Reviewerly/exHarmony huggingface dataset:
Efficient and Low Color Information Dependency Skin Segmentation Model

Skin segmentation involves segmenting the human skin region in an image. It is a preprocessing technique mainly used in many applications such as face detection, hand gesture recognition, and remote biosignal measurements. As the performance of skin segmentation directly affects the performance of these applications, precise skin segmentation methods have been studied. However, previous skin segmentation methods are unsuitable for real-world environments because they rely heavily on color information. In addition, deep-learning-based skin segmentation methods incur high computational costs, even though skin segmentation is mainly used for preprocessing. This study proposes a lightweight skin segmentation model with a high performance. Additionally, we used data augmentation techniques that modify the hue, saturation, and values, allowing the model to learn texture or contextual information better without relying on color information. Our proposed model requires 1.09M parameters and 5.04 giga multiply-accumulate. Through experiments, we demonstrated that our proposed model shows high performance with an F-score of 0.9492 and consistent performance even for modified images. Furthermore, our proposed model showed a fast processing speed of approximately 68 fps, based on 3 × 512 × 512 images and an NVIDIA RTX 2080TI GPU (11GB VRAM) graphics card.
Hybrid Multichannel-Based Deep Models Using Deep Features for Feature-Oriented Sentiment Analysis

With the rapid growth of user-generated content on social media, several new research domains have emerged, and sentiment analysis (SA) is one of the active research areas due to its significance. In the field of feature-oriented sentiment analysis, both convolutional neural network (CNN) and gated recurrent unit (GRU) performed well. The former is widely used for local feature extraction, whereas the latter is suitable for extracting global contextual information or long-term dependencies. In existing studies, the focus has been to combine them as a single framework; however, these approaches fail to fairly distribute the features as inputs, such as word embedding, part-of-speech (PoS) tags, dependency relations, and contextual position information. To solve this issue, in this manuscript, we propose a technique that combines variant algorithms in a parallel manner and treats them equally to extract advantageous informative features, usually known as aspects, and then performs sentiment classification. Thus, the proposed methodology combines a multichannel convolutional neural network (MC-CNN) with a multichannel bidirectional gated recurrent unit (MC-Bi-GRU) and provides them with equal input parameters. In addition, sharing the information of hidden layers between parallelly combined algorithms becomes another cause of achieving the benefits of their combined abilities. These abilities make this approach distinctive and novel compared to the existing methodologies. An extensive empirical analysis carried out on several standard datasets confirms that the proposed technique outperforms the latest existing models.
Image inpainting in acoustic microscopy

Scanning acoustic microscopy (SAM) is a non-ionizing and label-free imaging modality used to visualize the surface and internal structures of industrial objects and biological specimens. The image of the sample under investigation is created using high-frequency acoustic waves. The frequency of the excitation signals, the signal-to-noise ratio, and the pixel size all play a role in acoustic image resolution. We propose a deep learning-enabled image inpainting for acoustic microscopy in this paper. The method is based on training various generative adversarial networks (GANs) to inpaint holes in the original image and generate a 4× image from it. In this approach, five different types of GAN models are used: AOTGAN, DeepFillv2, Edge-Connect, DMFN, and Hypergraphs image inpainting. The trained model’s performance is assessed by calculating the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) between network-predicted and ground truth images. The Hypergraphs image inpainting model provided an average SSIM of 0.93 for 2× and up to 0.93 for the final 4×, respectively, and a PSNR of 32.33 for 2× and up to 32.20 for the final 4×. The developed SAM and GAN frameworks can be used in a variety of industrial applications, including bio-imaging.
A Lightweight Traffic Lights Detection and Recognition Method for Mobile Platform

Traffic lights detection and recognition (TLDR) is one of the necessary abilities of multi-type intelligent mobile platforms such as drones. Although previous TLDR methods have strong robustness in their recognition results, the feasibility of deployment of these methods is limited by their large model size and high requirements of computing power. In this paper, a novel lightweight TLDR method is proposed to improve its feasibility to be deployed on mobile platforms. The proposed method is a two-stage approach. In the detection stage, a novel lightweight YOLOv5s model is constructed to locate and extract the region of interest (ROI). In the recognition stage, the HSV color space is employed along with an extended twin support vector machines (TWSVMs) model to achieve the recognition of multi-type traffic lights including the arrow shapes. The dataset, collected in naturalistic driving experiments with an instrument vehicle, is utilized to train, verify, and evaluate the proposed method. The results suggest that compared with the previous YOLOv5s-based TLDR methods, the model size of the proposed lightweight TLDR method is reduced by 73.3%, and the computing power consumption of it is reduced by 79.21%. Meanwhile, the satisfied reasoning speed and recognition robustness are also achieved. The feasibility of the proposed method to be deployed on mobile platforms is verified with the Nvidia Jetson NANO platform.
Designing Situated Vocabularies to Counter Social Polarizations: A Case Study of Nolo Neighbourhood, Milan

