Reviewerly/exHarmony huggingface dataset:
Semantic Segmentation Algorithm of Remote Sensing Images Based on Improved Panoptic

Semantic segmentation of images can play an important role in land use, building extraction, road extraction and vehicle detection using remote sensing images. In the scenario of applying Panoptic FPN algorithm for remote sensing images, we found that the decoder of this algorithm is not robust in feature extraction and cannot do weighted enhancement of key spaces and channels, and its encoder loses a lot of high-dimensional semantic features when simply fusing semantic information at all levels by simple addition. To address these two problems, we propose the Se-Resnext encoder based on the attention mechanism and the Concat-based feature fusion mechanism, respectively, and verify the effectiveness of the methods through experiments. The accuracy of semantic segmentation is improved in both suichang dataset and posdam dataset.
A Small-Scale Pedestrian Detection Method in Railway Scene

It is typically difficult to balance the accuracy and speed of small-scale pedestrian detection in the railway scene. To solve this problem, we have designed a novel network, in which we proposed a new loss function by introducing new parameters. In addition, we introduced the coordinate attention mechanism, which can significantly improve the detection performance of the network. In addition, we also designed the lightweight version of this network to apply to the Unmanned Aerial Vehicle (UAV) platform. The experimental results showed that the algorithm had higher detection accuracy than the basic network, and the LAMR (Log-Average Miss Rate) was reduced from 19.49% to 10.70%. Furthermore, its lightweight network can improve the detection frame rate from 14.92 to 19.60 FPS while ensuring high detection performance.
Residue adder design for the modulo set {2<sup>n</sup> − 1, 2<sup>n</sup>, 2<sup>n+1</sup> − 1} and its application in DCT architecture for HEVC

Modulo adders are the standard building blocks of Residue Number System (RNS)-based applications. Traditional modulo adders use an end-around carry strategy and a parallel prefix adder that consumes a lot of space. In this paper, a hardware architecture with a low-area and low-power is suggested for the modulo adder with the modulo set P = {2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> − 1; 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ; 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+1</sup> − 1}. Experiments show that suggested architecture performs approximately 92% and 26% better than standard architectures in terms of power usage and area consumption, respectively. Its application in the High Efficiency Video Coding (HEVC), Transform module (DCT) is also discussed. When compared to current ones in the literature, the proposed RNS adder-designed 1D-DCT architecture has an area improvement of 18
Enhanced Adaptive Combined Ant Colony Algorithm

To solve the problems that ant colony algorithm (ACO) has long iterations, slow convergence, and is difficult to find the optimum, an ACO based on the annealing tempering coefficient (AHACO) is proposed, which can speed up convergence and improve the ability to find optimum. According to the distribution characteristics of path, an adaptive state transition probability (APACO) is introduced, and two types of adaptive coefficient are given. Subsequently, an adaptive evaporation coefficient is introduced to optimize convergence (AEACO). enhanced adaptive combined ACO is introduced to combine all advantages. Finally, parameters selection and simulation experiments are designed and executed. The results indicate that the effectiveness of EACACO.
An Inaccurate Median Filter Architecture for Salt and Pepper Noise Removal

In this paper, an inaccurate median filter is proposed to eliminate the salt and pepper noise in the grayscale image. The corrupted pixel is detected and replaced using the Inaccurate Median Filter (IAMF). The IAMF excludes the corrupted pixel while computing the median value of the provided pixels in the 3×3 window. The input data bits are divided into different segments whose Partial Median (PM) are computed and concatenated to obtain the final median. The PM for each segment is computed without any comparators. The Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) are used to analyze the image quality of the proposed architecture. The synthesis results are obtained using GPDK 90nm library at a clock frequency of 500MHz. These results shows that the proposed architecture consumes less area and produces good image quality compared to the existing architectures.
TRANSFORMATION OF SOCIAL COMMUNICATION MODELS IN THE DIGITAL AGE

