Reviewerly/exHarmony huggingface dataset:
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of "emotion", coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a "Holy Grail" research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.
A comprehensive taxonomy on multimedia video forgery detection techniques: challenges and novel trends

Thousands of videos are posted on websites and social media every day, including Twitter, Facebook, WhatsApp, Instagram, and YouTube. Newspapers, law enforcement publications, criminal investigations, surveillance systems, Banking, the museum, the military, imaging in medicine, insurance claims, and consumer photography are just a few examples of places where important visual data may be obtained. Thus, the emergence of powerful processing tools that can be easily made available online poses a huge threat to the authenticity of videos. Therefore, it's vital to distinguish between true and fake data. Digital video forgery detection techniques are used to validate and check the realness of digital video content. Deep learning algorithms lately sparked a lot of interest in the field of digital forensics, such as Recurrent Neural Networks (RNN), Deep Convolutional Neural Networks (DCNN), and Adaptive Neural Networks (ANN). In this paper, we give a soft taxonomy as well as a thorough overview of recent research on multimedia falsification detection systems. First, the basic knowledge needed to comprehend video forgery is provided. Then, a summary of active and passive video manipulation detection approaches is provided. Anti-forensics, compression video methods, datasets required for video forensics, and challenges of video detection approaches are also addressed. Following that, we presented an overview of deepfake, and the datasets required for detection were also provided. Also, helpful software packages and forensics tools for video detection are covered. In addition, this paper provides an overview of video analysis tools that are used in video forensic applications. Finally, we highlight research difficulties as well as interesting research avenues. In short, this survey provides detailed information and a broader investigation to extract data and detect fraud video contents under one umbrella.
Classification and recycling of recyclable garbage based on deep learning

Garbage classification and recycling have a lot of benefits, which help protect water and soil resources, improve the living environment's quality, and speed up the green circular economy development. However, traditional garbage collection is weak in effectiveness, requiring a lot of workforces, material, and financial resources. This paper combines ShuffleNet v2 and the depth-separable convolution method to create lightweight YOLOv5s for classifying and positioning recyclable waste. Experimental results show that the enhanced model is only 62% parameters of the original model. In the case of the input resolution being 640 × 640, the mAP (mean Average Precision) of the enhanced model is 94% in accuracy, which is 2.1% higher than the original YOLOv5s. Regarding speed, the reference time is 11.5% faster than the original YOLOv5s on Jetson Nano. In addition, compared with the current mainstream target detection models, the proposed model also expresses the characteristics of recyclable garbage well and can provide a reference value for the classification of recyclable garbage and the lightweight development of recycling.
Comparing and Describing the Characteristics of Swarm Optimization in Bio-inspired Algorithm

The majority of medical data research includes bio-inspired algorithms to identify the best answers, mainly swarm optimizations techniques. Up to this point, there are nine swarm optimization approaches derived through publications. All these bio-inspired algorithms are based on animal behavior, lifestyles, and environments. To directly locate the best procedures, we must overcome various challenges to choose an optimal answer for medical research. Even if we can identify some algorithms as the best methodologies, for various research projects, we choose various algorithms as the major ones depending on the qualities that they require. As a result, we examine all the optimization strategies’ characteristics in detail in our work. The readers can determine the optimization strategy they want to apply in their application by reading the qualities they want to incorporate in their project since many optimization strategies cannot be anticipated as the best approaches based on a single criterion. As a result, we covered the characteristics of swarm optimization strategies, took five main characteristics of bio-inspired algorithms, and mentioned the best suitable algorithm for each of them.
Mixed local channel attention for object detection

Attention mechanism, one of the most extensively utilized components in computer vision, can assist neural networks in emphasizing significant elements and suppressing irrelevant ones. However, the vast majority of channel attention mechanisms only contain channel feature information and ignore spatial feature information, resulting in poor model representation effect or object detection performance, and the spatial attention modules were often complex and expensive. In order to strike a balance between performance and complexity, this paper proposes a lightweight Mixed Local Channel Attention (MLCA) module to improve the performance of the object detection network, and it can simultaneously incorporate both channel information and spatial information, as well as local information and global information to improve the expression effect of the network. On this basis, the MobileNet-Attention-YOLO(MAY) algorithm for comparing the performance of various attention modules is presented. On the Pascal VOC and SMID datasets, MLCA achieves a better balance between model representation efficacy, performance, and complexity than alternative attention techniques. Against the Squeeze-and-Excitation(SE) attention mechanism on the PASCAL VOC dataset and the Coordinate Attention(CA) method on the SIMD dataset, the mAP is enhanced by 1.0 % and 1.5 %, respectively.
Can Gross Primary Productivity Products be effectively evaluated in regions with few observation data?