Many neighbourhoods are currently serving as laboratories where new methods are being explored for collaboratively redesigning cities and tackling the social, environmental, and cultural issues affecting them. These redesign processes are often supported by local communities who increasingly develop bottom-up initiatives to innovate and preserve the neighbourhood’s “common goods.” This is certainly the case of Nolo, an area in the city of Milan (Italy) that has recently undergone an urban regeneration process thanks to the presence of a proactive community of actors living and working in the neighbourhood. Despite effective social innovation practices enacted by part of the local community, several “voices” in Nolo—mainly belonging to marginalized communities—are still excluded from the current process of urban regeneration. This lack of attention is rather problematic for the whole community, as it is leading to increasing rather than mitigating social polarization. To address this issue, we approached Nolo and its community through a participatory design experimentation, generating a series of collaborative platforms to enable those marginalized voices—humans as well as non-humans—to be heard, to enter into agonistic conversations with one another, and to question what they (should) all care about. What this (still ongoing) experimentation is currently showing is that to co-design collaborative platforms to counter polarization needs to be carefully balanced, negotiating between all the actors involved and acknowledging their thick entanglements to finally unravel how they radically inter-depend on one another. This kind of “ontologizing” practice is currently proving to be pivotal to counter “antagonisms” (and, therefore, mitigate social polarizations), and re-framing them in “agonistic” terms. This article reports how we operated this “ontologizing” practice in a particularly debated area of the neighbourhood by embracing the perspective of marginalized actors, encouraging them to collaborative and transformative actions for their own situated context.
Re-Evaluation Method by Index Finger Position in the Face Area Using Face Part Position Criterion for Sign Language Recognition

Several researchers have proposed systems with high recognition rates for sign language recognition. Recently, there has also been an increase in research that uses multiple recognition methods and further fuses their results to improve recognition rates. The most recent of these studies, skeleton aware multi-modal SLR (SAM-SLR), achieved a recognition rate of 98.00% on the RGB video of the Turkish Sign Language dataset AUTSL. We investigated the unrecognized parts of this dataset and found that some signs where the fingers touch parts of the face were not correctly recognized. The proposed method is as follows: First, those with slight differences in top-1 and top-2 evaluation values in the SAM-SLR recognition results are extracted and re-evaluated. Then, we created heatmaps of the coordinates of the index finger in one-handed sign language in the face region of the recognition result in the top-1 to top-3 training data of the candidates based on the face part criteria, respectively. In addition, we extracted four index finger positions from the test data where the index finger stayed longer and obtained the product of the heatmap values of these positions. The highest value among them was used as the result of the re-evaluation. Finally, three evaluation methods were used: the absolute and relative evaluation with two heatmaps and an evaluation method integrating the absolute and relative evaluation results. As a result of applying the proposed method to the SAM-SLR and the previously proposed model, respectively, the best method achieved 98.24% for the highest recognition rate, an improvement of 0.30 points.
Pricing Multiasset Derivatives by Variational Quantum Algorithms

Pricing a multiasset derivative is an important problem in financial engineering, both theoretically and practically. Although it is suitable to numerically solve partial differential equations to calculate the prices of certain types of derivatives, the computational complexity increases exponentially as the number of underlying assets increases in some classical methods, such as the finite difference method. Therefore, there are efforts to reduce the computational complexity by using quantum computation. However, when solving with naive quantum algorithms, the target derivative price is embedded in the amplitude of one basis of the quantum state, and so an exponential complexity is required to obtain the solution. To avoid the bottleneck, our previous study utilizes the fact that the present price of a derivative can be obtained by its discounted expected value at any future point in time and shows that the quantum algorithm can reduce the complexity. In this article, to make the algorithm feasible to run on a small quantum computer, we use variational quantum simulation to solve the Black–Scholes equation and compute the derivative price from the inner product between the solution and a probability distribution. This avoids the measurement bottleneck of the naive approach and would provide quantum speedup even in noisy quantum computers. We also conduct numerical experiments to validate our method. Our method will be an important breakthrough in derivative pricing using small-scale quantum computers.
Fundamentals and issues of user experience in the process of designing consumer products

