Reviewerly/exHarmony huggingface dataset:
TIToK: A solution for bi-imbalanced unsupervised domain adaptation

Unsupervised domain adaptation (UDA) aims to transfer knowledge via domain alignment, and typically assumes balanced data distribution. When deployed in real tasks, however, (i) each domain usually suffers from class imbalance, and (ii) different domains may have different class imbalance ratios. In such bi-imbalanced cases with both within-domain and across-domain imbalance, source knowledge transfer may degenerate the target performance. Some recent efforts have adopted source re-weighting to this issue, in order to align label distributions across domains. However, since target label distribution is unknown, the alignment might be incorrect or even risky. In this paper, we propose an alternative solution named TIToK for bi-imbalanced UDA, by directly Transferring Imbalance-Tolerant Knowledge across domains. In TIToK, a class contrastive loss is presented for classification, in order to alleviate the sensitivity to imbalance in knowledge transfer. Meanwhile, knowledge of class correlation is transferred as a supplementary, which is commonly invariant to imbalance. Finally, discriminative feature alignment is developed for a more robust classifier boundary. Experiments over benchmark datasets show that TIToK achieves competitive performance with the state-of-the-arts, and its performance is less sensitive to imbalance.
Research on the Model of Rectifying the Longitude and Latitude of the Base Station in the Main Cell Based on the Adjacent Cell Data

Abstract In order to solve the problem that the location prediction rate of the base station is low due to the insufficient MR data within the coverage range of the base station, based on the inspiration of the grip rule, the longitude and latitude correction model of the base station in the main cell based on big data is established by using the coarse positioning data and fine positioning data of adjacent cells. Taking Guangdong Telecom as an example, the experimental results show that the prediction rate of base station location is significantly improved from 69% to 92%.
PiouCrypt: Decentralized lattice-based method for visual symmetric cryptography

In recent years, establishing secure visual communications has turned into one of the essential problems for security engineers and researchers. However, only limited novel solutions are provided for image encryption, and limiting visual cryptography to only limited schemes can bring up negative consequences, especially with emerging quantum computational systems. This paper presents a novel algorithm for establishing secure private visual communication. The proposed method has a layered architecture with several cohesive components, and corresponded with an NP-hard problem, despite its symmetric structure. This two-step technique is not limited to gray-scale pictures, and furthermore, it is relatively secure from the theoretical dimension.
Improved Transformer Based Hashing for Cased-based Pneumonia Retrieval

Abstract The deep hash method has been proved to be the most effective nearest neighbor search technique in large-scale image retrieval. However, up to now, deep hashing in medical image retrieval has been dominated by convolution network architecture, such as Resnet. Inspired by Transformer, we integrate Transformer into the deep learning framework. Our framework is mainly composed of two main modules: (1) In order to solve the problem of big difference among different subclasses of medical image categories, the Transformer model is improved in this paper. The external attention is used to replace the self-attention in the original Transformer model, and the feature acquisition ability of the model is improved by capturing the correlation between samples. (2) In addition, in order to learn compact binary coding, we adopt Bayesian framework for end-to-end learning. We conduct experiments on ChestX-ray Pneumonia dataset, and compare the proposed algorithm with the commonly used depth hashing method. Experimental results show that the MAP values under different hash bit codes are better than the existing deep hash methods, which proves the effectiveness of the proposed method.
Privacy-enhanced generative adversarial network with adaptive noise allocation

Generative adversarial networks (GANs) have become hugely popular by virtue of their impressive ability to generate realistic samples. Although GANs alleviate the arduous data-collection problem, they are prone to memorize training samples as a result of their complex model structure. Thus, GANs may not provide sufficient privacy guarantees, and there is a considerable chance of inadvertently divulging data privacy. To alleviate this issue, we design a privacy-enhanced GAN based on differential privacy. We first integrate truncated concentrated differential privacy technique into GAN for mitigating privacy leakage with tighter privacy bound. Then, according to different privacy demands of users in real-world scenarios, we design two adaptive noise allocation strategies, which enable us to dynamically inject noise into gradients at each iteration. Different strategies provide us with an intuitive handle to adopt a suitable strategy and achieve an elegant compromise between privacy and utility in distinct scenarios. Furthermore, we offer rigorous illustrations from the perspective of privacy preservation and privacy defense to demonstrate that our algorithm can fulfill differential privacy guarantees. Extensive experiments on real-world datasets manifest that our algorithm can generate high-quality samples while achieving an excellent trade-off between model performance and privacy guarantees.
Enhanced features in image manipulation detection

