Reviewerly/exHarmony huggingface dataset:
Dual-Encoder Unet for Single Image HDR Reconstruction with Denoising

High dynamic range (HDR) image shows richer scene brightness and details for better visual effects than conventional low dynamic range (LDR) images since they utilize more bits to express pixel values. With little input information, the challenge of HDR reconstruction is to recover the details lost in the under- /over-exposed regions of an image. The majority of current methods for single-frame HDR reconstruction fail to focus on image denoising and color balance. In this work, we address these difficulties by proposing the notion of extracting image luminance features and texture features separately. The method is based on a dual-input channel encoder-decoder structure and utilizes a spatial feature transform module to implement information interaction between both input branches at different feature scales. In addition, our proposed network includes a weighting network to preserve useful information about the image selectively. Through both quantitative and qualitative experiments, we demonstrate the effectiveness of the components proposed in the network. In comparison to other existing mainstream methods in the field on publicly available datasets, we have demonstrated that the proposed method enables noise reduction while recovering the lost image details. The experimental results show that our method achieves state-of-the-art performance in the task of single-frame HDR reconstruction. The code is available at https://github.com/AMSTL-PING/DEUNet-HDRI.git
A Comprehensive Analysis for P2P Lending Credit Risk Assessment on Imbalanced Data and Feature Selection

In the field of P2P lending, the problem of data imbalance has led to poor results of traditional credit risk assessment models. Addressing the issue of imbalanced data leading to ineffective classification is a popular research topic in data mining and machine learning. However, existing research has mainly focused on treating data imbalance to improve model performance, while largely overlooking the importance of data feature completeness. There is currently no systematic theoretical support for the relationship between imbalanced data and feature selection. To address this gap, our paper aims to investigate the impact of data feature completeness on imbalanced data in P2P lending. For this purpose, we selected 1,345,310 observations from Lending Club 2007-2018 Q4 as an empirical sample. We used LR, XGBoost, and DT as base classifiers and XGBoost as a tool for feature selection. Imbalanced learning methods were employed for evaluation. Our findings reveal that datasets with more complete features yielded better evaluation results when compared to the incomplete datasets used in existing studies. Moreover, we found that the Easy Ensemble-XGBoost approach showed the best results in solving the imbalance issue. Furthermore, we delved into the dynamics of imbalanced ratio and feature selection. Ultimately, by employing feature selection importance ranking, we obtained a more complete set of optimal features, thus enhancing the credit risk assessment model's performance in P2P lending.
An Anchor-free Small Object Detection Algorithm Based On Feature Enhancement And Feature Fusion

Small object detection has the problem of not being able to obtain enough semantic information and rich detail information at the same time, and it is prone to missed detection and false detection. Based on this, we propose an anchor-free small object detection algorithm based on feature enhancement and multi-scale feature fusion. Unlike most complex feature fusion networks, we utilize large kernel attention to assign weights of input features with different importance while preserving bottom-up and top-down multi-scale feature fusion. And we use the feature enhancement module to filter more effective feature layers. Extensive experiments are conducted on the MS COCO 2017 and VisDrone 2019. Our algorithm achieves state-of-the-art on both datasets with clear advantages for detecting small objects.
Ecological Risk Assessment of PTEs in Topsoils Impacted by Diamond Mining (Yakutia, Russia)

The concentrations of potentially toxic elements (PTEs) in the topsoils of Udachny mining area were determined. The soil samples were taken from the 42 topsoil samples at a depth of 0-20 cm. The concentrations of PTEs (Pb, Cd, Cr, Zn) were analyzed by the atomic absorption method on MGA-1000. In addition, the pH and soil organic matter (humus) were measured for each sample. The potential ecological risk was quantitatively estimated for each site. The most severe PTEs soil contamination in the study area was Cd, followed by As, Pb, Cr, and Zn. Local areas with a significant potential environmental risk (300 ≤ RI &lt; 600) have been established near the kimberlite pipe and overburden dump.
A Sea-Surface Target Detection Method based on Weighted Aggregation Network

