Reviewerly/exHarmony huggingface dataset:
A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts

Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.
CN-Celeb: Multi-genre speaker recognition

Research on speaker recognition is extending to address the vulnerability in the wild conditions, among which genre mismatch is perhaps the most challenging, for instance, enrollment with reading speech while testing with conversational or singing audio. This mismatch leads to complex and composite inter-session variations, both intrinsic (i.e., speaking style, physiological status) and extrinsic (i.e., recording device, background noise). Unfortunately, the few existing multi-genre corpora are not only limited in size but are also recorded under controlled conditions, which cannot support conclusive research on the multi-genre problem. In this work, we firstly publish CN-Celeb, a large-scale multi-genre corpus that includes in-the-wild speech utterances of 3000 speakers in 11 different genres. Secondly, using this dataset, we conduct a comprehensive study on the multi-genre phenomenon, in particular the impact of the multi-genre challenge on speaker recognition and the performance gain when the new dataset is used to conduct multi-genre training.
A Hybrid CNN for Image Denoising

Deep convolutional neural networks (CNNs) with strong learning abilities have been used in the field of image super-resolution. However, some CNNs depends on a single deep network to training an image super-resolution model, which will have poor performance in complex screens. To address this problem, we propose a hybrid denoising CNN (HDCNN). HDCNN is composed of a dilated block (DB), RepVGG block (RVB) and feature refinement block (FB), a single convolution. DB combines a dilated convolution, batch normalization (BN), common convolutions, activation function of ReLU to obtain more context information. RVB uses parallel combination of convolution and BN, ReLU to extract complementary width features. FB is used to obtain more accurate information via refining obtained feature from the RVB. A single convolution collaborates a residual learning operation to construct a clean image. These key components make the HDCNN have good performance in image denoising. Experiment shows that the proposed HDCNN enjoys good denoising effect in public datasets.
Error Mitigation for Universal Gates on Encoded Qubits

The Eastin-Knill theorem states that no quantum error-correcting code can have a universal set of transversal gates. For Calderbank-Shor-Steane codes that can implement Clifford gates transversally, it suffices to provide one additional non-Clifford gate, such as the T gate, to achieve universality. Common methods to implement fault-tolerant T gates, e.g., magic state distillation, generate a significant hardware overhead that will likely prevent their practical usage in the near-term future. Recently, methods have been developed to mitigate the effect of noise in shallow quantum circuits that are not protected by error correction. Error mitigation methods require no additional hardware resources but suffer from a bad asymptotic scaling and apply only to a restricted class of quantum algorithms. In this Letter, we combine both approaches and show how to implement encoded Clifford+T circuits where Clifford gates are protected from noise by error correction while errors introduced by noisy encoded T gates are mitigated using the quasiprobability method. As a result, Clifford+T circuits with a number of T gates inversely proportional to the physical noise rate can be implemented on small error-corrected devices without magic state distillation. We argue that such circuits can be out of reach for state-of-the-art classical simulation algorithms.
Learning Compact Features for Human Activity Recognition Via Probabilistic First-Take-All

With the popularity of mobile sensor technology, smart wearable devices open a unprecedented opportunity to solve the challenging human activity recognition (HAR) problem by learning expressive representations from the multi-dimensional daily sensor signals. This inspires us to develop a new algorithm applicable to both camera-based and wearable sensor-based HAR systems. Although competitive classification accuracy has been reported, existing methods often face the challenge of distinguishing visually similar activities composed of activity patterns in different temporal orders. In this paper, we propose a novel probabilistic algorithm to compactly encode temporal orders of activity patterns for HAR. Specifically, the algorithm learns an optimal set of latent patterns such that their temporal structures really matter in recognizing different human activities. Then, a novel probabilistic First-Take-All (pFTA) approach is introduced to generate compact features from the orders of these latent patterns to encode the entire sequence, and the temporal structural similarity between different sequences can be efficiently measured by the Hamming distance between compact features. Experiments on three public HAR datasets show the proposed pFTA approach can achieve competitive performance in terms of accuracy as well as efficiency.
Tunable single-photon quantum router

We propose an efficient single-photon router comprising two resonator waveguide channels coupled by several sequential cavities with embedded three-level atoms. We show that the system can operate as a perfect four-way single-photon switch. We also demonstrate that an incident single-photon propagating in one of the waveguides can be routed into one or the other output channels; such routing can be controlled by the external classical electromagnetic field driving the atoms. We argue that, under appropriate conditions, the efficiency of such routing can be close to 100\% within a broad operational bandwidth, suggesting various applications in photonics.
Evaluating visual analytics for health informatics applications: a systematic review from the American Medical Informatics Association Visual Analytics Working Group Task Force on Evaluation

