Reviewerly/exHarmony huggingface dataset:
Bunched LPCNet: Vocoder for Low-Cost Neural Text-To-Speech Systems

LPCNet is an efficient vocoder that combines linear prediction and deep neural network modules to keep the computational complexity low. In this work, we present two techniques to further reduce it's complexity, aiming for a low-cost LPCNet vocoder-based neural Text-to-Speech (TTS) System. These techniques are: 1) Sample-bunching, which allows LPCNet to generate more than one audio sample per inference; and 2) Bit-bunching, which reduces the computations in the final layer of LPCNet. With the proposed bunching techniques, LPCNet, in conjunction with a Deep Convolutional TTS (DCTTS) acoustic model, shows a 2.19x improvement over the baseline run-time when running on a mobile device, with a less than 0.1 decrease in TTS mean opinion score (MOS).
Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.
Attribute Prototype Network for Zero-Shot Learning

From the beginning of zero-shot learning research, visual attributes have been shown to play an important role. In order to better transfer attribute-based knowledge from known to unknown classes, we argue that an image representation with integrated attribute localization ability would be beneficial for zero-shot learning. To this end, we propose a novel zero-shot representation learning framework that jointly learns discriminative global and local features using only class-level attributes. While a visual-semantic embedding layer learns global features, local features are learned through an attribute prototype network that simultaneously regresses and decorrelates attributes from intermediate features. We show that our locality augmented image representations achieve a new state-of-the-art on three zero-shot learning benchmarks. As an additional benefit, our model points to the visual evidence of the attributes in an image, e.g. for the CUB dataset, confirming the improved attribute localization ability of our image representation.
Agile ways of working: A team maturity perspective

Abstract With the agile approach to managing software development projects comes an increased dependability on well‐functioning teams, since many of the practices are built on teamwork. The objective of this study was to investigate if, and how, team development from a group psychological perspective is related to some work practices of agile teams. Data were collected from 34 agile teams (200 individuals) from six software development organizations and one university in both Brazil and Sweden using the Group Development Questionnaire (Scale IV) and the Perceptive Agile Measurement (PAM). The result indicates a strong correlation between levels of group maturity and the two agile practices iterative development and retrospectives . We, therefore, conclude that agile teams at different group development stages adopt parts of team agility differently, thus confirming previous studies but with more data and by investigating concrete and applied agile practices. We thereby add evidence to the hypothesis that an agile implementation and management of agile projects need to be adapted to the group maturity levels of the agile teams.
Limitations of the Recall Capabilities in Delay-Based Reservoir Computing Systems

Abstract We analyse the memory capacity of a delay-based reservoir computer with a Hopf normal form as nonlinearity and numerically compute the linear as well as the higher order recall capabilities. A possible physical realization could be a laser with external cavity, for which the information is fed via electrical injection. A task-independent quantification of the computational capability of the reservoir system is done via a complete orthonormal set of basis functions. Our results suggest that even for constant readout dimension the total memory capacity is dependent on the ratio between the information input period, also called the clock cycle, and the time delay in the system. Optimal performance is found for a time delay about 1.6 times the clock cycle.
Local Obfuscation Mechanisms for Hiding Probability Distributions

We introduce a formal model for the information leakage of probability distributions and define a notion called distribution privacy as the local differential privacy for probability distributions. Roughly, the distribution privacy of a local obfuscation mechanism means that the attacker cannot significantly gain any information on the distribution of the mechanism’s input by observing its output. Then we show that existing local mechanisms can hide input distributions in terms of distribution privacy, while deteriorating the utility by adding too much noise. For example, we prove that the Laplace mechanism needs to add a large amount of noise proportionally to the infinite Wasserstein distance between the two distributions we want to make indistinguishable. To improve the tradeoff between distribution privacy and utility, we introduce a local obfuscation mechanism, called a tupling mechanism, that adds random dummy data to the output. Then we apply this mechanism to the protection of user attributes in location based services. By experiments, we demonstrate that the tupling mechanism outperforms popular local mechanisms in terms of attribute obfuscation and service quality.
DeepPhasePick: A method for detecting and picking seismic phases from local earthquakes based on highly optimized convolutional and recurrent deep neural networks