This article presents an overview of the models developed by researchers for the visual representation of a complex and multidimensional communicative process, which reflect the most essential elements of the real communication process, the views of scientists on the communication process, as well as the most pressing problems that attracted their attention at one time or another. According to the author, at present, due to the development and active introduction of electronic and digital communication technologies into communication processes, the key components of communication models have been significantly transformed, and their functional purpose has changed significantly. In this regard, considerable attention is paid in the article to the vectors of the indicated transformation, among which is the qualitative expansion of the sphere of social communication, the change in the way information is transmitted and such elements of the communicative process as the communicator – audience. The article presents the author's interpretation of the transformation of the H. Lasswell model in the digital age. The author believes that when transporting a message with information, in addition to the physical medium of distribution (air, computer network, etc.), the distribution structure, which is increasingly acquiring a network character, has a great importance on efficiency. In the case of a network structure for the distribution of a message with information and the simultaneous receipt of a message with information by a large number of recipients, the "time of interest" of the recipients to the incoming information is of great importance. The article analyzes new properties characteristic of digital communications. Thus, the source of information (communicator) in the digital era may be unknown, the message sent by the communicator in the digital era may be adjusted according to the recipient, communication channels in the digital era can also be adjusted to a specific user, the number of information sources for the recipient in the digital era increases significantly, but in practice the recipient of information turns to a limited number of sources, when at the same time, believing that he has full freedom of access to information. The most important feature of the communication effect in the digital age is that in the conditions of rapid development of virtual social networks, the recipient himself can be a source of mass information. Thus, the development of digital technologies and network communication structures, although it is an important vector of transformation of the communication structure of society in the digital age, has ambiguous consequences for the development of society
Cleaning Method of Equipment Fault Database Based on ISOMAP Equidistant Mapping Clustering

With the development of artificial intelligence algorithms, monitoring data of power equipment is widely used in early fault diagnosis of equipment. Since the original monitoring data may contain some abnormal “dirty” data, which will seriously affect the accuracy of intelligent diagnosis of equipment faults, it is necessary to clean the equipment fault database. In this paper, the linear model and the proximity model are combined, and a data cleaning method based on ISOMAP(isometric feature mapping) equidistant map clustering is constructed. Firstly, the ISOMAP analysis method is used to extract the features of the original data, and on this basis, the DBSCAN(density-based spatial clustering of applications with noise) density clustering analysis is carried out. This method fully considers the nonlinear relationship and clustering characteristics between the characteristic quantities of power equipment, removes outliers, and improves data quality. This paper takes the cleaning of the fault database of the dissolved gas concentration in the oil of the transformer as an example. The results show that the fault diagnosis learning network trained by the cleaned database can improve the diagnosis accuracy by 17%. Compared with other data cleaning algorithm models, the classification and recognition rate of the method proposed in this paper is improved by 5%.
The DVC Integer-Voxel Displacement Search Method Based on ant Colony Algorithm

The enumeration method is very inefficient for integer-voxel displacement search of the digital volume correlation (DVC) method. In this paper, three intelligent algorithms with global search capabilities, the ant colony algorithm (ACA), the genetic algorithm (GA) and the simulated annealing algorithm (SAA), are introduced into DVC. Through simulated speckle experiments, the search accuracy and speed of these three algorithms are studied and compared. The results of simulated speckle experiments show that: (1) The accuracy of the ant colony algorithm is the highest, followed by the genetic algorithm, and finally the simulated annealing algorithm, with average relative errors of 0, 3.6% and 3.9%, respectively. (2) The genetic algorithm is the fastest, followed by the simulated annealing algorithm, and finally the ant colony algorithm. The time-consuming of the three algorithms is 10%, 11% and 23% of the enumeration method.
Research on Prototype System for Electromagnetic Spectrum Management and Control Based on GIS

Controlling electromagnetic space has become an important support for the fight for comprehensive control over the battlefield. How to effectively use limited electromagnetic spectrum resources has become a major problem that restricts future operations. The GIS-based electromagnetic spectrum management and control prototype system can provide commanders with spectrum planning and frequency assignment suggestions, and give full play to the combat effectiveness of frequency equipment. First introduced the basic structure of the prototype system and the overview of the command and equipment side; then designed the system composition and the command side workflow of the prototype system; finally, in order to demonstrate the effectiveness of the prototype system, use the border conflict as the background to distinguish the pre-war spectrum The electromagnetic spectrum is managed and controlled in three situations: assignment, suffering from ground-based fixed interference and suffering from space-based mobile interference. Through teaching and training practice, it is helpful to effectively use electromagnetic spectrum resources and avoid intentional interference, and has certain promotion and application value
Smart Grid Data Traceability System based on Blockchain Technologies

