Reviewerly/exHarmony huggingface dataset:
Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction

In this investigation, we have shown that the combination of deep learning, including natural language processing, and conformal prediction results in highly predictive and efficient temporal test set sentiment estimates for 12 categories of Amazon product reviews using either in-category predictions, i.e. the model and the test set are from the same review category or cross-category predictions, i.e. using a model of another review category for predicting the test set. The similar results from in- and cross-category predictions indicate high degree of generalizability across product review categories. The investigation also shows that the combination of deep learning and conformal prediction gracefully handles class imbalances without explicit class balancing measures.
A Target Recognition Algorithm of Multi-Source Remote Sensing Image Based on Visual Internet of Things

Abstract Multi-source remote sensing images have the characteristics of large differences in texture and gray level. Mismatch and low recognition accuracy are easy to occur in the process of identifying targets. Thus, in this paper, the target recognition algorithm of multi-source remote sensing image based on IoT vision is investigated. The infrared sensor and SAR radars are set in the visual perception layer of the iVIOT. The visual perception layer transmits the collected remote sensing image information to the application layer through the wireless networks. The data processing module in the application layer uses the normalized central moment idea to extract the features of multi-source remote sensing image. Contourlet two-level decomposition is performed on the image after feature extraction to realize multi-scale and multi-directional feature fusion. A two-step method of primary fineness is used to match the fused features and the random sampling consensus algorithm is used to eliminate false matches for obtaining the correct match pairs. After the image feature matching is completed, the BVM target detection operator is used to complete the target recognition of multi-source remote sensing image. Experimental results show that the use of the IoT to visually recognizing the desired remote sensing image target has low communication overhead, and the recognition reaches 99% accuracy.
A Study of the Human Perception of Synthetic Faces

Advances in face synthesis have raised alarms about the deceptive use of synthetic faces. Can synthetic identities be effectively used to fool human observers? In this paper, we introduce a study of the human perception of synthetic faces generated using different strategies including a state-of-the-art deep learning-based GAN model. This is the first rigorous study of the effectiveness of synthetic face generation techniques grounded in experimental techniques from psychology. We answer important questions such as how often do GAN-based and more traditional image processing-based techniques confuse human observers, and are there subtle cues within a synthetic face image that cause humans to perceive it as a fake without having to search for obvious clues? To answer these questions, we conducted a series of large-scale crowdsourced behavioral experiments with different sources of face imagery. Results show that humans are unable to distinguish synthetic faces from real faces under several different circumstances. This finding has serious implications for many different applications where face images are presented to human users.
Examining the Relationships Between English Teachers’ Lifelong Learning Tendencies with Professional Competencies and Technology Integrating Self-Efficacy

The aim of this study is to examine the lifelong learning tendencies of English teachers, their professional competencies, and their self-efficacy in integrating technology. For this purpose, a descriptive survey model was used in this study. The participants of the research consist of 561 English teachers working in institutions (private and state) affiliated to the Ministry of National Education selected from every geographical region of Turkey. Participants work in primary, secondary, and secondary education institutions. Non-random stratified purposive sampling method was used in the sample selection of this study. Personal Information Form and 3 different scales were used for the data collection process of the research. Analysis of the data was performed by one-way analysis of variance (ANOVA), t-test, and simple linear regression analysis. As a result of this research, it was seen that there is a positive correlation between English teachers' technology ıntegrating self-efficacy, professional competency, and lifelong learning tendencies. It was determined that teachers' self-efficacy in integrating technology and professional competencies are predictors of their lifelong learning tendencies. In addition, it was observed that there was a significant difference in English teachers' self-efficacy in integrating technology, lifelong learning tendencies, and professional competencies according to gender. English teachers' professional competencies differ according to the type of institution, while their self-efficacy in integrating technology differs according to the type of institution and age. It is thought that the results of this study will contribute to raising awareness about the impact of teachers' self-efficacy and professional development on technology integration on issues surrounding their lifelong learning tendencies.
Time-Space Dimension Reduction of Millimeter-Wave Radar Point-Clouds for Smart-Home Hand-Gesture Recognition

