Miracle-dz/UniFAR-FTUs huggingface dataset:
User-Guided Anime Line Art Colorization with Spatially-adaptive Normalization

Line art plays an essential role in the process of anime creation. Colorization of the line art images is a tough task for the reason that neither grayscale values nor coloring semantic information exists in grayscale line art images. In this paper, we propose a novel line art colorization architecture with spatially-adaptive normalization for user-guide colorization. Our method can obtain high-quality colorized anime images by reserving more semantic information of inputs. Specifically, we integrate spatially-adaptive normalization block as well as spectral normalization, and the hybrid normalization enables us to train the network robustly while preserving the semantic information as much as possible. Our model is based on U-net architecture, which is regarded as a successful feature extraction architecture and is widely applied in the field of image segmentation and image generation. Also, We propose an anime data processing paradigm, combining the colored picture and the synthetic line art to generate simulated strokes in line with human intuition, which is conducive to making the generated images more realistic and perform on in-the-wild data better. With the proposed model, we evaluate a collected anime dataset and compare it with the existing method. The experimental results demonstrate the excellent and stable coloring effect of our proposed model.
Object-Aware Semantic Scene Completion Through Attention-Based Feature Fusion and Voxel-Points Representation

Semantic scene completion is a computer vision technique that combines semantic segmentation and shape completion. Its purpose is to infer a complete 3D scene with semantic information from single-view RGB-D images. In recent years, some methods have adopted the voxel-points-based approach, converting voxelized scenes into point clouds to reduce the computational cost associated with 3D convolutions. However, majority of such methods do not fully consider the geometric details of the objects in the scene. In this paper, we propose ASPNet (Attention-based Semantic Point Completion Network), a two-branch semantic scene completion algorithm that combines scene-level completion and object refinement. In the scene level completion branch, we design the SPT (Semantic-based Point Transformer) module, which introduces semantic information into the traditional Point Transformer layer to realize the feature aggregation of neighboring keypoints of the same category. Using the object detection module and the object refinement module, ASPNet refines the rough semantic complementation results obtained from direct coding and decoding of RGB-D inputs. The quantitative results show that ASPNet has much less computational overhead than the 3D convolution-based semantic scene completion algorithm, while the reconstruction results have more geometric details.
BERT-Enhanced Graph Convolutional Network for News Text Classification

With the rapid development of the Internet, on-line news text data is growing explosively. Traditional manual annotation methods are no longer sufficient for the current order of magnitude of news classification tasks. Most of the current news text classification tasks use the pre-trained language model BERT for contextual semantic information extraction and achieve good results. However, BERT is unable to make full use of the structural features of the text. In contrast, Graph Convolutional Network (GCN) has a natural advantage in modeling the structural information of the text. In order to comprehensively utilize the structural information and semantic information of news texts to improve the accuracy of news text classification, this paper proposes a BERT-Enhanced Graph Convolutional Network model (BEGCN) to classify news text. For each news text, the text graph is constructed based on word co-occurrence, a semantic dictionary is introduced to enrich the construction of the text graph, and text structure features are extracted using GCN. In addition, semantic features are extracted using BERT. These two different granularity features are interacted through the multi-head attention module, and three aggregation methods are used for feature aggregation so that they can influence each other to obtain a final more effective representation. The experiments can be proved the effectiveness of the proposed model on three text datasets of news categories.
Using Deep Learning Models for COVID-19 Related Sentiment Analysis on Twitter Data

The significance of sentiment analysis has increased in modern times due to the extensive use of social media platforms as a medium for individuals to express their opinions. Twitter is widely acknowledged as a popular social media site mostly used for microblogging. People often voice their opinions on current events, making it challenging for researchers to accurately classify the mood expressed in these opinions. This research study presents a novel and efficient method for identifying and detecting false or misleading information pertaining to the COVID-19 pandemic. The dataset including artificially created news stories is obtained from a collection of texts and processed via the cycle of natural language processing (NLP). For this research study, three advanced deep learning models were used to predict the emotion of news items, accurately differentiating between genuine and fraudulent ones. This study utilizes Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) as deep learning classifiers. Subsequently, it compares the outcomes achieved from these classifiers. The findings suggest that the BiGRU deep learning classifier has exceptional accuracy and efficiency, achieving a remarkable accuracy rate of 91%.
Multimodal Context-Based Continuous Authentication

