Reviewerly/exHarmony huggingface dataset:
Application of Generative Task Methods to Domain Adaptive Tasks

Our previous work found that some generative tasks have some commonalities with domain adaptation tasks, such as image style transfer. Therefore, in our work, after adding the regularization method commonly used in image style conversion, instance normalization, to the feature extractor, the experimental results in the domain adaptation task have been significantly improved. Later, in order to further strengthen the domain adaptability of our model, for the classifier, we used the Batch Nuclear-norm Maximization algorithm. Because the Domain-Symmetric Network takes into account the category alignment, the Batch Nuclear-norm Maximization algorithm can give full play to its role, so we choose Domain-Symmetric Network as our basic network framework in our work. We tested the above work on two benchmark datasets, and both achieved good performance. The subsequent ablation experiments demonstrate the effectiveness of each of our work.
ClothFace: Battery-Free On-body Interface Platform for Future Human-Machine Interaction

Smooth communication between people and machines plays an important role in the intelligent environments of the future, where the best sides of both people and machines are to be exploited in the name of efficiency and flexibility. This requires a functional human-machine interface that allows the necessary control actions but does not require any use of robot-specific devices. In this paper, we further introduce a passive ultra-high frequency (UHF) radio frequency identification (RFID)-based platform, which is an attractive solution for on-body interfaces, as it is passive and maintenance-free, does not require a line-of-sight to function, and has reading distances of several meters. The unique aspect of this study is the established fully textile-based battery-free touchpad for writing digits, which can be integrated into everyday clothing and worn on body. Further, the paper presents various use cases for the developed technology.
A single image dehazing algorithm based on black and bright channel fusion

To solve the problem of inaccurate estimation of atmospheric light and transmission, this paper proposes a single image dehazing algorithm based on the fusion of black and bright channel. First, finding the block with the largest mean-gradient ratio in the image and the point closest to the maximum pixel value in the block, then taking the pixel value of this point as the atmospheric light. Secondly, the black channel map of haze image is obtained through a minimum filter and a corrosion operation, and the black channel transmission is calculated. Then, maximum filtering twice for haze image to obtain the bright channel map, and the transmission of bright channel map is calculated. The transmission of bright and black channel is fused to obtain the total transmission and then it is handled by guidedfiltering. Finally, the dehazing image is restored by the atmospheric scattering model. The experimental results show that our method can not only obtain more accurate value of atmospheric light and transmission, but also get a good dehazing result. The evaluation indicators of dehazing images are better than other two dehazing algorithms.
Study of Chinese Shadow Mapping Classification with the Application of Deep Learning Algorithms

Shadow puppetry is a traditional Chinese fascinating theatre act performed by large group of artists. An artist generally uses sticks, transparent cloth screen, and flat puppets behind an illuminated background to create illusion of moving pictures during the act. These acts showcase the culture, heritage, social belief, and customs of Chinese and are a popular form of entertainment especially to youths. The modern method of digital shadow puppetry has gained a tremendous interest in the diversifying entertainment industry. Proper identification and classification of shadow puppetry is a tedious process, demanding significant research studies attention to solve the real-world vision-based problem. The proposed research studies focus on the design of artificial intelligence-based modified Grey Wolf Optimized Classifier (mGWOC) for the digital shadow puppetry problem. Data augmentation process is performed in the initial stage of the work to increase the size of the dataset used for training and testing. Secondly, to derive feature vectors from shadow puppet images, Alex Net-a deep neural network model as a part of feature extraction is adopted. Finally, Extreme Learning Classifier (ELC) is applied to allocate proper class labels. The experimental results of the proposed mGWOC reports betterment over the ResNet model, DenseNet model, and grey wolf optimization algorithm in terms of precision, recall, F-score, and kappa statistical performance measure reporting average accuracy as 0.951.
Dependence of subhourly solar variability statistics on time interval and cloud vertical position

Solar variability corresponds to strong variations of the solar irradiance, caused mainly by the presence of clouds. Practical uses of solar resource data, such as the design of photovoltaic solar plants, usually employ several years of hourly data, neglecting subhourly features. The effect of clouds on short-time variability can differ by cloud type, suggesting that some cloud effects could be ignored when working with hourly data. In this work, we compare statistics of solar variability calculated at different time intervals and separate the analysis by cloud categories. We use 1 min solar data and cloud radar products from the Atmospheric Radiation Measurement (ARM) cloud, aerosol, and complex terrain interactions campaign in Córdoba, Argentina, where a wide variety of clouds exist. We classify the clouds based on their vertical position and observe solar variability using the mean and standard deviation of the clear sky index for varying time intervals of 5, 15, 30, and 60 min. Time intervals affect the mean and standard deviation of the clear sky index differently for each cloud type: longer time intervals neglect small variability and overestimate the mean clear sky index of low and mid-clouds, while high clouds do not change as much. The effect is also palpable when measuring ramps: the percentile 95 of the ramps obtained for 1 min is 21 times greater compared to 1 h. This ratio varies per cloud type with the strongest differences occurring for mid-clouds, having ramps that are 73 times stronger.
Visual Detection Algorithm of Foreign Object Intrusion in High-Speed Railway Traction Substation Based on Patch Clustering Learning

