Reviewerly/exHarmony huggingface dataset:
Making Models Shallow Again: Jointly Learning to Reduce Non-Linearity and Depth for Latency-Efficient Private Inference

Large number of ReLU and MAC operations of Deep neural networks make them ill-suited for latency and compute-efficient private inference. In this paper, we present a model optimization method that allows a model to learn to be shallow. In particular, we leverage the ReLU sensitivity of a convolutional block to remove a ReLU layer and merge its succeeding and preceding convolution layers to a shallow block. Unlike existing ReLU reduction methods, our joint reduction method can yield models with improved reduction of both ReLUs and linear operations by up to 1.73× and 1.47×, respectively, evaluated with ResNet18 on CIFAR-100 without any significant accuracy-drop.
M<sup>2</sup>DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer

Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed videos, namely M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DAR, with a particular focus on detecting distracted driving behaviors. Our system features a weight-sharing, multi-scale Transformer-based action recognition network that learns robust hierarchical representations. Furthermore, we propose a new election algorithm consisting of aggregation, filtering, merging, and selection processes to refine the preliminary results from the action recognition module across multiple views. Extensive experiments conducted on the 7th AI City Challenge Track 3 dataset demonstrate the effectiveness of our approach, where we achieved an overlap score of 0.5921 on the A2 test set. Our source code is available at https://github.com/PurdueDigitalTwin/M2DAR.
N-pad : Neighboring Pixel-based Industrial Anomaly Detection

Identifying defects in the images of industrial products has been an important task to enhance quality control and reduce maintenance costs. In recent studies, industrial anomaly detection models were developed using pre-trained networks to learn nominal representations. To employ the relative positional information of each pixel, we present N-pad, a novel method for anomaly detection and segmentation in a one-class learning setting that includes the neighborhood of the target pixel for model training and evaluation. Within the model architecture, pixel-wise nominal distributions are estimated by using the features of neighboring pixels with the target pixel to allow possible marginal misalignment. Moreover, the centroids from clusters of nominal features are identified as a representative nominal set. Accordingly, anomaly scores are inferred based on the Mahalanobis distances and Euclidean distances between the target pixel and the estimated distributions or the centroid set, respectively. Thus, we have achieved state-of-the-art performance in MVTec-AD with AUROC of 99.37 for anomaly detection and 98.75 for anomaly segmentation, reducing the error by 34% compared to the next best performing model. Experiments in various settings further validate our model.
Experimental Benchmarking of an Automated Deterministic Error-Suppression Workflow for Quantum Algorithms

Excitement about the promise of quantum computers is tempered by the reality that the hardware remains exceptionally fragile and error prone, forming a bottleneck in the development of alternative applications. In this paper, we describe and experimentally test a fully autonomous workflow designed to deterministically suppress errors in quantum algorithms from the gate level through to circuit execution and measurement. We introduce the key elements of this workflow, delivered as a software package called Fire Opal, and survey the underlying physical concepts: error-aware compilation, automated system-wide gate optimization, automated dynamical decoupling embedding for circuit-level error cancellation, and calibration-efficient measurement-error mitigation. We then present a comprehensive suite of performance benchmarks executed on IBM hardware, demonstrating up to $>1000\ifmmode\times\else\texttimes\fi{}$ improvement over the best alternative expert-configured techniques available in the open literature. Benchmarking includes experiments using up to 16 qubit systems executing the following: Bernstein Vazirani, quantum Fourier transform, Grover's search, quantum approximate optimization algorithm, variational quantum eigensolver, syndrome extraction on a five-qubit quantum error-correction code, and quantum volume. Experiments reveal a strong contribution of Non-Markovian errors to baseline algorithmic performance; in all cases the deterministic error-suppression workflow delivers the highest performance and approaches incoherent error bounds without the need for any additional sampling or randomization overhead, while maintaining compatibility with all additional probabilistic error suppression techniques.
Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model

Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user interacts with a non-sign language user, it becomes difficult for a signer to express themselves to another person. A sign language recognition system can help a signer to interpret the sign of a non-sign language user. This study presents a sign language recognition system that is capable of recognizing Arabic Sign Language from recorded RGB videos. To achieve this, two datasets were considered, such as (1) the raw dataset and (2) the face–hand region-based segmented dataset produced from the raw dataset. Moreover, operational layer-based multi-layer perceptron “SelfMLP” is proposed in this study to build CNN-LSTM-SelfMLP models for Arabic Sign Language recognition. MobileNetV2 and ResNet18-based CNN backbones and three SelfMLPs were used to construct six different models of CNN-LSTM-SelfMLP architecture for performance comparison of Arabic Sign Language recognition. This study examined the signer-independent mode to deal with real-time application circumstances. As a result, MobileNetV2-LSTM-SelfMLP on the segmented dataset achieved the best accuracy of 87.69% with 88.57% precision, 87.69% recall, 87.72% F1 score, and 99.75% specificity. Overall, face–hand region-based segmentation and SelfMLP-infused MobileNetV2-LSTM-SelfMLP surpassed the previous findings on Arabic Sign Language recognition by 10.970% accuracy.
Impact of Financial Development and Remittances on Educational Attainment within the Context of Sustainable Development: A Panel Evidence from Emerging Markets

Education is one of the Sustainable Development Goals (SDGs) of the United Nations and is also a vital factor for nearly all the other SDGs. Therefore, factors underlying educational attainment are crucial for achieving the SDGs by 2030. In this context, the financial system has become critical in building various schools and covering educational expenditures such as teachers’ salaries, teaching materials, and training. This paper uses static and dynamic regression methods to study the impact of financial sector development, remittances, real GDP per capita, information and communications technologies (ICT) development, and globalization on educational attainment in 18 emerging economies over the 2000–2020 period. The results indicate that financial development, remittances, real GDP per capita, ICT development, and globalization positively impact educational attainment. Real GDP per capita, ICT development, globalization, and financial development have the highest impact of these factors. In contrast, remittances have a limited positive influence on educational attainment compared with other variables.
A study of dynamic populations in geometric semantic genetic programming

Allowing the population size to variate during the evolution can bring advantages to evolutionary algorithms (EAs), retaining computational effort during the evolution process. Dynamic populations use computational resources wisely in several types of EAs, including genetic programming. However, so far, a thorough study on the use of dynamic populations in Geometric Semantic Genetic Programming (GSGP) is missing. Still, GSGP is a resource-greedy algorithm, and the use of dynamic populations seems appropriate. This paper adapts algorithms to GSGP to manage dynamic populations that were successful for other types of EAs and introduces two novel algorithms. The novel algorithms exploit the concept of semantic neighbourhood. These methods are assessed and compared through a set of eight regression problems. The results indicate that the algorithms outperform standard GSGP, confirming the suitability of dynamic populations for GSGP. Interestingly, the novel algorithms that use semantic neighbourhood to manage variation in population size are particularly effective in generating robust models even for the most difficult of the studied test problems.
Nuclei segmentation with point annotations from pathology images via self-supervised learning and co-training

Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Codes are available at https://github.com/hust-linyi/SC-Net.
A Simple Transformer-style Network for Lightweight Image Super-resolution

The task of single image super resolution (SISR) has taken much attention in the last few years due to the wide range of real-world applications. However, most of the recently developed methods are computationally expensive and need much more memory. To solve this issue, we propose a simple Transformer-style network (STSN) for the image super resolution (SR) task. The idea of this method is based on using convolutional modulation (Conv2Former), which is a very simple block with a linearly compared to quadratically as in Transformers. This Conv2Former is simplified the self-attention mechanism based on utilizing only convolutions and Hadamard product. Also, the original Conv2Former is further improved to be able to extract local features, which is helpful for SR task. Based on this Conv2Former and multi-layer perceptron (MLP), we propose a convolutional modulation block (Conv2FormerB) which is similar to the Transformers block. Based on this Conv2FormerB, 3 × 3 convolution and enhanced spatial attention (ESA) block, an STSN is designed for the SISR task. This STSN achieved good results in multiple SR benchmarks. Finally, our STSN model attained 5.6 × faster run time compared to LWSwinIR.
IDF-Sign: Addressing Inconsistent Depth Features for Dynamic Sign Word Recognition

Inconsistent hand and body features pose barriers to sign language recognition and translation leading to unsatisfactory models.Existing recognition models are built up on the spatial-temporal depth S p features.Finding suitable expert features for the S p model is challenging especially for dynamic sign words because many inconsistent features exist across hand motions and shapes.In this article, we propose IDF-Sign: an efficient and consistent S p model from a spatial-temporal multivariate pairwise consistency feature ranking (PairCFR) approach.The temporal features are obtained by computing the 3D position vector of skeletal hand joint coordinates, while the spatial features were obtained by taking every ten spatial coordinates in the 3D video frames and averaging it and doing so until the end of the frames.The PairCFR was used to rank and select the best S p model features at different feature thresholds.We employed a threshold selection to compute a mid-point value of each ranked feature according to its weight.The receiver operating characteristics (ROC) scheme was employed to identify the relationship between the sensitive parameters and the S p features, and the obtained values were utilized as modeling inputs.To verify the IDF-Sign, we design a real-life experiment with a leap motion sensor (LMS) consisting of ten signers with a total of ninety dynamic sign words.LMS provides the depth videos, since depth videos are too dense for the S p model to treat directly, we read the depth videos in comma-separated files in real time.Extensive IDF-Sign evaluations using machine learning on ASL, GSL, DSG, and ASL-similar datasets prove the Optimized Forest achieved an average recognition performance of 95%, 78%, 65.07%, and 95.33% of the top-1, respectively.
Unveiling Copy-Move Forgeries: Enhancing Detection With SuperPoint Keypoint Architecture

The authentication of digital images poses a significant challenge due to the wide range of image forgery techniques employed, with one notable example being a copy-move forgery.This form of forgery involves duplicating and relocating segments of an image within the same image, often accompanied by geometric transformations to deceive viewers into perceiving the forged image as authentic.Furthermore, additional processing techniques like scaling, rotation, JPEG compression, and the application of Additive White Gaussian Noise (AWGN) are frequently employed to further obscure any traces of forgery, making the detection and verification process even more complex.This paper presents a novel approach for detecting copy-move forgery in digital images using the self-supervised image keypoint detector, SuperPoint.Our approach leverages the advanced capabilities of SuperPoint, which combines keypoint detection and descriptor extraction, to identify and localize copy-move forgery accurately.One important aspect of our approach is its ability to handle images with different textures, including smooth and self-similar structural images.The proposed approach is able to produce stable results in images with various attacks, making it a functional and reliable tool for detecting copy-move forgery in a diverse range of forged images.Comparative analysis with existing forgery detection methods shows the superior performance of our proposed approach.Furthermore, the computational efficiency of our algorithm enables real-time forgery detection.Our approach using SuperPoint offers an effective solution for detecting copy-move forgery in digital images, making it valuable for image forensics and authenticity
Let You See in Haze and Sandstorm: Two-in-One Low-Visibility Enhancement Network

Scattering particles (such as water vapor and dust) in the atmosphere excessively absorb and scatter light, which causes the image collected by imaging sensors with low visibility, low contrast, and color shifting, etc. This limitation hinders the development of vision-driven artificial intelligence applications, such as the intelligent traffic monitoring system (ITMS). To improve imaging quality in hazy and sandstorm conditions, this paper proposes a novel two-in-one low-visibility enhancement network (termed TOENet). Specifically, we take into account the changes in the histogram distributions of the red, green, and blue channels of the hazy and sandstorm images and present a multilayer perceptron (MLP)-based channel correlation extraction module (CCEM) to learn and reconstruct the correlation between the channels of low-visibility images. The multi-scale channel attention features generated by CCEM will be embedded into the backbone (i.e., the encoder-decoder network) of TOENet to extract additional latent feature information from low-visibility images. TOENet can achieve both dehazing and sandstorm enhancement through a single deep network model without incurring extra computational costs. Extensive experimental results demonstrate that the proposed TOENet outperforms several state-of-the-art methods on challenging datasets. Furthermore, the running time and object detection experiments indicate the practical benefits of our method for high-level visual task under low-visibility weathers in ITMS. The source code is available at https://github.com/YuanGao-YG/TOENet.
Edge Intelligence Over the Air: Two Faces of Interference in Federated Learning

Federated edge learning is envisioned as the bedrock of enabling intelligence in next-generation wireless networks, but the limited spectral resources often constrain its scalability. In light of this challenge, a line of recent research suggested integrating analog over-the-air computations into federated edge learning systems, to exploit the superposition property of electromagnetic waves for fast aggregation of intermediate parameters and achieve (almost) unlimited scalability. Over-the-air computations also benefit the system in other aspects, such as low hardware cost, reduced access latency, and enhanced privacy protection. Despite these advantages, the interference introduced by wireless communications also influences various aspects of the model training process, while its importance is not well recognized yet. This article provides a comprehensive overview of the positive and negative effects of interference on over-the-air computation-based edge learning systems. The potential open issues and research trends are also discussed.
CRNet: Channel-Enhanced Remodeling-Based Network for Salient Object Detection in Optical Remote Sensing Images

Despite the remarkable progress made by the salient object detection of natural sensing images (NSI-SOD), the complex background and scale diversity issues of remote sensing images (RSIs) still pose a substantial obstacle. In this study, we build an end-to-end channel-enhanced remodeling-based network (CRNet) for optical RSIs (ORSIs) to highlight salient objects through feature augmentation. First, the backbone convolutional block is used to suggest the fundamental characteristics. Then, we use the channel enhance module (CEM) to enhance the shallow features. CEM primarily relies on the channel attention mechanism and employs a no-downscaling strategy to produce local cross-channel interaction, which lowers model complexity while enhancing extraction performance. Meanwhile, we use the redefined feature module (RFM) to reconstruct the deep features and generate global attention features by dimensional transformation and feature relationship aggregation to achieve the role of locating salient targets. Finally, the cascade combines the multi-scale features to provide the final saliency map. To further enhance the representational power of the network, we use a hybrid loss function to improve performance. The proposed approach outperforms current state-of-the-art methods, as shown by several experiments on three available datasets. The source code of the proposed CRNet is available publicly at https://github.com/hilitteq/CRNet.git.
Constructing n-dimensional discrete non-degenerate hyperchaotic maps using QR decomposition

Lyapunov exponents (LEs) characterize the average exponential rate of convergence or divergence between adjacent orbits in phase space. Thus, the number of positive LEs can reflect the complexity of chaotic systems from a certain point of view. To resist the dynamic degradation of digital chaos, we propose a novel universal method that is based on QR decomposition for constructing non-degenerate hyperchaotic maps. A large number of positive LEs can be generated to increase the complexity of chaotic systems. Furthermore, we construct a 4-D discrete non-degenerate hyperchaotic map as an example to demonstrate the adaptability and efficacy of the proposed scheme. For the proposed method, the related control parameters can not only effectively adjust Lyapunov exponents, but also carry out chaotic regulation on the discrete map, such as amplitude control and offset boosting. In addition, a pseudorandom number generator (PRNG) is designed with desirable statistical properties. Then, a microcontroller-based platform was developed to implement the proposed chaotic map. This study is interesting and has a potential for real-world applications, such as secure communication and cryptography.
Reflection equivariant quantum neural networks for enhanced image classification

Abstract Machine learning is among the most widely anticipated use cases for near-term quantum computers, however there remain significant theoretical and implementation challenges impeding its scale up. In particular, there is an emerging body of work which suggests that generic, data agnostic quantum machine learning (QML) architectures may suffer from severe trainability issues, with the gradient of typical variational parameters vanishing exponentially in the number of qubits. Additionally, the high expressibility of QML models can lead to overfitting on training data and poor generalisation performance. A promising strategy to combat both of these difficulties is to construct models which explicitly respect the symmetries inherent in their data, so-called geometric quantum machine learning (GQML). In this work, we utilise the techniques of GQML for the task of image classification, building new QML models which are equivariant with respect to reflections of the images. We find that these networks are capable of consistently and significantly outperforming generic ansatze on complicated real-world image datasets, bringing high-resolution image classification via quantum computers closer to reality. Our work highlights a potential pathway for the future development and implementation of powerful QML models which directly exploit the symmetries of data.