We present a new multimodal, context-based dataset for continuous authentication. The dataset contains 27 subjects, with an age range of [8, 72], where data has been collected across multiple sessions while the subjects are watching videos meant to elicit an emotional response. Collected data includes accelerometer data, heart rate, electrodermal activity, skin temperature, and face videos. We also propose a baseline approach for fair comparisons when using the proposed dataset. The approach uses a combination of a pretrained backbone network with supervised contrastive loss for face. Time-series features are also extracted, from the physiological signals, which are used for classification. This approach, on the proposed dataset, results in an average accuracy, precision, and recall of 76.59%, 88.90, and 53.25, respectively, on electrical signals, and 90.39%, 98.77, and 75.71, respectively on face videos.
Concentration Chromatography Analysis of Online Learners

In the era of resource sharing, online learning courses have gained popularity among learners due to their affordability, flexibility in terms of time and space, and the abundance of resources available. The issue of learners' concentration has become a prominent topic of discussion. While various expression-based concentration detection models exist, most of the literature focuses on extracting discrete data from videos to identify concentration levels. However, it is important to note that learners' concentration states are continuously changing and they are unable to maintain a high level of concentration for extended periods. Therefore, it is necessary to consider the continuous state changes and overall distribution of learners' concentration when studying this phenomenon. In this paper, we propose the utilization of concentration chromatography to represent the distribution and changes in concentration within a video, enabling concentration detection by classifying the concentration chromatography. Experimental results demonstrate the effectiveness of our proposed concentration chromatography in identifying different concentration situations, exhibiting favorable performance.
Question-Guided Graph Convolutional Network for Visual Question Answering Based on Object-Difference

The diversity of VQA questions bring new challenge for VQA model to predict the answer. Existing models focus on the construction of new attention mechanisms and object recognition, but ignore the understanding of visual and semantic object relationships. To solve this problem, we propose a graph convolutional network based on Fine-grained Question and object-difference (FQOD-GCN). Firstly, the question is presented in a fine-grained way. The fine-grained question features can effectively identify the relationship between objects in the image. Then, under the guidance of fine-grained question features, the differences between visual objects are calculated to learn the semantic relationship appropriate to the question, and the object relationship graph is constructed based on the object differences. In addition, the graph convolutional network is used to focus on the neighborhood information of each object in the object graph, which magnifies the objects related to the question to reduce the redundancy. The performance of our model on VQA 2.0 data set is 3% ~ 4% higher than that of the classical method, which proves the effectiveness of the model.
Gaussian Process Models for Manufacturing and Industrial Processes

Gaussian processes are a class of probabilistic models which offer a flexible approach to the statistical modelling of arbitrary functions. Their robust uncertainty estimates and ability to handle complex and non-linear relationships makes them particularly effective for time-series interpolation and prediction problems. Here, we give a brief overview of the mathematical and conceptual framework for Gaussian processes and demonstrate their versatility for time-series analysis through illustrative examples of their application to frequently encountered scenarios involving manufacturing and industrial data. Such examples serve to highlight the flexibility and effectiveness of Gaussian processes for modelling diverse phenomena, monitoring and predicting machine performance and enabling probabilistic predictions and data-driven decision-making in the presence of uncertainty.
A Novel Intelligent Inspection Robot Navigation Strategy for Autonomous Defect Search in Pipeline Anti-Corrosion Coating

The pipeline external inspection technology utilizing DC voltage gradient (DCVG) is widely used to detect the defects of buried pipeline anti-corrosion coating due to the merits of high precision and accuracy. However, the current DCVG detection process relies on experienced operators, leading to a substantial workload. To address these issues, a new navigation strategy is first proposed for defect searching in the pipeline anti-corrosion coating based on an intelligent inspection robot. The proposed scheme transforms the autonomous defect search into an optimization problem by utilizing the measured environmental information. Firstly, the intelligent inspection robot, incorporating the coordinated operation of track wheels and a robotic arm, is designed to enhance the stability of movement and the flexibility of detection. Secondly, an ideal polynomial regression model is established to represent the relationship between the ground potential and the distance to the defect point. Finally, a comprehensive defect point trajectory planning scheme is proposed for the designed robot. Through the four phases of the scheme: initialization, search, decision-making, and fixed-point, optimal control of the direction and walking step size is achieved without relying on location information. The accuracy and feasibility of the proposed intelligent inspection robot are verified through simulation and experiment.
Tracking Analysis of Maximum Versoria Criterion Based Adaptive Filter

