored the impact of these factors in online contexts. This article addresses this gap by examining the psychosocial and behavioral factors associated with common types of adolescent cyberdeviance. This is accomplished through an empirical study of 327 adolescents enrolled in a high school located in a large Australian city. The study assesses various aspects of psychosocial and behavioral functioning using the Strengths and Difficulties Questionnaire (total difficulties, internalizing problems, externalizing problems, prosocial behavior), as well as numerous types of cyberdeviance relevant to young people, including cyberfraud, cyberhate, cyberviolence, sexting, digital piracy, hacking, and cyberbullying. A series of multivariate logistic regression analyses were conducted to demonstrate the association between psychosocial and behavioral difficulties and various types of cyberdeviance, independent of gender, school grade, socioeconomic status, and engagement in offline delinquency. Results indicate that total difficulties, internalizing problems, and externalizing problems were significantly associated with greater likelihood of engagement in most types of cyberdeviance examined in this study, whereas prosocial behavior was associated with a lower likelihood of engagement in digital piracy only. A discussion of the findings highlights the importance of understanding these factors in a digital context, as well as demonstrating the need to account for them when designing targeted interventions.