Abstract Objective This article reports results from a systematic literature review related to the evaluation of data visualizations and visual analytics technologies within the health informatics domain. The review aims to (1) characterize the variety of evaluation methods used within the health informatics community and (2) identify best practices. Methods A systematic literature review was conducted following PRISMA guidelines. PubMed searches were conducted in February 2017 using search terms representing key concepts of interest: health care settings, visualization, and evaluation. References were also screened for eligibility. Data were extracted from included studies and analyzed using a PICOS framework: Participants, Interventions, Comparators, Outcomes, and Study Design. Results After screening, 76 publications met the review criteria. Publications varied across all PICOS dimensions. The most common audience was healthcare providers (n = 43), and the most common data gathering methods were direct observation (n = 30) and surveys (n = 27). About half of the publications focused on static, concentrated views of data with visuals (n = 36). Evaluations were heterogeneous regarding setting and measurements used. Discussion When evaluating data visualizations and visual analytics technologies, a variety of approaches have been used. Usability measures were used most often in early (prototype) implementations, whereas clinical outcomes were most common in evaluations of operationally-deployed systems. These findings suggest opportunities for both (1) expanding evaluation practices, and (2) innovation with respect to evaluation methods for data visualizations and visual analytics technologies across health settings. Conclusion Evaluation approaches are varied. New studies should adopt commonly reported metrics, context-appropriate study designs, and phased evaluation strategies.
A new quantum approach to binary classification

This paper proposes a new quantum-like method for the binary classification applied to classical datasets. Inspired by the quantum Helstrom measurement, this innovative approach has enabled us to define a new classifier, called Helstrom Quantum Centroid (HQC). This binary classifier (inspired by the concept of distinguishability between quantum states) acts on density matrices—called density patterns—that are the quantum encoding of classical patterns of a dataset. In this paper we compare the performance of HQC with respect to twelve standard (linear and non-linear) classifiers over fourteen different datasets. The experimental results show that HQC outperforms the other classifiers when compared to the Balanced Accuracy and other statistical measures. Finally, we show that the performance of our classifier is positively correlated to the increase in the number of “quantum copies” of a pattern and the resulting tensor product thereof.
Studying Summarization Evaluation Metrics in the Appropriate Scoring Range

In summarization, automatic evaluation metrics are usually compared based on their ability to correlate with human judgments.Unfortunately, the few existing human judgment datasets have been created as by-products of the manual evaluations performed during the DUC/TAC shared tasks.However, modern systems are typically better than the best systems submitted at the time of these shared tasks.We show that, surprisingly, evaluation metrics which behave similarly on these datasets (average-scoring range) strongly disagree in the higher-scoring range in which current systems now operate.It is problematic because metrics disagree yet we can't decide which one to trust.This is a call for collecting human judgments for high-scoring summaries as this would resolve the debate over which metrics to trust.This would also be greatly beneficial to further improve summarization systems and metrics alike.
Using Platform Signals for Distinguishing Discourses: The Case of Men’s Rights and Men’s Liberation on Reddit

Reddit’s men’s rights community (/r/MensRights) has been criticized for the promotion of misogynistic language, toxic masculinity and discourses that reinforce alt-right ideologies. Conversely, the men’s liberation (/r/MensLib) community integrates inclusive politics, intersectionality and masculinity within a broad umbrella of self-reflection that suggests toxic masculinity harms men as well as women.&#x0D; We use machine learning text classifiers, keyword frequencies, and qualitative approaches first to distinguish these two subreddits, and second to interpret the differences ideologically rather than topically. We further integrate platform metadata (referred to as ‘platform signals’) to distinguish the subreddits. These signals help us understand how similar terms can be used to arrive at different interpretations of gender and discrimination. Where /r/MensLib tends to see masculinity as an adjective and women as peers, /r/MensRights views being a man as an essential quality, men as the target of discrimination, and women as sources of personalized grievances.
ActionNet: Vision-Based Workflow Action Recognition From Programming Screencasts

Programming screencasts have two important applications in software engineering context: study developer behaviors, information needs and disseminate software engineering knowledge. Although programming screencasts are easy to produce, they are not easy to analyze or index due to the image nature of the data. Existing techniques extract only content from screencasts, but ignore workflow actions by which developers accomplish programming tasks. This significantly limits the effective use of programming screencasts in downstream applications. In this paper, we are the first to present a novel technique for recognizing workflow actions in programming screencasts. Our technique exploits image differencing and Convolutional Neural Network (CNN) to analyze the correspondence and change of consecutive frames, based on which nine classes of frequent developer actions can be recognized from programming screencasts. Using programming screencasts from Youtube, we evaluate different configurations of our CNN model and the performance of our technique for developer action recognition across developers, working environments and programming languages. Using screencasts of developers' real work, we demonstrate the usefulness of our technique in a practical application for actionaware extraction of key-code frames in developers' work.
Deep Hierarchical Network for Automatic Modulation Classification

