Reviewerly/exHarmony huggingface dataset:
Improved ECG Watermarking Technique Using Curvelet Transform

Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient’s data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient’s data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance—even when the hidden messages were large size.
Tight typings and split bounds, fully developed

Abstract Multi types – aka non-idempotent intersection types – have been used. to obtain quantitative bounds on higher-order programs, as pioneered by de Carvalho. Notably, they bound at the same time the number of evaluation steps and the size of the result. Recent results show that the number of steps can be taken as a reasonable time complexity measure. At the same time, however, these results suggest that multi types provide quite lax complexity bounds, because the size of the result can be exponentially bigger than the number of steps. Starting from this observation, we refine and generalise a technique introduced by Bernadet and Graham-Lengrand to provide exact bounds. Our typing judgements carry counters, one measuring evaluation lengths and the other measuring result sizes. In order to emphasise the modularity of the approach, we provide exact bounds for four evaluation strategies, both in the λ -calculus (head, leftmost-outermost, and maximal evaluation) and in the linear substitution calculus (linear head evaluation). Our work aims at both capturing the results in the literature and extending them with new outcomes. Concerning the literature, it unifies de Carvalho and Bernadet &amp; Graham-Lengrand via a uniform technique and a complexity-based perspective. The two main novelties are exact split bounds for the leftmost strategy – the only known strategy that evaluates terms to full normal forms and provides a reasonable complexity measure – and the observation that the computing device hidden behind multi types is the notion of substitution at a distance, as implemented by the linear substitution calculus.
TRACER: A Framework for Facilitating Accurate and Interpretable Analytics for High Stakes Applications

In high stakes applications such as healthcare and finance analytics, the interpretability of predictive models is required and necessary for domain practitioners to trust the predictions. Traditional machine learning models, e.g., logistic regression (LR), are easy to interpret in nature. However, many of these models aggregate time-series data without considering the temporal correlations and variations. Therefore, their performance cannot match up to recurrent neural network (RNN) based models, which are nonetheless difficult to interpret. In this paper, we propose a general framework TRACER to facilitate accurate and interpretable predictions, with a novel model TITV devised for healthcare analytics and other high stakes applications such as financial investment and risk management. Different from LR and other existing RNN-based models, TITV is designed to capture both the time-invariant and the time-variant feature importance using a feature-wise transformation subnetwork and a self-attention subnetwork, for the feature influence shared over the entire time series and the time-related importance respectively. Healthcare analytics is adopted as a driving use case, and we note that the proposed TRACER is also applicable to other domains, e.g., fintech. We evaluate the accuracy of TRACER extensively in two real-world hospital datasets, and our doctors/clinicians further validate the interpretability of TRACER in both the patient level and the feature level. Besides, TRACER is also validated in a critical financial application. The experimental results confirm that TRACER facilitates both accurate and interpretable analytics for high stakes applications.
Improving qubit readout with hidden Markov models

We demonstrate the application of pattern recognition algorithms via hidden Markov models (HMM) for qubit readout. This scheme provides a state-path trajectory approach capable of detecting qubit-state transitions and makes for a robust classification scheme with higher starting-state assignment fidelity than when compared to a multivariate Gaussian or a support vector machine scheme. Therefore, the method also eliminates the qubit-dependent readout time optimization requirement in current schemes. Using a HMM state discriminator we estimate fidelities reaching the ideal limit. Unsupervised learning gives access to transition matrix, priors, and $IQ$ distributions, providing a toolbox for studying qubit-state dynamics during strong projective readout.
Getting Playful with Explainable AI: Games with a Purpose to Improve Human Understanding of AI

Explainable Artificial Intelligence (XAI) is an emerging topic in Machine Learning (ML) that aims to give humans visibility into how AI systems make decisions. XAI is increasingly important in bringing transparency to fields such as medicine and criminal justice where AI informs high consequence decisions. While many XAI techniques have been proposed, few have been evaluated beyond anecdotal evidence. Our research offers a novel approach to assess how humans interpret AI explanations; we explore this by integrating XAI with Games with a Purpose (GWAP). XAI requires human evaluation at scale, and GWAP can be used for XAI tasks which are presented through rounds of play. This paper outlines the benefits of GWAP for XAI, and demonstrates application through our creation of a multi-player GWAP that focuses on explaining deep learning models trained for image recognition. Through our game, we seek to understand how humans select and interpret explanations used in image recognition systems, and bring empirical evidence on the validity of GWAP designs for XAI.
ENANO: Encoder for NANOpore FASTQ files

