Reviewerly/exHarmony huggingface dataset:
A Blockchain System with Lightweight Full Node Based on Dew Computing

The blockchain technology enabled cryptocurrencies and a lot of other applications that trust is needed among different entities. Because every blockchain client needs to keep huge amount of blockchain data, some personal computers and mobile devices cannot be used to run blockchain clients. To make things worse, the size of blockchain data is always increasing. In this paper, a new kind of blockchain system, Dewblock, is introduced. In this system, a blockchain client does not need to keep the blockchain data and it also has the features of a blockchain full node. Dewblock was developed based on dew computing principles and the cloud-dew architecture. A node connection protocol, Pair Connection Protocol, was proposed, which could be used in similar cloud-dew applications.
Imaging and Localizing Individual Atoms Interfaced with a Nanophotonic Waveguide

Single particle-resolved fluorescence imaging is an enabling technology in cold-atom physics. However, so far, this technique has not been available for nanophotonic atom-light interfaces. Here, we image single atoms that are trapped and optically interfaced using an optical nanofiber. Near-resonant light is scattered off the atoms and imaged while counteracting heating mechanisms via degenerate Raman cooling. We detect trapped atoms within 150 ms and record image sequences of given atoms. Building on our technique, we perform two experiments which are conditioned on the number and position of the nanofiber-trapped atoms. We measure the transmission of nanofiber-guided resonant light and verify its exponential scaling in the few-atom limit, in accordance with Beer-Lambert's law. Moreover, depending on the interatomic distance, we observe interference of the fields that two simultaneously trapped atoms emit into the nanofiber. The demonstrated technique enables postselection and possible feedback schemes and thereby opens the road toward a new generation of experiments in quantum nanophotonics.
Convolutional-Neural-Network-Based Approach for Segmentation of Apical Four-Chamber View from Fetal Echocardiography

An apical four-chamber (A4C) view from early fetal echocardiography is an extremely significant step in early diagnosis and timely treatment of congenital heart diseases. The objective is to perform automated segmentation of cardiac structures, namely, the epicardium, left ventricle, left atrium, descending aorta, right atrium, right ventricle, and thorax, in ultrasound A4C views in one shot in order to assist clinicians in prenatal examination. However, such a segmentation task is often faced with the following challenges: 1) low imaging resolution; 2) incomplete tissue boundary; 3) overall contrast of the image. To address these issues, in this study, we propose a cascaded U-net, named CU-net, with structural similarity index measure (SSIM) loss. First, the CU-net with two branch supervisions helps gain clear tissue boundaries and alleviate the gradient vanishing problem caused by increasing network depth. Second, between-net connections in the CU-net can transmit the prior information from the shallow layer to the deeper layer and obtain more refined segmentation results. Third, the method leverages on SSIM loss to preserve fine-grained structural information and obtain clear boundaries. Extensive experiments on a dataset of 1712 A4C views demonstrate that the proposed method achieves a high dice coefficient of 0.856, Hausdorff distance of 3.33, and pixel accuracy of 0.929, revealing its effectiveness and potential as a clinical tool.
Video Frame Prediction by Deep Multi-Branch Mask Network

Future frame prediction in video is one of the most important problem in computer vision, and useful for a range of practical applications, such as intention prediction or video anomaly detection. However, this task is challenging because of the complex and dynamic evolution of scene. The difficulty of video frame prediction is to model the inherent spatio-temporal correlation between frames and pose an adaptive and flexible framework for large motion change or appearance variation. In this paper, we construct a deep multi-branch mask network (DMMNet) which adaptively fuses the advantages of optical flow warping and RGB pixel synthesizing methods, i.e., the common two kinds of approaches in this task. In the procedure of DMMNet, we add mask layer in each branch to adaptively adjust the magnitude range of estimated optical flow and the weight of predicted frames by optical flow warping and RGB pixel synthesizing, respectively. In other words, we provide a more flexible masking network for motion and appearance fusion on video frame prediction. Exhaustive experiments on Caltech pedestrian and UCF101 datasets show that the proposed model can obtain favorable video frame prediction performance compared with the state-of-the-art methods. In addition, we also put our model into the video anomaly detection problem, and the superiority is verified by the experiments on UCSD dataset.
Adversarial Example Detection by Classification for Deep Speech Recognition

