Reviewerly/exHarmony huggingface dataset:
Evaluation of Painting Artistic Style Transfer Based on Generative Adversarial Network

Unlike most image style transfer tasks, which are to convert a photograph into an image with a certain artist’s style, this paper focuses on artistic style transfer in Monet painting, which is transferring a Monet painting into an image in another artist’s style. In our experiments, we successfully implemented the Cycle-Consistent GAN model and applied Neural Style Transfer (NST) model for contrasting effects. In order to evaluate the result of artistic style transfer quantitatively and effectively with a low requirement for computational resources, we also proposed a quantitative method called style transfer indicator to make the comparison more obvious as the comparisons of the effect of image style transfer were mostly done by subjective analysis previously. This method takes both the style and content of the transferred image into account because whether the transferred image belongs to the new style is as important as whether the content of the image is saved. A ResNet18 pre-trained model and structural similarity index are used for the evaluation of style and content respectively. The human survey that we conducted also proved the validity of our style transfer indicator. Moreover, our proposed indicator could also be applied for the evaluation of other image style transfer tasks.
DLVS: Time Series Architecture for Image-Based Visual Servoing

A novel deep learning-based visual servoing architecture “DLVS” is proposed for control of an unmanned aerial vehicle (UAV) capable of quasi-stationary flight with a camera mounted under the vehicle to track a target consisting of a finite set of stationary points lying in a plane. Current Deep Learning and Reinforcement Learning (RL) based end-to-end servoing approaches rely on training convolutional neural networks using color images with known camera poses to learn the visual features in the environment suitable for servoing tasks. This approach limits the application of the network to available environments where the dataset was collected. Moreover, we cannot deploy such networks on the low-power computers present onboard the UAV. The proposed solution employs a time series architecture to learn temporal data from sequential values to output the control cues to the flight controller. The low computational complexity and flexibility of the DLVS architecture ensure real-time onboard tracking for virtually any target. The algorithm was thoroughly validated in real-life environments and outperformed the current state-of-the-art in terms of time efficiency and accuracy.
Path Planning of Mobile Robots Based on Genetic Algorithm

On the path planning of planar mobile robot, the grid map model is transformed into a two-dimensional matrix, and a mobile operator with probability attribute is designed. A genetic coding method based on Number of matrix rows and columns is proposed, and the reciprocal of path distance is used as the normalized fitness. The path planning based on genetic algorithm is realized by using single point duplication on different paths to cross and multi point duplication to mutate. The optimal path can be obtained by designing the appropriate number of groups. The experimental results show that the method is feasible and effective.
Bokeh Rendering Based on Adaptive Depth Calibration Network

Bokeh rendering is a popular and effective technique used in photography to create an aesthetically pleasing effect. It is widely used to blur the background and highlight the subject in the foreground, thereby drawing the viewer's attention to the main focus of the image. In traditional digital single-lens reflex cameras (DSLRs), this effect is achieved through the use of a large aperture lens. This allows the camera to capture images with shallow depth-of-field, in which only a small area of the image is in sharp focus, while the rest of the image is blurred. However, the hardware embedded in mobile phones is typically much smaller and more limited than that found in DSLRs. Consequently, mobile phones are not able to capture natural shallow depth-of-field photos, which can be a significant limitation for mobile photography. To address this challenge, we propose a novel method for bokeh rendering based on the Vision Transformer. The proposed method employs an adaptive depth calibration network (ADCN) that acts as a confidence level to compensate for errors in monocular depth estimation. The ADCN is used to supervise the rendering process in conjunction with depth information, allowing for the generation of high-quality bokeh images at high resolutions. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods, achieving about 24.7% improvements on LPIPS and obtaining higher PSNR scores.
3D Visualization of Forest Fire Spread Model Based on Cellular Automata

Whereas the traditional forest fire spread model cannot clearly and dynamically display the fire spread process, this paper optimizes the cell structure and evolution rules of cellular automata (CA) in the traditional forest fire spread model to improve the simulation accuracy and develops a forest fire spread simulation system using Unity 3D technology, which realizes the dynamically display and 3D visualization of forest fire spread process. It plays an important role in forest fire prevention and extinguishing.
Unsupervised Sentence Embedding Model Based on Contrastive Learning