The development of smart grid systems is based on the concept of integrating intelligence into all grid components for management, which contains a large amount of heterogeneous data such as various assets and equipment. Big data analytics is considered a practical technology to provide smart grid data management solutions. In order to overcome the problems of artificially introduced label confusion and storage duplication in smart grid data, this paper proposes a smart grid data traceability system based on the principle of blockchain. The experimental results of data traceability based on blockchain technology in the smart grid data traceability system show that by using blockchain technology-based data traceability in the smart grid procurement data platform, the data traceability capability of the smart grid data traceability system is improved. In addition, the tests conducted in the edge computing device scenario also obtained good usability and feasibility.
Implementation of Cache Timing Attack Based on Present Algorithm

Traditional side-channel attacks have shortcomings such as low efficiency, extremely difficult collection and injection of fault information in real environments, and poor applicability of attacks. The cache timing attack proposed in recent years is a new type of side-channel attack method. This attack method uses the difference in the reading speed of the computer CPU cache to enable the attacker to obtain the confidential information during the execution of the algorithm. The attack efficiency is high, and the cost is relatively low. little. Present algorithm is a lightweight block cipher proposed in 2007. The algorithm has excellent hardware implementation and concise round function design. On this basis, scholars at home and abroad have carried out different side-channel attacks on it, such as differential attacks., multiple differential chain attacks, algebraic attacks, etc. At present, there is no published research on the Cache timing attack against the Present algorithm at home and abroad. In this paper, the output value of the S box in the first and second rounds of the encryption process is obtained through the combination of the Cache timing attack and the side-channel Trojan horse, and Combined with the key recovery algorithm, the master key of the algorithm is finally recovered.
A Chaotic Image Encryption Algorithm Using Pixel Permutation-diffusion and Bit Replacement

A new chaotic image encryption algorithm using pixel permutation-diffusion and bit plane operations is presented. Firstly, the system parameter and initial value of 1D skew tent system are modified by applying the characteristic quantity of plain-image to strengthen the cryptosystem resisting known/chosen plaintext attacks. The generated chaotic sequence is then utilized to scramble the plain-image. Secondly, two chaotic image sequences are generated by 2D skew tent system, and the scrambled plain-image is split and embedded into the two chaotic image sequences by bit plane replacement. The bitwise XOR operation and forward diffusion operation are then conducted on the two resulted chaotic sequences embedded plain-image information to strengthen the security of the proposed cryptosystem. Experimental results and performance analysis demonstrate that the proposed encryption algorithm can effectively withstand statistical analysis like entropy analysis, histogram analysis, adjacent pixels correlation analysis, etc. The proposed image encryption algorithm has strong key sensitivity, strong plain-image sensitivity, and large enough key space as well.
Graph Neural Networks based Recommendation Methods in Different Scenarios: A Survey

Recommendation system is one of the most important service applications in the world, and graph neural network is currently the most popular research direction in the field of recommendation system. In this survey, we conduct a literature survey on graph neural network-based recommender systems. Firstly, we introduce the classification for graph neural networks. Secondly, different recommendation scenarios in the recommendation system are introduced, and the challenges faced by graph neural networks in different recommendation scenarios are analyzed. Finally, the solutions to these challenges are summarized.
gCoRF: Generative Compositional Radiance Fields

3D generative models of objects enable photorealistic image synthesis with 3D control. Existing methods model the scene as a global scene representation, ignoring the compositional aspect of the scene. Compositional reasoning can enable a wide variety of editing applications, in addition to enabling generalizable 3D reasoning. In this paper, we present a compositional generative model, where each semantic part of the object is represented as an independent 3D representation learnt from only in-the-wild 2D data. We start with a global generative model (GAN) and learn to decompose it into different semantic parts using supervision from 2D segmentation masks. We then learn to composite independently sampled parts in order to create coherent global scenes. Different parts can be independently sampled, while keeping rest of the object fixed. We evaluate our method on a wide variety of objects and parts, and demonstrate editing applications.
Quality of Experience Assessment of Online Server/Cloud Gaming

Nowadays, gaming is very popular among the young generation, and game players use heterogeneous devices to play games from cloud/server and PC installation/mobile app. Better gaming can be considered as where the game runs smoothly, speedy, and quickly responds to the user's input. The game player and developer are unaware of how a better gaming experience will be achieved. In this paper, we used quality of experience (QoE) to assess the game experience of game players by using different ways of gaming such as PC installation, Mobile app, and online from server/cloud using both devices. The results show that the PC installation method provides a better QoE for both games, whereas, gaming online from server/cloud using both devices gave the worst user experience.
High-Speed and Low-Latency 3D Sensing with a Parallel-Bus Pattern

