Reviewerly/exHarmony huggingface dataset:
A Natural Language Processing Pipeline of Chinese Free-Text Radiology Reports for Liver Cancer Diagnosis

Despite the rapid development of natural language processing (NLP) implementation in electronic medical records (EMRs), Chinese EMRs processing remains challenging due to the limited corpus and specific grammatical characteristics, especially for radiology reports.In this study, we designed an NLP pipeline for the direct extraction of clinically relevant features from Chinese radiology reports, which is the first key step in computer-aided radiologic diagnosis.The pipeline was comprised of named entity recognition, synonyms normalization, and relationship extraction to finally derive the radiological features composed of one or more terms.In named entity recognition, we incorporated lexicon into deep learning model bidirectional long short-term memory-conditional random field (BiLSTM-CRF), and the model finally achieved an F1 score of 93.00%.With the extracted radiological features, least absolute shrinkage and selection operator and machine learning methods (support vector machine, random forest, decision tree, and logistic regression) were used to build the classifiers for liver cancer prediction.For liver cancer diagnosis, random forest had the highest predictive performance in liver cancer diagnosis (F1 score 86.97%, precision 87.71%, and recall 86.25%).This work was a comprehensive NLP study focusing on Chinese radiology reports and the application of NLP in cancer risk prediction.The proposed NLP pipeline for the radiological feature extraction could be easily implemented in other kinds of Chinese clinical texts and other disease predictive tasks.
Unjustified Classification Regions and Counterfactual Explanations in Machine Learning

Post-hoc interpretability approaches, although powerful tools to generate explanations for predictions made by a trained black-box model, have been shown to be vulnerable to issues caused by lack of robustness of the classifier. In particular, this paper focuses on the notion of explanation justification, defined as connectedness to ground-truth data, in the context of counterfactuals. In this work, we explore the extent of the risk of generating unjustified explanations. We propose an empirical study to assess the vulnerability of classifiers and show that the chosen learning algorithm heavily impacts the vulnerability of the model. Additionally, we show that state-of-the-art post-hoc counterfactual approaches can minimize the impact of this risk by generating less local explanations (Source code available at: https://github.com/thibaultlaugel/truce ).
Automatic Pose Recognition for Monitoring Dangerous Situations in Ambient-Assisted Living

Continuous monitoring of frail individuals for detecting dangerous situations during their daily living at home can be a powerful tool toward their inclusion in the society by allowing living independently while safely. To this goal we developed a pose recognition system tailored to disabled students living in college dorms and based on skeleton tracking through four Kinect One devices independently recording the inhabitant with different viewpoints, while preserving the individual's privacy. The system is intended to classify each data frame and provide the classification result to a further decision-making algorithm, which may trigger an alarm based on the classified pose and the location of the subject with respect to the furniture in the room. An extensive dataset was recorded on 12 individuals moving in a mockup room and undertaking four poses to be recognized: standing, sitting, lying down, and "dangerous sitting." The latter consists of the subject slumped in a chair with his/her head lying forward or backward as if unconscious. Each skeleton frame was labeled and represented using 10 discriminative features: three skeletal joint vertical coordinates and seven relative and absolute angles describing articular joint positions and body segment orientation. In order to classify the pose of the subject in each skeleton frame we built a two hidden layers multi-layer perceptron neural network with a "SoftMax" output layer, which we trained on the data from 10 of the 12 subjects (495,728 frames), with the data from the two remaining subjects representing the test set (106,802 frames). The system achieved very promising results, with an average accuracy of 83.9% (ranging 82.7 and 94.3% in each of the four classes). Our work proves the usefulness of human pose recognition based on machine learning in the field of safety monitoring in assisted living conditions.
Quantum computer-aided design of quantum optics hardware