Recently, maximum Versoria criterion-based adaptive algorithms have been introduced as a new solution for robust adaptive filtering. This paper studies the steady-state tracking analysis of an adaptive filter with maximum Versoria criterion (MVC) in a non-stationary (Markov time-varying) system. Our analysis relies on the energy conservation method. Both Gaussian and general non-Gaussian noise are considered, and for both cases, the closed-form expression for steady-state excess mean square error (EMSE) is derived. Regardless of noise type, unlike the stationary environment, the EMSE curves are not increasing functions of step-size parameter. The validity of the theoretical results is justified via simulation.
Machine Learning for QUIC Traffic Flood Detection

In order to ensure dependability of connected devices, machine learning is used in the analysis of network traffic to enable faster detection of abnormal behavior and congestion. The use of machine learning techniques enhances the capacity for handling traffic and helps to maintain service quality. Additionally, the purpose of machine learning on securing networks is to detect anomalies, classify traffic in real time, in order to optimize network performance and detect potential threats. This work emphasizes the positive impact of employing machine learning techniques to enhance network reliability and security. Our contributions include an analysis of sample traffic interacting with a web server using HTTP/3. We applied machine learning algorithms to distinguish between normal traffic and potential HTTP/3 flood. Additionally, we created a dataset of traffic samples with 23 features classified into six subcategories. From traffic captured from an emulated environment, we also assessed the relevance of these features and find that leveraging machine learning techniques has the potential to significantly improve both network security and reliability. We used four supervised classification algorithms, namely Support Vector Machine (SVM), Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN). These algorithms are a type of supervised classification algorithm. They were instrumental in training datasets of network traffic, which were meticulously labeled to differentiate between Distributed Denial-of-Service (DDoS) attacks and regular traffic. The findings of this study showcase the effectiveness of machine learning algorithms when applied to network traffic for detecting specific types of DDoS attacks, particularly those utilizing QUIC traffic. This demonstrates the considerable potential of machine learning techniques in bolstering the overall security and dependability of networks.
Leveraging FMCW Radar for Monitoring On-bed States of Human Presence, Posture, and Motion

In this study, we propose a non-invasive solution for on-bed monitoring of human states using FMCW radar. Our unique approach models the bed area and region of interest (ROI) from the radar perspective, projecting them into azimuth-range and elevation-range maps for precise state detection. The processing pipeline uses the Minimum Variance Distortionless Response (MVDR) for target localization and Fast Fourier Transform (FFT) and Weighted Range-Time-Frequency Transform (WRTFT) for Doppler data extraction. We introduce a new method for on-bed state detection by capitalizing on chest localization within the azimuth-elevation-range-ROI mapping, enhancing detection accuracy. Our solution concurrently monitors on-bed presence, posture, motion, and turning, providing a holistic view of sleep behavior. Extensive experimental evaluation confirms the solution’s effectiveness, efficiency, and robustness, with an F1-score of 100% for presence detection and similar high scores for posture, motion, and turning detection.
Where is the Sound: User Sound Source Perception in Virtual Reality Environment

In recent years, virtual reality (VR) technology has advanced significantly, and VR spatial audio technology has greatly enhanced the immersive experience for users. Despite the progress made in VR spatial audio technology, the acceptance and perception of sound source by users in VR environments remains relatively low, which has a negative impact on the immersive experience. The field of virtual reality user perception is currently under-researched, and existing studies have not adequately addressed The impact of sound source orientation on user perception and immersion and the categorization of sound source types. Therefore, this paper mainly discusses the following two questions: Do differences in sound source orientation and sound mode affect user perception in virtual reality environments? Is the user’s immersion in a virtual reality environment related to the sound mode? To answer these questions, this study recruited 66 participants to conduct experiments, questionnaires, and semi-structured interviews. Using statistical methods, we analyzed the collected dataset and found that sound source orientation and type significantly influence user perception. The results demonstrate that users’ perception and immersion in VR spatial audio mode are significantly better than those in traditional 5.1 and 7.1 surround sound modes.
Advent of Artificial Intelligence in Automotive Engineering

Artificial intelligence (AI) has long been a topic of interest and with its constant development and growing popularity and functions, it is no surprise that it has made its way into the automotive industry. For ages, people have done research regarding AI in the automotive industry, and with the increasing popularity of this subject, the research only goes deeper. This paper gives an analysis of previous research under different areas which involve AI in automotives, somewhat singling out autonomous vehicles. We also go into the basis of artificial intelligence, as well as highlight a few challenges which face the integration of AI into the automotive industry.
Spatio-Temporal Consistency for Multivariate Time-Series Representation Learning

