Reviewerly/exHarmony huggingface dataset:
Resonance for Analog Recurrent Neural Network

There is a strong interest in using physical waves for artificial neural computing because of their unique advantages in fast speed and intrinsic parallelism. Resonance, as a ubiquitous feature across many wave systems, is a natural candidate for analog computing in temporal signals. We demonstrate that resonance can be used to construct stable and scalable recurrent neural networks. By including resonators with different lifetimes, the computing system develops both short-term and long-term memories simultaneously.
Hybrid Cloud Detection Algorithm Based on Intelligent Scene Recognition

Abstract All-sky images derived from ground-based imaging equipment have become an important means of recognizing and quantifying cloud information. Accurate cloud detection is a prerequisite for obtaining important cloud information from an all-sky image. Existing cloud segmentation algorithms can achieve high accuracy. However, for different scenes, such as completely cloudy with obscured sun and partly cloudy with unobscured sun, the use of specific algorithms can further improve segmentation. In this study, a hybrid cloud detection algorithm based on intelligent scene recognition (HCD-ISR) is proposed. It uses suitable cloud segmentation algorithms for images in different scenes recognized by ISR, so as to utilize the various algorithms to their full potential. First, we developed an ISR method to automatically classify the all-sky images into three scenes. In scene A, the sky is completely clear; in scene B, the sky is partly cloudy with unobscured sun; and in scene C, the sun is completely obscured by clouds. The experimental results show that the ISR method can correctly identify 93% of the images. The most suitable cloud detection algorithm was selected for each scene based on the relevant features of the images in that scene. A fixed thresholding (FT) method was used for the images in scene C. For the most complicated scene, that is, scene B, the clear-sky background difference (CSBD) method was used to identify cloud pixels based on a clear-sky library (CSL). The images in the CSL were automatically filtered by ISR. Compared to FT, adaptive thresholding (AT), and CSBD methods, the proposed HCD-ISR method has the highest accuracy (95.62%). The quantitative evaluation and visualization results show that the proposed HCD-ISR algorithm makes full use of the advantages of different cloud detection methods, and is more flexible and robust.
High-quality 3D shape measurement with binary half truncated sinusoidal fringe pattern

Digital fringe projection (DFP) with defocused binary fringe patterns could overcome the projector nonlinearity and achieve a high-speed 3D measurement. Most of the binary defocusing techniques take sinusoidal fringe as the target of binarization, hoping the binary fringes can become "quasi-sinusoidal" after defocusing. However, considering that the phase-shifting algorithm (PSA) has an inherent resistance to certain order harmonics components, it is not the best choice to make the defocused binary fringe become more sinusoidal. This paper proposed a new kind of fringe called half truncated sinusoidal (ht-sinusoidal) fringe, which has the same phase result as the sinusoidal fringe with 4-step PSA theoretically. And this kind of fringe is easier to be binarized for the reason that half of it has 0 intensity values already. Therefore it can achieve significant phase quality improvements compared with the binary sinusoidal fringe. We mainly verified that the ht-sinusoidal fringe will help substantially improve the measurement accuracy of different error diffusion methods through both simulations and experiments.
Super Resolution CT Imaging Using Higher Order Total Variation (HOTV) Technique

The present study focuses on the performance upgradation of a table-top computed tomography (CT) scanning instrument. The size restriction of the detectors of table-top CT scanner imposes limitation on the allowable size of the object to be scanned. The object with increasing size has to be put in a far end from the X-ray source to be fit into the scan field of view of the scanner. This situation affects the pixel resolution of the tomography images. The limited resolution capacity restricts the CT image zoomed-in view facility which is very often an essential requirement for scientific measurement and analysis. The hardware-based solution involving larger detectors will significantly increase the monetary cost, and thus a cost-effective solution is very much required. The new super resolution (SR) or resolution up-scaling technique based on higher order total variation minimization has been proposed to overcome the above-mentioned problem associated with a table-top tomography instrument. The proposed technique is capable of producing SR image from a single low-resolution image. The experimental results have been compared with some of the existing SR techniques, in terms of various image quality parameters (mean squared error, correlation coefficient, structural similarity index, and feature similarity index) to show betterment over other existing SR techniques. The experiments are carried out with different objects to validate the universality of the proposed scheme. The pixel-enhanced tomographic images are further checked with Sobolev space error analysis to ensure the quality of the images from tomography perspective. It can be inferred that the presented technique can properly up-scale the resolution of CT images, and therefore it presents a cost-effective possible solution to the resolution problem caused by the size constraint of the detectors of table-top CT scanning instruments.
Blockchain-Based Model for Secure IoT Communication in Smart Healthcare