Since high-speed railway traction substation is usually built in an open area, the intrusion of foreign objects will cause hidden trouble to the operation safety of the substation, so it is of great significance and practical value to study the foreign object intrusion detection method in traction substation. Therefore, this paper proposes a patch-based clustering learning foreign invasion of visual detection algorithm. Firstly, the global region image of the high-speed railway traction substation is divided into patches, and then features are extracted from the segmented image patches based on the MobileNetV2 network. Then, the image patches are clustered according to these features by the K-means method and the classification results are obtained. Finally, the Patch-SVDD method is used to train the encoder and classifier to detect and locate foreign object intrusion. Based on the real traction substation data, the optimal input size and sampling step size of the image patch were obtained by selecting segmentation image patches of different sizes and sampling step sizes, and the validity and accuracy of the proposed method were verified. The detection accuracy of foreign object intrusion was 96.6%, and the positioning accuracy was 98.8%.
Overlapping speech analysis in Slovak conversational interactions

Overlapping speech is a situation when more than one dialogue interlocutors speak simultaneously. Such situations are common in the human-human conversations and are located during turn-shifts as a result of cooperative or competitive interruption of the current speaker by the listener. From the human-machine speech communication point of view, understanding of mechanisms behind overlaps can help to make turn taking more rapid and human-like. We analyzed the corpus of human-human discussions in Slovak language and all overlapping segments were manually labeled with the interaction intent of the interrupting utterance. Especially, we focused on the duration of analyzed overlaps to observe patterns related to the concrete interaction intentions. Our aim is to bring new findings, which can be used in the automatic classification of interruption intentions.
Sentiment Analysis Method for Web Short Texts Based on Fusion Features

Danmaku is a special kind of short text, highly associated with video content, with few features and sparse semantics. Existing methods only consider the text itself and are not suitable for sentiment analysis of danmaku. To solve the above problems, a dataset of time-based synchronized videos for annotation is firstly constructed. Then, a dual-channel sentiment analysis method based on text and time is proposed. The text channel uses ERNIE and TextCNN to extract the deep semantic features of words and characters of danmaku, which introduces external knowledge and enhances the feature representation; the temporal features associate danmaku with the video content; after feature fusion, the BiLSTM combined with attention mechanism is used for sentiment classification. The experiment results show that the method is better than the mainstream models and can be effectively applied to the sentiment analysis of danmaku.
ID-based Data Integrity Auditing Scheme from RSA with Forward Security

Cloud data integrity verification was an important means to ensure data security. We used public key infrastructure (PKI) to manage user keys in Traditional way, but there were problems of certificate verification and high cost of key management. In this paper, RSA signature was used to construct a new identity-based cloud audit protocol, which solved the previous problems caused by PKI and supported forward security, and reduced the loss caused by key exposure. Through security analysis, the design scheme could effectively resist forgery attack and support forward security.
Optimized Implementation of PRESENT 8-bit S-box structure in Secure Adiabatic Logic

Adiabatic logic is one of the prime solutions to resist the hardware device against side channel power analysis security attacks. Adiabatic circuit design technique possesses attractive energy-efficient and security characteristics which makes them suitable for lightweight devices. Design of an 8-bit S-box from the 4-bit S-box definition of the PRESENT lightweight cipher definition has been attempted with optimal number of stages. This paper also presents the circuit level implementation of the 8-bit S-box circuit with appropriate transistor sizing to achieve security characteristics using the existing secure Charge Balancing Symmetric Pre-Resolve Adiabatic Logic (CBSPAL). Secure CBSPAL provides a uniform current waveform irrespective of the input data transitions. Power analysis robustness against side channel attacks has been analyzed with one round circuit realization of PRESENT-80 algorithm in both CBSPAL and static CMOS logic. CBSPAL based S-box implementation demonstrates 83% of energy saving per cycle in comparison against the static CMOS counterpart at 12.5 MHz. Post layout simulations of the circuit have been carried out in Cadence® Virtuoso environment using UMC 90 nm technology operating at 12.5 MHz operating frequency. The CBSPAL is more effective for frequencies in the MHz band.
Research on book recommendation algorithm based on attribute comprehensive similarity

