Reviewerly/exHarmony huggingface dataset:
Distribution-Preserving Steganography Based on Text-to-Speech Generative Models

Steganography is the art and science of hiding secret messages in public communication so that the presence of secret messages cannot be detected. There are two distribution-preserving steganographic frameworks, one is sampler-based and the other is compression-based. The former requires a perfect sampler which yields data following the same distribution, and the latter needs the explicit distribution of generative objects. However, these two conditions are too strict even unrealistic in the traditional data environment, e.g., the distribution of natural images is hard to seize. Fortunately, generative models bring new vitality to distribution-preserving steganography, which can serve as the perfect sampler or provide the explicit distribution of generative media. Taking text-to-speech generation task as an example, we propose distribution-preserving steganography based on WaveGlow and WaveRNN, which corresponds to the former two categories. Steganalysis experiments and theoretical analysis are conducted to demonstrate that the proposed methods can preserve the distribution.
Deep learning approaches for human-centered IoT applications in smart indoor environments: a contemporary survey

The widespread Internet of Things (IoT) technologies in day life indoor environments result in an enormous amount of daily generated data, which require reliable data analysis techniques to enable efficient exploitation of this data. The recent developments in deep learning (DL) have facilitated the processing and learning from the massive IoT data and learn essential features swiftly and professionally for a variety of IoT applications on smart indoor environments. This study surveys the recent literature on exploiting DL for different indoor IoT applications. We aim to give insights into how the DL approaches can be employed from various viewpoints to develop improved Indoor IoT applications in two distinct domains: indoor positioning/tracking and activity recognition. A primary target is to effortlessly amalgamate the two disciplines of IoT and DL, resultant in a broad range of innovative strategies in indoor IoT applications, such as health monitoring, smart home control, robotics, etc. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three beforementioned domains. Eventually, we proposed and discussed a set of matters, challenges, and some new directions in incorporating DL to improve the efficiency of indoor IoT applications, encouraging and stimulating additional advances in this auspicious research area.
Inversion Study of Heavy Metals in Soils of Potentially Polluted Sites Based on UAV Hyperspectral Data and Machine Learning Algorithms

We established a risk screening and grading model and a content estimation model for zinc pollution in bare soil. We built these models using the machine learning algorithms Support Vector Machine (SVM), Generalized Linear Model (GLM), Multivariate Adaptive Regression Spline (Mars), Random Forest (RF), XGBoost, Ridge Regression (Ridge), and Cubist based on UAV hyperspectral data and heavy metal field fast detection data in typical potentially contaminated sites. The parameters of the hyperspectral data were original reflectivity, smoothed reflectivity, first-order derivative, second-order derivative, and the de-enveloping spectrum. The results showed that to classify soil zinc pollution risk, the machine learning model based on the second-order derivative spectrum performed better than the other hyperspectral parameter independent variables. The overall classification accuracy of the MARS model based on the second-order derivative spectrum was 89.29%. The XGBoost model based on the second-order derivative spectrum performed the best in estimating zinc content, with results of R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.59. When the zinc (Zn) content was less than 1000mg/kg, the model accuracy was stable. This method doesn't rely on soil samples, and thus avoids uncertainty caused by the selection of sensitive bands in heavy metal inversion. This method provides a basis for large-scale fast investigation of soil heavy metal pollution based on limited ground monitoring point data.
Information and communication activity of students when writing a course work on linguistics

The experience of using information space in the process of students’ work over a course paper in linguistics has been examined in the article. The importance of information and communication technologies in this type of educational activity is determined. There are three levels of students’ information and communication activities depending on the development of the students’ information competence and the involvement of information and communication technologies as the main and auxiliary resources for writing a course paper. Adaptive, productive and creative levels of information and communication activities are highlighted. The type of the above-mentioned student actively engages various online platforms for surveys, classifications and processing of the source base of research, etc. It is stressed that information resources are mostly used at the preparatory stage and less in the final one. The need to create a single information base of linguistic resources has been emphasized.
Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labels