In non-cooperative communication scenarios, automatic modulation classification (AMC) is the premise of information acquisition. It has been a difficult issue for decades due to the attenuation and interference during wireless transmission. In this paper, a novel deep hierarchical network (DHN) based on convolutional neural network (CNN) is proposed for the AMC. The model is designed to combine the shallow features with high-level features. Thus, it can simultaneously have global receptive field and location information through multi-level feature extraction and does not require any transformation of the raw data. To make full use of limited data, a new method is proposed to use signal-to-noise ratio (SNR) as a weight in training instead of working as an indicator of system robustness. Furthermore, some other deep learning methods have been used to explore whether they could improve the performance of the proposed model. Several new techniques have been chosen to be applied in the DHN at last. Then, a detailed analysis of the improvement in network performance is provided. Combination of the DHN and the weighted-loss can achieve more than 93% classification accuracy which is the best performance in an open source dataset.
Path Planning in Multiple-AUV Systems for Difficult Target Traveling Missions: A Hybrid Metaheuristic Approach

Multiple autonomous underwater vehicles (AUVs) are popular for challenging submarine missions. In this article, we focus on the multi-AUV path planning for a common class of missions that need to traverse lots of mission targets in large and complex environments. Given that AUVs are often launched from a movable surface vehicle, e.g., a ship, a multi-AUV target traveling problem is formulated with a requirement of surface point location. Thereafter, a hybrid metaheuristic approach is developed by sequentially performing a cube-based environment modeling, cost map building, voyage planning, and detailed trajectory planning. Specifically, the shortest path faster algorithm (SPFA) is adopted to build a cost map among targets and candidate surface points, and the A* search is utilized for trajectory planning. The optimality of both SPFA and A* can indeed be guaranteed. Thus, voyage planning becomes critical and an algorithm namely DE-C-ACO is proposed by combining the ant colony optimization (ACO) and the differential evolution (DE) with a cluster-based adjustment strategy, i.e., DE-C. DE-C and ACO evolve in parallel for surface point location and voyage generation, respectively. Experiments based on realistic bathymetries are conducted and the results validate the effectiveness and efficiency of the proposed DE-C-ACO.
Real-Time Motion Planning Approach for Automated Driving in Urban Environments

Autonomous vehicles must be able to react in a timely manner to typical and unpredictable situations in urban scenarios. In this connection, motion planning algorithms play a key role as they are responsible of ensuring driving safety and comfort while producing human-like trajectories in a wide range of driving scenarios. Typical approaches for motion planning focus on trajectory optimization by applying computation-intensive algorithms, rather than finding a balance between optimatily and computing time. However, for on-road automated driving at medium and high speeds, determinism is necessary at high sampling rates. This work presents a trajectory planning algorithm that is able to provide safe, human-like and comfortable trajectories by using cost-effective primitives evaluation based on quintic Bézier curves. The proposed method is able to consider the kinodynamic constrains of the vehicle while reactively handling dynamic real environments in real-time. The proposed motion planning strategy has been implemented in a real experimental platform and validated in different real operating environments, successfully overcoming typical urban traffic scenes where both static and dynamic objects are involved.
Learning a Deep Dual Attention Network for Video Super-Resolution

Recently, deep learning based video super-resolution (SR) methods combine the convolutional neural networks (CNN) with motion compensation to estimate a high-resolution (HR) video from its low-resolution (LR) counterpart. However, most previous methods conduct downscaling motion estimation to handle large motions, which can lead to detrimental effects on the accuracy of motion estimation due to the reduction of spatial resolution. Besides, these methods usually treat different types of intermediate features equally, which lack flexibility to emphasize meaningful information for revealing the high-frequency details. In this paper, to solve above issues, we propose a deep dual attention network (DDAN), including a motion compensation network (MCNet) and a SR reconstruction network (ReconNet), to fully exploit the spatio-temporal informative features for accurate video SR. The MCNet progressively learns the optical flow representations to synthesize the motion information across adjacent frames in a pyramid fashion. To decrease the mis-registration errors caused by the optical flow based motion compensation, we extract the detail components of original LR neighboring frames as complementary information for accurate feature extraction. In the ReconNet, we implement dual attention mechanisms on a residual unit and form a residual attention unit to focus on the intermediate informative features for high-frequency details recovery. Extensive experimental results on numerous datasets demonstrate the proposed method can effectively achieve superior performance in terms of quantitative and qualitative assessments compared with state-of-the-art methods.
A Dynamic Bayesian Network Approach for Analysing Topic-Sentiment Evolution

Sentiment analysis is one of the key tasks of natural language understanding.Sentiment Evolution models the dynamics of sentiment orientation over time.It can help people have a more profound and deep understanding of opinion and sentiment implied in user generated content.Existing work mainly focuses on sentiment classification, while the analysis of how the sentiment orientation of a topic has been influenced by other topics or the dynamic interaction of topics from the aspect of sentiment has been ignored.In this paper, we propose to construct a Gaussian Process Dynamic Bayesian Network to model the dynamics and interactions of the sentiment of topics on social media such as Twitter.We use Dynamic Bayesian Networks to model time series of the sentiment of related topics and learn relationships between them.The network model itself applies Gaussian Process Regression to model the sentiment at a given time point based on related topics at previous time.We conducted experiments on a real world dataset that was crawled from Twitter with 9.72 million tweets.The experiment demonstrates a case study of analysing the sentiment dynamics of topics related to the event Brexit.
Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

Abstract The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications.