Reviewerly/exHarmony huggingface dataset:
DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation that leverages weight-sharing and SGD for cost reduction of NAS. In DARTS, all candidate operations are trained simultaneously during the network weight training step. Our empirical results show that this training procedure leads to the co-adaption problem and Matthew Effect: operations with fewer parameters would be trained maturely earlier. This causes two problems: firstly, the operations with more parameters may never have the chance to express the desired function since those with less have already done the job; secondly, the system will punish those underperforming operations by lowering their architecture parameter and backward smaller loss gradients, this causes the Matthew Effect. In this paper, we systematically study these problems and propose a novel grouped operation dropout algorithm named DropNAS to fix the problems with DARTS. Extensive experiments demonstrate that DropNAS solves the above issues and achieves promising performance. Specifically, DropNAS achieves 2.26% test error on CIFAR-10, 16.39% on CIFAR-100 and 23.4% on ImageNet (with the same training hyperparameters as DARTS for a fair comparison). It is also observed that DropNAS is robust across variants of the DARTS search space. Code is available at https://github.com/wiljohnhong/DropNAS.
Use of digital tools as a component of STEM education ecosystem

The authors investigate the theoretical foundations of the concept of ecosystem, interdisciplinary approaches and features of integrated STEM education in the educational process, analyzed the components of the educational ecosystem and proved that they all directly affect the quality of STEM subjects. The authors conducted a survey to identify ways to develop components of the ecosystem of STEM education, which involved 105 respondents, students of pedagogical specialties of Borys Grinchenko Kyiv University and teachers of secondary schools in different regions of Ukraine. The analysis showed that the successful implementation of STEM education, involving all components of the ecosystem, involves the development of science education, in particular, the introduction of inquiry-based learning and the use of digital technologies in the educational process. Process skills are described, which are formed using inquiry-based learning, examples of tasks for the formation of basic and integrated skills of the scientific process are presented. Based on the research, the role of digital tools in the development of science education is demonstrated and groups of digital tools are identified that are necessary for ecosystem development and will help increase the efficiency of the educational process, make STEM learning interesting and productive; examples of digital resources that support students' learning in various integrated STEM environments are given and their role in the development of the STEM education ecosystem is described. Digital tools can be used to increase students' positive motivation, expand their experience and accelerate learning, they help to study STEM subjects and encourage students to explore scientific ideas in new ways.
Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operations, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivated us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employed local transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts. The outputs from different views and sides were concatenated and fed into global transformer blocks, to jointly learn patch relationships between four images representing two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of mammograms, which included 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our four-image (two-view-two-side) transformer-based model achieves case classification performance with an area under ROC curve (AUC = 0.818 ± 0.039), which significantly outperforms AUC = 0.784 ± 0.016 achieved by the state-of-the-art multi-view CNNs (p = 0.009). It also outperforms two one-view-two-side models that achieve AUC of 0.724 ± 0.013 (CC view) and 0.769 ± 0.036 (MLO view), respectively. The study demonstrates the potential of using transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms.
Textless Speech-to-Speech Translation on Real Data

Ann Lee, Hongyu Gong, Paul-Ambroise Duquenne, Holger Schwenk, Peng-Jen Chen, Changhan Wang, Sravya Popuri, Yossi Adi, Juan Pino, Jiatao Gu, Wei-Ning Hsu. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
True wide convolutional neural network for image denoising

Deep convolutional neural networks (CNNs) have achieved significant success in image-denoising tasks. However, increasing the network depth arbitrarily results in vanishing/exploding gradients, which does not improve the image denoising quality. In this study, we propose a true wide CNN (WCNN) to reorganize several convolutional layers. Through wavelet decomposition, all convolutional layers that are originally concentrated on a single information stream to train one image are now distributed among multiple independent subnetworks, each of which has few convolutional layers and is arranged in parallel in the WCNN. Each subnetwork has its own input and output and is supervised by its own loss function to capture image features with a specific direction and scale, allowing the WCNN to have sufficient convolutional layers to capture image features while avoiding vanishing/exploding gradients. In addition, each subnetwork can adopt a suitable CNN structure based on the characteristics of a subband, ensuring that the WCNN is competent in handling a specific range of noise levels using a single set of trained parameters. Extensive quantitative and qualitative evaluations of benchmark datasets and real images reveal that the WCNN outperforms state-of-the-art denoising methods.
Privacy is What We Care About

Federated Learning enables training of a general model through edge devices without sending raw data to the cloud. Hence, this approach is attractive for digital health applications, where data is sourced through edge devices and users care about privacy. Here, we report on the feasibility to train deep neural networks on the Raspberry Pi4s as edge devices. A CNN, a LSTM and a MLP was successfully trained on the MNIST data-set. Further, federated learning was demonstrated experimentally on IID and non-IID samples in a parametric study, to benchmark the model convergence. The weight updates from the workers are shared with the cloud to train the general model through federated learning. With the CNN and the non-IID samples a test-accuracy of up to 85% could be achieved within a training time of 2 minutes, while exchanging less than 10 MB data per device. In addition, we discuss federated learning from an use-case standpoint, elaborating on privacy risks and labeling requirements for the application of emotion detection from sound. Based on the experimental findings, we discuss possible research directions to improve model and system performance. Finally, we provide best practices for a practitioner, considering the implementation of federated learning.
Self-Supervised Hypergraph Transformer for Recommender Systems

Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN-based recommender systems is to recursively perform the message passing along the user-item interaction edge for refining the encoded embeddings. Despite their effectiveness, however, most of the current recommendation models rely on sufficient and high-quality training data, such that the learned representations can well capture accurate user preference. User behavior data in many practical recommendation scenarios is often noisy and exhibits skewed distribution, which may result in suboptimal representation performance in GNN-based models. In this paper, we propose SHT, a novel Self-Supervised Hypergraph Transformer framework (SHT) which augments user representations by exploring the global collaborative relationships in an explicit way. Specifically, we first empower the graph neural CF paradigm to maintain global collaborative effects among users and items with a hypergraph transformer network. With the distilled global context, a cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph, so as to enhance the robustness of recommender systems. Extensive experiments demonstrate that SHT can significantly improve the performance over various state-of-the-art baselines. Further ablation studies show the superior representation ability of our SHT recommendation framework in alleviating the data sparsity and noise issues. The source code and evaluation datasets are available at: https://github.com/akaxlh/SHT.
Attention-Based Multi-Level Feature Fusion for Object Detection in Remote Sensing Images

We study the problem of object detection in remote sensing images. As a simple but effective feature extractor, Feature Pyramid Network (FPN) has been widely used in several generic vision tasks. However, it still faces some challenges when used for remote sensing object detection, as the objects in remote sensing images usually exhibit variable shapes, orientations, and sizes. To this end, we propose a dedicated object detector based on the FPN architecture to achieve accurate object detection in remote sensing images. Specifically, considering the variable shapes and orientations of remote sensing objects, we first replace the original lateral connections of FPN with Deformable Convolution Lateral Connection Modules (DCLCMs), each of which includes a 3×3 deformable convolution to generate feature maps with deformable receptive fields. Additionally, we further introduce several Attention-based Multi-Level Feature Fusion Modules (A-MLFFMs) to integrate the multi-level outputs of FPN adaptively, further enabling multi-scale object detection. Extensive experimental results on the DIOR dataset demonstrated the state-of-the-art performance achieved by the proposed method, with the highest mean Average Precision (mAP) of 73.6%.
Local binary pattern‐based reversible data hiding

A novel local binary pattern-based reversible data hiding (LBP-RDH) technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding capacity. During embedding, the image is divided into various 3×3 blocks. Then, using the LBP-based image descriptor, the LBP codes for each block are computed. Next, the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment process. Further, each cover image (CI) pixel produces two different stego-image pixels. Likewise, during extraction, the CI pixels are restored without the loss of a single bit of information. The outcome of the proposed technique with respect to perceptual transparency measures, such as peak signal-to-noise ratio and structural similarity index, is found to be superior to that of some of the recent and state-of-the-art techniques. In addition, the proposed technique has shown excellent resilience to various stego-attacks, such as pixel difference histogram as well as regular and singular analysis. Besides, the out-off boundary pixel problem, which endures in most of the contemporary data hiding techniques, has been successfully addressed.
Next Generation IoT and Blockchain Integration

The Internet of Things (IoT) refers to the interconnection of smart devices to collect data and make intelligent decisions. However, a lack of intrinsic security measures makes next generation IoT more vulnerable to privacy and security threats. With its “security by design,” Blockchain (BC) can help in addressing major security requirements in IoT. Blockchain is an ever-growing list of records that are linked and protected using cryptographic methods. It offers its users the flexibility to conduct transactions with lower costs and faster speeds. Blockchain ledgers are also decentralized and a ledger is maintained at each node in the network. Blockchain’s security and adaptability help in making even entire systems on it a much easily task with the benefit of decentralization. BC capabilities like immutability, transparency, auditability, data encryption, and operational resilience can help solve most architectural shortcomings of IoT. In the vision of the Internet of Things, traditional devices are becoming smarter and more autonomous. This vision is becoming reality as technology advances but there are still challenges to be resolved. This is especially true in a security domain like data trust, and with the expected evolution of the IoT in the coming years, it is important to ensure that this great source of data arrives. This paper began with an overview of blockchain and IoT, as well as explore the IoT blockchain application challenges. This article also focuses to review the most relevant tasks to analyse how IoT blockchain can improve and examine current research concerns and developments in the use of blockchain-related techniques and technologies in the context of IoT security in depth. One of the best parts of working or learning about blockchain and its application is the curiosity about how it can impact the things that we have been accustomed to without trying to improve and make things more efficient and productive.
Mathematical operations and equation solving with reconfigurable metadevices

Performing analog computations with metastructures is an emerging wave-based paradigm for solving mathematical problems. For such devices, one major challenge is their reconfigurability, especially without the need for a priori mathematical computations or computationally-intensive optimization. Their equation-solving capabilities are applied only to matrices with special spectral (eigenvalue) distribution. Here we report the theory and design of wave-based metastructures using tunable elements capable of solving integral/differential equations in a fully-reconfigurable fashion. We consider two architectures: the Miller architecture, which requires the singular-value decomposition, and an alternative intuitive direct-complex-matrix (DCM) architecture introduced here, which does not require a priori mathematical decomposition. As examples, we demonstrate, using system-level simulation tools, the solutions of integral and differential equations. We then expand the matrix inverting capabilities of both architectures toward evaluating the generalized Moore-Penrose matrix inversion. Therefore, we provide evidence that metadevices can implement generalized matrix inversions and act as the basis for the gradient descent method for solutions to a wide variety of problems. Finally, a general upper bound of the solution convergence time reveals the rich potential that such metadevices can offer for stationary iterative schemes.
Reconstructing Earth’s atmospheric oxygenation history using machine learning

Abstract Reconstructing historical atmospheric oxygen (O 2 ) levels at finer temporal resolution is a top priority for exploring the evolution of life on Earth. This goal, however, is challenged by gaps in traditionally employed sediment-hosted geochemical proxy data. Here, we propose an independent strategy—machine learning with global mafic igneous geochemistry big data to explore atmospheric oxygenation over the last 4.0 billion years. We observe an overall two-step rise of atmospheric O 2 similar to the published curves derived from independent sediment-hosted paleo-oxybarometers but with a more detailed fabric of O 2 fluctuations superimposed. These additional, shorter-term fluctuations are also consistent with previous but less well-established suggestions of O 2 variability. We conclude from this agreement that Earth’s oxygenated atmosphere may therefore be at least partly a natural consequence of mantle cooling and specifically that evolving mantle melts collectively have helped modulate the balance of early O 2 sources and sinks.
Which factors drive Bitcoin volatility: Macroeconomic, technical, or both?

Abstract Academic research relies heavily on exogenous drivers to improve the forecasting accuracy of Bitcoin volatility. The present study provides additional insight into the role of both macroeconomic and technical indicators in forecasting the realized volatility of Bitcoin. Using 17 famous macroeconomic variables and 18 technical indicators between December 2011 and April 2021, the results reveal that the shrinkage methods, including elastic net and LASSO, can powerfully extract predictive information from macroeconomic and technical indicators. We further investigate the forecasting power of macroeconomic factors and technical indicators in terms of variable selection, business cycle, and volatility levels, and the results show strong evidence that the macroeconomic indicators (namely, S&amp;P 500 realized volatility, global real economic activity index, and trade‐weighted USD index return) are the most frequently selected by shrinkage method, suggesting that their ability to forecast Bitcoin volatility is stronger than that of technical indicators. However, technical indicators are more powerful in forecasting Bitcoin volatility during the low volatility state.
A Lightweight Model for Ship Detection and Recognition in Complex-Scene SAR Images