Abstract User experience (UX) application in the practice of engineering and product design is still limited. The present paper provides insights into research on UX design and recommendations for design practitioners by pointing out common criticalities. These outcomes are achieved through a literature review on how UX relates to design. First, issues in benefitting from UX understanding in design are identified with a specific focus on theoretical contributions. Second, experimental papers investigating UX and design are analysed in relation to previously identified issues. Although issues are present to some extent in all the contributions, the empirical studies dealing with UX in design are overall valid. The results highlight UX’s support in revealing design requirements, but its capability of steering design processes is arguable, as concrete guidelines for practitioners are not well described. Based on identified issues, the authors propose a checklist to make UX studies in design more reliable and their outcomes more comparable.
Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network

The short-term forecasting of photovoltaic (PV) power generation ensures the scheduling and dispatching of electrical power, helps design a PV-integrated energy management system, and enhances the security of grid operation. However, due to the randomness of solar energy, the output of the PV system will fluctuate, which will affect the safe operation of the grid. To solve this problem, a high-precision hybrid prediction model based on variational quantum circuit (VQC) and long short-term memory (LSTM) network is developed to predict solar irradiance 1 hour in advance. VQC is embedded in LSTM to iteratively optimize the weight parameters of four gates (forgetting gate, input gate, cell state, and output gate) to improve prediction accuracy. To evaluate the prediction performance of this model, five solar radiation observatories located in China are selected, together with widely used models including seasonal autoregressive integrated moving average, convolution neural network, recurrent neural network (RNN), gate recurrent unit, (GRU), and LSTM; comparisons are made under different seasons and months. The experimental results show that the annual average root mean square error of the quantum long short-term memory model is 61.756 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{W/m}^{2}$</tex-math></inline-formula> , which is reduced by 10.7%, 13.9%, 8.1%, 3.8%, and 3.4%, respectively, compared with other models; the annual average mean absolute error is 24.257 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{W/m}^{2}$</tex-math></inline-formula> , which is reduced by 28.1%, 28.9%, 24.1%, 12.2%, and 12.8%, respectively, compared with other models; the annual average R-Square ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> ) is 0.946, which is improved by 1.5%, 1.9%, 1.2%, 0.4%, and 0.4%, respectively, compared with other models.
FedLC: Optimizing Federated Learning in Non-IID Data via Label-Wise Clustering

As contemporary systems are being operated in dynamic situations alternating into decentralized and distributed environments from conventional centralized frameworks, Federated Learning (FL) has been gaining attention for an effective architecture when aggregating the information and correspondingly trained AI model of geographically distributed datasets. However, the convergence of FL suffers from the skewed and biased non-IID local dataset acquired from the heterogeneous environment, which is common in real-world practice. To address this issue, we propose a novel Label-wise clustering algorithm in FL (FedLC) that guarantees the convergence among local clients that hold unique distribution. We theoretically analyze the non-IID attributes that potentially affect the performance, defining the six non-IID scenarios in hierarchical order. Through conducting experiments on the suggested non-IID scenarios, we empirically show that the vanilla FL aggregation model is incapable of gaining stable convergence, generating a biased model. By pre-evaluating the local dataset prior to training models, FedLC determines the potential contributions with respect to trainability in a global view, and adaptively selects the locals to collaboratively take part in while aggregation. Our experimental results show that the FedLC outperforms the state-of-the-art non-IID FL optimization studies, offering a robust convergence in a highly skewed and biased non-IID dataset.
Multi-Person Pose Estimation Using Group-Based Convolutional Neural Network Model

Human pose estimation has drawn extensive attention recently and there has been significant progress on it due to the rising popularity of convolutional neural networks (CNN). However, existing state-of-the-art approaches suffer from occlusion, complicated backgrounds, and substantial position fluctuations because of disregarding the human body form. Human parsing is a very pertinent activity that can provide crucial semantic data about bodily parts for position estimation. To overcome the aforesaid limitations, this paper introduces a human pose estimation method using a group-based convolutional neural network model. The proposed method adopts a bottom-up parsing strategy that yields features to extract skeletal key points in the human body. Moreover, it creates a grouping of anatomical key points for individuals by utilizing the non-parametric description for the key point association vector field. Experimental results indicate that the proposed method provides superior performance than the state-of-the-art algorithms in terms of accuracy. In addition, it optimizes its output and detects occluded as well as invisible key points by incorporating feature representation. The proposed method surpasses the recent methods, achieving 93% of the mean average accuracy.
DPVis: Automatic Visual Encoding Based on Deep Learning