This paper proposes a novel time-space dimension reduction method for millimeter-wave radar point-clouds on long-distance or large-field-of-view hand-gesture recognition. First, a two-dimensional antenna array is used to obtain radar point-clouds composed of range, Doppler, azimuth, elevation, and amplitude. The point-cloud time series are further reduced in dimensionality to reconstruct radar spectrums with less interference and better time-space continuity in the Range-Doppler-Azimuth-Elevation (RDAE) domain, and generate new spatial position spectrums after transforming to the three-dimensional cartesian coordinate system, which are superior to radar spectrums in the RDAE domain in terms of space invariance. Then a spatial position alignment method is proposed to improve the spatial consistency of multi-position dataset. Finally, a multi-channel convolutional neural network is designed for multi-position hand-gesture recognition. Experimental results show that the proposed method can solve the problems of poor space invariance in the RDAE domain and poor generalization performance for different spatial positions and different users in existing methods. For hand-gesture recognition within a long distance of 3 meters and a large field-of-view of 60 degrees, the average classification accuracies of 5 waving hand-gestures collected at multiple positions for trained user A, non-trained user B, and non-trained user C can reach 99.7%, 91.1%, and 87.1%, respectively.
Problems and Solutions of Art Professional Service Rural Revitalization Strategy Based on Random Forest Algorithm

The rural revitalization strategy proposal is not a passive water, but rather one with its own internal historical logic. The rural revitalization strategy has been implemented in a consistent manner with China’s rural construction practice over the last 100 years. Since modern times, the Chinese agricultural civilization has been severely harmed by the collision and friction with western industrial and commercial civilizations, which has directly aroused the strong reflection of people with lofty ideals from all walks of life in Chinese society and led them down the path of rural construction aimed at “rejuvenating” the countryside. Accelerating the development of rural culture is beneficial to the overall improvement of social culture and the happiness of the Chinese people. As a result, the development and construction of rural culture will have a significant impact on China. The random forest algorithm simulates the data learning process based on data mining, creates a model, and returns a judgment result. The strategy of art professional service is examined in this study. The random forest algorithm was used to revitalize rural areas. The random forest algorithm’s learning process is very quick, and it is still very effective in dealing with art professional services. The current random forest algorithm weighs the importance of all variables to determine the significance of switching from independent to dependent variables.
Automated Prescreening of Mild Cognitive Impairment Using Shank-Mounted Inertial Sensors Based Gait Biomarkers

The mild symptoms in Mild Cognitive Impairment (MCI), a precursor of dementia, often go unnoticed and are assumed as normal aging signs. Such negligence result in late visits which consequently, lead to the diagnosis and progression of dementia. An instrumented gait assessment in home settings may facilitate the detection of subtle MCI-related motor deficits thus, allowing early diagnosis and intervention. This paper investigates potential gait biomarkers derived from shank mounted inertial sensors signals under normal and dual-task walking conditions using data collected from thirty MCI and thirty cognitively normal (CN) subjects. To identify potential gait biomarkers for MCI screening, we assess the variance and predictive power of each feature. Moreover, multiple classification models using different machine learning and feature selection techniques are built to automate MCI detection by leveraging the gait biomarkers. Statistical analysis reveal multiple gait parameters that are significantly different under both single and dual-task settings. However, we show that dual-task walking provides better distinction between MCI and CN subjects. The machine learning model employed for MCI pre-screening based on the inertial sensor-derived gait biomarkers achieves accuracy and sensitivity of 71.67% and 83.33%, respectively.
SPIQ: A Self-Supervised Pre-Trained Model for Image Quality Assessment

Blind image quality assessment (BIQA) has witnessed a flourishing progress due to the rapid advances in deep learning technique. The vast majority of prior BIQA methods try to leverage models pre-trained on ImageNet to mitigate the data shortage problem. These well-trained models, however, can be sub-optimal when applied to BIQA task that varies considerably from the image classification domain. To address this issue, we make the first attempt to leverage the plentiful unlabeled data to conduct self-supervised pre-training for BIQA task. Based on the distorted images generated from the high-quality samples using the designed distortion augmentation strategy, the proposed pre-training is implemented by a feature representation prediction task. Specifically, patch-wise feature representations corresponding to a certain grid are integrated to make prediction for the representation of the patch below it. The prediction quality is then evaluated using a contrastive loss to capture quality-aware information for BIQA task. Experimental results conducted on KADID-10 k and KonIQ-10 k databases demonstrate that the learned pre-trained model can significantly benefit the existing learning based IQA models.
IoT-ID: Robust IoT Device Identification Based on Feature Drift Adaptation

