Reviewerly/exHarmony huggingface dataset:
Expectation-Maximization via Pretext-Invariant Representations

Contrastive learning methods have been widely adopted in numerous unsupervised and self-supervised visual representation learning methods. Such algorithms aim to maximize the cosine similarity between two positive samples while minimizing that of the negative samples. Recently, Grill et al. propose an algorithm, BYOL [1], to utilize only positive samples, completely giving up on negative ones, by introducing a Siamese-like asymmetric architecture. Although many recent state-of-the-art (SOTA) methods adopt the architecture, most of them simply introduce the additional neural network, the predictor, without much exploration of the asymmetrical architecture. In contrast, He et al. propose SimSiam [2], a simple Siamese architecture relying on the stop-gradient operation instead of the momentum encoder and describe the framework from the perspective of Expectation-Maximization. We argue that BYOL-like algorithms attain suboptimal performance due to representation inconsistency during training. In this work, we explain and propose a novel self-supervised objective, Expectation-Maximization via Pretext-Invariant Representations (EMPIR), which enhances Expectation-Maximization-based optimization in BYOL-like algorithms by enforcing augmentation invariance within a local region of k nearest neighbors, resulting in consistent representation learning. In other words, we propose Expectation-Maximization as a core task of asymmetric architectures. We show that it consistently outperforms other (SOTA) algorithms by a decent margin. We also demonstrate its transfer learning capabilities on downstream image recognition tasks.
Underwater Image Clarification Based on Double-Opponency Light Estimation and Red Channel Prior

Underwater images typically exhibit color bias and low contrast resulting from the absorption and scattering effects of light propagating in water.Such degraded images often have difficulty in satisfying the requirements of underwater operations.Traditional underwater image restoration and enhancement methods generally require long computation times with unsatisfactory results.In this study, we propose a new underwater image restoration method involving background light estimation based on doubleopponency and transmission estimation based on the red channel prior.For background light estimation, double-opponency is adopted to imitate human visual color constancy, whereby background light information consistent with human perception is obtained.Transmission estimation uses the prior knowledge that red wavelength has the fastest attenuation in water.To enhance the contrast effect of the resulting image, the restored image is fused with that of contrast stretch processing in HSV space to obtain the final output image.Experiments indicate that the proposed method can reduce computation time by 40% or more relative to comparison methods, thereby producing a clearer normal color image according to both objective and subjective evaluations
Learning Multi-Scale Features Using Dilated Convolution for Contour Detection

For the contour detection task, we use the EfficientNet model as the backbone network and propose a network model that uses dilated convolution for multi-scale optimization.The network is accumulated top-down layer by layer, combining multiple optimization modules concat together to achieve a richer feature representation.To fuse feature information at different scales, we introduce a new Multiscale optimization module to replace the use of deeper network structures or more complex decoding methods, which uses channel attention module to learn the correlation between channels and then uses dilated convolution of different scales to enhance contextual information.High generalization performance and accuracy are obtained in comparison with recent deep learning-based contour detection models.We evaluate our approach on two datasets, i.e., BSDS500 and NYUD-v2, achieving an ODS F-measure value of 0.828 on BSDS500.In particular, the results of BSDS500 exceed the human-level performance under more stringent criteria.
Reverse Image Search for Collage: A Novel Local Feature-Based Framework

Collage, a popular form of visual-content summarization technique is commonly used by internet users and digital artists. Social media usage is a rising trend that significantly affects the increasing demand for collages. The primary source of collage generation is social media, but other sources also generate it. The searching for a required query image in this corpus is a crucial demand and also valuable. The query image can be retrieved using Reverse Image Search (RIS), either in its exact form or with a small variation. Well-known search engines like Google and Yandex have this functionality, but their method has not been made public. In this research, we propose a consolidated framework for reverse image searching for the problem of collage. Essentially, the local features of collage images are extracted by using SIFT, SURF, and ORB algorithms. These features undergo the localization of the region of interest (ROI) process which handles by binning technique. We propose to use the Manhattan distance to calculate the similarity. The proposed model is extensively evaluated on standard databases and is shown to always have good results using SIFT algorithm. The proposed model is entirely generic and attains 90.96%, accuracy using the SIFT algorithm. The proposed approach is also evaluated on flip and scale variant college and achieves a result of 83% and 78% respectively, using SIFT algorithm.
Exploring Potential Applications of Ising Machines for Power System Operations