Abstract The parameters of a quantum system grow exponentially with the number of involved quantum particles. Hence, the associated memory requirement to store or manipulate the underlying wavefunction goes well beyond the limit of the best classical computers for quantum systems composed of a few dozen particles, leading to serious challenges in their numerical simulation. This implies that the verification and design of new quantum devices and experiments are fundamentally limited to small system size. It is not clear how the full potential of large quantum systems can be exploited. Here, we present the concept of quantum computer designed quantum hardware and apply it to the field of quantum optics. Specifically, we map complex experimental hardware for high-dimensional, many-body entangled photons into a gate-based quantum circuit. We show explicitly how digital quantum simulation of Boson sampling experiments can be realized. We then illustrate how to design quantum-optical setups for complex entangled photonic systems, such as high-dimensional Greenberger–Horne–Zeilinger states and their derivatives. Since photonic hardware is already on the edge of quantum supremacy and the development of gate-based quantum computers is rapidly advancing, our approach promises to be a useful tool for the future of quantum device design.
GAN-Based Data Generation for Speech Emotion Recognition

In this work, we propose a GAN-based method to generate synthetic data for speech emotion recognition. Specifically, we investigate the usage of GANs for capturing the data manifold when the data is eyes-off, i.e., where we can train networks using the data but cannot copy it from the clients. We propose a CNN-based GAN with spectral normalization on both the generator and discriminator, both of which are pre-trained on large unlabeled speech corpora. We show that our method provides better speech emotion recognition performance than a strong baseline. Furthermore, we show that even after the data on the client is lost, our model can generate similar data that can be used for model bootstrapping in the future. Although we evaluated our method for speech emotion recognition, it can be applied to other tasks.
Unsupervised Morphological Paradigm Completion

We propose the task of unsupervised morphological paradigm completion.Given only raw text and a lemma list, the task consists of generating the morphological paradigms, i.e., all inflected forms, of the lemmas.From a natural language processing (NLP) perspective, this is a challenging unsupervised task, and high-performing systems have the potential to improve tools for low-resource languages or to assist linguistic annotators.From a cognitive science perspective, this can shed light on how children acquire morphological knowledge.We further introduce a system for the task, which generates morphological paradigms via the following steps: (i) EDIT TREE retrieval, (ii) additional lemma retrieval, (iii) paradigm size discovery, and (iv) inflection generation.We perform an evaluation on 14 typologically diverse languages.Our system outperforms trivial baselines with ease and, for some languages, even obtains a higher accuracy than minimally supervised systems. 1
Information set decoding in the Lee metric with applications to cryptography

&lt;p style='text-indent:20px;'&gt;We convert Stern's information set decoding (ISD) algorithm to the ring &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ \mathbb{Z}/4 \mathbb{Z} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; equipped with the Lee metric. Moreover, we set up the general framework for a McEliece and a Niederreiter cryptosystem over this ring. The complexity of the ISD algorithm determines the minimum key size in these cryptosystems for a given security level. We show that using Lee metric codes can substantially decrease the key size, compared to Hamming metric codes. In the end we explain how our results can be generalized to other Galois rings &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ \mathbb{Z}/p^s\mathbb{Z} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Measuring Independence between Statistical Randomness Tests by Mutual Information

The analysis of independence between statistical randomness tests has had great attention in the literature recently. Dependency detection between statistical randomness tests allows one to discriminate statistical randomness tests that measure similar characteristics, and thus minimize the amount of statistical randomness tests that need to be used. In this work, a method for detecting statistical dependency by using mutual information is proposed. The main advantage of using mutual information is its ability to detect nonlinear correlations, which cannot be detected by the linear correlation coefficient used in previous work. This method analyzes the correlation between the battery tests of the National Institute of Standards and Technology, used as a standard in the evaluation of randomness. The results of the experiments show the existence of statistical dependencies between the tests that have not been previously detected.
Quantum coherence and speed limit in the mean-field Dicke model of superradiance

Dicke superrandiance is a cooperative phenomenon which arises from the collective coupling of an ensemble of atoms to the electromagnetic radiation. Here we discuss the quantifying of quantum coherence for the Dicke model of superradiance in the mean-field approximation. We found the single-atom ${l}_{1}$ norm of coherence is given by the square root of the normalized average intensity of radiation emitted by the superradiant system. This validates quantum coherence as a useful figure of merit towards the understanding of superradiance phenomenon in the mean-field approach. In particular, this result suggests probing the single-atom coherence through the radiation intensity in superradiant systems, which might be useful in experimental realizations where is unfeasible to address atoms individually. Furthermore, given the nonlinear unitary dynamics of the time-dependent single-atom state that effectively describes the system of $N$ atoms, we analyze the quantum speed limit time and its interplay with the ${l}_{1}$ norm of coherence. We verify the quantum coherence speeds up the evolution of the superradiant system, i.e., the more coherence stored on the single-atom state, the faster the evolution. These findings unveil the role played by quantum coherence in superradiant systems, which in turn could be of interest for communities of both condensed matter physics and quantum optics.
The importance of open science for biological assessment of aquatic environments