Internet of Things (IoT) devices deployed in publicly accessible locations increasingly encounter security threats from device replacement and impersonation attacks. Unfortunately, the limited memory and poor computing capability on such devices make solutions involving complex algorithms or enhanced authentication protocols untenable. To address this issue, device identification technologies based on traffic characteristics finger-printing have been proposed to prevent illegal device intrusion and impersonation. However, because of time-dependent distribution of traffic characteristics, these approaches often become less accurate over time. Meanwhile insufficient attention has been paid to the impact of possible changes on the accuracy of device identification. Therefore, we propose a novel feature selection method based on degree of feature drift and genetic algorithm to keep high accuracy and stability of device identification. The degree of feature drift&#x2014; relevance of features through time and gain ratio are combined as a composite metric to filter out stable features. Furthermore, in order to perform equally well in device identification, we use the genetic algorithm to select the most discriminate feature subset. Experiments show that the accuracy of device recognition compared with other methods is increased from 86.4&#x0025; to 94.5&#x0025;, and the robustness of recognition is also improved.
Ultrasound Speckle Reduction Using Wavelet-Based Generative Adversarial Network

The visual quality of ultrasound (US) images is crucial for clinical diagnosis and treatment. The main source of image quality degradation is the inherent speckle noise generated during US image acquisition. Current deep learning-based methods cannot preserve the maximum boundary contrast when removing noise and speckle. In this paper, we address the issue by proposing a novel wavelet-based generative adversarial network (GAN) for real-time high-quality US image reconstruction, viz. WGAN-DUS. First, we propose a batch normalization module (BNM) to balance the importance of each sub-band image and fuse sub-band features simultaneously. Then, a wavelet reconstruction module (WRM) integrated with a cascade of wavelet residual channel attention block (WRCAB) is proposed to extract distinctive sub-band features used to reconstruct denoised images. A gradual tuning strategy is proposed to fine-tune our generator for better despeckling performance. We further propose a wavelet-based discriminator and a comprehensive loss function to effectively suppress speckle noise and preserve the image features. Besides, we have designed an algorithm to estimate the noise levels during despeckling of real US images. The performance of our network was then evaluated on natural, synthetic, simulated and clinical US images and compared against various despeckling methods. To verify the feasibility of WGAN-DUS, we further extend our work to uterine fibroid segmentation with the denoised US image of the proposed approach. Experimental result demonstrates that our proposed method is feasible and can be generalized to clinical applications for despeckling of US images in real-time without losing its fine details.
Improving Out-of-Distribution Detection by Learning From the Deployment Environment

Recognition systems in the remote sensing domain often operate in “open-world” environments, where they must be capable of accurately classifying data from the in-distribution categories while simultaneously detecting and rejecting anomalous/out-of-distribution (OOD) inputs. However, most modern designs use deep neural networks (DNNs) to perform this recognition function that are trained under “closed-world” assumptions in offline-only environments. As a result, by construction, these systems are ill-posed to handle anomalous inputs and have no mechanism for improving OOD detection abilities during deployment. In this work, we address these weaknesses from two aspects. First, we introduce advanced DNN training methods to codesign for accuracy and OOD detection in the offline training phase. We then propose a novel “learn-online” workflow for updating the DNNs during deployment using a small library of carefully collected samples from the operating environment. To show the efficacy of our methods, we consider experimenting with two popular recognition tasks in remote sensing: scene classification in electro-optical satellite images and automatic target recognition in synthetic aperture radar imagery. In both, we find that our two primary design contributions can individually improve detection performance, while also being complementary. Additionally, we find that detection performance on difficult and highly granular OOD samples can be drastically improved using only tens or hundreds of samples collected from the environment. Finally, through analysis, we determine that the logic for adding/removing samples from the collection library is of key importance and using a proper learning rate during the model update step is critical.
Reservoir Computing Using DNA Oscillators