Recent advancements in Ising machines, both via quantum and quantum-inspired annealing, have been showing promising results in solving difficult optimization problems. Due to the potential of these dedicated machines, there is a rising interest in applying Ising machines to power system operation. However, despite Ising machines exhibiting computational advantages over conventional computational resources, modeling capability of Ising machines limit potential applications. Hence, this work explores these potential applications in two steps. First, using known characteristics of Ising machines and theoretical derivation, this work constructs an Ising machine applicability table for optimization problems. Then, using the constructed table, this work assesses the suitability of well-known optimization problems in the power system operation to identify potential applications. Furthermore, to understand the performance in applying Ising machines, a phasor measurement unit placement problem is solved using a quantum inspired Ising machine. The results show potential for solving large-scale problems that are unsolvable by conventional methods. The findings of the work are summarized in an Ising machine usage guideline for power system operations.
Multi-Exposure Fusion With Guidance Information: Night Color Image Enhancement for Roadside Units

The computer vision-based roadside occupation surveillance system is a key infrastructure component of Cooperative Intelligent Transport Systems. However, traffic images captured under low-light conditions suffer from low visibility and unexpected noise, leading to negative effects on traffic supervision and management. Despite the great progress achieved in recent years, the existing night image enhancement algorithms often suffer from color deviation, ghosting, and overexposure problems in practical traffic applications, especially when the image captured by low-cost traffic cameras is too degraded to meet the prerequisites for single-frame image enhancement. Thus, we present a novel night color image enhancement approach to overcome this issue by combining multi-sensor fusion and pseudo-multi-exposure fusion techniques. Unlike the traditional exposure adjustment-based approaches, we performed a novel bidirectional region segmentation-based inverse tone mapping operator to generate pseudo-multi-exposure sequences from day and night image pairs. Meanwhile, to solve the problem that moving objects are diluted after fusion, a partial differential equation (PDE)-based luminance stretching is applied to the moving areas to guarantee that the enhanced image always highlights the traffic targets. Instead of image feature-based methods for moving object detection, we generate more accurate moving regions by fusing data from the radar and camera sensors. Finally, a pyramid-based fusion method with an improved weight function is conducted to generate high-quality traffic images. The proposed method and five state-of-the-art methods are evaluated on randomly selected images from the Rope-3D database and nighttime images captured by an Intelligent Roadside Surveillance System. The experimental results demonstrate that the proposed method effectively improves the visibility of night surveillance images and significantly facilitates object detection in nighttime traffic scenes. Compared with the state-of-the-art baselines, our method has significant advantages in enhancing details and making colors more natural for human observation.
Abnormal Traffic Detection Based on Attention and Big Step Convolution

Abnormal traffic detection is critical to network security and quality of service. However, the similarity of features and the single dimension of the detection model cause great difficulties for abnormal traffic detection, and thus a big-step convolutional neural network traffic detection model based on the attention mechanism is proposed. Firstly, the network traffic characteristics are analyzed and the raw traffic is preprocessed and mapped into a two-dimensional grayscale image. Then, multi-channel grayscale images are generated by histogram equalization, and an attention mechanism is introduced to assign different weights to traffic features to enhance local features. Finally, pooling-free convolutional neural networks are combined to extract traffic features of different depths, thus improving the defects such as local feature omission and overfitting in convolutional neural networks. The simulation experiment was carried out in a balanced public data set and an actual data set. Using the commonly used algorithm SVM as a baseline, the proposed model is compared with ANN, CNN, RF, Bayes and two latest models. Experimentally, the accuracy rate with multiple classifications is 99.5%. The proposed model has the best anomaly detection. And the proposed method outperforms other models in precision, recall, and F1. It is demonstrated that the model is not only efficient in detection, but also robust and robust to different complex environments.
SCRN: Stepwise Change and Refine Network Based Semantic Distribution for Human Pose Transfer