Machine Learning systems are vulnerable to adversarial attacks and will highly likely produce incorrect outputs under these attacks. There are white-box and black-box attacks regarding to adversary's access level to the victim learning algorithm. To defend the learning systems from these attacks, existing methods in the speech domain focus on modifying input signals and testing the behaviours of speech recognizers. We, however, formulate the defense as a classification problem and present a strategy for systematically generating adversarial example datasets: one for white-box attacks and one for black-box attacks, containing both adversarial and normal examples. The white-box attack is a gradient-based method on Baidu DeepSpeech with the Mozilla Common Voice database while the black-box attack is a gradient-free method on a deep model-based keyword spotting system with the Google Speech Command dataset. The generated datasets are used to train a proposed Convolutional Neural Network (CNN), together with cepstral features, to detect adversarial examples. Experimental results show that, it is possible to accurately distinct between adversarial and normal examples for known attacks, in both single-condition and multi-condition training settings, while the performance degrades dramatically for unknown attacks. The adversarial datasets and the source code are made publicly available.
ADRN: Attention-Based Deep Residual Network for Hyperspectral Image Denoising

Hyperspectral image (HSI) denoising is of crucial importance for many subsequent applications, such as HSI classification and interpretation. In this paper, we propose an attention-based deep residual network to directly learn a mapping from noisy HSI to the clean one. To jointly utilize the spatial-spectral information, the current band and its K adjacent bands are simultaneously exploited as the input. Then, we adopt convolution layer with different filter sizes to fuse the multi-scale feature, and use shortcut connection to incorporate the multi-level information for better noise removal. In addition, the channel attention mechanism is employed to make the network concentrate on the most relevant auxiliary information and features that are beneficial to the de-noising process best. To ease the training procedure, we reconstruct the output through a residual mode rather than a straightforward prediction. Experimental results demonstrate that our proposed ADRN scheme outperforms the state-of-the-art methods both in quantitative and visual evaluations.
PaletteViz: A Visualization Method for Functional Understanding of High-Dimensional Pareto-Optimal Data-Sets to Aid Multi-Criteria Decision Making

To represent a many-objective Pareto-optimal front having four or more dimensions of the objective space, a large number of points are necessary. However, for choosing a single preferred point from a large set is problematic and time-consuming, as they provide a large cognitive burden on the part of the decision-makers (DMs). Hence, many-objective optimization and decision-making researchers and practitioners have been interested in effective visualization methods to filter down a few critical points for further analysis. While some ideas are borrowed from data analytics and visualization literature, they are generic and do not exploit the functionalities that DMs are usually interested. In this paper, we outline some such functionalities: a point's trade-off among conflicting objectives in its neighborhood, closeness of a point to the boundary or core of the high-dimensional Pareto set, specific desired geometric properties of points, spatial distance of one point to another, closeness of a point to constraint boundary, and others, in developing a new visualization technique. We propose a novel way to map a high-dimensional Pareto-optimal front (points or data-set) into two-and-half dimensions by revealing functional features of points that may be of great interest to DMs. As a proof-of-principle demonstration, we apply our proposed palette visualization (PaletteViz) technique to a number of different structures of Pareto-optimal data-sets and discuss how the proposed technique is different from a few popularly used visualization techniques.
Overlap Local-SGD: An Algorithmic Approach to Hide Communication Delays in Distributed SGD

Distributed stochastic gradient descent (SGD) is essential for scaling the machine learning algorithms to a large number of computing nodes. However, the infrastructures variability such as high communication delay or random node slowdown greatly impedes the performance of distributed SGD algorithm, especially in a wireless system or sensor networks. In this paper, we propose an algorithmic approach named Overlap Local-SGD (and its momentum variant) to overlap communication and computation so as to speedup the distributed training procedure. The approach can help to mitigate the straggler effects as well. We achieve this by adding an anchor model on each node. After multiple local updates, locally trained models will be pulled back towards the synchronized anchor model rather than communicating with others. Experimental results of training a deep neural network on CIFAR-10 dataset demonstrate the effectiveness of Overlap Local-SGD. We also provide a convergence guarantee for the proposed algorithm under non-convex objective functions.A full version of this paper with additional examples and proofs is accessible at: http://andrew.cmu.edu/user/gaurij/overlap_local_SGD.pdf.
Cooperative Learning VIA Federated Distillation OVER Fading Channels

Cooperative training methods for distributed machine learning are typically based on the exchange of local gradients or local model parameters. The latter approach is known as Federated Learning (FL). An alternative solution with reduced communication overhead, referred to as Federated Distillation (FD), was recently proposed that exchanges only averaged model outputs. While prior work studied implementations of FL over wireless fading channels, here we propose wireless protocols for FD and for an enhanced version thereof that leverages an offline communication phase to communicate "mixed-up" covariate vectors. The proposed implementations consist of different combinations of digital schemes based on separate source-channel coding and of over-the-air computing strategies based on analog joint source-channel coding. It is shown that the enhanced version FD has the potential to significantly outperform FL in the presence of limited spectral resources.
Accurate Object Detection System on HoloLens Using YOLO Algorithm