The unsupervised sentence embedding model of the contrastive learning framework SimCSE uses dropout noise as a data expansion method, which often defaults to having sentences of the same length to have more similar semantic information, and the random nature of dropout may lead to loss of semantic information or large differences due to sentence embedding. For this reason, we propose two agent tasks random deletion as well as R-Dropout to solve these problems. We conducted experiments on the text semantic similarity task on the publicly available datasets STS12-16, STS B, and SICK-R. The experimental results show that our proposed sentence embedding model improves the average Spearman correlation coefficient to 77.67 %, compared with the benchmark models IS-BERT <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base</inf> , CT-BERT <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">base</inf> , and SimCSE- We also used the SenEval toolkit to evaluate the quality of sentence embed dings generated by the model, and used sentence embeddings as features of migration tasks MR, SUBJ, MPQA, TREC, and MRPC for classification tasks using SenEval, and the experimental results showed that our proposed sentence embedding model achieves better performance in the accuracy of classification in all cases.
NF-Net: Near and Far Network for Crowd Counting

In recent years, the research of crowd counting has aroused widespread attention in many fields such as video surveillance, public security, biomedicine, etc., and crowd counting has become one of the research hotspots in the field of computer vision. Due to factors such as the uncertain distribution of crowds, challenging scale changes, and complex scenes, most state-of-the-art methods adopt the method of constructing a density map and then use it as a learning objective to train a density estimator, however, this method generally performs poorly in the sparse crowd scene with few people. On the contrary, this paper proposes an end-to-end far-near distance network, called NF-Net. First of all, we designed a depth information module (DIM). The introduction of depth information weights into the module can distinguish far and near features well, divide the picture into four different patches, and understand the importance of each feature of each image position. Different receptive fields are assigned to groups of people with different degrees of distance. Secondly, a Near-block is proposed for patch application space for sparse crowds. After feature fusion, the output feature map enters the Near-block, and the method of deepening the network depth is used to enhance the feature extraction ability of pedestrians in sparse scenes, and a new network loss is proposed. Computational method A-Loss. Finally, we conduct extensive experiments on public datasets, demonstrating the effectiveness of NF-Net over baselines. In particular, our method currently achieves state-of-the-art performance on the ShanghaiTech dataset over previous methods. It has a great advantage in scenes where the crowd is sparser.
Low Cost Object Tracking by Computer Vision Using 8 Bits Communication with a Viper Robot

Computer vision systems have been an area of study for multiple areas of engineering. Vision systems have been studied greatly in the area of robotics, as it is of great interest to better automate processes. Many perception systems have been created to improve object handling capabilities for robots. This article seeks to create a computer vision system capable of detecting objects and communicating with a robot in a cost-effective way. To achieve the development of the project the incremental methodology is used, having as first increment the object detection algorithm, the second increment the communication system and the third increment the implementation of an object classification library. In this project, object segmentation tools and neural networks are used to create an intelligent object classification system. The cost for this project are lower than others solution from the market. Although the costs show notorious differences, no motion detection tests were performed, which will be done as a second phase of the project.
Research on Satellite Traffic Classification based on Deep packet recognition and convolution Neural Network

Over the past decade, Satellite communication has been a crucial topic. In particular, as we move from the 5G era to the 6G era, satellites will play an increasingly critical role in providing coverage and flexibility. With the increasing complexity of satellite network environment, the resource allocation of satellite network is becoming more and more important. Accurate identification of traffic types for classification can allocate network resources more effectively. Each method has its own advantages and disadvantages. In order to minimize the impact of the disadvantages, a reasonable combination of them is a new way to accomplish this task. In this paper, we propose a traffic classification method based on deep packet inspection (DPI) and convolution neural network (CNN), and verify it with open data sets. Experimental results show the effectiveness of our proposed method.
Frequency-Domain Analysis of Traces for the Detection of AI-based Compression

The JPEG algorithm is the most popular compression method on the internet. Its properties have been extensively studied in image forensics for examining image origin and authenticity. However, the JPEG standard will in the near future be extended with AI-based compression. This approach is fundamentally different from the classic JPEG algorithm, and requires an entirely new set of forensics tools.As a first step towards forensic tools for AI compression, we present a first investigation of forensic traces in HiFiC, the current state-of-the-art AI-based compression method. We investigate the frequency space of the compressed images, and identify two types of traces, which likely arise from GAN upsampling and in homogeneous areas. We evaluate the detectability on different patch sizes and unseen postprocessing, and report a detectability of 96.37%. Our empirical results also suggest that further, yet unidentified, compression traces can be expected in the spatial domain.
Semi-synthetic Data Generation for Tattoo Segmentation