Open science principles that seek to improve science can effectively bridge the gap between researchers and environmental managers. However, widespread adoption has yet to gain traction for the development and application of bioassessment products. At the core of this philosophy is the concept that research should be reproducible and transparent, in addition to having long-term value through effective data preservation and sharing. In this article, we review core open science concepts that have recently been adopted in the ecological sciences and emphasize how adoption can benefit the field of bioassessment for both prescriptive condition assessments and proactive applications that inform environmental management. An example from the state of California demonstrates effective adoption of open science principles through data stewardship, reproducible research, and engagement of stakeholders with multimedia applications. We also discuss technical, sociocultural, and institutional challenges for adopting open science, including practical approaches for overcoming these hurdles in bioassessment applications.
Blockchain challenges in adoption

Purpose Blockchain technology can be used to record virtually anything of value – your identity, a will, a deed or almost any type of secure transfer of information. But in many respects, blockchain is lacking some critical capabilities to make it ready for widescale adoption by business. This paper on IBM blockchain attempts to uncover the promise of blockchain for enterprise, which goes beyond its role as an industry disruptor. It also has tremendous potential to improve existing business processes, as well as to improve efficiencies in existing transaction systems, leading to exponential cost saving for the enterprise and the end consumer. Disintermediation and disruption is the investment magnet for blockchain-related ideas, riding on the success of the business and underpinned by peer-to-peer and crowdsourcing models. Blockchain technology promises a similar explosion in trade, ownership and trust, as the tenets of both technologies rely on principles of distributed governance and rules established for a time-tested protocol. Design/methodology/approach The idea behind defining the path to blockchain enterprise adoption is to ensure that we have a microscopic focus on a singular use case and that we distill the existing business into a blockchain paradigm. This implies both business and technology models. We take a singular use case that has an industry and enterprise impact and apply business and technology acumen to the problem domain. Findings The result is a well-thought-out business architecture and technology blueprint, along with requirements for compliance, audit and enterprise integration. The point of this exercise is to expend time and energy with the right business domain expertise and blockchain technology expertise to derive an adoption model that enlists and surfaces hurdles, challenges and factors in the costs and economic viability of the blockchain solution. Originality/value The resulting artifacts/collateral of the blockchain client led engagement are envisioned to be instrumental in socialization and in providing a blueprint for a business seeking executive sponsorship and the necessary funding for first project.
E-Learning and Industry 4.0: A Chatbot for Training Employees

Within what is called the Fourth Industrial Revolution, one of the problems that companies encounter frequently, in order to ensure themselves and their products and services over time, is the need for continuous training of the people who work in synergy for them. If continuing education is a problem, e-learning is its natural solution. The research concerns the realization of a system capable of providing a constant, reliable, and friendly help through a practical and nice chatbot based on context processing. In particular, the proposed chatbot acts as a reminder and follows the user during his personal corporate training, ready to provide, when needed, the necessary and useful teaching material to complete the educational course.
Multi-Task Deep Dual Correlation Filters for Visual Tracking

Correlation filters combined with deep features have delivered impressive results in visual tracking task. However, existing approaches treat deep features produced by different network layers independently, limiting their representation power. To address this issue, this article proposes a multi-task deep dual correlation filters (MDDCF) based method for robust visual tracking. First, a new multi-task learning scheme is designed to take full advantage of the multi-level features of deep networks, where target representation with individual features is regarded as a single task. As such, the interdependencies between different levels of features can be better explored. Second, we reformulate the objective function of the dual correlation filters and propose a new alternating optimization method, allowing joint training of the correlation filters and network parameters. Third, we design an effective object template update scheme which can well capture the target appearance variations. Extensive experimental evaluations on seven benchmark datasets show that the proposed MDDCF tracker performs favorably against state-of-the-art methods.
“Show this thread”: policing, disruption and mobilisation through Twitter. An analysis of UK law enforcement tweeting practices during the Covid-19 pandemic