The dynamics of Gross Primary Productivity (GPP) is key to understand the global carbon cycle. Multiple GPP products are currently available based on remote sensing, Light Use Efficiency model (LUE) or diagnostic biophysical model. However, little knowledge is available on the spatial patterns of the uncertainty of different GPP products and their potential drivers over the Central Asia (CA), a fragile environment for accurate GPP estimation. This study investigates the sensitivity of the 8-day, monthly and yearly GPP uncertainties based on the three-cornered hat (TCH) method and Shapley additive explanation (SHAP) model in terms of vegetation, energy, water, climate and terrain factors in the dryland ecosystem during the 2003–2015 period. Ten GPP products were examined, including one product (FLUXCOM) from machine learning (ML), six products (EC-LUE, FluxSat, LUEopt, MODIS, MuSyQ and VPM) based on the (LUE), two products (GOSIF and NIRv) from satellite-based direct proxies (Proxies) and one product (PML) from the diagnostic biophysical model. The results indicate that the spatial distribution of the ten GPP products in CA showed similar patterns at different time scales, but with values varied at different products and time scales. According to the eddy covariance (EC) observations and the TCH-based uncertainties, the FLUXCOM product showed smaller relative uncertainties than other products. The attribution analysis denotes that the sources of uncertainty of the GPP varied for each product, and thus the improvement strategies for different products should be implemented separately The FLUXCOM should adapt the vegetation- related module to the dryland environment of CA. The LUE model should optimize the LUE parameters for the dryland ecosystem and incorporate the water related variables in the model. The Proxies model should incorporate the water and energy variables (such as soil moisture and radiation) as input data to improve their performance in CA. The diagnostic model should consider the elevation variable as input data, which may improve the performance of the PML in CA. Our results do not only provide an important basis for the selection of GPP products in the study of the carbon cycle in CA, but also offer a new insight into the GPP model development and improvement for the dryland ecosystem.
Poverty, entrepreneurship, and economic growth in Africa

Abstract Literature abounds on the impact of entrepreneurship on economic growth, but with little attention to the role of entrepreneurship in poverty reduction. Furthermore, little attention has been paid to the triangular relationship among poverty reduction, entrepreneurship, and growth in Africa. In this study, we examine the dynamic relationship among these three variables using data collected from 18 African countries. Data are sourced from World Bank development indicators and the World Bank's entrepreneurship database. The study uses batteries of panel data estimation techniques to analyze the data. The following findings emerged: (1) there is bidirectional causality between poverty reduction and growth. (2) There is bidirectional causality between growth and entrepreneurship in Africa. (3) There is unidirectional causality moving from entrepreneurship to poverty reduction. (4) Entrepreneurship significantly reduces poverty in Africa. (5) Impulse response shows that all the variables positively respond to shocks in each other. The study concludes that entrepreneurship enhances poverty reduction in Africa and has a significant impact on economic growth.
SCL: Self-supervised contrastive learning for few-shot image classification

Few-shot learning aims to train a model with a limited number of base class samples to classify the novel class samples. However, to attain generalization with a limited number of samples is not a trivial task. This paper proposed a novel few-shot learning approach named Self-supervised Contrastive Learning (SCL) that enriched the model representation with multiple self-supervision objectives. Given the base class samples, the model is trained with the base class loss. Subsequently, contrastive-based self-supervision is introduced to minimize the distance between each training sample with their augmented variants to improve the sample discrimination. To recognize the distant sample, rotation-based self-supervision is proposed to enable the model to learn to recognize the rotation degree of the samples for better sample diversity. The multitask environment is introduced where each training sample is assigned with two class labels: base class label and rotation class label. Complex augmentation is put forth to help the model learn a deeper understanding of the object. The image structure of the training samples are augmented independent of the base class information. The proposed SCL is trained to minimize the base class loss, contrastive distance loss, and rotation class loss simultaneously to learn the generic features and improve the novel class performance. With the multiple self-supervision objectives, the proposed SCL outperforms state-of-the-art few-shot approaches on few-shot image classification benchmark datasets.
A novel image compression and encryption scheme based on conservative chaotic system and DNA method