Label sparsity in multivariate time series (MTS) makes using label information for practical applications challenging. Thus, unsupervised representation learning methods have gained attention to learn effective representations suitable for various MTS tasks without relying on labels. Recently, contrastive learning has emerged as a promising approach to generate robust representations by capturing underlying MTS information. However, the existing methods have some limitations, such as insufficient consideration of cross-variable relationships of MTS and high sensitivity to positive pairs. Therefore, we proposed a novel spatio-temporal contrastive representation learning method (STCR) designed to address these limitations. STCR focuses on learning robust representations by encouraging spatio-temporal consistency, which comprehensively considers spatial information as well as temporal dependencies in MTS. The results of extensive experiments on MTS classification and forecasting tasks demonstrate the efficacy of STCR in generating high-quality representations, achieving state-of-the-art performance on both tasks.
Adaptive Adversarial Patch Attack on Face Recognition Models

Face recognition models have become widely used for identity authentication in scenarios such as cell phone unlocking and financial payment, but they are vulnerable to adversarial examples. Due to the realizability in the physical world, adversarial patch attack has emerged as a significant security threat. However, most existing adversarial patch attack methods focus on only one aspect of patch generation, such as patch location or shape. To overcome this limitation, we propose a novel unified Adaptive Adversarial Patch (AAP) attack framework for targeted attack on face recognition models. Our method comprehensively considers various factors during patch generation, including location, shape, and number. Our approach adaptively selects patch location and number based on saliency map and clustering, while simultaneously deforming patch shape and optimizing perturbations. Extensive experiments under both white-box and black-box settings demonstrate that our proposed method achieves higher attack success rates compared to SOTA methods.
Pressure induced deformation measurements on atherosclerotic porcine iliac arteries: An MRI study

Aim: Inspect the MRI image sequence to see if it can image the components of an atherosclerotic plaque and investigate if pressure induced deformation measurements can be done on these images. Methods: Four porcine iliac arteries with atherosclerotic plaques were scanned with a T2 imaging sequence in a 7T MRI scanner. One high resolution scan with an in-plane resolution of 56x56mm and three low resolution scans with an in-plane resolution of 98x98mm were obtained. The pressure inside the artery for the high-resolution scan was 100mmHg and for the low resolution scans the pressure was 80, 100 and 120mmHg, respectively. For each artery one high resolution MRI slice was compared with two histology staining to see if the lumen, intima, media, adventitia, calcium, and lipid and collagen poor/rich regions could be indentified. The surfaces of the lumen and external elastic lamina (EEL) were compared for the three different pressures to see what the global deformations were. Also a pilot has been performed to see if local deformations could be indentified with the program Elastix [1]. Results: In all four MRI slices of the porcine arteries the lumen, intima, media, adventitia and calcium could clearly been seen. The lipid poor/rich and collagen poor/rich regions were not always indentified correctly. The surface area of the lumen and EEL seem to increase as the pressure increases. However, the increase in surface area for these diseased arteries seem to be smaller then expected. The local deformations obtained with the program Elastix showed a logical deformation pattern for the artery with the largest deformations, but for the smaller deformations the deformation field was contradictory to expectations. Conclusion: With the high-resolution T2 sequence it is possible to clearly delineate the lumen, intima, media, adventitia and calcium, but not lipid or collagen poor/rich regions. Global deformations can be measured from the low resolution MRI scans, but the local deformations are hard to indentify with the program Elastix if the displacements are close to the resolution of the MRI scans.
F-CACNN: A framework integrating FNet and adaptive attention for end-to-end EEG emotion recognition

An important area of study in human-computer interaction is emotional brain-computer interface based on electroencephalogram (EEG). A challenge in this area is how to make full use of channel correlation and provide timely feedback on effective emotion recognition results. In this study, we present a FNet-based and channel attention convolution neural network-based model (F-CACNN) for emotion recognition. We thoroughly explore the EEG channel information with the adaptive channel attention mechanism and employ the FNet mechanism for the first time to realize emotion recognition more efficiently. On the one hand, we include the adaptive channel attention technique into the convolutional neural network to adaptively assign the weights according to the importance of each channel and extract EEG spatial features. On the other hand, the FNet mechanism is integrated into long short-term memory. It takes the Fourier Transform to extract the EEG temporal feature without learnable parameterization. Experimental results show that our method can effectively realize emotion recognition without handcrafted features extraction, and significantly reduce time consumption, which meets the current demand for end-to-end emotion recognition.