Over the last decade, a dramatic growth has been witnessed in healthcare monitoring systems and wireless healthcare monitoring systems have been developed in many countries of the world. Smart healthcare is one of the key areas where IoT infrastructures and solutions are widely used to facilitate the best possible patient surveillance, accurate diagnosis, and timely operation of patients with existing diseases. Smart healthcare systems on the other hand, face various challenges including data transparency, traceability, immutability, and security. Also, most of the existing healthcare systems are often controlled by a centralized monitoring unit that poses potential risks of single-point failures during natural calamities. Being motivated by the aforementioned issues, this paper aims at leveraging blockchain technology for smart healthcare to overcome the issues and challenges that smart healthcare faces as a result of traditional security policies. We propose a theoretical Autonomous Healthcare model, which divides the whole healthcare scenario into hospital-patient collaborations. The proposed model uses multiple blockchains, a global blockchain (GBC), which is used for collaboration among hospitals. Thus, creating a global healthcare system in which a patient is not limited to a certain set of region-based hospitals and doctors. Other blockchains (RBC) are specific to the regions of the patients for problems that are minor and require less interaction and consultation of doctors. Global blockchain (GBC) is a permission-less blockchain while region blockchains (RBC) are permissioned ones. The proposed model promises a decentralized framework that facilitates sharing and exchanging, and integration of information across all the users and third parties.
A reliable and unobtrusive approach to display area detection for imperceptible display camera communication

Object framework detection has been extensively studied in computer vision for applications such as document digitization and whiteboard scanning. Similarly, it is essential for display-camera communication systems, particularly when imperceptible data modulation is employed to enable simultaneous video playback and data transmission. Reliable and accurate localization of the encoded display area is critical for data demodulation and decoding. However, existing systems typically adapt established methods developed for other applications that do not meet the system requirements for high-rate data transmission. In this article, we propose a novel method for display area detection in the camera images by embedding a new localization marker into the display corners. While the localization marker is less obtrusive than conventional fiducial markers, our detection algorithm demonstrated excellent reliability regardless of the display content and background, according to simulation and experimental results. In addition, the detector achieved subpixel accuracy and real-time performance with modern smartphones.
Boosting Robust Learning Via Leveraging Reusable Samples in Noisy Web Data

Webly-supervised fine-grained visual classification (FGVC) has attracted increasing attention in recent years because of the unaffordable cost of obtaining correctly-labeled large-scale fine-grained datasets. However, due to the existence of label noise in web images and the high memorization capacity of deep neural networks, training deep fine-grained (FG) models directly through web images tends to have an inferior recognition ability. In the literature, to alleviate this issue, loss correction methods try to estimate the noise transition matrix, but the inevitable false correction would cause accumulated errors. Sample selection methods identify clean (“easy”) samples based on the fact that small losses can alleviate the accumulated errors. However, “hard” and mislabeled examples that can both boost the robustness of FG models are also dropped. To this end, we propose a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images. Our key idea is to additionally identify and correct reusable samples, and then leverage them together with clean examples to update the network. Furthermore, in order to endow our model with the capability to capture richer and more discriminative feature representations, we propose a cross-layer attention-based feature refinement (CLAR) block. We demonstrate the superiority of the proposed approach from both theoretical and experimental perspectives.
Privacy-Aware Double Auction With Time-Dependent Valuation for Blockchain-Based Dynamic Spectrum Sharing in IoT Systems

For future Internet of Things (IoT) systems, data-driven and dynamic spectrum-sharing schemes can significantly improve the spectrum utilization and efficiency. However, conventional centralized architecture of such dynamic IoT spectrum-sharing systems is often considered to be nontransparent, costly, and vulnerable to potential attacks and single-point failures. To address the aforementioned issues, a blockchain-based dynamic spectrum-sharing scheme has been proposed and investigated in this work, which aims at enhancing the system by providing desirable features, such as decentralization, transparency, immutability, and auditability. By considering the privacy and transaction dynamics issues when blockchain is integrated into spectrum-sharing systems, a privacy-preserving double auction mechanism based on differential privacy is developed for incentivizing spectrum sharing, where the time-varying valuations of the spectrum resources are also taken into consideration. In the proposed auction, a winner determination problem (WDP) is formulated to decide the winning bidders and spectrum allocation. A deep reinforcement learning (DRL)-based method is then proposed for efficiently solving the WDP. The proposed auction mechanism can be integrated with smart contracts on blockchain platforms. Furthermore, the computation of the DRL-based method for solving the WDP is designed as part of the consensus mechanism in the blockchain. Theoretical analysis show that the proposed privacy-aware double auction mechanism satisfies the properties of differential privacy, individual rationality, and truthfulness. Finally, simulation results are provided to validate the performance of the spectrum-sharing approach.
Experimental Shot-by-Shot Estimation of Quantum Measurement Confidence