Deep learning increasingly accelerates biomedical research, deploying neural networks for multiple tasks, such as image classification, object detection, and semantic segmentation. However, neural networks are commonly trained supervised on large-scale, labeled datasets. These prerequisites raise issues in biomedical image recognition, as datasets are generally small-scale, challenging to obtain, expensive to label, and frequently heterogeneously labeled. Furthermore, heterogeneous labels are a challenge for supervised methods. If not all classes are labeled for an individual sample, supervised deep learning approaches can only learn on a subset of the dataset with common labels for each individual sample; consequently, biomedical image recognition engineers need to be frugal concerning their label and ground truth requirements. This paper discusses the effects of frugal labeling and proposes to train neural networks for multi-class semantic segmentation on heterogeneously labeled data based on a novel objective function. The objective function combines a class asymmetric loss with the Dice loss. The approach is demonstrated for training on the sparse ground truth of a heterogeneous labeled dataset, training within a transfer learning setting, and the use-case of merging multiple heterogeneously labeled datasets. For this purpose, a biomedical small-scale, multi-class semantic segmentation dataset is utilized. The heartSeg dataset is based on the medaka fish’s position as a cardiac model system. Automating image recognition and semantic segmentation enables high-throughput experiments and is essential for biomedical research. Our approach and analysis show competitive results in supervised training regimes and encourage frugal labeling within biomedical image recognition.
Best Practices for the Implementation of Home-based, Hands-on Lab Activities to Effectively Engage STEM Students During a Pandemic

He has over eighteen years of experience in practicing, teaching and research in civil engineering.His academic background and professional skills allows him to teach a range of courses across three different departments in the school of engineering.This is a rare and uncommon achievement.Within his short time at Morgan, he has
RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection

Unsupervised anomaly detection (AD) plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interest in applying deep neural networks (DNNs) to AD problems. A common approach is using autoencoders to learn a feature representation for the normal observations in the data. The reconstruction error of the autoencoder is then used as outlier score to detect the anomalies. However, due to the high complexity brought upon by over-parameterization of DNNs, the reconstruction error of the anomalies could also be small, which hampers the effectiveness of these methods. To alleviate this problem, we propose a robust framework using collaborative autoencoders to jointly identify normal observations from the data while learning its feature representation. We investigate the theoretical properties of the framework and empirically show its outstanding performance as compared to other DNN-based methods. Empirical results also show resiliency of the framework to missing values compared to other baseline methods.
Madrid Quantum Communication Infrastructure: a testbed for assessing QKD technologies into real production networks

We report on the Madrid Quantum Communication Infrastructure, which demonstrates that a telecommunications network can also host quantum communications in a unified, logical and physical infrastructure. Using new Quantum Key Distribution systems paired with modern networking paradigms, we demonstrate a high technology readiness level of QKD installing the network in production facilities and running relevant use cases.
HG-News: News Headline Generation Based on a Generative Pre-Training Model

Neural headline generation models have recently shown great results since neural network methods have been applied to text summarization.In this paper, we focus on news headline generation.We propose a news headline generation model based on a generative pre-training model.In our model, we propose a rich features input module.The headline generation model we propose only contains a decoder incorporating the pointer mechanism and the n-gram language features, while other generation models use the encoder-decoder architecture.Experiments on news datasets show that our model achieves comparable results in the field of news headline generation.
Sequences with almost Poissonian pair correlations

Although a generic uniformly distributed sequence has Poissonian pair correlations, only one explicit example has been found up to now. Additionally, it is even known that many classes of uniformly distributed sequences, like van der Corput sequences, Kronecker sequences and LS sequences, do not have Poissonian pair correlations. In this paper, we show that van der Corput sequences and the Kronecker sequence for the golden mean are as close to having Poissonian pair correlations as possible: they both have α-pair correlations for all 0<α<1 but not for α=1 which corresponds to Poissonian pair correlations.
A Fuzzy Sliding-Mode Control Based on Z-Numbers and LAMDA

Z-Numbers is a recent concept related to fuzzy logic where the restriction and reliability criteria are characterized as fuzzy sets. Due to the potential of Z-numbers, this paper presents the development of a fuzzy controller that combines the fundamentals of LAMDA (Learning Algorithm for Multivariate Data Analysis) with the concepts of the Total Utility of Z-numbers, to establish an inference method to improve the performance in a control system. The controller uses criteria from the sliding mode control (SMC) and the Lyapunov concepts to guarantee robustness and stability respectively. The LAMDA method is applied to compute a chattering-free control action which is applied to systems with model uncertainties and variable dynamics. The fuzzy controller has been tested by simulation in two different tasks: 1) Control of a process that consists of a mixing tank with variable dynamics, and 2) Trajectory tracking of a mobile robot. The proposed approach provides suitable results at runtime and outperforms the results of the other tested controllers in terms of performance, minimizing the deviation between the current system output and the reference. Finally, a complexity analysis is presented to evaluate the feasibility in the implementation of the proposal. The obtained results prove the suitability of using a LAMDA Z-number-based controller in the tested systems.
Efficient tree-traversals: reconciling parallelism and dense data representations