SAR ship detection and recognition are important components of the application of SAR data interpretation, allowing for the continuous, reliable, and efficient monitoring of maritime ship targets, in view of the present situation of SAR interpretation applications. On the one hand, because of the lack of high-quality datasets, most existing research on SAR ships is focused on target detection. Additionally, there have been few studies on integrated ship detection and recognition in complex SAR images. On the other hand, the development of deep learning technology promotes research on the SAR image intelligent interpretation algorithm to some extent. However, most existing algorithms only focus on target recognition performance and ignore the model’s size and computational efficiency. Aiming to solve the above problems, a lightweight model for ship detection and recognition in complex-scene SAR images is proposed in this paper. Firstly, in order to comprehensively improve the detection performance and deployment capability, this paper applies the YOLOv5-n lightweight model as the baseline algorithm. Secondly, we redesign and optimize the pyramid pooling structure to effectively enhance the target feature extraction efficiency and improve the algorithm’s operation speed. Meanwhile, to suppress the influence of complex background interference and ships’ distribution, we integrate different attention mechanism into the target feature extraction layer. In addition, to improve the detection and recognition performance of densely parallel ships, we optimize the structure of the model’s prediction layer by adding an angular classification module. Finally, we conducted extensive experiments on the newly released complex-scene SAR image ship detection and recognition dataset, named the SRSDDv1.0 dataset. The experimental results show that the minimum size of the model proposed in this paper is only 1.92 M parameters and 4.52 MB of model memory, which can achieve an excellent F1-Score performance of 61.26 and an FPS performance of 68.02 on the SRSDDv1.0 dataset.
Resolution of 100 photons and quantum generation of unbiased random numbers

Macroscopic quantum phenomena, such as observed in superfluids and superconductors, have led to promising technological advancements and some of the most important tests of fundamental physics. At present, quantum detection of light is mostly relegated to the microscale, where avalanche photodiodes are very sensitive to distinguishing single-photon events from vacuum but cannot differentiate between larger photon-number events. Beyond this, the ability to perform measurements to resolve photon numbers is highly desirable for a variety of quantum information applications including computation, sensing, and cryptography. True photon-number resolving detectors do exist, but they are currently limited to the ability to resolve on the order of 10 photons, which is too small for several quantum state generation methods based on heralded detection. In this work, we extend photon measurement into the mesoscopic regime by implementing a detection scheme based on multiplexing highly quantum-efficient transition-edge sensors to accurately resolve photon numbers between zero and 100. We then demonstrate the use of our system by implementing a quantum random number generator with no inherent bias. This method is based on sampling a coherent state in the photon-number basis and is robust against environmental noise, phase and amplitude fluctuations in the laser, loss and detector inefficiency as well as eavesdropping. Beyond true random number generation, our detection scheme serves as a means to implement quantum measurement and engineering techniques valuable for photonic quantum information processing.
Context-Free Word Importance Scores for Attacking Neural Networks

Leave-One-Out (LOO) scores provide estimates of feature importance in neural networks, for adversarial attacks. In this work, we present context-free word scores as a query-efficient alternative. Experiments show that these approximations are quite effective for black box attacks on neural networks trained for text classification, particularly for CNNs. The model query count for this method scales as 0(vocan_size * model_input_length). It is independent of the number of examples and features to be perturbed.
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to produce RL algorithms whose policies generalise well to novel unseen situations at deployment time, avoiding overfitting to their training environments. Tackling this is vital if we are to deploy reinforcement learning algorithms in real world scenarios, where the environment will be diverse, dynamic and unpredictable. This survey is an overview of this nascent field. We rely on a unifying formalism and terminology for discussing different ZSG problems, building upon previous works. We go on to categorise existing benchmarks for ZSG, as well as current methods for tackling these problems. Finally, we provide a critical discussion of the current state of the field, including recommendations for future work. Among other conclusions, we argue that taking a purely procedural content generation approach to benchmark design is not conducive to progress in ZSG, we suggest fast online adaptation and tackling RL-specific problems as some areas for future work on methods for ZSG, and we recommend building benchmarks in underexplored problem settings such as offline RL ZSG and reward-function variation.