Reviewerly/exHarmony huggingface dataset:
Around-device finger input on commodity smartwatches with learning guidance through discoverability

User interactions with smartwatches are limited due to one-finger usage on small touch screens. However, smartwatch interaction capabilities can be extended beyond the screen into the around-device space by leveraging multi-finger interactions. In this paper, we explore suitable finger postures and gestures in mid-air and on the back of the palm to extend interaction capabilities on the unmodified commodity smartwatch while ensuring their learnability through discoverability. We conduct a design study to find a set of finger postures and gestures suitable for interacting with off-the-shelf smartwatches. An application is then designed to detect the posture-gesture set with on-device dual cameras while examining their classification accuracy. To facilitate learnability through discoverability of the gesture-posture set, we explore ‘context-aware-hints’ that shows a sub-set of gesture-posture and their associated actions based on the usage context. Results from a user study show that the guidance helps users to discover and learn them quickly and accurately.
Variational adaptive Kalman filter for unknown measurement loss and inaccurate noise statistics

Considering a common situation that the measurements are obtained from independent sensors and the accurate noise statistics are not available, we propose a novel variational adaptive Kalman filter (KF), which can selectively treat measurement loss and adaptively estimate inaccurate state and measurement noise covariance matrices. Firstly, a multiple inverse-Wishart mixture (MIWM) distribution is utilized to modeled state transition probability density function (PDF), which reduces the dependence on the pre-selected nominal state noise covariance matrix (SNCM). Then, a modified measurement model is constructed and a new measurement likelihood PDF is provided, where the measurement losses from different sensors are considered independently and the measurement noise covariance matrix (MNCM) is modeled as an inverse Gamma distribution. Finally, based on the modified state transition and measurement likelihood PDFs, a novel variational adaptive KF is derived by variational Bayesian method, and the feasibility and superiority of the filter are demonstrated by the numerical simulation.
Efficiency of Distributed Selection of Edge or Cloud Servers under Latency Constraints

We consider a set of network users (nodes) that generate latency-constrained service requests requiring the execution of computing tasks on servers located either in the cloud or at the network edge. We explore the efficiency of a distributed server selection strategically performed by individual nodes. In an earlier analysis, we argued for a stateless centralized allocation based on a probabilistic selection between edge and cloud servers. In that proposal, the optimal share of edge and cloud tasks was computed according to static network characteristics, with no knowledge of the actual network state. In this new study, we perform an analysis based on game theory, where we compare the globally optimal allocation performed at a central level against a distributed server selection driven by the selfish objectives of individual nodes. The inefficiency of the selfish allocation can be computed as the price of anarchy, which is shown to be very small, thus justifying a distributed strategic implementation of stateless policies. This insight is precious for designing algorithms for server selection and quantitatively proves the efficiency of distributed selfish approaches.
LSTM-based Viewport Prediction for Immersive Video Systems

Immersive multimedia content delivery is becoming increasingly popular due to the spread of Head Mounted Displays. In particular, omnidirectional video streaming is gaining ground among video delivery platforms. Delivering 360° video content over the Internet requires much larger bandwidth compared to classic 2D videos. Therefore, for the purpose of reducing bandwidth consumption, the tiling technique breaks down the video into smaller portions so that those falling outside the user’s viewport are encoded at a low resolution whereas those in the viewport are encoded at a higher resolution. This operation can be performed only when the user’s future viewports are known in advance. Thus, it is necessary to provide a trustworthy prediction of future viewports. In this work, we show that users have a tendency to explore the environment at the beginning of the video and then to focus on one of the regions attracting more attention (Points of Interest). This insight is helpful when it comes to designing viewport-adaptive streaming techniques. On this basis, we propose a viewport prediction approach that combines Long Short-Term Memory (LSTM) networks and the classic naive technique. Preliminary simulative tests show promising results.
Research on medical image denoising based on wavelet pyramid fusion

The noise in medical images will affect the subsequent image processing. In order to reduce the noise in the prostate capsule, we proposed a new noise reduction technology using principal component analysis, wavelet pyramid fusion and adaptive median filtering. Compared with other commonly used denoising algorithms, this method is simple and efficient, and can better maintain image details while reducing noise.
Research on military target detection method based on YOLO method