Abstract Motivation The amount of genomic data generated globally is seeing explosive growth, leading to increasing needs for processing, storage and transmission resources, which motivates the development of efficient compression tools for these data. Work so far has focused mainly on the compression of data generated by short-read technologies. However, nanopore sequencing technologies are rapidly gaining popularity due to the advantages offered by the large increase in the average size of the produced reads, the reduction in their cost and the portability of the sequencing technology. We present ENANO (Encoder for NANOpore), a novel lossless compression algorithm especially designed for nanopore sequencing FASTQ files. Results The main focus of ENANO is on the compression of the quality scores, as they dominate the size of the compressed file. ENANO offers two modes, Maximum Compression and Fast (default), which trade-off compression efficiency and speed. We tested ENANO, the current state-of-the-art compressor SPRING and the general compressor pigz on several publicly available nanopore datasets. The results show that the proposed algorithm consistently achieves the best compression performance (in both modes) on every considered nanopore dataset, with an average improvement over pigz and SPRING of &amp;gt;24.7% and 6.3%, respectively. In addition, in terms of encoding and decoding speeds, ENANO is 2.9× and 1.7× times faster than SPRING, respectively, with memory consumption up to 0.2 GB. Availability and implementation ENANO is freely available for download at: https://github.com/guilledufort/EnanoFASTQ. Supplementary information Supplementary data are available at Bioinformatics online.
A New Haze Removal Algorithm for Single Urban Remote Sensing Image

The remote sensing imaging detection technology is an important means to effectively monitor and manage urban environment and resources, and remote sensing images are an important data source of smart city and digital city.The existence of haze has a serious impact on the quality of optical remote sensing image acquisition, resulting in remote sensing image blurred, detail information loss, contrast decreased and color distortion.To reduce the impact of haze and give full play to the value of remote sensing images, a new urban remote sensing haze removal (URSHR) algorithm is proposed in this paper, which combines the image phase consistency feature, multi-scale Retinax theory and histogram characteristic.In URSHR method, firstly the image haze is removed by using multi-scale Retinex theory and histogram characteristic, and then the detail information of the image is enhanced by using the phase consistency features, finally they are fused with the multi-scale wavelet transform.It achieves the purpose of both removing haze and enhancing geometric detail information.The many verification experiments were carried out by using real urban remote sensing image data, and good results were obtained.This shows that the new algorithm is a feasible and effective for urban remote sensing image haze removal, and it has good application and promotion value.
Automatic Construction of Subject Knowledge Graph based on Educational Big Data

In this paper, we propose an automatic construction method of subject knowledge graph for educational applications. The subject knowledge graph is constructed based on educational big data by using a bootstrapping strategy to gradually expand knowledge points and connections between them. In this paper two different datasets are used. One is the subject teaching resources such as syllabuses, teaching plans, textbooks and etc., which is used to automatically construct the core of subject knowledge graph so as to reduce the dependence on the manual annotation. Meanwhile the high-quality of subject teaching resources is the guarantee of accuracy of the knowledge graph core. The other dataset is the massive Internet encyclopedia texts, which is used to expand and complete the subject knowledge graph. As to algorithm, this paper utilizes the BERT-BiLSTM-CRF model to automatically identify the subject knowledge points, and then evaluates the relationship between the knowledge points by calculating their semantic similarity, PMI and Normalized Google Distance between them. The experimental results show that BERT-BiLSTM-CRF outperforms the baselines significantly, and the three kinds of relationship evaluation models have achieved good results. Finally, computer science and physics science are taken as examples to construct the subject knowledge graphs successfully, which show the effectiveness of our method.
An Improved Proof-of-Trust Consensus Algorithm for Credible Crowdsourcing Blockchain Services