Recent work showed that compiling functional programs to use dense, serialized memory representations for recursive algebraic datatypes can yield significant constant-factor speedups for sequential programs. But serializing data in a maximally dense format consequently serializes the processing of that data, yielding a tension between density and parallelism. This paper shows that a disciplined, practical compromise is possible. We present Parallel Gibbon, a compiler that obtains the benefits of dense data formats and parallelism. We formalize the semantics of the parallel location calculus underpinning this novel implementation strategy, and show that it is type-safe. Parallel Gibbon exceeds the parallel performance of existing compilers for purely functional programs that use recursive algebraic datatypes, including, notably, abstract-syntax-tree traversals as in compilers.
A Codesigned Photonic Electronic MAC Neuron with ADC-Embedded Nonlinearity

We present a reduced-precision integrated photonic electronic multiply-accumulate (MAC) neuron with ADC-embedded nonlinearity. The proposed device trades off speed with resolution, outperforming both analog and digital electronic solutions in terms of speed and energy consumption.
Identifying casualty changes in software patches

Noise in software patches impacts their understanding, analysis, and use for tasks such as change prediction. Although several approaches have been developed to identify noise in patches, this issue has persisted. An analysis of a dataset of security patches for the Tomcat web server, which we further expanded with security patches from five additional systems, uncovered several kinds of previously unreported noise which we call nonessential casualty changes. These are changes that themselves do not alter the logic of the program but are necessitated by other changes made in the patch. In this paper, we provide a comprehensive taxonomy of casualty changes. We then develop CasCADe, an automated technique for automatically identifying casualty changes. We evaluate CasCADe with several publicly available datasets of patches and tools that focus on them. Our results show that CasCADe is highly accurate, that the kinds of noise it identifies occur relatively commonly in patches, and that removing this noise improves upon the evaluation results of a previously published change-based approach.
Speech Activity Detection Based on Multilingual Speech Recognition System

To better model the contextual information and increase the generalization ability of Speech Activity Detection (SAD) system, this paper leverages a multi-lingual Automatic Speech Recognition (ASR) system to perform SAD. Sequence discriminative training of Acoustic Model (AM) using Lattice-Free Maximum Mutual Information (LF-MMI) loss function, effectively extracts the contextual information of the input acoustic frame. Multi-lingual AM training, causes the robustness to noise and language variabilities. The index of maximum output posterior is considered as a frame-level speech/non-speech decision function. Majority voting and logistic regression are applied to fuse the language-dependent decisions. The multi-lingual ASR is trained on 18 languages of BABEL datasets and the built SAD is evaluated on 3 different languages. On out-of-domain datasets, the proposed SAD model shows significantly better performance with respect to baseline models. On the Ester2 dataset, without using any in-domain data, this model outperforms the WebRTC, phoneme recognizer based VAD (Phn Rec), and Pyannote baselines (respectively by 7.1, 1.7, and 2.7% absolute) in Detection Error Rate (DetER) metrics. Similarly, on the LiveATC dataset, this model outperforms the WebRTC, Phn Rec, and Pyannote baselines (respectively by 6.4, 10.0, and 3.7% absolutely) in DetER metrics.
On the continuous time limit of ensemble square root filters

We provide a continuous time limit analysis for the class of Ensemble Square Root Filter algorithms with deterministic model perturbations. In the particular linear case, we specify general conditions on the model perturbations implying convergence of the empirical mean and covariance matrix towards their respective counterparts of the Kalman-Bucy Filter. As a second main result we identify additional assumptions for the convergence of the whole ensemble towards solutions of the Ensemble Kalman-Bucy filtering equations introduced in [1]. The latter result can be generalized to nonlinear Lipschitz-continuous model operators. A striking implication of our results is the fact that the limiting equations for the ensemble members are universal for a large class of Ensemble Square Root Filters. This yields a mathematically rigorous justification for the analysis of these algorithms with the help of the Ensemble Kalman-Bucy Filter. [1] de Wiljes, Jana, Reich, Sebastian, Stannat, Wilhelm, Long-Time Stability and Accuracy of the Ensemble Kalman-Bucy Filter for Fully Observed Processes and Small Measurement Noise. SIAM Journal on Applied Dynamical Systems, Vol. 17, No. 2, 1152-1181, 2018
Efficient Indexing of 3D Human Motions