Automatic visual encoding is frequently employed in automatic visualization tools to automatically map data to visual elements. This paper proposed an automatic visual encoding approach based on deep learning. This approach constructs visual encoding dataset in a more comprehensive and reliable manner to extract and label widely available visualization graphics on the Internet in accordance with three essentials of visualization. The deep learning model is then trained to create a visual encoding model with powerful generalization performance, enabling automated effective visual encoding recommendations for visual designers. The results demonstrated that our approach extends the automatic visual encoding techniques used by existing visualization tools, enhances the functionality and performance of visualization tools, uncovers previously undiscovered data, and increases the coverage of data variables.
A Study on Neural Style Transfer Methods for Images

Image style transfer techniques have been around for almost twenty years. In particular, the rise of deep learning has gradually influenced traditional image transfer techniques, eventually giving rise to new neural image transfer techniques. This paper covers an introduction to traditional style transfer techniques, including Stroke-based rendering, image filtering, image analogy and texture synthesis, as well as a categorization of various current classic neural image style transfer methods including slow neural method, fast neural method and generative adversarial networks (GAN) based neural style transfer method. In addition, this paper covers commonly used training datasets for style transfer. Finally, the challenges of neural image style transfer are discussed, and the possible future research directions are contained.
Adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm

&lt;abstract&gt; &lt;p&gt;How to improve the robustness to resist attacks and how to adaptively match the key parameters of the watermarking algorithm with the performance requirements to achieve the best performance in different applications are two hot issues in the research of audio watermarking algorithms. An adaptive and blind audio watermarking algorithm based on dither modulation and butterfly optimization algorithm (BOA) is proposed. Based on the convolution operation, a stable feature is designed to carry the watermark, which will improve the robustness by means of the stability of this feature to prevent the watermark loss. Blind extraction will be achieved only by comparing the feature value and the quantized value without the original audio. The BOA is used to optimize the key parameters of the algorithm which can be matched with the performance requirements by coding the population and constructing the fitness function. Experimental results confirm that this proposed algorithm can adaptively search for the optimal key parameters that match the performance requirements. Compared with other related algorithms in recent years, it exhibits strong robustness against various signal processing attacks and synchronization attacks.&lt;/p&gt; &lt;/abstract&gt;
A BERT-CNN Based Approach on Movie Review Sentiment Analysis

Sentiment analysis plays a vital role in the decision-making of multiple fields. Specifically, in movies and television, audiences’ reviews can help with casting, the direction of the plot, etc. To further improve the performance of the original BERT model, a BERT-CNN-based approach is proposed in this paper to do sentiment analysis on IMDb dataset. Although their performances are nearly the same throughout the research, the BERT-CNN approach is better at negative sentiment detection. It got an average elevation of 3.6% in accuracy after the ensemble. Apart from that, topic modeling is also performed to show that most negative reviews are commented on from multiple aspects instead of criticizing only, making sentiment analysis of movie reviews a complex problem.
Deep Learning on Ultrasound Imaging for Breast Cancer Diagnosis and Treatment: Current Applications and Future Perspectives

Abstract:
Investigating High-Level Decision Making for Automated Driving

As the quality of perception systems available for automated driving (AD) increases, we investigate the development of an AD agent based on Reinforcement Learning which exploits underlying systems for longitudinal and lateral control. The goal is addressed by designing high-level actions, trying to imitate the commands of a real driver. The proposed agent is trained in a simulated motorway environment and compared to an agent which outputs low-level actions. Our preliminary results show similar performance results, a more pronounced human-like behaviour and a huge reduction in needed training time because of the higher-level of the available actions.
Research on countermeasures for the development of ecological civilization education in schools in the context of cultural diversity

Abstract Ecological civilization is a form of civilization and has cultural values. In the process of economic and social development, different countries, regions and ethnic groups are bound to differ in their understanding of the connotation of ecological civilization and its external expression due to differences in social practices. In the context of cultural diversity, the development of ecological civilization education in China is a long-term systematic project, which requires all people’s participation, and can generally be carried out at three levels, including the social level, the national level and the school level. This paper argues that the whole society is the soil for developing ecological civilization education in China, the government is the external guarantee, the schools are the main battlefield and the government mainly relies on schools to achieve the goal of ecological civilization education. Therefore, this paper will focus on the school level to discuss the countermeasures for the development of ecological civilization education in China in the present day of cultural diversity, which includes the goal system, teachers, students, curriculum, methodology and environment. Besides, this article takes Sichuan-Tibet region and Tianjin city as examples to elaborate the specific actions of integrating ethnic ecological culture into ecological civilization education in schools.