Image editing technology leads to tampering traces hidden in images hard to be identified, which results in great challenges for image manipulation detection. Therefore, it is necessary to enhance the characteristic response of tampering traces. In this paper, a two-stream network is exploited to extract tampering features from both semantic level and noise level through end-to-end training. At the input end of the network, a multi-scale high-frequency constrained convolution kernel module is proposed to extract multi-channel and multi-scale noise fingerprint information, which enhances the tampering traces. Moreover, we leverage multi-task learning to realize the classification of tampering techniques, the detection and segmentation of tampering regions. The tampered segmentation mask attention is introduced as a novel constraint to be fed back to the detection branch, which further enhances the tampering characteristics and improves the detection performance. Experiments on COVER, CASIA and NIST16 datasets prove that the proposed framework outperforms existing methods quantitatively and qualitatively. The proposed method effectively enhances the characteristics of tampered regions. Moreover, the proposed method has high robustness on JPEG recompression and image resize.
Privacy preserving for AI-based 3D human pose recovery and retargeting

As an essential research task in artificial intelligence (AI), the estimation of 3D human poses has important application value in virtual reality, medical diagnosis, athlete training and other fields. However, human pose recovery and retargeting require the acquisition of detailed visual data containing private information, which has led to increasing concerns about user privacy and security. Therefore, we build a lightweight framework, called Human Motion Parameters Prediction (HMPP), which can infer the 3D mesh and 3D skeletal joint points of the human body while protecting the privacy of the user. The proposed method successfully reduces or suppresses privacy attributes while ensuring important features to perform human pose estimation. The 2D and 3D joints are used for supervision to improve the interpretability of the model at each stage. In addition, the prediction of the camera's internal parameters is added so that the model can be augmented with projection supervision, thereby using more 2D datasets for training and improving the generalization ability of the model. Finally, the predicted motion parameters are used for 3D reconstruction and motion retargeting. Experiments show that our approach can achieve excellent evaluation results on multiple datasets and avoid inadvertently compromising private and sensitive data.
Deformable 3D medical image registration with convolutional neural network and transformer

Abstract Deformable registration of medical images based on deep learning has been the research focus this year. Convolutional Neural Network (CNN) and the transformer are the most common backbone and have been shown to enhance registration accuracy. However, CNN lacks the ability to contact long-distance information, and the transformer lacks the ability to capture local information. Whichever subtle feature loss may lead to disastrous consequences in the analysis of clinical medicine. This paper presented a novel registration network named Information Complementation Network (ICN). We aim to improve the registration accuracy by complementing the lost information. Pure transformers can establish long-distance spatial information about the image. Proposed meshing patch embedding can minimize the loss of local information and expand the receptive field to extract long-distance information. The dual-path decoder in ICN is designed to restore information furthest. We experimented on 3D brain MRI data and quantitatively compared several excellent registration models. Compared with conventional methods, the dice coefficient increased by 3%. Compared with the advanced methods, the dice coefficient increased by 1%. The number of foldings was reduced by about 50% without any loss of registration accuracy. Each evaluation metric of the trained models on liver CT images was higher than other methods. By fully complementing the lost or invalid information, ICN achieved higher registration accuracy and smoother deformation field. The innovation and contribution of this paper have the potential to be applied to clinical research or medical image processing.
Simulation study of a box type solar cooker for different locations

Solar cooker which relies on solar energy can be a viable alternative for the reduction of pollution during the combustion of biomass during the cooking process. The emission from biomass contains hydrocarbons as its major source. This simulation study of box type solar cooker performed using ANSYS Fluent version R1 2022 helps in optimizing the design of solar cooker to predict solar heat flux values for different locations such as Chennai, Vellore, Karur and Rameswaram for a particular day and particular time intervals such as 9 am, 12 pm, 2 pm, 5 pm. The proposed work further highlights the region where the solar cooker can be efficiently used by utilizing non-conventional energy sources for cooking process. In the simulation process, Discrete Transfer Radiation Model (DTRM) was used for tracking the solar radiation and solar calculator to predict the direct and diffuse solar irradiation for a particular day, by considering the latitude, longitude and Greenwich Mean Time (GMT) for specified locations. The specified geographical regions are plotted in a spatial map with solar radiation data obtained from Indian Meteorological Department (IMD) through ArcGIS geospatial techniques. The maximum solar heat flux values obtained at 12 pm, in the simulation results are 718 W/m2, 698 W/m2, 726 W/m2 and 720 W/m2, it is identified that among the four regions karur recorded the highest solar heat flux value of 726 W/m2. The minimum solar heat flux values at 5 pm are 215 W/m2, 202 W/m2, 265 W/m2 and 230 W/m2. Vellore was observed to have the minimum solar heat flux value of 202 W/m2. Solar radiation data obtained from the solar calculator and IMD radiation data were in good agreement with each other with a maximum error percentage of 2.36% because of the variation in the sunshine factor.
Design of An Intelligent Manipulator Controlled by Gestures Based on FPGA