Summary Seismic phase detection, identification and first-onset picking are basic but essential routines to analyse earthquake data. As both the number of seismic stations, globally and regionally, and the number of experiments greatly increase due to ever greater availability of instrumentation, automated data processing becomes more and more essential. E.g., for modern seismic experiments involving 100s to even 1,000s instruments, conventional human analyst-based identification and picking of seismic phases is becoming unfeasible, and the introduction of automatic algorithms mandatory. In this paper, we introduce DeepPhasePick, an automatic two-stage method that detects and picks P and S seismic phases from local earthquakes. The method is entirely based on highly optimized deep neural networks, consisting of a first stage that detects the phases using a convolutional neural network, and a second stage that uses two recurrent neural networks to pick both phases. Detection is performed on three-component seismograms. P- and S-picking is then conducted on the vertical and the two-horizontal components, respectively. Systematic hyperparameter optimization was applied to select the best model architectures and to define both the filter applied to preprocess the seismic data as well as the characteristics of the window sample used to feed the models. We trained DeepPhasePick using seismic records extracted from two sets of manually-picked event waveforms originating from northern Chile (∼39,000 records for detection and ∼36,000 records for picking). In different tectonic regimes, DeepPhasePick demonstrated the ability to both detect P and S phases from local earthquakes with high accuracy, as well as predict P- and S-phase time onsets with an analyst level of precision. DeepPhasePick additionally computes onset uncertainties based on the Monte Carlo Dropout technique as an approximation of Bayesian inference. This information can then further feed an associator algorithm in an earthquake location procedure.
Smartphone Sensor-Based Human Activity Recognition Robust to Different Sampling Rates

There is a research field of human activity recognition that automatically recognizes a user's physical activity through sensing technology incorporated in smartphones and other devices. When sensing daily activity, various measurement conditions, such as device type, possession method, wearing method, and measurement application, are often different depending on the user and the date of the measurement. Models that predict activity from sensor values are often implemented by machine learning and are trained using a large amount of activity-labeled sensor data measured from many users who provide labeled sensor data. However, collecting activity-labeled sensor data using each user's individual smartphones causes data being measured in inconsistent environments that may degrade the estimation accuracy of machine learning. In this study, I propose an activity recognition method that is robust to different sampling rates -- even in the measurement environment. The proposed method applies an adversarial network and data augmentation by downsampling to a common activity recognition model to achieve the acquisition of feature representations that make the sampling rate unspecifiable. Using the Human Activity Sensing Consortium (HASC), which is a dataset of basic activity recognition using smartphone sensors, I conducted an evaluation experiment to simulate an environment in which various sampling rates were measured. As a result, I found that estimation accuracy was reduced by the conventional method in the above environment and could be improved by my proposed method.
Human Perception-Optimized Planning for Comfortable VR-Based Telepresence

This paper introduces an emerging motion planning problem by considering a human that is immersed into the viewing perspective of a remote robot. The challenge is to make the experience both effective (such as delivering a sense of presence) and comfortable (such as avoiding adverse sickness symptoms, including nausea). We refer to this challenging new area as human perception-optimized planning and propose a general multiobjective optimization framework that can be instantiated in many envisioned scenarios. We then consider a specific VR telepresence task as a case of human perception-optimized planning, in which we simulate a robot that sends 360 video to a remote user to be viewed through a head-mounted display. In this particular task, we plan trajectories that minimize VR sickness (and thereby maximize comfort). An A* type method is used to create a Pareto-optimal collection of piecewise linear trajectories while taking into account criteria that improve comfort. We conducted a study with human subjects touring a virtual museum, in which paths computed by our algorithm are compared against a reference RRT-based trajectory. Generally, users suffered less from VR sickness and preferred the paths created by the presented algorithm.
Cyber Security Incident Handling, Warning and Response System for the European Critical Information Infrastructures (CyberSANE)

