Reviewerly/exHarmony huggingface dataset:
TensorFlow on State-of-the-Art HPC Clusters: A Machine Learning use Case

The recent rapid growth of the data-flow programming paradigm enabled the development of specific architectures, e.g., for machine learning.The most known example is the Tensor Processing Unit (TPU) by Google.Standard data-centers, however, still can not foresee large partitions dedicated to machine learning specific architectures.Within data-centers, the High-Performance Computing (HPC) clusters are highly parallel machines targeting a broad class of compute-intensive workflows, as such they can be used for tackling machine learning challenges.On top of this, HPC architectures are rapidly changing, including accelerators and instruction sets other than the classical x86 CPUs.In this blurry scenario, identifying which are the best hardware/software configurations to efficiently support machine learning workloads on HPC clusters is not trivial.In this paper, we considered the workflow of TensorFlow for image recognition.We highlight the strong dependency of the performance in the training phase on the availability of arithmetic libraries optimized for the underlying architecture.Following the example of Intel leveraging the MKL libraries for improving the TensorFlow performance, we plugged the Arm Performance Libraries into TensorFlow and tested on an HPC cluster based on Marvell ThunderX2 CPUs.Also, we performed a scalability study on three state-of-the-art HPC clusters based on different CPU architectures, x86 Intel Skylake, Arm-v8 Marvell ThunderX2, and PowerPC IBM Power9.
Efficient Multi-agent Cooperative Navigation in Unknown Environments with Interlaced Deep Reinforcement Learning

This work addresses a multi-agent cooperative navigation problem that multiple agents work together in an unknown environment in order to reach different targets without collision and minimize the maximum navigation time they spend. Typical reinforcement learning-based solutions directly model the cooperative navigation policy as a steering policy. However, when each agent does not know which target to head for, this method could prolong convergence time and reduce overall performance. To this end, we model the navigation policy as a combination of a dynamic target selection policy and a collision avoidance policy. Since these two policies are coupled, an interlaced deep reinforcement learning method is proposed to simultaneously learn them. Additionally, a reward function is directly derived from the optimization objective function instead of using a heuristic design method. Extensive experiments demonstrate that the proposed method can converge in a fast way and generate a more efficient navigation policy compared with the state-of-the-art.
Sleep Gesture Detection in Classroom Monitor System

This paper proposes a novel method to detect sleep persons in real classroom scenes, which is useful for detecting the attention of students. There are several challenges for sleep gesture detection, including occlusion, various sleep gestures, interference terms with similar features like writing, and small sleep gesture targets. To solve these challenges, we first build a sleep gesture dataset from hundreds of real classes among schools in Shanghai. Second, to detect sleep gestures we propose a modified R-FCN integrated with feature pyramid and deformable convolution. Moreover, we design an efficient local multiscale testing algorithm to address small sleep gesture detection. Experiments on our sleep dataset have shown that our approach significantly outperforms the basic R-FCN and reaches 0.74 AP@0.5. Especially, for small-size sleep gestures, our method gets an impressive improvement at 90% on our hard-samples dataset with just little additional time consumption. Besides, experiments on a large number of real video streams have proven our algorithm is applicable in real classroom scenes.
Outer approximating coherent lower probabilities with belief functions

From an epistemic point of view, coherent lower probabilities allow us to model the imprecise information about a partially unknown probability. However, there are some issues that hinder their use in practice. Since belief functions are easier to deal with, we propose to approximate the coherent lower probability by a belief function that is at the same time as close as possible to the initial coherent lower probability while not including additional information. We show that this problem can be tackled by means of linear programming, and investigate the features of the set of optimal solutions. Moreover, we emphasize the differences with the outer approximations by 2-monotone lower probabilities. We also study the problem for two particular cases of belief functions that are computationally easier to handle: necessity measures and probability boxes.
Security requirement prototyping with hyperledger composer for drug supply chain

Blockchain may have a potential to prove its value for the new US FDA regulatory requirements defined in the Drug Supply Chain Security Act (DSCSA) as innovative solutions are needed to support the highly complex pharmaceutical industry supply chain as it seeks to comply. In this paper, we examine how blockchain can be applied to meet with the security compliance requirement for the pharmaceutical supply chain. We explore the online playground of Hyperledger Composer, a set of tools for building blockchain business networks, to model the data and access control rules for the drug supply chain. Our experiment shows that this solution can provide a prototyping opportunity for compliance checking with certain limitations.
Readability and research impact