Abstract With the development of science and technology, manipulators are increasingly used in industrial production and daily life. The manipulator with traditional control mode has high requirements for operators, complicated operation, and human-computer interaction is inconvenient. In this paper, an intelligent manipulator system based on gesture control is designed. FPGA is used as the control core of the system. We apply the improved Canny algorithm processing, which can automatically recognize gesture information. After judgment, the control signal is sent to the manipulator to control the manipulator to complete the specified action. Through experimental testing, the system can successfully recognize all kinds of gestures, and can well control the manipulator to complete all kinds of actions. The human-computer interaction is more real, which greatly improves the intelligence of the manipulator system and helps to improve the work efficiency.
External Measurement System for Aircraft Structural Parts Based on Digital Image Correlation in Signal-limited Spaces

Abstract An experimental study was performed on the application of the digital image correlation (DIC) technique to characterize aircraft components for which accurate time information is required. This study builds an aircraft external measurement system based on Stereo DIC. This system obtains real-time satellite time information from a GPS clock source and transmits it to a trigger unit. A camera acquires this information and displays it on the image. This solves the problem that some closed laboratories can use only computer time and cannot obtain accurate satellite time when using camera measurements. Moreover, we verify the reliability and accuracy of this approach, finding an average angular accuracy of better than 0.02 degrees and a relative error of less than 1%. Using the binocular Stereo DIC system, the accuracy of the measurements is clarified for specific conditions. Furthermore, we use this system to measure simulated aircraft landing gear, which moves at an angular velocity of 52.4 °/ s over a distance of 5 m with a wide field of view of 16.8°. The satellite time information acquired by the camera and the time information of the captured time targets are highly stable. The system’s reliability is demonstrated for use in practical engineering, contributing to the engineering application of Stereo DIC for external aircraft measurements.
Dusky Detect: Facial Detection in Low Light Using CNN

At present, Identification of faces through image processing is a crucial task in the fields of Machine Learning and Artificial intelligence.Luminescence plays a key role in the face detection.Nowadays, detecting the faces of different people present in different lightings and different times of day has become strenuous.Dusky face is sticky to predict using traditional methods.In order to find a solution, this project proposes various classifiers and Convolutional Neural Networks to detect the face in Abnormal lighting situations.This idea provides the process and technique of the face detection in low light using CNN.The proposed idea in this project will improve the accuracy and facial recognition in different light intensities using the combinations of various facial recognition techniques and classifiers and also the CNN for obtaining a better model with higher performance metrics.
Automatic Control System of Manipulator Clamping Angle Based on Machine Vision

Abstract In order to shorten the time it takes for the manipulator components to reach the preset position and avoid the behavior state of excessively increasing the gripping angle, an automatic control system for the gripping angle of the manipulator based on machine vision is designed. According to the motion state of the mechanical components of the gripper, the connection sensitivity of the angular position sensor is controlled, and then the tension and pressure control structure is used to complete the on-demand adjustment of the clamping force action form and realize the construction of the application platform environment of the hardware structure of the automatic control system. On this basis, by grabbing the necessary manipulator operation image, following the principle of machine vision, completing the preprocessing, and then combining the kinematic functions to perform the force control switching operation and realizing the gripping force control based on machine vision, and completing the design and application of the automatic control system for the gripping angle of the manipulator. The experimental results show that when the motor driving speed is equal to 2000 PPS,4000 PPS, and 6000 PPS, the consumption time required for the manipulator components to reach the preset position will not exceed 9.5s. Compared with the double closed-loop control system, it is more in line with the application requirements of effectively controlling the behavior of the clamping angle.
Evaluation of Security Challenges and Proposed Solution in a Cloud Based E-Learning

Cloud based E-Learning is one of the booming technologies in IT field which brings powerful e-learning products with the help of cloud power.Cloud technology has numerous advantages over the existing traditional E-Learning systems but at the same time, security is a major concern in cloud based e-learning.So security measures are unavoidable to prevent the loss of users' valuable data from the security vulnerabilities.Cloud based e-learning products also need to satisfy the security needs of customers and overcome various security threats which attack valuable data stored in cloud servers.This research study several number of Challenges as follows (1) Cloud Computing Security Threats which comprises i-Availability ii-Data Lock-in iii-Insecure of incomplete data deletion iv-Increase authentication demand v-Browser security (2) E-learning Security Threats which include i-User authorization and authentication ii.Protection against manipulation iii.Non-repudiation (3) Social aspects of security which include i-Confidentiality ii-integrity iii.Availability.In view of these, number of solutions are proposed, these solutions include (1) Security measures in cloud computing including i. Software as a service (SAaS) Security ii.Security Management with users iii-Security governance iv-.Risk management v. Risk assessment vi.Security awareness vii.Education and training Viii.policies and standards ix.Third party risk management x.
Dynamic Image Difficulty-Aware DNN Pruning