It is a challenging and meaningful task to achieve person image synthesis by guiding pose. However, two problems have existed in past work: inaccurate generated poses and inconsistency with the target texture. To address these issues, we propose the Stepwise Change and Refine Network (SCRN), a two-stage network that aims to transfer given person images to the target pose while generating more reasonable and closer-to-real results. In the first stage, coarse images are generated using a series of modules with the same structure called Coarse Blocks. This process gradually changes the pose to achieve better shape consistency with the target image. In the second stage, style features are extracted from the original image by distributing semantic information. These features are used to optimize the rough image to obtain the final generated image, resulting in better consistency with the appearance of the target image. Our proposed method preserves both the pose’s spatial features and the original image’s texture features. Furthermore, we introduce a new loss function to make the generated image more in line with human perception. Qualitative and quantitative experiments with state-of-the-art models demonstrate significant improvements in SSIM, FID, PSNR, and LPIPS, validating the superiority of our model.
View Synthesis of Dynamic Scenes Based on Deep 3D Mask Volume

Image view synthesis has seen great success in reconstructing photorealistic visuals, thanks to deep learning and various novel representations. The next key step in immersive virtual experiences is view synthesis of dynamic scenes. However, several challenges exist due to the lack of high-quality training datasets, and the additional time dimension for videos of dynamic scenes. To address this issue, we introduce a multi-view video dataset, captured with a custom 10-camera rig in 120FPS. The dataset contains 96 high-quality scenes showing various visual effects and human interactions in outdoor scenes. We develop a new algorithm, Deep 3D Mask Volume, which enables temporally-stable view extrapolation from binocular videos of dynamic scenes, captured by static cameras. Our algorithm addresses the temporal inconsistency of disocclusions by identifying the error-prone areas with a 3D mask volume, and replaces them with static background observed throughout the video. Our method enables manipulation in 3D space as opposed to simple 2D masks, We demonstrate better temporal stability than frame-by-frame static view synthesis methods, or those that use 2D masks. The resulting view synthesis videos show minimal flickering artifacts and allow for larger translational movements.
An Efficient FPGA-Based Gaussian Random Number Generator Using an Accurate Segmented Box–Muller Method

In this paper, we introduce an accurate, efficient, and optimized design and implementation of a Gaussian pseudo random number generator (PRNG) on a field-programmable gate array (FPGA) platform.A second-order segmented Box-Muller (S 2 BM) transformation is proposed as an alternative approach to our previously presented segmented Box-Muller (S-BM) transformation.S 2 BM transformation targets two goals simultaneously: enhancing accuracy and improving efficiency by introducing two new upgrades to the S-BM transformation.The first upgrade was performed by splitting each segment into two parts, the body and tail, using two different Box-Muller (BM) transformation steps.The second upgrade was made by producing two uncorrelated samples per clock cycle with small additional FPGA logic.The achieved FPGA implementation scores prove that the designed 19-bit S 2 BM Gaussian PRNG generator offers an improvement of 21% and 29% in efficiency over the S-BM within the ranges of ±7.81σ and ±9.561σ respectively, when implemented on the cost-optimized Xilinx Spartan-6 devices (XC6SLX75T).An improvement of 86% and 149% in efficiency are obtained respectively, over the Central Limit Theorem (CTL) Hadamard transformation and the Multihat transformation.Furthermore, the obtained results and performance confirmed the superiority of the S 2 BM transformation in terms of accuracy and efficiency compared with the existing GRN generator architectures.
Application of big data technology in the design of TESOL system

In order to strengthen the teaching quality of ETL and promote the improvement of students' ESL (English; All of the following with ESL instead) quality, this paper analyzes the feasibility of information technology in college ETL (ETL, All of the following with ETL instead). It also analyzes the application of information technology in college ETL. Explore the application of information technology major in the field of TESOL (English teaching; All of the following with TESOL instead), classroom TESOL, specialized ESL textbooks, writing and examinations in colleges and universities. Explore the application of information technology major in the field of TESOL, TESOL in the course of TESOL, specialized ESL textbooks and examinations in colleges and universities. With the support of high-tech information resources, college TESOL will be combined with new technology resources to create a new pattern of modern TESOL in colleges and universities. It will be a new task for college TESOL. In the context of information age, college ESL education is characterized by multi- culture. Information technology can play an important role in college ETL.
TEN: temporal excitation network for video action recognition