I ask whether the writing quality affects a research paper's impact. Using papers published in the American Economic Review between 2000 and 2009 I apply a standardized readability metric to score the quality of the writing of the introduction section of each paper. Using citation counts to measure research's impact, I show a statistically significant relationship between the two. The hardest to read papers suffer a reduction in citations by up to 15 of a standard deviation.
A novel approach for text summarization using optimal combination of sentence scoring methods

In this paper, a novel multi-document summarization scheme based on metaheuristic optimization is introduced that generates a summary by extracting salient and relevant sentences from a collection of documents. The proposed work generates optimal combinations of sentence scoring methods and their respective optimal weights to extract the sentences with the help of a metaheuristic approach known as teaching–learning-based optimization. In addition, the proposed scheme is compared to two summarization methods that use different metaheuristic approaches. The experimental results show the efficacy of the proposed summarization scheme.
A Vignetting Model for Light Field Cameras With an Application to Light Field Microscopy

In standard photography, vignetting is considered mainly as a radiometric effect because it results in a darkening of the edges of the captured image. In this paper, we demonstrate that for light held cameras, vignetting is more than just a radiometric effect. It modihes the properties of the acquired light held and renders most of the calibration procedures from the literature inadequate. We address the problem by describing a model and camera-agnostic method to evaluate vignetting in phase space. This enables the synthesis of vignetted pixel values, which applied to a range of pixels yield images corresponding to the white images that are customarily recorded for calibrating light held cameras. We show that the commonly assumed reference points for microlens-based systems are incorrect approximations to the true optical reference, i.e., the image of the center of the exit pupil. We introduce a novel calibration procedure to determine this optically correct reference point from experimental white images. We describe the changes vignetting imposes on the light held sampling patterns and, therefore, the optical properties of the corresponding virtual cameras using the equivalent camera array model [L. Mignard-Debise, J. Restrepo, and I. Ihrke, “A unifying first-order model for light-field cameras: The equivalent camera array,” IEEE Trans. Comput. Imag., vol. 3, no. 4, pp. 798-810, Dec. 2017] and apply these insights to a custom-built light held microscope.
A novel clustering algorithm based on the natural reverse nearest neighbor structure

Cluster analysis plays an important role in identifying the natural structure of the target dataset. It has been widely used in many fields, such as pattern recognition, machine learning, image segmentation, document clustering and so on. There are many different methods to conduct cluster analysis. Namely, most real datasets are non-spherical and have complex shapes. Although these methods are widely used to deal with clustering tasks, they are susceptible to noise and arbitrary shapes. Thus, we propose a novel clustering algorithm (called RNN-NSDC) in this paper, which is based on the natural reverse nearest neighbor structure. Firstly, we apply the reverse nearest neighbors in the algorithm to extract core objects. Secondly, our algorithm uses the neighbor structure information of core objects to cluster. And excluding noise effects, core sets can well represent the structure of clusters. Therefore, the RNN-NSDC can obtain the optimal cluster numbers for the datasets which contain clusters of outliers and arbitrary shapes. To verify the efficiency and accuracy of the RNN-NSDC, synthetic datasets and real datasets are used for experiments. The results indicate the superiority of the RNN-NSDC compared with K-means, DBSCAN, DPC, SNNDPC, DCore and NaNLORE.
Robust stylometric analysis and author attribution based on tones and rimes

Abstract In this article, we propose an innovative and robust approach to stylometric analysis without annotation and leveraging lexical and sub-lexical information. In particular, we propose to leverage the phonological information of tones and rimes in Mandarin Chinese automatically extracted from unannotated texts. The texts from different authors were represented by tones, tone motifs, and word length motifs as well as rimes and rime motifs. Support vector machines and random forests were used to establish the text classification model for authorship attribution. From the results of the experiments, we conclude that the combination of bigrams of rimes, word-final rimes, and segment -final rimes can discriminate the texts from different authors effectively when using random forests to establish the classification model. This robust approach can in principle be applied to other languages with established phonological inventory of onset and rimes.
Analyzing Uncertainties in Speech Recognition Using Dropout

The performance of Automatic Speech Recognition (ASR) systems is often measured using Word Error Rates (WER) which requires time-consuming and expensive manually transcribed data. In this paper, we use state-of-the-art ASR systems based on Deep Neural Networks (DNN) and propose a novel framework which uses "Dropout" at the test time to model uncertainty in prediction hypotheses. We systematically exploit this uncertainty to estimate WER without the need for explicit transcriptions. In addition, we show that the predictive uncertainty can also be used to accurately localize the errors made by the ASR system. We study the performance of our approach on Switchboard database where it predicts WER accurately within a range of 2.6% and 5.0% for HMM-DNN and Connectionist Temporal Classification (CTC) ASR systems, respectively.
Improving Layer Trajectory LSTM with Future Context Frames