Tattoos have been successfully employed to assist law enforcement in the identification of criminals and victims. Due to various privacy issues in acquiring images containing tattoos, only a limited number of databases exist. This lack of databases has slowed down the development of new tattoo segmentation and retrieval methods. In our work, we propose a new unsupervised generator that allows generating a large number of semi-synthetic images with tattooed subjects. To successfully generate realistic images, a database including the respective skin segmentation map is also proposed. Using this new generator and the skin database, 5,500 semi-synthetic images were created and evaluated for the tattoo segmentation use case. Experimental results on real data show the usefulness of using semi-synthetic images to train semantic segmentation algorithms: several manually mislabelled real samples were successfully corrected. The tattoo generator code, the skin database and generated images have been made available at https://dasec.h-da.de/hda-sstd/.
A Systematic Review on the Development of Speech Synthesis

This paper systematically summarizes and analyzes the development of speech synthesis technology. Based on the architecture of the speech synthesis system, this paper has conducted in-depth research on the related technologies of text front-end, acoustic model and vocoder. Especially, the neural network speech synthesis method which has been widely concerned in academia and industry in recent years is studied. Finally, the development direction of speech synthesis technology is prospected.
Optimization-Based Improvement of Face Image Quality Assessment Techniques

Contemporary face recognition (FR) models achieve near-ideal recognition performance in constrained settings, yet do not fully translate the performance to unconstrained (real-world) scenarios. To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality information from the input face images that can aid with the recognition process. While existing FIQA techniques are able to efficiently capture the differences between high and low quality images, they typically cannot fully distinguish between images of similar quality, leading to lower performance in many scenarios. To address this issue, we present in this paper a supervised quality-label optimization approach, aimed at improving the performance of existing FIQA techniques. The developed optimization procedure infuses additional information (computed with a selected FR model) into the initial quality scores generated with a given FIQA technique to produce better estimates of the “actual” image quality. We evaluate the proposed approach in comprehensive experiments with six state-of-the-art FIQA approaches (CR-FIQA, FaceQAN, SER-FIQ, PCNet, MagFace, SER-FIQ) on five commonly used benchmarks (LFW, CFP-FP, CPLFW, CALFW, XQLFW) using three targeted FR models (ArcFace, ElasticFace, CurricularFace) with highly encouraging results.
An Interpretable Deep Bayesian Model for Facial Micro-Expression Recognition

The Bayesian network is a powerful model for uncertain causal inference, but is limited to handle numerical data. In order to apply its excellent bidirectional inference ability to the image domain, this paper proposes an interpretable deep Bayesian model, which is based on deep learning technology to conduct semantic segmentation of facial micro-expressions and then extract features to construct the feature Bayesian network to analyze and infer causal relationships. Experiments show that the proposed model enables Bayesian networks to analyze image information, and enhances the interpretability of micro-expression recognition compared with deep learning models.
Design of PUF Based Chaotic Random Number Generator

The creation of key sequences for cryptographic protocols such as digital signatures, hashing, encryption, seed vector, one-time passwords (OTP), etc. depends heavily on pseudo random number generators (PRNG). Simple chaotic (PRNG)s exhibit superior randomness and unpredictability. The primary factor increasing area usage is the initial seed, which requires some memory to retain. When compared to other techniques, a simple chaotic pseudo random number generator (CPRNG) has the quality of being very unpredictable and sensitized to the initial seed. If the original seed is compromised, CPRNG is however exposed. We introduced a novel PUF-CPRNG key generation method that is useful for cryptographic applications in this paper. The purpose of PUF in this study is to generate a secured first seed. The proposed PUF-CPRNG which contains dynamic refresh logic to assurance correctness of the generated random numbers at all times. The Proposed Design Consumes 37.5% less Power than the Existing method [1].
Analysis of SAR ImagesDe-speckling using a Bilateral filter and Feed Forward Neural Networks

Speckle noise reduces the quality and nature of SAR imageries and diminishes the performance of SAR image processing. Thus, the multiplicative noise must be stifled before processing the image utilizing different image handling systems. Even though, there are number of speckle noise reduction techniques are available, all have its own merits and demerits. Therefore, noise reduction is still a major impediment in SAR image processing. In this paper, the speckle noise is reduced by using neural Network followed by the Bilateral Filter. This paper also presents the comparative analysis of two layered FFBPNN, TLFFBPNN and FLFFBPNN for speckle noise reduction of SAR images. Upon comparisons, it could be concluded that, TLFFBPNN de-speckling method provides good visual effects of SN reduction with better similarity and edging conservation metrics.
CRA: Text to Image Retrieval for Architecture Images by Chinese CLIP