High-speed 3D shape sensing is an essential technology for three-dimensional recognition and manipulation in dynamic scenes. However, conventional high-speed sensing methods mainly focus on the image capturing speed and the actual processing time to obtain a point cloud is not optimized in the measurement scheme and sensing pattern configuration. On the other hand, measurement latency is critical to respond in real-time and physically handle the dynamic scenes. This paper introduces a physically stereo-rectified projector-camera system for high-speed and low-latency 3D sensing with fast sequential memory access in the decoding process. Moreover, we configure a structured light pattern named “Parallel-bus pattern” with a De Bruijn torus and clock lines to maximize information density and robustly decode the pattern as data transfer in a parallel bus interface. We measured dynamically moving and deforming objects with the proposed system and evaluated the measurement performance. As a result, the developed 3D sensing system with the parallel-bus pattern achieved 27K-points measurement at higher than 1000 fps with 0.336 ms latency and 0.838 mm accuracy on average.
Dimensions of Motion: Monocular Prediction through Flow Subspaces

We introduce a way to learn to estimate a scene representation from a single image by predicting a low-dimensional subspace of optical flow for each training example, which encompasses the variety of possible camera and object movement. Supervision is provided by a novel loss which measures the distance between this predicted flow subspace and an observed optical flow. This provides a new approach to learning scene representation tasks, such as monocular depth prediction or instance segmentation, in an unsupervised fashion using in-the-wild input videos without requiring camera poses, intrinsics, or an explicit multi-view stereo step. We evaluate our method in multiple settings, including an indoor depth prediction task where it achieves comparable performance to recent methods trained with more supervision. Our project page is at https://dimensions-of-motion.github.io/.
Classroom Practical Teaching Based On Intelligent Data

The development of artificial intelligence and big data technology has brought brainstorming to the education field, and many new education models and concepts have emerged. As a new type of teaching form, teaching based on intelligent classrooms has been widely explored and practiced by various educational institutions. But in this kind of teaching practice, people often overemphasize the role of the intelligent hardware environment, while ignoring the data resources accumulated in the teaching process. Data resources should play an important role in the teaching process, help teachers improve teaching effects, and guide students to learn better. To this end, this article introduces a new method of using teaching data to help teachers carry out curriculum teaching design, curriculum teaching process control combined teaching effect prediction evaluation, to help students find insufficient knowledge and improve learning ability.
New Untrained Emitter Detection Based on SK-GAND Network

The increasing complexity of the spatial electromagnetic environment has triggered the need to discriminate individuals with unknown radiation sources, and the recognition effect of traditional deep networks and other methods based on supervised learning is poor. To address the problem of identifying new untrained devices in RF fingerprinting, this paper proposes an unsupervised SK-GAND neural network-based method for detecting new untrained radiation sources, adding the SK module to the traditional GAN network and setting two thresholds γand ω for identifying trained and new radiation sources. The thresholds <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$y$</tex> and ω are determined by training the difference between the samples obtained in the SK-GAND network for the existence of encoding and decoding re-encoding, and a multi-discriminator model is used to determine whether the input radiation source samples are new untrained sources. Based on the experimental comparison of different numbers of radiation sources, the detection effectiveness is obtained up to 80% for up to 12 sources and up to 90% for up to 5 sources.
TranSal: Depth-guided Transformer for RGB-D Salient Object Detection

In recent years, RGB-D salient object detection (SOD) has attracted increased attention and has been widely employed in various computer vision applications. Fully convolutional networks dominate many RGB-D SOD tasks and have already achieved outstanding results. However, solving the cross-modality fusion of low-quality depth and RGB cues remains challenging. We present TranSal, an innovative network, depth-guided transformer for RGB-D SOD, based on the Transformer's fantastic performance in image recognition and segmentation. A dual-branch U-Net architecture is used in the proposed model. To begin, it uses ResNet-50 to extract multi-level RGB and depth features. Second, it employs Transformer layers to represent image sequentiality. Finally, the representation is projected into spatial order, and the multi-scale cross-modality characteristics are fused to generate an accurate saliency map using a depth-guided fusion subnetwork. TranSal can successfully mitigate the negative impacts of low-quality depth information and create a saliency map with clear contours and accurate semantics compared to previous models. Experiments and analyses on five large-scale benchmarks verify that TranSal achieves satisfactory performance compared to the recent state-of-the-art methods.