We demonstrate in our paper, an implementation on Microsoft HoloLens, deep learning supported in the context of object detection. The main aim of this system is to create the more accurate object detection model for Augmented Reality using communication between the deep learning processing and the Microsoft HoloLens as Input/Output device. This system aims to help the wearable device user to detect and to recognize between objects in real world. For the object detection approach, a deep learning model has been used for the implementation of this system called YOLO. This model is near to real-time and it supports to detect more than 9000 objects. Our system provides the annotation of augmented object detected and its limitation area or bounding box via HoloLens. It allows to detect the new position of moving object in a few milliseconds. Preliminary results show a great rate of object detection with a detection time comparable.
Aipnet: Generative Adversarial Pre-Training of Accent-Invariant Networks for End-To-End Speech Recognition

As one of the major sources in speech variability, accents have posed a grand challenge to the robustness of speech recognition systems. In this paper, our goal is to build a unified end-to-end speech recognition system that generalizes well across accents. For this purpose, we propose a novel pre-training framework AIPNetbased on generative adversarial nets (GAN) for accent-invariant representation learning: Accent Invariant Pre-training Networks. We pre-train AIPNetto disentangle accent-invariant and accent-specific characteristics from acoustic features through adversarial training on accented data for which transcriptions are not necessarily available. We further fine-tune AIPNetby connecting the accent-invariant module with an attention-based encoder-decoder model for multi-accent speech recognition. In the experiments, our approach is compared against four baselines including both accent-dependent and accent-independent models. Experimental results on 9 English accents show that the proposed approach outperforms all the baselines by 2.3 ~ 4.5% relative reduction on average WER when transcriptions are available in all accents and by 1.6 ~ 6.1% relative reduction when transcriptions are only available in US accent.
TCLiVi: Transmission Control in Live Video Streaming Based on Deep Reinforcement Learning

Currently, video content accounts for the majority of network traffic. With increased live streaming, rigorous requirements have been introduced for better Quality of Experience (QoE). It is challenging to meet satisfactory QoE in live streaming, where the aim is to achieve a balance between 1) enhancing the video quality and stability and 2) reducing the rebuffering time and end-to-end delay, under different scenarios with various network conditions and user preferences, where the fluctuation in the network throughput degrades the QoE severely. In this paper, we propose an approach to improve the QoE for live video streaming based on Deep Reinforcement Learning (DRL). The new approach jointly adjusts the streaming parameters, including the video bitrate and target buffer size. With the basic DRL framework, TCLiVi can automatically generate the inference model based on the playback information, to achieve the joint optimization of the video quality, stability, rebuffering time and latency parameters. We evaluate our framework on real-world data in different live streaming broadcast scenarios, such as a talent show and a sports competition under different network conditions. We compare TCLiVi with other algorithms, such as the Double DQN, MPC and Buffer-based algorithms. The simulation results show that TCLiVi significantly improves the video quality and decreases the rebuffering time, consequently increasing the QoE score by 40.84% in average. We also show that TCLiVi is self-adaptive in different scenarios.
PickCapsNet: Capsule Network for Automatic P-Wave Arrival Picking

Microseismic monitoring is an effective technique to ensure the safety of rock mass engineering. Moreover, P-wave arrival picking is crucial in the seismic/microseismic monitoring process. The existing methods of P-wave arrival picking are not fully qualified for practical application because they are mostly semiautomatic or need too much training data. To overcome the shortcoming of today's most elaborate methods, we leverage the recent advances in artificial intelligence and present PickCapsNet, a highly scalable capsule network for P-wave arrival picking from a single waveform without feature extraction. We apply the PickCapsNet to study the induced microseismic events in Dongguashan Copper Mine, China, and compare it with Akaike information criterion (AIC), short- and long-time average ratio (STA/LTA), and convolutional neural network (CNN). The differences between the PickCapsNet and manual picks have a mean value of 0.0023 s and a standard deviation of 0.0033 s; moreover, 97.46% of the picks are within 0.01 s of the manual pick. Furthermore, at different signal-to-noise ratios (SNRs), it has a higher accuracy and stability than other methods. These results indicate that the proposed method is of high picking precision and robustness.
Spectral-density estimation with the Gaussian integral transform

The spectral density operator $\hat{\rho}(\omega)=\delta(\omega-\hat{H})$ plays a central role in linear response theory as its expectation value, the dynamical response function, can be used to compute scattering cross-sections. In this work, we describe a near optimal quantum algorithm providing an approximation to the spectral density with energy resolution $\Delta$ and error $\epsilon$ using $\mathcal{O}\left(\sqrt{\log\left(1/\epsilon\right)\left(\log\left(1/\Delta\right)+\log\left(1/\epsilon\right)\right)}/\Delta\right)$ operations. This is achieved without using expensive approximations to the time-evolution operator but exploiting instead qubitization to implement an approximate Gaussian Integral Transform (GIT) of the spectral density. We also describe appropriate error metrics to assess the quality of spectral function approximations more generally.
The Automatic Detection of Speech Disorders in Children: Challenges, Opportunities, and Preliminary Results