Text-to-image retrieval is revolutionized since the Contrastive Language-Image Pre-training model was proposed. Most existing methods learn a latent representation of text and then align its embedding with the corresponding image’s embedding from an image encoder. Recently, several Chinese CLIP models have supported a good representation of paired image-text sets. However, adapting the pre-trained retrieval model to a professional domain still remains a challenge, mainly due to the large domain gap between the professional and general text-image sets. In this paper, we introduce a novel contrastive tuning model, named CRA, using Chinese texts to retrieve architecture-related images by fine-tuning the pre-trained Chinese CLIP. Instead of fine-tuning the whole CLIP model, we engage the Locked-image Text tuning (LiT) strategy to adapt the architecture-terminology sets by tuning the text encoder and freezing the pre-trained large-scale image encoder. We further propose a text-image dataset of architectural design. On the text-to-image retrieval task, we improve the metric of R@20 from 44.92% by the original Chinese CLIP model to 74.61% by our CRA model in the test set.
A Real-time Machine Learning Framework for Smart Home-based Yoga Teaching System

Practicing yoga poses in a home-based environment has increased due to Covid19.Yoga poses without a trainer can be challenging, and incorrect yoga poses can cause muscle damage.Smart home-based yoga teaching systems may aid in performing accurate yoga poses.However, the challenge with such systems is the computational time required to detect yoga poses.This research proposes a real-time machine learning framework for teaching accurate yoga poses.It combines a pose estimation model, a pose classification model, and a real-time feedback mechanism.The dataset consists of five popular yoga poses namely the downdog pose, the tree pose, the goddess pose, the plank pose, and the warrior pose.The BlazePose model was used for yoga pose estimation which transforms the image data into 3D landmark points.The output of the pose estimation model was then passed to the pose classification model for yoga pose detection.Four machine learning classifiers namely, Random Forest, Support Vector Machine, XGBoost, Decision Tree, and two neural network classifiers LSTM and CNN were evaluated based on accuracy, latency and size.Results demonstrate that XGBoost outperforms other models with an accuracy of 95.14 percentage, latency of 8 ms, and size of 513 KB.The output of the XGBoost Classifier was then used to correct yoga poses by displaying real-time feedback to the user.This novel framework has the potential to be integrated into mobile applications which can be used by people for the unsupervised practice of yoga at home.
High-Automatical and High-Accurate Pupil Location Neural Network via FRST FPL

Pupil location refers to the location of the pupil or its center in an image. To solve the problem that the pupil location method is difficult to achieve high automation and high accuracy at the same time, this paper proposes a method combining image processing and statistical learning. In this paper, an improved algorithm of the fast radial symmetry transform (FRST) based on pupil location is proposed, namely FRSTFPL (fast radial symmetry transform for pupil location), which is used to coarsely localize the pupil in the image, followed by a shallow CNN to achieve precise localization. In addition, we construct a dataset based on the CASIA-IrisV4 iris image database and then conduct a variety of experiments. The results show that the location error of the proposed method in an image with a size of 640 × 480 pixels is 8.51 pixels, which exceeds the performance of the comparing methods. In our method, not only accurate radius and complex network are unnecessary, but also highly automated, low computational complexity, and relatively high localizing accuracy can be achieved together.
Visual Learning with Dynamic Recall

The domain of Computer Vision is based on understanding the human visual system and replicating its functionalities using artificial systems. Existing CV algorithms have been able to emulate the human visual system through tasks which include but are not limited to image captioning, classifying visuals and recognizing objects. The human visual system’s astounding capability to recognize objects after encountering a few instances of them has not been replicated by an artificial system. This is crucial in deploying object recognition systems in a real-world scenario as it is not possible to predetermine the categories of objects that the systems will encounter. The CV task that pertains to it is called few-shot learning. Although Deep Neural Networks have attained impressive accuracies in recognizing objects when sufficiently large amounts of data are available, it is not feasible to collect the necessary data for every single object category. This project is an attempt to study and implement a few-shot learning algorithm that extends an object recognition system with a few-shot classification weight generator using knowledge about the base categories to generate weights for the novel categories which have limited samples.
Are quantum computers really energy efficient?

Proponents often tout quantum computing as a more energy efficient alternative to classical computing methods. However, the extent to which it can reduce energy usage remains unclear, as experts have not yet agreed on metrics to determine its energy consumption.