An image compression encryption scheme on the strength of a conservative chaotic map, compressed sensing (CS) and DNA method is proposed. Built on the hash algorithm, a novel algorithm for generating the initial state of the chaotic map is designed, which makes the algorithm resistant to chosen-plaintext and known-plaintext attacks. The pseudo-random sequences which are produced by the conservative chaotic map are devoted to constructing the measurement matrix of CS and control the encoding and operation mode of DNA, which improves the complexity of the cryptosystem. In addition, a novel Integrated Cyclic Crossover and Mutation algorithm (ICCM) based on DNA method is proposed. ICCM simultaneously completes the scrambling and diffusion of images based on the loop structure, which greatly enhances the security of the algorithm. Experimental results and a number of security analyses demonstrate that the scheme has excellent performance and robustness, and has good resistance to various attacks.
Scene Reconstruction Algorithm for Unstructured Weak-Texture Regions Based on Stereo Vision

At present, Chinese 3D reconstruction solutions using stereo cameras mainly face known, indoor, structured scenes; for the reconstruction of unstructured, larger-scale scenes with a large variety of texture information of different intensities, there are certain difficulties in ensuring accuracy and real-time processing. For the above problems, we propose a scene reconstruction method using stereo vision. Firstly, considering the influence of outdoor lighting and weather on the captured 2D images, the optimized SAD-FAST feature detection algorithm and stereo-matching strategy were employed in the stereo-matching stage to improve the overall efficiency and matching quality at this stage. Then, a homogenized feature extraction algorithm with gradient value decreasing step by step (GVDS) was used in the depth value calculation to ensure a sufficient number of feature points for strong texture information while extracting features from weak-texture areas, which greatly improved the quality and speed of unstructured scene reconstruction. We conducted experiments to validate the proposed method, and the results showed the feasibility of the proposed method and its high practical value.
Cross-modal attention fusion network for RGB-D semantic segmentation

RGB-D semantic segmentation is crucial for robots to understand scenes. Most existing methods take depth information as an additional input, leading to cross-modal semantic segmentation networks that cannot achieve the purpose of multi-scale global and local cross-modal feature complementation. In this paper, we propose a cross-modal attention fusion network for RGB-D semantic segmentation. Specifically, we adopt a coordinate attention feature interaction module (CA-FIM) to aggregate RGB and depth features at the spatial and channel levels through the coordinate attention mechanism. Then, the gated cross-attention feature fusion module (GC-FFM) fuses the expanded modal features to achieve cross-modal global inference by the gated cross-attention mechanism. Utilizing the above two modules in four stages of the network, our framework can learn multi-modal and multi-level information to reduce the uncertainty of the final prediction. Extensive experiments on the NYU Depth V2, SUN RGB-D, and Cityscapes datasets demonstrate that our cross-modal attention fusion network is effective in RGB-D semantic segmentation for various complicated scenes.
QoE enhancement of the industrial metaverse based on Mixed Reality application optimization

As a comprehensive integration of many new-generation information technologies, the metaverse has become a research hotspot that has attracted much attention. As a part of the metaverse, the industrial metaverse is expected to break through the constraints of space and time and promote high-quality industrial development. The industrial metaverse is human-centric, so its quality of experience (QoE) is a key topic. As one of the enabling technologies of the industrial metaverse, Mixed Reality (MR) can seamlessly integrate virtual information with the physical world and is widely regarded as an important window to the industrial metaverse. In close integration with other enabling technologies, industrial MR applications can be seen as a path toward the realization of the industrial metaverse; thus, the optimization of industrial MR applications can effectively achieve the QoE enhancement of the industrial metaverse. Based on the analysis of existing research and the characteristics of industrial scenarios, consistency, authenticity, smoothness, and comfort are identified as the factors that influence the user experience (UX) of industrial MR applications. Specific optimization methods for industrial MR applications are proposed to improve the UX with regard to these four factors. To verify the effectiveness of the proposed methods, a QoE evaluation model of the industrial metaverse based on the fuzzy analytic hierarchy process (FAHP) is established. Moreover, an industrial metaverse prototype for longwall mining that incorporates the proposed methods is developed and its QoE is evaluated. The results show that the proposed optimization methods for industrial MR applications significantly enhance the QoE in the industrial metaverse, and can provide better services for users in industrial systems, thus better serving these systems.
Strategies choice for blockchain construction and coordination in vaccine supply chain