Aiming at the problem that the detection performance is easily affected by objective conditions such as light environment and background noise in the complicated ocean environment and there is image scale transformation in congested scenarios and loss of deep semantic information during forwarding, this paper proposed an actual sea surface target detection algorithm combined with the Convolutional Block Attention Module (CBAM) and the Bidirectional Feature Pyramid Network (BiFPN). Firstly, an efficient weighted feature aggregation network was constructed based on the YOLOv5s model by adding the skip connection between input and output nodes, which integrated the deep and shallow semantic information and achieved a unified description of the cross-scale features. On the basis of original feature network, a series of attention feature map information with channel and space two dimensions was generated for meeting the needs of surface target detection for environmental noise suppression and enhancing the feature expression ability of the network in complex backgrounds. And then, for improving the training speed and inference accuracy, the SIoU loss function was introduced as the location loss, which redefined the penalty metrics considering the vector angle between the required regressions to ensure the accurate localization of the prediction box. Finally, the validation was carried out on the actual sea image dataset provided by the Key Laboratory of Marine Intelligent Equipment and System, Ministry of Education. The experimental results showed that the improved model had higher detection accuracy than the YOLOv5s model, the [email protected]:0.95 was increased by 1.8 percentage points, and the speed reached 77FPS. The model showed good generalization performance, and it could meet the requirements of both target accuracy and real-time detection, which could be effectively applied to the ship intelligent perception field.
Generating Head Animation for a Back-Projected Human-Like Robot Head

Head animation, including facial expression and head motion, is a critical part for human-robot interaction. Most of the existing head animation methods rely on a set of pre-established joint configurations for expression generation. Moreover, the facial expression and head motion are obtained by separate approaches. Automatically, variously and consistently head animation generation using an integrated framework provides potential advantages to simplify the design process of human-robot. In this paper, we introduce a novel deep neural network based head animation synthesis method on humanoid robots. The facial image sequence as well as head motion arguments are driven simultaneously from a video. To project the facial expression onto a 3D facial sculpture, we added an non-rigid image registration module to correct the synthesized facial images. Experimental evaluations on a projection model, show that the ability of the proposed method to generate realistic facial expressions was further validated.
DAGP-Face Restorer: Blind Face Restoration Network with Domain-aligned Generative Prior

Blind face restoration is an extremely challenging task since it often relies on suitable reference priors, which are unavailable in real-world scenarios. Generative prior encapsulated in pre-trained face generator has shown its effectiveness in providing rich and realistic facial details. However, trained on a specific face dataset, face GAN prior mainly focus on face region but limited background information and local details. In this work, we propose a framework named DAGP-Face Restorer to perform restoration procedure by fusing multi-scale generative prior features and convolutional features extracted from degraded images. We first train a domain-alignment-guided encoder to project degraded inputs into the latent space aligning with that of high quality images, which allows our network to provide generative prior with better quality. Another encoder-decoder framework with feature fusion blocks is designed to extract convolutional features and fuse them with generative priors to output the final results. Experiment results show that, with the carefully design and fusion of two features, our model can better take into account restoration of face regions as well as image backgrounds and achieves superior performance in recovering faithful details and retaining fidelity.
Modality Coupling for Privacy Image Classification

Privacy image classification (PIC) has attracted increasing attention as it can help people make appropriate privacy decisions when sharing images. Most recently, some pioneer research efforts have been made to utilize multimodal information for PIC, since multi-modality can provide richer information than single modality. Those research efforts on multimodal PIC are under the assumption of independently identically distribution. However, connections between different modalities commonly exist in real-world cases. Taking the modalities of scene and object as example, in the scene of ’library/indoor’, the object ’book jacket’ resides with high probabilities. To this end, in this paper, a novel PIC approach, called CoupledPIC, is proposed to bridge this gap by comprehensively capturing the coupling relations between different modalities. In CoupledPIC, two submodules are designed to capture explicit and implicit coupling relations between different modalities respectively. The explicit modality coupling is learned with a tensor fusion networks based submodule, via the direct interaction of features. For the implicit modality coupling, a graph convolutional networks based submodule is proposed to learn on both the initial graphs and attention guided graphs, via information aggregation on graphs. Extensive experiments on the public benchmark, PicAlert, demonstrate the effectiveness of the proposed CoupledPIC, yielding significant improvement by modeling inter-modality coupling information.
WCDANN: A Lightweight CNN Post-Processing Filter for VVC-Based Video Compression