In the field of military target recognition, in order to solve the problems of easy loss of small target detection results and slow detection speed, this paper proposes a target recognition detection algorithm YOLO-G2S, which reduces the parameters and reduces the model size by sacrificing a little accuracy. In the original model of YOLO v5, the 2nd, 3rd and 4th C3 modules in the backbone were replaced by C3G2 modules, thus reducing the model size and improving the detection speed. Then the accuracy is improved by changing the RELU activation function in C3G2 module. The experimental results show that the average accuracy of YOLO-G2S reaches 95%, the number of parameters is reduced by 7.3%, and the amount of calculation is reduced by 17.8%. Therefore, the improved algorithm in this paper is more suitable for deployment in the field of military target recognition.
Security Protection Mechanism of Core Data of Industrial Internet Identification Analysis in Power Industry

Based on the innovative industrial Internet identity resolution architecture, and focusing on the security risks existing in the industrial Internet identity resolution system, the industrial Internet identity resolution registration authentication and trusted resolution mechanism are designed. The information interaction rules and identity authentication methods of the system module are designed for enterprise node access, enterprise user registration, update, identification resolution and other processes. And tamper proof verification is carried out for the parsed entity ID mapping data results to further enhance the data security of the innovative ID resolution system.
Respecting and Protecting Cultural Values in an Indigenous Virtual Reality Project

Co-design and development of technology with indigenous communities requires respect and close partnership. Here, we reflect on our experiences working with a Māori (indigenous New Zealand) community as Pākehā textasciimacron (non-Māori). <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> In particular, we consider the importance of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">protection</i> as an underlying principle. We believe that our experiences can inform other researchers and practitioners in respecting and protecting cultural values in similar projects.
Generalized Design of Spacecraft Electromechanical Product Software Based on Permanent Magnet Synchronous Motor

Software reuse is an important means to improve the productivity and quality of spacecraft embedded software. A variety of complex products based on permanent magnet synchronous motors have similar software requirements. Based on the structured requirements decomposition, this paper divides the software hierarchy, analyzes the data interaction relationship between layers, proposes a domain software component model, and designs 25 types of software components. In terms of program implementation, the software can be reused to the greatest extent from three levels of granularity: file level, project level, and product level. The software reusable assets in this paper have been successfully applied to 153 sets of products in 3 categories, and the software production efficiency has increased by 33 %.
Agricultural Planting Big Data Q &amp; A System Technology Research Based on Knowledge Graph

In the process of agricultural production, planting has the characteristics of decentralization, complexity, seasonal differences, and regional differences, which affects agricultural growers to easily obtain effective planting information to help their own agricultural planting. The purpose of this study is to apply knowledge graph technology and natural language processing technology to the process of agricultural planting, and use the intelligent Q & A system to provide agricultural growers with regional natural conditions and suggestions and decisions of agricultural plant problems. Provide agricultural growers with a new solution for question and answer to high - efficiency and professional crop planting decisions. Finally, the intelligent decision -making and knowledge generation of farming planting, production, and operational process is achieved, and it is agricultural growers and improves farming income and farmland production efficiency. Change the agriculture from “watching the sky” to the intelligent “knowing the sky”.
Research on fast stitching method of UAV aerial images based on OpenCV

Most of the current aerial images of UAVs are stitched together with the help of image post-processing software on the PC side, which has certain inconvenience. Based on the Android platform, the aerial images are transmitted back to the cell phone, and OpenCV is used to realize the fast-stitching and synthesis of the aerial images of UAVs. At the same time, considering the limitation of hardware image processing capability of mobile devices, the method is optimized and experimentally verified, and the results show that the image stitching effect on the mobile side meets the expectation.
EfficientRF: Facial Age Estimation Based on EfficientNet and Random Forest

Age estimation by analyzing facial images has always been a challenging problem in computer vision. With the maturity of face recognition technology and the popularity of mobile devices, it is of great significance to deploy face age estimation algorithms on mobile devices. Aiming at the problems of high model complexity and low running speed of the current face age estimation model, this paper proposes a new end-to-end model named EfficientRF for the age estimation task. EfficientRF integrates random forest based on EfficientNet network architecture, wherein the split nodes of the random forest are connected to the last fully connected layer of the convolutional neural network. The experimental results show that, on the premise of ensuring high accuracy, the new method uses 1.6 times less parameters and 60 times faster running speed in face age estimation compared to three public age estimation benchmarks, and is superior to the most advanced standard on FG-NET dataset.
Systematic Construction and Application of Intelligent Teaching Environment in Medical Colleges