Given the limited accessibility to Speech and Language Pathologists (SLPs) children in need often have, pediatric Computer-Aided Speech Therapy (CAST) tools can play an important role in the early diagnosis and treatment of speech disorders. However, various challenges impede the implementation of accurate automated analysis of speech disorders in children. In this article, we first discuss three key challenges in processing child disordered speech: 1) the unreliability of low-level annotation and scarcity of speech corpora, 2) speaker diarization of therapy sessions and 3) inaccurate children's acoustic models. We next explore opportunities to overcome some of these challenges. First, we investigate the effectiveness of high-level paralinguistic features in disordered speech detection to reduce the dependency on annotated data. A binary classifier trained using paralinguistic features extracted from both typically developing children and those suffering from Speech Sound Disorders (SSD) achieved 87% subject-level classification accuracy. Second, we tackle the speech disorder detection problem as an anomaly detection problem where models are trained merely on typically developing speech, reducing the need for disordered training data. A phoneme-level <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</i> 1 score of 0.77 was obtained from an anomaly detection-based system trained on speech attribute features to classify between typical and atypical phoneme pronunciations of children with speech disorder. Finally, we test the efficiency of an x-vector based speaker diarization technique in pediatric therapy sessions. The method successfully distinguished between therapist and child speech with a Diarization Error Rate (DER) of 10%.
Behavioral and Game-Theoretic Security Investments in Interdependent Systems Modeled by Attack Graphs

In this article, we consider a system consisting of multiple interdependent assets, and a set of defenders, each responsible for securing a subset of the assets against an attacker. The interdependencies between the assets are captured by an attack graph, where an edge from one asset to another indicates that if the former asset is compromised, an attack can be launched on the latter asset. Each edge has an associated probability of successful attack, which can be reduced via security investments by the defenders. In such scenarios, we investigate the security investments that arise under certain features of human decision making that have been identified in behavioral economics. In particular, humans have been shown to perceive probabilities in a nonlinear manner, typically overweighting low probabilities and underweighting high probabilities. We show that suboptimal investments can arise under such weighting in certain network topologies. We also show that pure strategy Nash equilibria exist in settings with multiple (behavioral) defenders, and study the inefficiency of the equilibrium investments by behavioral defenders compared to a centralized socially optimal solution.
Inferring Sentiments from Supervised Classification of Text and Speech cues using Fuzzy Rules

In last half decade an increasing number of works published has manifested the tremendous progress in multimodal sentiment analysis. In real-life communication, people are spontaneously modulating their tone to accentuate specific points or to express their sentiments. This research work introduces a supervised fuzzy rule-based system for multimodal sentiment classification, that can identify the sentiment expressed in video reviews on social media platform. It has been demonstrated that multimodal sentiment analysis can be effectively performed by the joint use of linguistic and acoustic modalities. In this paper computation of the sentiment using an ingenious set of fuzzy rules has been applied to label the review into: positive or negative sentiment. The confidence score from supervised Support Vector Machine (SVM) classification of text and speech cues is considered as the input variable for the fuzzy rules. The fusion of fuzzy logic with acoustic and linguistic features for classifying sentiment contributes a new exemplar in multimodal sentiment analysis. Our fuzzy approach has been compared with eight state-of-the-art techniques for supervised machine learning. The experiments on benchmark datasets yield 82.5 % accuracy for our approach which is higher in contrast to the state-of-the-art.
Mammography Image Quality Assurance Using Deep Learning

Objective: According to the European Reference Organization for Quality Assured Breast Cancer Screening and Diagnostic Services (EUREF) image quality in mammography is assessed by recording and analyzing a set of images of the CDMAM phantom. The EUREF procedure applies an automated analysis combining image registration, signal detection and nonlinear fitting. We present a proof of concept for an end-to-end deep learning framework that assesses image quality on the basis of single images as an alternative. Methods: Virtual mammography is used to generate a database with known ground truth for training a regression convolutional neural net (CNN). Training is carried out by continuously extending the training data and applying transfer learning. Results: The trained net is shown to correctly predict the image quality of simulated and real images. Specifically, image quality predictions on the basis of single images are of similar quality as those obtained by applying the EUREF procedure with 16 images. Our results suggest that the trained CNN generalizes well. Conclusion: Mammography image quality assessment can benefit from the proposed deep learning approach. Significance: Deep learning avoids cumbersome pre-processing and allows mammography image quality to be estimated reliably using single images.