In online crowdsourcing services, credible accountability mechanisms are crucial for guaranteeing a good interactive environment.However, the crowdsourcing systems are established in virtual environments, the identities of the participants are various and complicated, the systems could scarcely identify malicious nodes automatically.So it is very hard to preserve the complete evidence of malicious behaviors and investigate relevant legal responsibilities.Blockchain is regarded as a very promising solution to these problems because it possesses characteristics of decentration, non-modifiability and traceability.However, a main challenge is to design an applicable blockchain consensus algorithm which can reach an agreement on credibility of participants automatically, prevent transaction data from tampering, and trace to the source of malicious behaviors.In this paper, an improved Proof-of-Trust (PoT) consensus scheme is proposed with the underlying technology of blockchain, which is properly to the crowdsourcing service scenarios.Firstly, this PoT consensus selects nodes with high credibility using subjective logic reputation algorithm.Only selected nodes have the chance to generate blocks, participate in verification, and claim crowdsourcing tasks.Secondly, the choice scheme of generate-block nodes is further optimized through the unpredictability of timestamp and digital signature.Moreover, an incentive mechanism based on game theory is designed in this consensus.With this mechanism, candidate nodes prefer to give honest verification results rather than engage in collusion with malicious nodes.The analysis and simulation results demonstrate the effectiveness, feasibility and scalability of the proposed approach.
CMFALL: A Cascade and Parallel Multi-State Fall Detection Algorithm Using Waist-Mounted Tri-Axial Accelerometer Signals

As one of the main threats to people's health, especially for the elderly, falls have caused a large number of accidents. Detecting falls in time can minimize the severity of the injury and save lives. Therefore, designing fall detection algorithms to assist people's daily life has been a hot research topic. In the last decade, different fall detection approaches based on wearable sensors have been proposed. However, since the hardware resources of wearable sensors are very limited, designing accurate but energy-efficient fall detection algorithms remains an open challenge. To deal with this, an accurate but low-cost fall detection algorithm is proposed in this paper. Particularly, a novel cascade and parallel method that efficiently employs the characteristics of human falls and the advanced modeling ability of the neural network is proposed. Also, a novel design of a lightweight convolutional neural network with self-attention is proposed to achieve the best recognition/numerical complexity tradeoff. The proposed method, namely CMFALL, is evaluated together with a multitude of state-of-the-art models on a large dataset, where it performs the best with an F1-score exceeding 99%. Meanwhile, its computational cost and model size are only a few thousandths of other models. Such low computational cost and small size not only enable to embed it in a wearable sensor but also make the system power requirements quite low, which can enhance the autonomy of the wearable fall detector.
Cross-Lingual Image Caption Generation Based on Visual Attention Model

As an interesting and challenging problem, generating image caption automatically has attracted increasingly attention in natural language processing and computer vision communities.In this paper, we propose an end-to-end deep learning approach for image caption generation.We leverage image feature information at specific location every moment and generate the corresponding caption description through a semantic attention model.The end-to-end framework allows us to introduce an independent recurrent structure as an attention module, derived by calculating the similarity between image feature sequence and semantic word sequence.Additionally, our model is designed to transfer the knowledge representation obtained from the English portion into the Chinese portion to achieve the cross-lingual image captioning.We evaluate the proposed model on the most popular benchmark datasets.We report an improvement of 3.9% over existing state-of-the-art approaches for cross-lingual image captioning on the Flickr8k CN dataset on CIDEr metric.The experimental results demonstrate the effectiveness of our attention model.
Learning to Ask More: Semi-Autoregressive Sequential Question Generation under Dual-Graph Interaction

Traditional Question Generation (TQG) aims to generate a question given an input passage and an answer.When there is a sequence of answers, we can perform Sequential Question Generation (SQG) to produce a series of interconnected questions.Since the frequently occurred information omission and coreference between questions, SQG is rather challenging.Prior works regarded SQG as a dialog generation task and recurrently produced each question.However, they suffered from problems caused by error cascades and could only capture limited context dependencies.To this end, we generate questions in a semi-autoregressive way.Our model divides questions into different groups and generates each group of them in parallel.During this process, it builds two graphs focusing on information from passages, answers respectively and performs dual-graph interaction to get information for generation.Besides, we design an answer-aware attention mechanism and the coarse-to-fine generation scenario.Experiments on our new dataset containing 81.9K questions show that our model substantially outperforms prior works.
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection

One-class novelty detection is to identify anomalous instances that do not conform to the expected normal instances. In this paper, the Generative Adversarial Networks (GANs) based on encoder-decoder-encoder pipeline are used for detection and achieve state-of-the-art performance. However, deep neural networks are too over-parameterized to deploy on resource-limited devices. Therefore, Progressive Knowledge Distillation with GANs (P-KDGAN) is proposed to learn compact and fast novelty detection networks. The P-KDGAN is a novel attempt to connect two standard GANs by the designed distillation loss for transferring knowledge from the teacher to the student. The progressive learning of knowledge distillation is a two-step approach that continuously improves the performance of the student GAN and achieves better performance than single step methods. In the first step, the student GAN learns the basic knowledge totally from the teacher via guiding of the pre-trained teacher GAN with fixed weights. In the second step, joint fine-training is adopted for the knowledgeable teacher and student GANs to further improve the performance and stability. The experimental results on CIFAR-10, MNIST, and FMNIST show that our method improves the performance of the student GAN by 2.44%, 1.77%, and 1.73% when compressing the computation at ratios of 24.45:1, 311.11:1, and 700:1, respectively.
Predicting Declension Class from Form and Meaning

The noun lexica of many natural languages are divided into several declension classes with characteristic morphological properties.Class membership is far from deterministic, but the phonological form of a noun and its meaning can often provide imperfect clues.Here, we investigate the strength of those clues.More specifically, we operationalize "strength" as measuring how much information, in bits, we can glean about declension class from knowing the form and meaning of nouns.We know that form and meaning are often also indicative of grammatical gender-which, as we quantitatively verify, can itself share information with declension class-so we also control for gender.We find for two Indo-European languages (Czech and German) that form and meaning share a significant amount of information with class (and contribute additional information beyond gender).The three-way interaction between class, form, and meaning (given gender) is also significant.Our study is important for two reasons: First, we introduce a new method that provides additional quantitative support for a classic linguistic finding that form and meaning are relevant for the classification of nouns into declensions.Second, we show not only that individual declension classes vary in the strength of their clues within a language, but also that the variations between classes vary across languages.The code is publicly available at https://github.com/rycolab/declension-mi.
Universal Decompositional Semantic Parsing

We introduce a transductive model for parsing into Universal Decompositional Semantics (UDS) representations, which jointly learns to map natural language utterances into UDS graph structures and annotate the graph with decompositional semantic attribute scores. We also introduce a strong pipeline model for parsing into the UDS graph structure, and show that our transductive parser performs comparably while additionally performing attribute prediction. By analyzing the attribute prediction errors, we find the model captures natural relationships between attribute groups.
SolarFinder: Automatic Detection of Solar Photovoltaic Arrays

Smart cities, utilities, third-parties, and government agencies are having pressure on managing stochastic power generation from distributed rooftop solar photovoltaic (PV) arrays, such as predicting and reacting to the variations in electric grid. Recently, there is a rising interest to identify solar PV arrays automatically and passively. Traditional approaches such as online assessment and utilities interconnection filings are time consuming and costly, and limited in geospatial resolution, and thus do not scale up to every location. Significant recent work focuses on using aerial imagery to train machine learning or deep learning models to automatically detect solar PV arrays. Unfortunately, these approaches typically require Very High Resolution (VHR) images and human handcrafted solar PV array templates for training, which have a minimum cost of $15 per km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and are not always available at every location.To address the problem, we design a new system—SolarFinder that can automatically detect distributed solar PV arrays in a given geospatial region without any extra cost. SolarFinder first automatically fetches regular resolution satellite images within the region using publicly-available imagery APIs. Then, SolarFinder leverages multi-dimensional K-means algorithm to automatically segment solar arrays on rooftop images. Eventually, SolarFinder employs hybrid linear regression approach that integrates support vector machine (SVM) modeling with a deep convolutional neural networks (CNNs) approach to accurately identify solar PV arrays and characterize each solar deployment. We evaluate SolarFinder using 269,632 satellite images that include 1,143,636 contours from 13 geospatial regions in U.S. We find that pre-trained SolarFinder yields a Matthews Correlation Coefficient (MCC) of 0.17, which is 3 times better than the most recent pre-trained CNNs approach and the same as a re-trained CNNs approach.