In our recent work, we proposed a layer trajectory long short-term memory (ltLSTM) model which decouples the tasks of temporal modeling and senone classification with time-LSTMs and depth-LSTMs. The ltLSTM model achieved significant accuracy improvement over the traditional multi-layer LSTM models from our previous study. Considering the future context frames carrying valuable information for predicting the target label evidenced by the success of bi-directional LSTMs, in this work we investigate how to incorporate this kind of information with hidden vectors from either time-LSTM or depth-LSTM. Trained with 30 thousand hours of EN-US Microsoft internal data, the best ltLSTM model with future context frames can improve the baseline ltLSTM with up to 11.5% relative word error rate (WER) reduction and improve the baseline LSTM with up to 24.6% relative WER reduction across different tasks.
Epsilon-Near-Zero Grids for On-chip Quantum Networks

Abstract Realization of an on-chip quantum network is a major goal in the field of integrated quantum photonics. A typical network scalable on-chip demands optical integration of single photon sources, optical circuitry and detectors for routing and processing of quantum information. Current solutions either notoriously experience considerable decoherence or suffer from extended footprint dimensions limiting their on-chip scaling. Here we propose and numerically demonstrate a robust on-chip network based on an epsilon-near-zero (ENZ) material, whose dielectric function has the real part close to zero. We show that ENZ materials strongly protect quantum information against decoherence and losses during its propagation in the dense network. As an example, we model a feasible implementation of an ENZ network and demonstrate that information can be reliably sent across a titanium nitride grid with a coherence length of 434 nm, operating at room temperature, which is more than 40 times larger than state-of-the-art plasmonic analogs. Our results facilitate practical realization of large multi-node quantum photonic networks and circuits on-a-chip.
Bangla Handwritten Digit Recognition Using an Improved Deep Convolutional Neural Network Architecture

Deep Convolutional Neural Network has recently gained popularity because of its improved performance over the typical machine learning algorithms. However, it has been very rarely used on recognition of Bangla handwritten digit. This paper proposes a Deep Convolutional Neural Network (DCNN) based Bangla handwritten digits recognition scheme. The proposed method applies a seven layered D-CNN containing three convolution layers, three average pool layers and one fully connected layer for recognizing Bangla handwritten digits. Rigorous experimentation on a relatively large Bangla digit dataset namely, CMATERdb 3.1.1 provides considerable recognition accuracies.
RIWT and QR factorization based hybrid robust image steganography using block selection algorithm for IoT devices

The development of the Internet of Things (IoT) can be attributed to the sudden rise in miniature electronic devices, as well as their computing power and ability to make interconnections. These devices exchange large volumes of confidential information from diverse locations. Similar to the Intern et, the IoT has also encountered various issues with information security. Due to limited computing and energy resources in the field of IoT, it is necessary to develop a scheme to ensure feasible and more effective concealment and security properties. This paper proposes a unique methodology that captures an image using IoT sensors, which are subjected to lighter cryptographic operations for conversion into a cipher image, and is then sent to a home server. At the home server, a combined cryptography and steganography approach is employed to conceal the cipher image in a cover image, camouflaging the presence of the secret image, which is then sent to the IoT-Cloud server for storage. During the embedding process, QR decomposition is performed on the RIWT transformed secret image and RIWT - DCT transformed cover image. Modification performed on the R matrix of QR decomposition does not affect the structural properties of the cover image. A block selection algorithm is used to select optimal blocks with high contrast areas to embed the secret image. The experimental results indicate that our scheme enhances imperceptibility, robustness, and resistance to steganalysis attacks.
Post-editing of machine translation

This chapter analyses the evolutionary process that human post-editing of Machine Translation (MT) output has undergone in previous years. It is well known that post-editing can improve translating productivity as well as target-text quality relative to translation carried out ‘from scratch’, but post-editing has many facets. Initially, it was akin to a step in the MT development pipeline where humans tidied the machine output to make it usable. More recently, post-editing is a professional service with its own international standard. In addition, the focus of translation tasks involving MT is shifting towards more integrated and human-centred environments. The chapter reviews previous post-editing research and discusses emerging issues and future directions on the use of MT in professional translation. It outlines how post-editing has matured as a practice and service and adopts an encompassing understanding of post-editing tasks that reflects MT’s changing role in professional translation processes.
A Deep Neural Network Based End to End Model for Joint Height and Age Estimation from Short Duration Speech