Smarter learning plays an important role in promoting the reform of higher education and teaching. This paper first analyzes the problems of intelligent teaching in the field of medical education. On the basis of the current campus learning environment, a more systematic, comprehensive and interrelated intelligent teaching environment is constructed, and the intelligent teaching mode is explored, which provides favorable software and hardware conditions for better network teaching in medical colleges and universities in the future, and makes the intelligent teaching research in the medical field more accurate and in-depth. Teaching behavior pays more attention to the reasonable connection between theory and practice.
A Novel Algorithm for Exploiting RSA Plain’s LSB Oracle

Plain’s least significant bit oracle is an common vulnerability of RSA. Though its idea is simple, most published algorithms for exploiting the vulnerability is wrong, so that they cannot reveal the original plain precisely. In this paper, we enumerate and analysis three kinds of common defective algorithms, and find out that no matter how to approximate the middle value of possible value range bounds, it will always cause error. Then a novel algorithm for exploiting the vulnerability is proposed. It can reveal the original plain precisely, by calculating the original plain’s eigenvalue, which is equal to its interval serial number, and converting it to the original plain. The algorithm’s completeness and correctness are proved finally.
Research on The Design and Application of Visual Navigation Control for Patrol Wheeled Robot

The traveling speed of inspection wheeled robot is always constant in the visual navigation process, that is, the inspection wheeled robot is always traveling at a constant speed in the lateral position deviation control process. In order to make the inspection wheeled robot approach navigation line towards the side where navigation line is located in the case of position deviation, and constantly correct the error during driving, it is necessary to design a visual navigation control algorithm according to the proportional link control mechanism in PID. In this paper, firstly, proportional control is used as the deviation control method for visually guiding the inspection wheeled robot to navigation line, and its control effect is analyzed. Then, the influence of heading angle on the driving error trend is analyzed, and then the visual control function is designed, and the designed connection function is used to connect the control function in segments, so as to realize the lateral deviation control of the inspection wheeled robot in the visual navigation process and the dynamic acceleration and deceleration control when the speed changes.
A Network of Generating High Quality Hemoglobin Images

In recent years, image-based facial skin pigment extraction has made great progress, and the extraction methods are mainly divided into traditional methods and deep learning methods. Due to the early introduction of traditional methods, there are some defects, such as low efficiency, some black pixels will go white, etc. Therefore, this paper mainly proposes a deep learning method based on Generative Adversarial Network (GAN) to achieve hemoglobin image generation. Our goal is similar to image translation. Currently, the mainstream GAN networks for image translation include pix2pix, pix2pixHD, cycleGAN, etc. This paper proposes a network GHNet based on UNet to generate higher quality images. In the data set part, we built the paired data set of polarized light-hemoglobin image by ourselves. In the network part, we reduced the number of encoders and decoders in the traditional UNet network, which made the network more lightweight. Based on this, we define a neural network module and integrate the attention mechanism SE module to enhance the ability to capture the input image features. The experimental results show that, compared with ASAPNet, DGC and CLD networks, the proposed network training speed is the same as that of the baseline network, and the PSNR index and SSIM index of the generated images are improved.
An Efficient Keywords Spotting System with Speaker Verification based on Binary Neural Networks

Keywords spotting plays an important role for enabling voice interface between human and smart devices. It is challenging to build a real-time system with high wake-up accuracy and low false alarm rate, especially when computational resources are limited. In this paper, to achieve the trade-off between accuracy and false alarm rate, we employ a two-stage system that uses a primary keyword spotting stage and a secondary speaker verification stage. Speaker verification model based on deep neural networks requires massive memory and computational resources, which makes it difficult to be deployed on memory and power constrained devices. To solve this problem, we proposed a speaker verification model based on binary neural networks (BNNs) where floating point weights and activations are compressed into 1-bit. To mitigate the performance degradation caused by information loss, BNNs structure is optimized by adding shortcuts to connect layers. Experimental results on text-dependent and text-independent datasets show that performance of proposed speaker verification model reach the level full-precision neural networks with much less memory and computational cost. We implement this model into a two-stage keywords spotting system and experiments show that this system can reduce false alarm rate significantly while keeping wake-up accuracy.
A Method to Improve the Accuracy of Face Recognition in Application Scenarios

This paper mainly studies the method to improve the accuracy of face recognition by using the multi sample discrimination method and the automatic optimization method of the sample library through analyzing the problems in the face recognition application scenarios with continuous input data such as intelligent video analysis. In the automatic optimization process of the sample library, a method of dynamically updating the face sample library while face recognition is proposed. The essence of this method is to find the sample combination corresponding to the optimal constraint function value. The conditional constraint function contains two criteria: global center approximation and single distance approximation. In the simulation experiment, the recognition accuracy has been improved after using this method.
Robust Smoothing for the Reconstructed Trajectory using RLWRS-QMS Method