Temporal modeling has attracted the attention of a large number of researchers in the past few years. In this work, we propose a new video architecture, termed as Temporal Excitation Network (TEN). The core of TEN is Temporal Excitation Module (TEM) block, which consists of Temporal Convolution Module (TCM) and Temporal Difference Module (TDM). TCM applies a channel-wise convolution to supplements the short-range temporal information. TDM works by computing feature-level long-range temporal differences and then exploiting it to excite motion-sensitive channels. These two-stage modeling scheme can be fused into existing 2D CNNs to model temporal structures flexibly and efficiently. Extensive experiments demonstrate the effectiveness of the proposed TEN on several benchmarks (e.g., UCF101, HMDB51, Something-Something V1 and Jester). The proposed TEN can guarantee high recognition accuracy while maintaining high recognition efficiency on these datasets.
Binarization for time-frequency images of LPI radar signals based on K-means

Abstract Low probability of intercept radar signal is widely used because it is difficult to be intercepted by non-cooperative receivers in electronic warfare. We need to binarize the time-frequency images when analyzing LPI radar signals based on time-frequency distribution. However, the existing binarization algorithms cannot distinguish noise from the signal frequency at low signal-to-noise ratios. In this paper, we propose to use K-means algorithm to binarize the gray time-frequency images of LPI radar signals. We use F1-score to comprehensively consider the effect of binarization. Based on linear frequency modulation signals, the simulation experiments show that the F1-score of the proposed algorithm exceeds the F1-score of Otsu’s method by 40% to 80% when the signal-to-noise ratio is from -6 dB to 2 dB. The binarization effect of K-means is excellent and has great application prospects.
Identity-based key-preserved compacted representative signature as a teaching case of cryptography

An compacted representative signature is a string that persuade the checker to believe that, for all 1 &le; <i>i </i>&le; n, the <i>i</i>-th representative signer approved a piece of information. The loss of clandestine keys threatens a security system. We come up with the idea of Identity-Based Key-Preserved Compacted Representative Signature (IBKPCRS) in which representative signing keys vary over time. The usage of IBKPCRS is to meet the key-revealing challenge. In the IBKPCRS system, the user sets his telephone number or other overt string as his public key. We give the IBKPCRS scheme as a teaching case of cryptography.
Recommendation system-application of unbiased deep learning in recommendation system with causal inference

All data for training and evaluating recommended systems are subject to selection biases. In much research, principled approaches have been found to manage selection biases by adapting estimation techniques and models from causal inference. However, no matter what kind of method is adopted, the problem of data randomly missing always exists. This paper tries to discover whether the deeper model can effectively bring better prediction results and debias. We theoretically and experimentally examine whether the models are robust or not.
Legitimate User Review Based on Sentiment Analysis

Abstract: Item surveys assume a significant part in choosing the offer of a specific item on the online business sites or applications like Flipkart, Amazon, Snap deal, and so on. In this, we propose a structure to identify counterfeit item surveys or spam audits by utilizing Opinion Mining. The Opinion mining is otherwise called Sentiment Analysis. In assumption investigation, we attempt to sort out the assessment of a client through a piece of text. We first take the audit and check if the survey is identified with the item with the assistance of Decision tree.
An efficient differential privacy federated learning scheme with adaptive privacy budget

The combination of Differential Privacy (DP) technology and Federated Learning (FL) can solve the problem of privacy leakage in FL. However, most of the existing schemes allocate the same privacy budget to clients during training. This also causes the problems of slow convergence speed and low training accuracy of FL. To solve these problems, we propose an adaptively privacy budget allocating scheme, in which clients are assigned different privacy budget according to their contributions. On the one hand, adding less noise on the client with higher convergence contribution value can accelerate the model convergence and improve the accuracy of model training; On the other hand, fast model convergence can reduce the number of training iterations, thus reducing the overall noise added and the communication cost of the model. We experiment on different data sets and network models. The result shows that the scheme proposed in this paper can improve the accuracy of model training and accelerate the convergence under the premise of ensuring the privacy of the model.
Algorithm for identifying college students with financial difficulties

Evaluating the consumption of students' canteens at school is helpful to accurately identify students with financial difficulties from families. To obtain the canteen consumption data, the daily average consumption of all students and students with difficulties should be calculated as the reference standard, and then the daily difficulty index of each student should be calculated. Accumulate a student's daily difficulty index to obtain the difficulty index, and then convert it into the difficulty degree through feature scaling. According to the scope of the difficulty degree, students are evaluated as poor, medium, and rich. By comparing the consumption evaluation results with the manual evaluation results, we can evaluate the funding compliance of colleges and classes.