Vaccine safety management is a top priority of vaccine supply chain (VSC) management. Faced with the increasingly serious vaccine safety problems, blockchain technology is regarded as an effective solution strategy. However, from the perspective of the supply chain operations and consumers, how to carry out blockchain construction is an issue that needs to be discussed. Considering the reality that vaccine manufacturers and vaccination units are implementing blockchain construction, we construct a VSC game model consisting of a vaccine manufacturer and a vaccination unit, analyze the conditions for applying blockchain technology in the VSC, and discuss who undertakes the blockchain construction between the vaccine manufacturer and the vaccination unit, which is more beneficial to the VSC and consumers. We further study the coordination of blockchain-based vaccine supply chain. The results show that, from the perspective of enterprises and consumers, blockchain technology will be adopted only when certain conditions are met. Compared with the vaccination unit, the blockchain construction undertaken by the vaccine manufacturer is more beneficial to the VSC and consumers. The vaccine manufacturer and the vaccination unit jointly undertake the blockchain construction, that is, sign a cost-sharing contract, which can improve the performance of the VSC, but cannot make the total profit reach the optimal level. When certain conditions are met, the two-part tariff contract can realize the blockchain-based VSC coordination.
Non-local adaptive hysteresis despeckling approach for medical ultrasound images

Speckle noise is a major problem in medical ultrasound imaging that reduces the image quality and leads to a negative impact on further diagnosis. In this paper, aiming to alleviate the effect of speckle, a new ultrasound image noise reduction approach called Non-local Adaptive Hysteresis Despeckling is proposed, which effectively exploits data redundancy not only for suppression of speckle noise but also for preserving meaningful structural details in ultrasound images. Since Hysteresis smoothing-based filters are initially introduced to eliminate additive white Gaussian noise, we attempt to adapt this category of filters to the signal-dependent and multiplicative nature of speckle noise in ultrasound images. Furthermore, the local functionality of Hysteresis smoothing-based filters, which is a limitation of their effectiveness, is addressed by proposing more efficient non-local weighting and averaging schemes to be more compatible with the speckle noise model. The qualitative and quantitative evaluations of the simulated and real clinical ultrasound images show that the proposed method can outperform other state-of-the-art methods.
Human skeletons and change detection for efficient violence detection in surveillance videos

In our constantly monitored world, surveillance cameras play a crucial role in curbing crime and violence in public spaces by serving as a deterrent. To enhance their effectiveness, there is a growing need for automated tools that can detect crimes in real time. In this paper, we propose a novel deep learning architecture that accurately and efficiently detects violent crimes in surveillance videos. We rely on what we believe are the most essential pieces of information to detect violence, namely: human bodies and their interaction. To this end, we employ human pose extractors and change detectors as the input of our proposal. Subsequently, we combine them using a novel method, which relies on additions instead of multiplications to guarantee the transmission of information even when one of the inputs provides a zero-valued signal; outperforming other combination alternatives of the literature. Finally, to account for both spatial and temporal information, we use a convolutional alternative of the standard LSTM, the ConvLSTM. The experiments performed on several benchmark datasets demonstrate the efficacy and efficiency of our proposal, achieving state-of-the-art results with much fewer trainable parameters. We release the code to replicate the proposed architecture at https://github.com/atmguille/Violence-Detection-With-Human-Skeletons.
Innovative Solutions for Economic Growth: Exploring the Impact of Economic Freedoms on Foreign Direct Investment Attraction

Abstract Economic freedoms are essential for the growth and development of countries, but their distribution varies across nations, resulting in differences in economic growth. This study aims to explore the impact of economic freedoms on economic growth, measured by gross domestic product (GDP) per capita and foreign direct investment (FDI) inflow, by examining the influence of institutional quality. Economic freedom indicators were obtained from the 2022 report by the Heritage Foundation, and GDP per capita and FDI indicators were sourced from the World Bank report. This study employs multivariate regression analysis to investigate the relationship between economic freedoms and economic growth. The findings suggest that economic freedoms have a positive effect on both GDP per capita growth and FDI inflow. Government integrity was found to be the primary indicator for GDP per capita growth, while Business freedom was identified as the key factor in attracting FDI. The implications of these results are significant, as they can inform policy decisions aimed at improving the standard of living by increasing GDP per capita and creating employment opportunities through FDI.