The common problems of less user information and missing item ratings in recommended systems greatly affect the accuracy of recommending users. Aiming at this problem, a collaborative filtering algorithm based on comprehensive similarity of attributes is proposed. Its application in book recommendation improves the utilization of resources and the quality of recommendation service, and improves the accuracy of book recommendation.
A Comprehensive Study on a Meta Heuristics Optimization Algorithm- Hybridized Mayfly Algorithm

Several optimization techniques have been proposed to solve the optimization problem. This study introduces a novel method known as the Mayfly algorithm (MA), which is one of the swarm optimization algorithms. Every problem in the environment has an answer at its core. Based on this, researchers in the present day are preparing one another for the intelligence of nature. Given that the Mayfly Algorithm (MA) was recently introduced in the year 2020. The proposed algorithm is based on the process of mayflies mating and flying behaviour with the key benefits of both the evolutionary algorithm and swarm optimization. This mayfly algorithm is introduced as a result of the PSO's good hybridization and superior algorithm with the structure of genetic algorithm and firefly algorithm. According to several research studies, the Mayfly algorithm outperforms other prominent Metaheuristic algorithms. The exceptional performance of mayfly algorithm is demonstrated by comparing the results in terms of the rate of convergence speed. This article will be a great resource for book reviewers to gain good understanding on the upcoming advanced Mayfly algorithm in which a lot of debate has been done on both the sides by analyzing its implementations, applications, and recent advancements for enhancing the proposed Mayfly algorithm enforced by the researcher.
Short-Term Visual-IMU Fusion Memory Agent For Drone's Motion Planning

This paper provides a deep reinforcement learning motion planning algorithm that uses time series image information as input. In this paper, we use a special visual state structure. This structure is composed of two kinds of sensor data. We combine the monocular visual information compressed by the variational autoencoder (VAE) and the inertial measurement unit (IMU) data. Then we use four consecutive combinations of the sensor data as the state of the reinforcement learning model. This method greatly simplifies the neural network complexity of continuous action space deep reinforcement learning on visual control tasks. After training in a drone simulation environment, this method has been proven to enable the drone to learn to avoid obstacles of various shapes and obstacles on the side blind corner of the camera.
The path to a useful quantum computer

Quantum computing is the grand challenge of the 21st century and is poised to revolutionizes our daily lives on a nearly unfathomable level. To truly understand the impact, we must first build a universal, fully error corrected machine capable of exploring the application space envisioned by both academic and industry. Numerous pathways have been identified and demonstrated to fabricate single, unique qubits using a myriad of platforms (superconducting, ions, nanoparticles, photons, etc…). However, it is now widely accepted that a truly useful quantum computer will require millions of interconnected, identical qubits to perform fully error corrected, general purpose calculations. To that end, photon-based qubits offer a path to such a general-purpose machine by leveraging the mature silicon photonics high-volume manufacturing ecosystem. Creating quantum devices from silicon photonics components requires patterning innovation to bring leading-edge nanolithography to near macroscopic scale, a unique challenge for an industry where the future relies on progressive device shrink.
Self-Study System Assessment of Spoken English considering the Speech Scientific Computing Knowledge Assessment Algorithm

For the purpose of effectively improving the assessment accuracy of spoken English self-study quality of English learners, a speech scientific computing knowledge assessment algorithm is introduced into the assessment of the self-study system of spoken English. Through the combination of spoken English speech assessment and accuracy detection, a DSP-based self-study assessment system for spoken English is designed. This system is mainly divided into two parts: voice signal processing and hardware circuit design. The features of spoken English self-study speech are extracted based on the multilayer wavelet feature conversion method, and the English pronunciation is detected and analyzed by adaptive filtering based on the characteristic values obtained. The corresponding wavelet entropy features of spoken English self-study speech are automatically assessed and assessed for the self-study quality. Finally, a practical case is analyzed, and the results indicate that the system proposed in this paper has high accuracy and excellent stability in assessing the quality of self-study in spoken English. Hence, it is of practical value in self-study assessment of spoken English.
Design of ID Card Automatic Identification System Based on Retinex Algorithm

For Chinese citizens, the identity card is very important. An efficient identification system can greatly make social identity information management more intelligent and convenient, this field has important practical significance and good application prospects. This paper studies and summarizes the recent technical achievement of pattern recognition and image processing. Combined with the Retinex illumination correction algorithm, an ID card automatic recognition system based on the MATLAB platform is designed. The system mainly includes image preprocessing, image feature extraction, image recognition. After testing, the recognition rate of the identity card numbers is 96.50%, and the recognition rate of Chinese characters is 92.6%.
Vehicle count prediction using machine learning