Automatic height and age prediction of a speaker has a wide variety of applications in speaker profiling, forensics etc. Often in such applications only a few seconds of speech data is available to reliably estimate the speaker parameters. Traditionally, age and height were predicted separately using different estimation algorithms. In this work, we propose a unified DNN architecture to predict both height and age of a speaker for short durations of speech. A novel initialization scheme for the deep neural architecture is introduced, that avoids the requirement for a large training dataset. We evaluate the system on TIMIT dataset where the mean duration of speech segments is around 2.5s. The DNN system is able to improve the age RMSE by at least 0.6 years as compared to a conventional support vector regression system trained on Gaussian Mixture Model mean supervectors. The system achieves an RMSE error of 6.85 and 6.29 cm for male and female height prediction. In case of age estimation, the RMSE errors are 7.60 and 8.63 years for male and female respectively. Analysis of shorter speech segments reveals that even with 1 second speech input the performance degradation is at most 3% compared to the full duration speech files.
A steganography based on orthogonal moments

The increase in data transmission volume and repository size have increased the need to secure and protecting a message such that it does not draw attention. in this paper, a new steganography algorithm in the transform domain of images based on orthogonal polynomials (OPs) is presented. The algorithms get benefits from the low energy moments, where the message is embedded. First, the proposed algorithm transforms the cover image into the transform domain using squared form of Krawtchouk-Tchebichef polynomials. After that, the low energy moments (transform coefficients) is substituted by the message data. Finally, the transform domain coefficients are transformed into the spatial domain to obtain the stego-image. Experimental results show that the proposed algorithm is competent in embedding information compared to other methods in terms of message size and image quality.
Multi-CubeSat Relative Position and Attitude Determination Based on Array Signal Detection in Formation Flying

High-precision relative position and attitude measurement is crucial for consensus control and collision avoidance in multi-CubeSat formation flying. However, the traditional relative navigation systems comprise many sensors and are not suitable for CubeSats due to large volume, complexity, and cost. In this paper, we propose a new approach, called Multi-CubeSat relative State determination by Array Signal detection (MUSAS). The approach utilizes the existing communication systems and antenna arrays on CubeSats without the need of extra components. In MUSAS, deputy vehicle (DV) CubeSats in a formation broadcast orthogonal spread spectrum signals simultaneously. Two chief vehicle (CV) CubeSats receive and separate the signals and extract the multiple-input multiple-output channel response of each DV CubeSat. Then, by utilizing the bi-directional spatial spectrum estimation, the angles-of-arrival and angles-of-departure of the propagation paths from each DV CubeSat to the CV CubeSats are estimated. Finally, the attitudes and positions of all DV CubeSats relative to the CV CubeSats are determined using the derived rotation matrices. We have theoretically proved the proposed MUSAS algorithm and performed extensive simulations to compare its performance with existing methods. Furthermore, we also developed the testbed of MUSAS and conducted field experiments. The simulation and experiment results have verified that, by exploiting the spread spectrum gain and antenna array gain, MUSAS can achieve high accuracy in relative state determination, even using small antenna arrays and low transmission power.
Autonomous waypoints planning and trajectory generation for multi-rotor UAVs

Autonomous trajectory generation in a complex environment is a challenging task for multi-rotor unmanned aerial vehicles (UAVs), which have high maneuverability in three-dimensional motion. Safe and effective operations for these UAVs demand obstacle avoidance strategies and advanced trajectory planning and control schemes for stability and energy efficiency. To solve those problems in one framework analytically is extremely challenging when the UAV needs to fly large distance in a complex environment. To address this challenge, a two-level optimization strategy is adopted. At the higher-level, a sequence of waypoints is selected that lead the UAV from its current position to the destination. At the lower-level, an optimal trajectory is generated between each pair of adjacent waypoints analytically. While the goal of trajectory generation is to maintain the stability of the UAV, the goal of the waypoints planning is to select waypoints with the lowest control thrust consumption throughout the entire trip while avoiding collisions with obstacles. The entire framework is implemented using deep reinforcement learning, which learns the highly complicated and non-linear interaction between those two levels, and the impact from the environment. A progressive learning strategy is investigated that not only reduces convergence time but also improves result quality. We further investigate and provide results regarding the tuning of gains in the optimal trajectory scheme using genetic algorithm. The experimental results demonstrate that our proposed approach is able to generate a list of obstacle-free waypoints with minimum control energy and develop an optimal trajectory with optimized platform velocity, acceleration, jerk and control thrust.