In the rocket projectile firing assessment test, the measurement data from a ground-based tracking radar may have poor quality due to complex interferences, especially in the rocket projectile’s low-altitude flight phase corresponding to the radar’s low-elevation detection that is corrupted by ground clutter. In this work, we are concerned about the problem that large errors accompany the rocket projectile’s trajectory that is reconstructed with polluted radar measurements, and affect the accuracy of assessment. We present a simple but practical robust smoothing method that combines robust local weighted regression smoothing (RLWRS) and quartile mean smoothing (QMS). Concretely, we use the RLWRS method to smooth the reconstructed sub-trajectory in the radar’s low-elevation detecting phase, whereas we use the QMS method to smooth the rest. Finally, a relatively realistic trajectory of the rocket projectile is obtained by splicing these smoothed sub-trajectories. A simulation experiment is provided and performed to verify the effectiveness of the RLWRS-QMS method.
Research on the Construction of Agricultural Planting Big Data Corpus Based on Knowledge Graphs

Agriculture has always been an important pillar of China, and the continuous development of science and technology has always been inclusive to the people. How to help more growers more scientific and convenient planting through intelligent technology and solving problems and challenges in the field of agricultural planting have always been a problem worth thinking and practice. The purpose of this study is to build a new-style agricultural field of the new agricultural field of the conceptual system, and build a standardized, systematic knowledge corpus, agricultural planting data entity extraction corpus in the agricultural field through the structural concept of the new agricultural planting knowledge base. Agricultural planting data is due to basic data during data mining and model construction. Provide an efficient and professional crop planted big data corpus solution for agricultural knowledge graph applications.
Research on Performance Optimization of MySQL Database

The application system based on the MySQL database has gradually penetrated into various fields, and the performance of the MYSQL database has attracted more and more attention. This paper studies the performance optimization of MySQL databases from five aspects: SQL statement optimization, index optimization, table structure optimization, schema optimization, and server-side optimization.
Individual Recognition of Big Data Radar Digital Waveform Based on Long Short-Term Memory Network

Individual recognition of Big Data radar digital waveform is based on the classification of target individuals using radar target waveform. Traditional recognition methods suffer from problems such as low accuracy and complex recognition processes. To address these issues, this paper proposes an individual recognition method for radar digital waveform based on Long Short-Term Memory (LSTM) network. The actual collected radar target digital waveform is simulated to generate big data of radar digital waveform, and then the basic frequency domain feature data is constructed. The LSTM network is used to extract the individual data features of the radar, and the network parameters are iteratively trained using the SGD optimization algorithm to achieve effective recognition of individual radar signals. Experimental results show that the radar signal individual recognition method based on LSTM has significantly improved accuracy compared to SVM and XGBOOST methods.
Business systems KPI anomaly detection based on an adversarial variational autoencoder

An anomaly KPI detection method is proposed based on an adversarial variational autoencoder (AVAE) for business systems. The proposed AVAE model is a combination of conditional variational autoencoder (CVAE) and generative adversarial network (GAN). Firstly, to effectively extract the periodic characteristics of KPI data, time information is introduced into the CVAE network as the input condition. Secondly, the discriminator of GAN network is introduced on the basis of CVAE to improve the sample inference ability of the generated network. Finally, CVAE, whose encoder can better obtain the mapping from real samples to hidden space, is used as a generator. Experimental results show the effectiveness of the proposed method.
A Channel-enhanced Cross-modality Person Re-identification Method Based on CE-DGTL

For visible-thermal person re-identification. This paper presents the CE-DGTL model. Using channel enhancement based on the DGTL model. By randomly swapping the image color channels, color-independent images are generated. Thus, the connection with the nighttime modalities is approximated, while the robustness of the model to color changes is enhanced. this paper also introduces a joint learning method for image enhancement from the channel level. Joint training is performed using visible images, enhanced images, grayscale images and nocturnal modal images. Using DGTL as the backbone network, coarse-grained and fine-grained information is learned to achieve intra-class compactness and inter-class differentiation. cbam attention module is introduced to make the model focus on specific regions. the model is jointly constrained using identity loss, triplet loss and hetero-center triplet loss. After using channel enhancement and cbam compared to backbone network DGTL. We experimented on the SYSU-MM01 dataset. In the case of All search, Rank-1 is improved by 7.85%. mAP improves by 5.39%. In the case of Indoor, Rank-1 increased by 7.04%. mAP increased by 5.81%. The above experiments also demonstrate the effectiveness of our method.