Reviewerly/exHarmony huggingface dataset:
Joint extraction of entities and relations by entity role recognition

Joint extracting entities and relations from unstructured text is a fundamental task in information extraction and a key step in constructing large knowledge graphs, entities and relations are constructed as relational triples of the form (subject, relation, object) or (s, r, o). Although triple extraction has been extremely successful, there are still continuing challenges due to factors such as entity overlap. Recent work has shown us the excellent performance of joint extraction models, however these methods still suffer from some problems, such as the redundancy prediction problem. Traditional methods for solving the overlap problem require triple extraction under the full class of relations defined in the dataset, however the number of relations in a sentence is much smaller than the full relational class, which leads to a large number of redundant predictions. To solve this problem, this paper decomposes the task into two steps: entity and potential relation extraction and entity-semantic role determination of triples. Specifically, we design several modules to extract the entities and relations in the sentence separately, and we use these entities and relations to construct possible candidate triples and predict the semantic roles (subject or object) of the entities under the relational constraints to obtain the correct triples. In general we propose a model for identifying the semantic roles of entities in triples under relation constraints, which can effectively solve the problem of redundant prediction, We also evaluated our model on two widely used public datasets, and our model achieved advanced performance with F1 scores of 90.8 and 92.4 on NYT and WebNLG, respectively.
Deformable Wiener Filter for Future Video Coding

In-loop filters have attracted increasing attention due to the remarkable noise-reduction capability in the hybrid video coding framework. However, the existing in-loop filters in Versatile Video Coding (VVC) mainly take advantage of the image local similarity. Although some non-local based in-loop filters can make up for this shortcoming, the widely-used unsupervised parameter estimation method by non-local filters limits the performance. In view of this, we propose a deformable Wiener Filter (DWF). It combines the local and non-local characteristics and supervisedly trains the filter coefficients based on the Wiener Filter theory. In the filtering process, local adjacent samples and non-local similar samples are first derived for each sample of interest. Then the to-be-filtered samples are classified into specific groups based on the patch-level noise and sample-level characteristics. Samples in each group share the same filter coefficients. After that, the local and non-local reference samples are adaptively fused based on the classification results. Finally, the filtering operation with outlier data constraints is conducted for each to-be-filtered sample. Moreover, the performance of the proposed DWF is analyzed with different reference sample derivation schemes in detail. Simulation results show that the proposed approach achieves 1.16%, 1.92%, and 2.67% bit-rate savings on average compared to the VTM-11.0 for All Intra, Random Access, and Low-Delay B configurations, respectively.
Hospital profiling using Bayesian decision theory

Abstract For evaluating the quality of care provided by hospitals, special interest lies in the identification of performance outliers. The classification of healthcare providers as outliers or non-outliers is a decision under uncertainty, because the true quality is unknown and can only be inferred from an observed result of a quality indicator. We propose to embed the classification of healthcare providers into a Bayesian decision theoretical framework that enables the derivation of optimal decision rules with respect to the expected decision consequences. We propose paradigmatic utility functions for two typical purposes of hospital profiling: the external reporting of healthcare quality and the initiation of change in care delivery. We make use of funnel plots to illustrate and compare the resulting optimal decision rules and argue that sensitivity and specificity of the resulting decision rules should be analyzed. We then apply the proposed methodology to the area of hip replacement surgeries by analyzing data from 1,277 hospitals in Germany which performed over 180,000 such procedures in 2017. Our setting illustrates that the classification of outliers can be highly dependent upon the underlying utilities. We conclude that analyzing the classification of hospitals as a decision theoretic problem helps to derive transparent and justifiable decision rules. The methodology for classifying quality indicator results is implemented in an R package (iqtigbdt) and is available on GitHub.
Error driven synapse augmented neurogenesis

Capturing the learning capabilities of the brain has the potential to revolutionize artificial intelligence. Humans display an impressive ability to acquire knowledge on the fly and immediately store it in a usable format. Parametric models of learning, such as gradient descent, focus on capturing the statistical properties of a data set. Information is precipitated into a network through repeated updates of connection weights in the direction gradients dictate will lead to less error. This work presents the EDN (Error Driven Neurogenesis) algorithm which explores how neurogenesis coupled with non-linear synaptic activations enables a biologically plausible mechanism to immediately store data in a one-shot, online fashion and readily apply it to a task without the need for parameter updates. Regression (auto-mpg) test error was reduced more than 135 times faster and converged to an error around three times smaller compared to gradient descent using ADAM optimization. EDN also reached the same level of performance in wine cultivar classification 25 times faster than gradient descent and twice as fast when applied to MNIST and the inverted pendulum (reinforcement learning).
Extended Belief Rule Base Model with Novel Rule Generation for Sensor-Based Human Activity Recognition Under Big Data