This paper aims to enhance the security and resilience of Critical Information Infrastructures (CIIs) by providing a dynamic collaborative, warning and response system (CyberSANE system) supporting and guiding security officers and operators (e.g. Incident Response professionals) to recognize, identify, dynamically analyse, forecast, treat and respond to their threats and risks and handle their daily cyber incidents. The proposed solution provides a first of a kind approach for handling cyber security incidents in the digital environments with highly interconnected, complex and diverse nature.
Cryptanalysis of a system based on twisted Reed–Solomon codes

Twisted Reed–Solomon (TRS) codes are a family of codes that contains a large number of maximum distance separable codes that are non-equivalent to Reed–Solomon codes. TRS codes were recently proposed as an alternative to Goppa codes for the McEliece code-based cryptosystem, resulting in a potential reduction of key sizes. The use of TRS codes in the McEliece cryptosystem has been motivated by the fact that a large subfamily of TRS codes is resilient to a direct use of known algebraic key-recovery methods. In this paper, an efficient key-recovery attack on the TRS variant that was used in the McEliece cryptosystem is presented. The algorithm exploits a new approach based on recovering the structure of a well-chosen subfield subcode of the public code. It is proved that the attack always succeeds and breaks the system for all practical parameters in $$O(n^4)$$ field operations. A software implementation of the algorithm retrieves a valid private key from the public key within a few minutes, for parameters claiming a security level of 128 bits. The success of the attack also indicates that, contrary to common beliefs, subfield subcodes of the public code need to be precisely analyzed when proposing a McEliece-type code-based cryptosystem. Finally, the paper discusses an attempt to repair the scheme and a modification of the attack aiming at Gabidulin–Paramonov–Tretjakov cryptosystems based on twisted Gabidulin codes.
Scrambling ability of quantum neural network architectures

This work proposes a quantity to characterize the information scrambling ability of different architectures of quantum neural networks and shows that it is positively correlated with the learning efficiency of the quantum neural network.
Overview and Insights from the Shared Tasks at Scholarly Document Processing 2020: CL-SciSumm, LaySumm and LongSumm

We present the results of three Shared Tasks held at the Scholarly Document Processing Workshop at EMNLP2020: CL-SciSumm, LaySumm and LongSumm.We report on each of the tasks, which received 18 submissions in total, with some submissions addressing two or three of the tasks.In summary, the quality and quantity of the submissions show that there is ample interest in scholarly document summarization, and the state of the art in this domain is at a midway point between being an impossible task and one that is fully resolved.
Semantic Role Labeling with Associated Memory Network

Semantic role labeling (SRL) is a task to recognize all the predicate-argument pairs of a sentence, which has been in a performance improvement bottleneck after a series of latest works were presented.This paper proposes a novel syntax-agnostic SRL model enhanced by the proposed associated memory network (AMN), which makes use of inter-sentence attention of label-known associated sentences as a kind of memory to further enhance dependency-based SRL.In detail, we use sentences and their labels from train dataset as an associated memory cue to help label the target sentence.Furthermore, we compare several associated sentences selecting strategies and label merging methods in AMN to find and utilize the label of associated sentences while attending them.By leveraging the attentive memory from known training data, Our full model reaches state-of-theart on CoNLL-2009 benchmark datasets for syntax-agnostic setting, showing a new effective research line of SRL enhancement other than exploiting external resources such as well pre-trained language models.
Distilling Knowledge from Reader to Retriever for Question Answering

The task of information retrieval is an important component of many natural language processing systems, such as open domain question answering. While traditional methods were based on hand-crafted features, continuous representations based on neural networks recently obtained competitive results. A challenge of using such methods is to obtain supervised data to train the retriever model, corresponding to pairs of query and support documents. In this paper, we propose a technique to learn retriever models for downstream tasks, inspired by knowledge distillation, and which does not require annotated pairs of query and documents. Our approach leverages attention scores of a reader model, used to solve the task based on retrieved documents, to obtain synthetic labels for the retriever. We evaluate our method on question answering, obtaining state-of-the-art results.
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation

Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded dialogue systems often require a large number of dialogue instances to learn as they fail to capture the correlations between different diseases and neglect the diagnostic experience shared among them. To address this issue, we propose a more natural and practical paradigm, i.e., low-resource medical dialogue generation, which can transfer the diagnostic experience from source diseases to target ones with a handful of data for adaptation. It is capitalized on a commonsense knowledge graph to characterize the prior disease-symptom relations. Besides, we develop a Graph-Evolving Meta-Learning (GEML) framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease, which effectively alleviates the needs of a large number of dialogues. More importantly, by dynamically evolving disease-symptom graphs, GEML also well addresses the real-world challenges that the disease-symptom correlations of each disease may vary or evolve along with more diagnostic cases. Extensive experiment results on the CMDD dataset and our newly-collected Chunyu dataset testify the superiority of our approach over state-of-the-art approaches. Besides, our GEML can generate an enriched dialogue-sensitive knowledge graph in an online manner, which could benefit other tasks grounded on knowledge graph.
Revisiting Low Resource Status of Indian Languages in Machine Translation

Indian language machine translation performance is hampered due to the lack of large scale multi-lingual sentence aligned corpora and robust benchmarks. Through this paper, we provide and analyse an automated framework to obtain such a corpus for Indian language neural machine translation (NMT) systems. Our pipeline consists of a baseline NMT system, a retrieval module, and an alignment module that is used to work with publicly available websites such as press releases by the government. The main contribution towards this effort is to obtain an incremental method that uses the above pipeline to iteratively improve the size of the corpus as well as improve each of the components of our system. Through our work, we also evaluate the design choices such as the choice of pivoting language and the effect of iterative incremental increase in corpus size. Our work in addition to providing an automated framework also results in generating a relatively larger corpus as compared to existing corpora that are available for Indian languages. This corpus helps us obtain substantially improved results on the publicly available WAT evaluation benchmark and other standard evaluation benchmarks.
Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction

We propose in this paper a contextualised graph convolution network over multiple dependency sub-graphs for relation extraction.A novel method to construct multiple sub-graphs using words in shortest dependency path and words linked to entities in the dependency graph is proposed.Graph convolution operation is performed over the resulting multiple sub-graphs to obtain more informative features useful for relation extraction.Our experimental results show that the proposed method achieves superior performance over existing GCN-based models achieving stateof-the-art performance on cross-sentence n-ary relation extraction and SemEval 2010 Task 8 sentence-level relation extraction task.Our model also achieves a comparable performance to the SoTA on the TACRED dataset.
Custom Face Classification Model for Classroom Using Haar-Like and LBP Features with Their Performance Comparisons

Face detection by electronic systems has been leveraged by private and government establishments to enhance the effectiveness of a wide range of applications in our day to day activities, security, and businesses. Most face detection algorithms that can reduce the problems posed by constrained and unconstrained environmental conditions such as unbalanced illumination, weather condition, distance from the camera, and background variations, are highly computationally intensive. Therefore, they are primarily unemployable in real-time applications. This paper developed face detectors by utilizing selected Haar-like and local binary pattern features, based on their number of uses at each stage of training using MATLAB’s trainCascadeObjectDetector function. We used 2577 positive face samples and 37,206 negative samples to train Haar-like and LBP face detectors for a range of False Alarm Rate (FAR) values (i.e., 0.01, 0.05, and 0.1). However, the study shows that the Haar cascade face detector at a low stage (i.e., at six stages) for 0.1 FAR value is the most efficient when tested on a set of classroom images dataset with 100% True Positive Rate (TPR) face detection accuracy. Though, deep learning ResNet101 and ResNet50 outperformed the average performance of Haar cascade by 9.09% and 0.76% based on TPR, respectively. The simplicity and relatively low computational time used by our approach (i.e., 1.09 s) gives it an edge over deep learning (139.5 s), in online classroom applications. The TPR of the proposed algorithm is 92.71% when tested on images in the synthetic Labeled Faces in the Wild (LFW) dataset and 98.55% for images in MUCT face dataset “a”, resulting in a little improvement in average TPR over the conventional face identification system.