Crisis and disruption are often unpredictable and can create opportunities for crime. During such times, policing may also need to meet additional challenges to handle the disruption. The use of social media by officials can be essential for crisis mitigation and crime reduction. In this paper, we study the use of Twitter for crime mitigation and reduction by UK police (and associated) agencies in the early stages of the Covid-19 pandemic. Our findings suggest that whilst most of the tweets from our sample concerned issues that were not specifically about crime, especially during the first stages of the pandemic, there was a significant increase in tweets about fraud, cybercrime and domestic abuse. There was also an increase in retweeting activity as opposed to the creation of original messages. Moreover, in terms of the impact of tweets, as measured by the rate at which they are retweeted, followers were more likely to 'spread the word' when the tweet was content-rich (discussed a crime specific matter and contained media), and account holders were themselves more active on Twitter. Considering the changing world we live in, criminal opportunity is likely to evolve. To help mitigate this, policy makers and researchers should consider more systematic approaches to developing social media communication strategies for the purpose of crime mitigation and reduction during disruption and change more generally. We suggest a framework for so doing.
Opinion-aware Answer Generation for Review-driven Question Answering in E-Commerce

Product-related question answering (QA) is an important but challenging task in E-Commerce. It leads to a great demand on automatic review-driven QA, which aims at providing instant responses towards user-posted questions based on diverse product reviews. Nevertheless, the rich information about personal opinions in product reviews, which is essential to answer those product-specific questions, is underutilized in current generation-based review-driven QA studies. There are two main challenges when exploiting the opinion information from the reviews to facilitate the opinion-aware answer generation: (i) jointly modeling opinionated and interrelated information between the question and reviews to capture important information for answer generation, (ii) aggregating diverse opinion information to uncover the common opinion towards the given question. In this paper, we tackle opinion-aware answer generation by jointly learning answer generation and opinion mining tasks with a unified model. Two kinds of opinion fusion strategies, namely, static and dynamic fusion, are proposed to distill and aggregate important opinion information learned from the opinion mining task into the answer generation process. Then a multi-view pointer-generator network is employed to generate opinion-aware answers for a given product-related question. Experimental results show that our method achieves superior performance in real-world E-Commerce QA datasets, and effectively generate opinionated and informative answers.
Markov-chain-inspired search for MH370

Markov-chain models are constructed for the probabilistic description of the drift of marine debris from Malaysian Airlines flight MH370. En route from Kuala Lumpur to Beijing, MH370 mysteriously disappeared in the southeastern Indian Ocean on 8 March 2014, somewhere along the arc of the 7th ping ring around the Inmarsat-3F1 satellite position when the airplane lost contact. The models are obtained by discretizing the motion of undrogued satellite-tracked surface drifting buoys from the global historical data bank. A spectral analysis, Bayesian estimation, and the computation of most probable paths between the Inmarsat arc and confirmed airplane debris beaching sites are shown to constrain the crash site, near 25°S on the Inmarsat arc.
Quantum Associative Memory in Hep Track Pattern Recognition

We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data triggering. We examine the practical limits of storage capacity, as well as store and recall errorless efficiency, from the viewpoints of the state-of-the-art IBM quantum processors and LHC real-time charged track pattern recognition requirements. We present a software prototype implementation of the QuAM protocols and analyze the topological limitations for porting the simplest QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.
Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence

We study the tradeoff between computational effort and classification accuracy in a cascade of deep neural networks. During inference, the user sets the acceptable accuracy degradation which then automatically determines confidence thresholds for the intermediate classifiers. As soon as the confidence threshold is met, inference terminates immediately without having to compute the output of the complete network. Confidence levels are derived directly from the softmax outputs of intermediate classifiers, as we do not train special decision functions. We show that using a softmax output as a confidence measure in a cascade of deep neural networks leads to a reduction of $$15\%$$ –50 $$\%$$ in the number of MAC operations while degrading the classification accuracy by roughly $$1\%$$ . Our method can be easily incorporated into pre-trained non-cascaded architectures, as we exemplify on ResNet. Our main contribution is a method that dynamically adjusts the tradeoff between accuracy and computation without retraining the model.