In this research, we provide a regression-based technique for counting and classifying freeway traffic. This technique does not need the normal division and monitoring of personal vehicles. Numerous pre-existing routines. It is thus important that this solution be used. Significant obstructions or low-resolution vehicles may benefit from this tool low, where the derived characteristics are notoriously unstable. For example, our method has two important contributions. Firstly, the backdrop segments are detected using a stretching approach that has been devised. Unclassified cars are found in these areas. Mathematical models vehicles may be tracked using Kaplan filtering (e.g. it's not necessary to minimize the deformation of the vehicle induced by the mesh grid with a quel motion parallax effect throughout the warping process, transformations are calculated and applied. N process. A subset of these low-level traits is then extracted. Construct a tumbled linear regression for the foreground section to directly number and categorize automobiles, without the aid of any third party pertaining to connected vehicles. There are three distinct ways to approach this. The creation and assessment of regression regressions. Our findings are supported by experiments. With low-quality datasets, linear extrapolation algorithms are accurate and resilient many present techniques fail effectively extract useful information from qualities you can rely on high index terms.
A Matching Scheme from Supply and Demand Sides of Electronic Health Records Based on Blockchain

This paper proposes a double-chains-parallelized electronic health record (EHR) matching system which combines asymmetric encryption technology (homomorphic encryption) and blockchain technology. In this double-chains parallelized system, EHR-supply-chain includes sharing nodes and EHR-demand-chain includes the nodes to be matched. The system achieves decentralization, safe and efficient sharing, voluntary circulation of value, effective growth of EHRs' resources by smart contracts in blockchain. This system manages to get effective supervision of EHR in the chains and give some ideas for solving the security problems in the health data sharing process.
User-Centred Co-Design in the Pandemic – A Reindeer Case

Abstract An innovative design process which has been conducted under the restrictions during the covid-19 pandemic is in focus in this study. Visits were banned so user-centred design activities were particularly challenged by the necessity to meet and work online. The purpose of the study is to present and reflect on how user-centred design was done under those conditions. The potential users, the reindeer herders, were merely contacted online, the user investigation were supported by for example using videos and storytelling. Also, the final concept is presented.
Task-Oriented Motion Planning of a Space Manipulator under Multiple Constraints

During task execution by a space manipulator, many factors need to be considered, such as joint motion range, external environment interference, running speed and task time. Studying the motion planning strategy under multi-constraint conditions is important for optimizing the task flow of the manipulator and improving the planning autonomy. Aiming at this, this paper proposes a method based on a hierarchical task network which has the ability to connect non-adjacent nodes of topology when generating the best path; it also carries out the control simulation of traditional path planning of a manipulator for typical tasks and the latter two working conditions using an optimal task planning method. The results show that this method is effective for realizing the Cartesian spatial task planning of a manipulator under multiple constraints and has important on-orbit application significance for improving the continuous autonomy of task completion.
In situ fast measurement of the radius of curvature for the plano-convex lens with deep-learning based on the point spread functions

As one of the most basic optical elements, lenses are widely used in lots of high-tech fields such as optical lithography, photoelectric detection, biological and medical engineering. The radius of curvature (ROC) is one of the key parameters determining the performances of the lens such as imaging, phase transform and spatial Fourier transform. In the process of lens manufacturing, the ROC needs to be measured to make sure the lens can meet the requirements for use. In this paper, we first develop a method to measure the ROC of plano-convex lens in situ by using deep-learning based on the point spread functions (PSFs). Through simulation, we utilize the convolutional neural network to establish the nonlinear mapping between the error of ROC and corresponding focus and defocus PSFs generated by the lens, and the ROC can be directly estimated from the actual PSFs generated by the lens under test by means of the network of simulation training in practical application. The experimental proofs show that our method could achieve a relative measurement error of 0.029% for the plano-convex lens at high measurement speed. This method can realize the in situ fast measurement of the ROC with the significant advantages of no damage, simple operation and non-interferometric technique.
IPAY (An Intelligence Payment System)

We all know that Nowadays technology has exceeded our humanity. Every day, the tech is evolving, and new things are coming out to ease the user experience. We are trying to develop an intelligent payment app with the same concept in mind. A mobile-based application that would, as the title suggests, will help customers easily checkout from departmental stores. This app works as we go to any supermarket then we have to scan the barcode of every product and make a list by ourselves, paying online and showing the receipt at the time of exit. This app also has the feature of declining the bill as nobody can misuse the previous bill. In this application, we are using the technology of UX/UI for design, frontend, and backend for the development of the app. We are using the language Kotlin. The goal of this project is to create an online payment system in all supermarkets. To avoid standing in a queue and the bill used in the bill is harmful to our health.