In this paper, we propose a weakly connected dense attention neural network for compression artifact removal, called WCDANN. WCDANN is a convolutional neural network (CNN)-based post-processing filter to enhance the quality of versatile video coding (VVC)-decoded videos without requiring any codec changes. WCDANN consists of several weakly connected dense attention blocks (WCDABs) based on residual learning, which takes the compressed video after codecs as the input. We use depthwise separable convolution for WCDANN as the basic convolution unit to generate a lightweight model. Moreover, we introduce attention mechanisms into the proposed filter to capture important features. Experimental results show that WCDANN achieves good performance in Bjøntegaard Delta Bit Rate (BD-BR). Compared with VTM-11.0-NNVC anchor, WCDANN achieves average 2.81%, 4.12% and 3.81% BD-rate reductions for Y channel on A1, A2, B, C, D and E classes in RA, AI and LDP configurations, respectively.
TiCTok: Time-Series Anomaly Detection With Contrastive Tokenization

Detecting anomalies in multivariate time-series data is an important task in various real world applications. Recent advances using deep learning have shown promising results in this area. Nowadays, Transformer-based models have shown outstanding performance and contrastive learning has emerged as a powerful technique for representation learning, however, it may not be directly applicable to the time-series domain. Here, we propose a time-series anomaly detection model with contrastive tokenization (TiCTok). We propose a time-series token encoder to transform raw time-series data into latent embeddings containing high-level wide-range temporal information. We exploit both token encoder and contrastive learning to produce high quality latent embeddings. In addition, we propose a novel anomaly scoring method simply utilizing the contrastive loss used in the training phase. According to our experimental results, the proposed model achieved better or comparable performance compared to the previous state-of-the-art on five widely used benchmark datasets in terms of F1-score.
Merge Cube as a New Teaching Tool for Augmented Reality

Due to the massive popularity of smartphones among people of all ages, Augmented Reality is becoming a commonly available technology for which we do not need to have special equipment, just an ordinary cheap smartphone with a camera.In this paper, we will deal with the use of Augmented Reality in education through the popular Merge cube, which has been enjoying increasing popularity in the educational process since 2017.In our article, we focus on the comparison of existing applications with the support of the Merge cube and the possibilities of it's use in education.We pay the main attention to the creation of our own educational application, which consists of five independent parts (three educational parts, like the solar system, world monuments, and marine animals, and two entertainment parts with the possibility of setting the difficulty, respectively-game type, like Pexeso and the 3D Labyrinth).Application development was carried out in the Unity 3D environment for mobile operating system Android.The application is mainly intended for hospitalized children, but it was tested due to pandemic restrictions among 134 children in regular primary and secondary schools.The main advantage of our application compared to other applications is that it is free, contains several educational areas and also that we can modify the content -it is open source.
Two New Lightweight Cryptographic Hash Functions Based on Saturnin and Beetle for the Internet of Things

With the enormous growth in Internet of Things (IoT) applications, the volume of data shared among IoT devices is vastly increasing. Extensive IoT device connectivity and substantial data transmission have made information integrity susceptible to various assaults. Therefore, hash functions are required to ensure data integrity in IoT networks. IoT systems are constrained by their complexity, necessitating the consumption of minimal computational power. As a result, lightweight hash functions have been selected as the solution for the IoT data integrity issue. We present two lightweight hash functions, Alit-Hash and Tjuilik-Hash, based on the Saturnin block cipher and the Beetle mode of operation. In particular, we created Tjuilik-Hash by modifying the Saturnin block cipher. The strength of the proposed hash functions is evaluated through security analysis and performance testing. Alit-Hash and Tjuilik-Hash both show reasonably good resistance to differential and linear cryptanalysis. Hardware implementations on a cost-effective and low-power microcontroller board (ATmega2560) demonstrate an average execution time of 0.746 microseconds for the Tjuilik-Hash algorithm. Performance evaluations on a 64-bit personal computer indicate that the Alit-Hash and Tjuilik-Hash implementations exhibit comparable speed and throughput to seven other evaluated hash functions. Simulation experiments employing Contiki-NG and the Cooja simulator confirm the good performance of these two hash functions relative to Photon-Beetle-Hash, Photon, and Spongent across five metrics. The hash functions pass seven cryptographic randomness tests and pass all tests in the National Institute of Standards and Technology (NIST) Statistical Test Suite (STS). Therefore, the implementation of both proposed hash functions should be considered, as they are both cost-effective and provide an adequate level of security, which is essential for IoT devices with limited resources.
Non-Profiled Deep Learning-Based Side-Channel Analysis With Only One Network Training