Digitization of human motion using 2D or 3D skeleton representations offers exciting possibilities for many applications but, at the same time, requires scalable content-based retrieval techniques to make such data reusable. Although a lot of research effort focuses on extracting content-preserving motion features, there is a lack of techniques that support efficient similarity search on a large scale. In this paper, we introduce a new indexing scheme for organizing large collections of spatio-temporal skeleton sequences. Specifically, we apply the motion-word concept to transform skeleton sequences into structured text-like motion documents, and index such documents using an extended inverted-file approach. Over this index, we design a new similarity search algorithm that exploits the properties of the motion-word representation and provides efficient retrieval with a variable level of approximation, possibly reaching constant search costs disregarding the collection size. Experimental results confirm the usefulness of the proposed approach.
Investigation of Branch Accessibility with a Robotic Pruner for Pruning Apple Trees

Highlights A branch accessibility simulation was performed for robotic pruning of apple trees. A virtual tree environment was established using a kinematic manipulator model and an obstacle model. Rapidly-exploring random tree (RRT) was combined with smoothing and optimization for improved path planning. Effects on RRT path planning of the approach angle of the end-effector and cutter orientation at the target were studied. Abstract . Robotic pruning is a potential solution to reduce orchard labor and associated costs. Collision-free path planning of the manipulator is essential for successful robotic pruning. This simulation study investigated the collision-free branch accessibility of a six rotational (6R) degrees of freedom (DoF) robotic manipulator with a shear cutter end-effector. A virtual environment with a simplified tall spindle tree canopy was established in MATLAB. An obstacle-avoidance algorithm, rapidly-exploring random tree (RRT), was implemented for establishing collision-free paths to reach the target pruning points. In addition, path smoothing and optimization algorithms were used to reduce the path length and calculate the optimized path. Two series of simulations were conducted: (1) performance and comparison of the RRT algorithm with and without smoothing and optimization, and (2) performance of collision-free path planning considering different approach poses of the end-effector relative to the target branch. The simulations showed that the RRT algorithm successfully avoided obstacles and allowed the manipulator to reach the target point with 23 s average path finding time. The RRT path length was reduced by about 28% with smoothing and by 25% with optimization. The RRT smoothing algorithm generated the shortest path lengths but required about 1 to 3 s of additional computation time. The lowest coefficient of variation and standard deviation values were found for the optimization method, which confirmed the repeatability of the method. Considering the different end-effector approach poses, the simulations suggested that successfully finding a collision-free path was possible for branches with no existing path using the ideal (perpendicular cutter) approach pose. This study provides a foundation for future work on the development of robotic pruning systems. Keywords: Agricultural robotics, Collision-free path, Manipulator, Path planning, Robotic pruning, Virtual tree environment.
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding

Word Error Rate (WER) has been the predominant metric used to evaluate the performance of automatic speech recognition (ASR) systems. However, WER is sometimes not a good indicator for downstream Natural Language Understanding (NLU) tasks, such as intent recognition, slot filling, and semantic parsing in task-oriented dialog systems. This is because WER takes into consideration only literal correctness instead of semantic correctness, the latter of which is typically more important for these downstream tasks. In this study, we propose a novel Semantic Distance (SemDist) measure as an alternative evaluation metric for ASR systems to address this issue. We define SemDist as the distance between a reference and hypothesis pair in a sentence-level embedding space. To represent the reference and hypothesis as a sentence embedding, we exploit RoBERTa, a state-of-the-art pre-trained deep contextualized language model based on the transformer architecture. We demonstrate the effectiveness of our proposed metric on various downstream tasks, including intent recognition, semantic parsing, and named entity recognition.
Predicting humans future motion trajectories in video streams using generative adversarial network

Understanding the behavior of human motion in social environments is important for various domains of a smart city, e.g, smart transportation, automatic navigation of service robots, efficient navigation of autonomous cars and surveillance systems. Examining past trajectories or environmental factors alone are not enough to address this problem. We propose a novel methodology to predict future motion trajectories of humans based on past attitude of individuals, crowd attitude and environmental context. Many researchers have proposed different techniques based on different features extraction and features fusion to predict the future motion trajectory. They used traditional machine learning algorithms like SVM,social forces, probabilistic models and LSTM to analyze the heuristic motion trajectories but they didn’t consider the other environmental factors e.g relative positions of other humans present in environment and positions of objects present in environment which can affect the motion trajectories of humans. We intend to achieve this goal by employing Long Short Term Memory(LSTM) units to analyze motion histories, convolution neural networks to environmental facts e.g. human-human, human-object interaction and relative positioning of 80 different objects including pedestrians and generative adversarial networks(GANs) to predict possible future motion paths. Our proposed method achieved 70% lower Average Displacement Error(ADE) and 41% lower Final Displacement Error(FDE) in comparison to other state of the art techniques.