This paper presents novel implementations for reservoir computing (RC) using DNA oscillators. An RC system consists of two parts: reservoir and readout layer. The reservoir projects input signals into a high-dimensional feature space which is formed by the state of the reservoir. The internal connectivity structure of the reservoir remains unchanged throughout computation. After training, the readout layer maps the projected features into the desired output. It has been shown in prior work that coupled deoxyribozyme oscillators can be used as the reservoir. In this paper, we utilize the n-phase molecular oscillator (n ≥ 3) presented in our prior work. The readout layer implements a matrix-vector multiplication using molecular reactions based on molecular analog multiplication. All molecular reactions are mapped to DNA strand displacement (DSD) reactions. We also introduce a novel encoding method that can significantly reduce the reaction time. The feasibility of the proposed RC systems based on the DNA oscillator is demonstrated for the handwritten digit recognition task and a second-order nonlinear prediction task.
Natural language based analysis of SQuAD: An analytical approach for BERT

In recent years, deep learning models have been used in the implementation of question answering systems. In this study, the performance of the question answering system was evaluated from the perspective of natural language processing using SQuAD, which was developed to measure the performance of deep learning language models. In line with the evaluations, in order to increase the performance, 3 natural language based methods, namely RNP, that can be used with pre-trained BERT language models have been proposed and they have increased the performance of the question answering system in which the pre-trained BERT models are used by 1.1% to 2.4%. As a result of the application of RNP methods with sentence selection, an increase in accuracy between 6.6% and 8.76% was achieved in answer detection. Since these methods don't require any training process, it has been shown that they can be used in question answering systems to increase the performance of any deep learning model.
No-Reference Image Quality Assessment via Multibranch Convolutional Neural Networks

No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific distortion scenarios, and lack of practicality, it is challenging to designing a universal NR-IQA method. In this article, a multibranch convolutional neural network (MB-CNN) based NR-IQA method is proposed, which includes a spatial-domain feature extractor, a gradient-domain feature extractor, and a weight mechanism. The spatial-domain feature extractor aims to extract the distortion features from the spatial domain. The gradient-domain feature extractor is used to guide the spatial-domain feature extractor to pay more attention to the distortions of the structure information. Particularly, the spatial-domain feature extractor uses the hierarchical feature merge module to realize multiscale feature representation, and the gradient-domain feature extractor uses pyramidal convolution to extract the multiscale structure information of the distorted image. In addition, a position vector is proposed to build the weight mechanism by considering the position relationships between patches and its entire image for improving the final prediction performance. We conduct the experiments on five representative databases: LIVE, TID2013, CSIQ, LIVE MD and Waterloo Exploration Database, and the experimental results show that the proposed NR-IQA method achieves the state-of-the-art performance, which demonstrate the effectiveness of our proposed NR-IQA method. The code ofthe proposed MB-CNN will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NUIST-Videocoding/MB-CNN</uri> .
JPEG Image Encryption With Adaptive DC Coefficient Prediction and RS Pair Permutation

JPEG image encryption aims at effectively converting the original JPEG image into a noise-like image that does not contain any useful information of original image. Existing schemes for JPEG image encryption, however, may not attain a good balance in terms of file size increment and encryption security. To address the problem, we design a novel JPEG image encryption scheme. Different from existing schemes, we first predict DC coefficients by an adaptive prediction method. Subsequently, the histogram of DC coefficient prediction errors is encrypted by combining the prediction errors and random integers to reduce the encoded length, which can ensure a very small increment of file size. Furthermore, we construct the RS (run/size) pairs in each DCT block and then implement the permutation for both RS pairs extracted from the upper left corner of each DCT block and all DCT blocks excluding DC coefficients, which can further distort the image contents. Extensive experiments demonstrate that, compared with existing JPEG image encryption schemes, our scheme can ensure not only the JPEG format compatibility for encrypted image, but also keep a very small file size increment and the superior security performance.
Grid-moving equivalent source method in a probability framework for the transformer discharge fault localization

The transformer discharge fault localization is important for the power supply reliability. The audible sound radiated by the transformer can reflect the extensive operation information. Thus, discharge faults can be localized by high spatial resolution acoustic imaging techniques to facilitate the inspection and maintenance of power equipment. In this paper, the grid-moving equivalent source method (GMESM) is proposed to visualize the discharge fault on a transformer, where the equivalent source grids can approach the discharge fault position adaptively. A probability framework is established in GMESM. The noise power can be firstly estimated by the Bayes rule to avoid an additional measurement of the background noise. Then, the expectation maximization algorithm is used to optimize the proposed model. Two experiment cases are used to validate the proposed method. It turns out that the proposed GMESM can visualize the discharge fault on the transformer with high spatial resolution and accuracy.