We propose efficient protocols for non-profiled deep learning-based side-channel analysis (DL-SCA). While the existing protocol, proposed by Timon in 2019, requires computational resources for training as many neural networks as the number of key candidates, our protocol requires training only one network, which can be transformed into a network associated with each key candidate. For instance, in the case of analysis for the AES, the network training complexity is 1/256 of that for the existing protocol. In this study, we describe our idea and formulate it as two protocols depending on the metrics used. We numerically examine them by implementing each protocol with two network architectures, multilayer perceptron and convolutional neural network. Using publicly available open data (ASCAD), we show that both protocols efficiently work as expected. We also clarify that our trained network, as in Timon’s original case, can be recycled for an attack against the same device with different key materials. Non-profiled DL-SCAs are superior to profiled ones in that they require no reference device for profiling before analyzing the target device. This property holds for our proposal as well.
Efficient and Stable Generation of High-Resolution Hair and Fur With ConvNet Using Adaptive Strand Geometry Images

This paper proposes a technique for transforming low-resolution (LR) simulations of hair and fur into high-resolution (HR) representations without noise, using strand geometry images in the form of lines and Convolutional Neural Network (CNN or ConvNet). LR and HR data for training are obtained through physics-based simulations, and LR-HR data pairs are set up. The training data involves converting the positions of hair strands into geometry images for use in the learning process. The proposed strand network in this paper is used as an image synthesizer to upscale LR images to HR images. When the obtained HR geometry image is transformed back into HR hair through the nonlinear transform proposed in this paper, it effectively captures the elastic movement of hair that is challenging to represent with a single mapping function alone. Furthermore, we extended the solver to accommodate hair structured in a triangular mesh form. We also propose a method for adaptively refining the hair particles of strands using the Bezier curves algorithm. Additionally, we improve the algorithm to ensure that the proposed strand geometry image can work effectively even in adaptively sampled hair particle structures. As a result, the neural network enables the stable generation of strands not only for straight and curly hair but also in various environments where different external forces are applied. Furthermore, the proposed research generates results faster than previous physics-based simulations and can be easily executed without requiring a deep understanding of complex numerical analysis processes.
Safety Barrier Certificates for Path Integral Control: Safety-Critical Control of Quadrotors

The safety issue arises as one of the most important requirements for autonomous quadrotor flight. Recently, control barrier function (CBF) technique has been developed to provide necessary and sufficient conditions on safety for robot systems. Previous works unified CBF with control techniques in an optimization fashion. However, these methods could not provide consistent performance, especially in clutter environments. In this letter, we present a novel predictive control algorithm that combines CBF and path integral control to develop a safety-critical controller for quadrotors. An efficient sequential projection algorithm is adopted to steer unsafe sampled inputs to a feasible region limited by safety barrier certificates. Comparing to existing methods, one distinguishing feature of our proposed method is that we provide collective thrusts and body rates (CTBR) control policies. Simulations and experiments demonstrate that our controller can deliver more robust maneuvers in the course of obstacle avoidance than state-of-the-art safe controllers.
A Novel Unsupervised Approach for Cross-Lingual Word Alignment in Low Isomorphic Embedding Spaces

Cross-lingual word alignment is the task for word translation between monolingual word embedding spaces of two different languages. Recent work is mostly based on supervised approaches, while their success relies on bilingual seed dictionaries derived from aligned data. The unsupervised adversarial approaches, which utilize generative adversarial networks (GANs) to map the global monolingual space to another space, can eliminate the need for aligned data. However, most GAN-based unsupervised approaches ignore the issues of mode collapse and gradient disappearance in GANs, leading to a training failure to converge. In addition, these approaches often fail to account for the low isomorphism between language pairs, which prevents capturing the non-linear relationship contained in cross-lingual embedding spaces. To address these issues, we propose a novel unsupervised unified framework with an adaptive training objective for the GANs' improvement (ATOGAN) and a local mapping (LM) strategy for exploring the non-linear relationship. We present ATOGAN to learn bi-directional global mapping using unaligned word embeddings, which integrates particle swarm optimization (PSO) to adaptively select the training objective for preventing mode collapse and gradient disappearance. Then, we design an LM strategy based on the guidance of dictionaries generated by trained ATOGAN to alleviate reliance on isomorphism assumption for purely linear mapping. Experimental results demonstrate the effectiveness of our proposed method for cross-lingual word alignment in low isomorphic embedding spaces (distant language pairs). Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/goFurtherLong/ATOGAN</uri> .