Deep Neural Networks (DNNs) have achieved impressive performance in various image recognition tasks, but their large model sizes make them challenging to deploy on resource-constrained devices. In this paper, we propose a dynamic DNN pruning approach that takes into account the difficulty of the incoming images during inference. To evaluate the effectiveness of our method, we conducted experiments on the ImageNet dataset on several state-of-art DNNs. Our results show that the proposed approach reduces the model size and amount of DNN operations without the need to retrain or fine-tune the pruned model. Overall, our method provides a promising direction for designing efficient frameworks for lightweight DNN models that can adapt to the varying complexity of input images.
I Want to Marry *the Merchant Banker: An Exploratory Self-Paced Reading Experiment on Arab Learners’ Processing of English Articles

Purpose. This study explores the processing of English articles by native speakers of Hejazi Arabic, a language with articles. The research aims to answer the question of whether offline (explicit) knowledge of specificity in English article choice mirrors online (implicit) knowledge. Existing research has found that Hejazi-Arabic learners of English misuse articles in the indefinite specific context when answering a written task; however, their performance is target-like in all other contexts of article use, which indicates that their production may be sensitive to specificity, similar to the production of learners from languages that do not include articles. Little has been done, though, to explore this phenomenon using online measures. &#x0D; Methods. To answer the research question, 68 speakers of Hejazi-Arabic were recruited alongside 23 native English speakers. The participants and native English speakers completed an article elicitation task and self-paced reading task. &#x0D; Results. The results of the article elicitation task show learners’ overuse of the in the indefinite specific context, which is consistent with the findings of existing research. Similarly, the real-time processing results indicate that there is a wider gap in reaction times between natives and L2 learners in the indefinite specific context, suggesting that learners’ online performance is not on target in this context.&#x0D; Conclusion. The study concludes that Hejazi Arabic speakers’ online knowledge of English articles show some resemblance to their offline knowledge. Theoretical implications and methodological issues are also discussed.
Generic Model of Max Heteroassociative Memory Robust to Acquisition Noise

Associative memories are a significant topic in pattern recognition, and therefore, throughout history, numerous memory models have been designed due to their usefulness. One such model is the associative memory minmax, which is highly efficient at learning and recalling patterns as well as being tolerant of high levels of additive and subtractive noise. However, it is not efficient when it comes to mixed noise. To solve this issue in the associative memory minmax, we present the generic model of heteroassociative memory max robust to acquisition noise (mixed noise). This solution is based on understanding the behavior of acquisition noise and mapping the location of noise in binary images and gray-scale through a distance transform. By controlling the location of the noise, the associative memories minmax become highly efficient. Furthermore, our proposed model allows patterns to contain mixed noise while still being able to recall the learned patterns completely. Our results show that the proposed model outperforms a model that has already solved this type of problem and has proven to overcome existing methods that show some solution to mixed noise. Additionally, we demonstrate that our model is applicable to all associative minmax memories with excellent results.
The Effect of Low Dropout Intention and In-depth Learning Approach of College Students on Online Class Satisfaction : Focusing on the Mediating Effect of Teaching Presence

Online classes have become a major teaching method for higher education during the COVID-19 pandemic. However, colleges mainly implemented face-to-face classes such as training and apprenticeship education before COVID-19, and lacked preparation for online classes compared to universities. Unexpected full-scale online classes showed low educational effects and satisfaction and as a result, the overall educational competitiveness of colleges is deteriorating, with students dropping out. In this context, the purpose of this study is to confirm the effect of students’ low dropout intention and in-depth learning approaches on online class satisfaction and verify the mediation effect of teaching presence. As a result, it was confirmed that students’ low dropout intention, in-depth approaches to learning, and teaching presence had significant effects on class satisfaction. Also, teaching presence had partial mediating effects in the both relationships between low dropout intention and online class satisfaction, and between in-depth learning approach and online class satisfaction. The research results show that the more teaching presence is mediated in college students’ learning and dropout, the higher class satisfaction can be, and that instructional design is necessary for the effect and satisfaction of online classes.