We demonstrate the single-shot confidence estimation for individual quantum measurement outcomes using the continuous measurement theory of the quantum counting process applied to the quantum state identification problem. We experimentally obtain single-shot and average confidences for quantum measurements and show that they favorably compare to that of the idealized classical measurement. Finally, we demonstrate that single-shot confidence estimations correctly represent observed experimental outcomes for a large ensemble of measurements.
Hardware Implementations of Pairings at Updated Security Levels

Pairings are cornerstones to several interesting cryptographic protocols including Non-interactive ARgument of Knowledge currently used in Zcash cryptocurrency. The Kim and Barbulescu Number Field Sieve attack has weakened pairing-friendly curves. Most impacted are the famous BN curves which now require an increase of the parameters to provide equivalent security. Recent cost estimations of pairings have recommended switching to other curves, but their selections are no longer clearly straightforward. This paper aims at providing the first hardware-based pairing implementations on the best curve candidates at both 128-bit and 192-bit security levels. The proposed architecture intends to fit both lightweight FPGA and ASIC purposes and the design is prototyped on a Kintex-7 FPGA device. It computes a pairing within 42.7 ms for 128-bit of security and 184.2 ms for 192-bit.
A Family of Distributed Momentum Methods Over Directed Graphs With Linear Convergence

We consider the distributed optimization to minimize a sum of smooth and strongly convex local objective functions over directed graphs. Using row- and column-stochastic weights, we propose a family of distributed momentum methods. It is a parametric distributed momentum (PDM) method, for which different values of parameter can lead to different distributed momentum methods. PDM includes the distributed heavy-ball method ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {AB}m$</tex-math></inline-formula> ) and the distributed Nesterov gradient method ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {ABN}$</tex-math></inline-formula> ) as its special cases. When the step sizes and the momentum coefficient do not exceed some upper bounds, we prove that PDM can converge to the optimal solution at a global <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R$</tex-math></inline-formula> -linear rate. The convergence result of PDM not only covers that of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {AB}m$</tex-math></inline-formula> , but also supplements that of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {ABN}$</tex-math></inline-formula> , which lacks theoretical convergence result. Simulation results on classification problems that arise in machine learning show that PDM with an appropriate negative parameter value can achieve faster acceleration than the existing distributed momentum algorithms.
Semi-supervised learning for optical fiber sensor road intrusion signal detection

This paper proposes a road intrusion detection model based on distributed optical fiber vibration sensors signals. Considering that the existing unsupervised classification method often has a high false alarm rate when meeting the new non-intrusion samples, we propose a one-dimensional semi-supervised generative adversarial network (1D-SSGAN) model for intrusion signal recognition. The 1D-SSGAN is composed of a generator and a discriminator. The output layer of the discriminator is mapped to N+1 classes, and the generator and discriminator are trained on the N class dataset. During the learning process of the generator against the discriminator, many new samples are generated based on a small number of samples, which effectively expands the datasets and assists the training of the discriminator. Experimental result analysis demonstrates the effectiveness of the proposed model.
A Virtual Knowledge Distillation via Conditional GAN

Knowledge distillation aims at transferring the knowledge from a pre-trained complex model, called teacher, to a relatively smaller and faster one, called student.Unlike previous works that transfer the teacher's softened distributions or feature spaces, in this paper, we propose a novel approach, called Virtual Knowledge Distillation (VKD), that transfers a softened distribution generated by a virtual knowledge generator conditioned on class label.A virtual knowledge generator is trained independently, but concurrently with a teacher, to mimic the teacher's softened distributions.Afterwards, when training a student, virtual knowledge generator can be exploited instead of the teacher's softened distributions or combined with the existing distillation methods in a straightforward manner.Moreover, with slight modifications, VKD can be utilized not only for the self-knowledge distillation method but also for the collaborative learning method.We compare our method with several representative distillation methods in various combinations of teacher and student architectures on the image classification tasks.Experimental results on various image classification tasks demonstrate that VKD show a competitive performance compared to the conventional distillation methods, and when combined with them, the performance is improved with a substantial margin.
Methods for Measurement and Analysis of Full Hand Angular Kinematics Using Electromagnetic Tracking Sensors

The human hand needs a large number of sensors to measure kinematics owing to its large number of degrees of freedom.Existing devices like data gloves and optical trackers are associated with calibration, line of sight, and accuracy problems.In this paper, we attempt to measure the full hand kinematics using Electromagnetic Tracking Sensors (EMTS) which are accurate, free of line-of-sight problems, and require no calibration.However, EMTS provides output in the form of rotation groups which are defined on a nonlinear manifold.Hence, linear operations required for experimental analysis such as linear dimensionality reduction are not valid.Also, these sensors are expensive, utilize space in terms of cabling, and require a reduced sensor layout.In this paper, we present measurement methods, test the utility of linear and non-linear dimensionality reduction techniques on quaternions and exponential maps.We also performed sensor reduction using a Gini feature selection based on random forest algorithm.The kinematic measurement results show that EMTS yield superior posture reproduction with an error of less than 1 degree.Autoencoder, a nonlinear dimensionality reduction technique, was successfully applied on quaternions which was tuned to perform better than Principal Component Analysis (PCA) in reducing dimensions.The reduced sensor layout with 8 sensors was able to predict full hand kinematics with a Root Mean Square Error (RMSE) of 5.1 degrees.
Dual Extended Kalman Filter Under Minimum Error Entropy With Fiducial Points

The multivariate autoregressive (MVAR) model is widely used in describing the dynamics of nonlinear systems, in which the estimates of model parameters and underlying states can be achieved by dual extended Kalman filter (DEKF). However, when the measurements are corrupted by complicated non-Gaussian noises, the DEKF based on the minimum mean-square error (MMSE) criterion may provide biased estimates. In the present article, we develop a novel dual Kalman-type filter, referred to as DEKF under minimum error entropy (MEE) with fiducial points (MEEFs-DEKF) to deal with the non-Gaussian noises. First, the equivalent state-space model and parameter-space model are presented based on the MVAR model. Then, an optimality criterion based on MEE with fiducial points (MEEFs) is applied in the batch-mode regression equation to improve the robustness. Finally, a fixed-point iteration algorithm gives the posterior estimates of state and parameter. Simulation results confirm that the proposed MEEF-DEKF can achieve excellent performance in various noises with different distributions, especially in heavy-tailed and multimodal noises.
Enabling Hyperparameter Tuning of Machine Learning Classifiers in Production

Machine learning (ML) classifiers are widely adopted in the learning-enabled components of intelligent Cyber-physical Systems (CPS) and tools used in designing integrated circuits. Due to the impact of the choice of hyperparameters on an ML classifier performance, hyperparameter tuning is a crucial step for application success. However, the practical adoption of existing hyperparameter tuning frameworks in production is hindered due to several factors such as inflexible architecture, limitations of search algorithms, software dependencies, or closed source nature. To enable state-of-the-art hyperparameter tuning in production, we propose the design of a lightweight library (1) having a flexible architecture facilitating usage on arbitrary systems, and (2) providing parallel optimization algorithms supporting mixed parameters (continuous, integer, and categorical), handling runtime failures, and allowing combined classifier selection and hyperparameter tuning (CASH). We present Mango, a black-box optimization library, to realize the proposed design. Mango is currently used in production at Arm for more than 25 months and is available open-source (https://github.com/ARM-software/mango). Our evaluation shows that Mango outperforms other black-box optimization libraries in tuning hyperparameters of ML classifiers having mixed param-eter search spaces. We discuss two use cases of Mango deployed in production at Arm, highlighting its flexible architecture and ease of adoption. The first use case trains ML classifiers on the Dask cluster using Mango to find bugs in Arm's integrated circuits designs. As a second use case, we introduce an AutoML framework deployed on the Kubernetes cluster using Mango. Finally, we present the third use-case of Mango in enabling neural architecture search (NAS) to transfer deep neural networks to TinyML platforms (microcontroller class devices) used by CPS/IoT applications.
Improving Contextual Coherence in Variational Personalized and Empathetic Dialogue Agents

In recent years, latent variable models, such as the Conditional Variational Auto Encoder (CVAE), have been applied to both personalized and empathetic dialogue generation. Prior work have largely focused on generating diverse dialogue responses that exhibit persona consistency and empathy. However, when it comes to the contextual coherence of the generated responses, there is still room for improvement. Hence, to improve the contextual coherence, we propose a novel Uncertainty Aware CVAE (UA-CVAE) framework. The UA-CVAE framework involves approximating and incorporating the aleatoric uncertainty during response generation. We apply our framework to both personalized and empathetic dialogue generation. Empirical results show that our framework significantly improves the contextual coherence of the generated response. Additionally, we introduce a novel automatic metric for measuring contextual coherence, which was found to correlate positively with human judgement.