As the population ages and health-care costs increase, smart environments can be an effective and economical way to provide care and support for the aged population. Human activity recognition (HAR), a key element of the smart environment research domain, has garnered a lot of attention lately. The present work is to provide a data-driven solution based on the extended belief rule base (EBRB) model for sensor-based HAR in the context of big data. More specifically, in order to increase the efficiency of the EBRB model, this research first offers a new rule generation method based on probability estimation, which forms the link between the extended belief rules and human activities. The number of extended belief rules used to extract knowledge from a sensor-based HAR dataset is exactly equal to the types of human activities, and each rule can be thought of as a collection of class conditional probability distributions. As a result, it is possible to create an EBRB-BD model, an EBRB model for HAR using big data that has a compact but representative rule base. The effectiveness of the EBRB-BD model is further supported by case studies. Experimental findings demonstrate that the modelling time of the EBRB-BD model is one in ten-thousand of the original EBRB model, and the EBRB-BD model also achieves the best area under the curve value (AUC) of 94.95 $$\%$$ , surpassing the original EBRB model and some other benchmark classifiers.
Exploring the Affordances of Immersive Visualization Spaces: A Use Case About COVID-19

This paper presents an immersive visualization of a taxonomy containing the most relevant words to describe the COVID-19 pandemic. In a hyper-informed world, people could feel frustrated when looking for news about a sensitive topic. Counterbalancing information overload involves offering reliable information to learn about a topic of interest in an engaging way. Our solution contributes to this end by providing a COVID taxonomy and an immersive visualization. To build the taxonomy, we involved an epidemiologist identifying the most meaningful words to describe the disease. The result is a hierarchical structure with sixty terms grouped into twelve categories representing symptoms, prevention measures, and treatments. The second contribution is a visualization designed to take advantage of the affordances of immersive spaces to improve the interaction with the information. The users can explore the taxonomy by interacting with terms and categories represented as connected spheres in a virtual world. To assess the experience, we involved 32 participants in a user engagement evaluation. The results show that the immersive space is an innovative and didactic way to understand a topic of interest.
Continuous Authentication on a Smartwatch

The purpose of this work is to leverage two types of sensors, motion and optical, to create a continuous authentication system for smart devices such as smartwatches. The proposed solution is based on an Android application that uses the accelerometer and gyroscope to measure movements and to classify them in normal and session-endangering classes. If suspicious movements are identified, then the app enacts a second decision level and activates the heart or body detection sensor to check if the watch is actually still on the user’s wrist. The two-level architecture tries to optimize energy consumption. To validate our system, various measurements were carried out with the aim of mapping the typical gestures of users who wear a smartwatch. The goal is therefore to be able to recognize certain movements, limit checks involving the optical sensors that are extremely energy hungry, and, thus, achieve a better battery recharge cycle.
Adversarial reinforcement learning for dynamic treatment regimes

Treatment recommendation, as a critical task of delivering effective interventions according to patient state and expected outcome, plays a vital role in precision medicine and healthcare management. As a well-suited tactic to learn optimal policies of recommender systems, reinforcement learning is promising to address the challenge of treatment recommendation. However, existing solutions mostly require frequent interactions between treatment recommender systems and clinical environment, which are expensive, time-consuming, and even infeasible in clinical practice. In this study, we present a novel model-based offline reinforcement learning approach to optimize a treatment policy by utilizing patient treatment trajectories in Electronic Health Records (EHRs). Specifically, a patient treatment trajectory simulator is firstly constructed based on the ground-truth trajectories in EHRs. Thereafter, the constructed simulator is utilized to model the online interactions between the treatment recommender system and clinical environment. In this way, the counterfactual trajectories can be generated. To alleviate the bias deriving from the ground-truth and the counterfactual trajectories, an adversarial network is incorporated into the proposed model, such that a large space of treatment actions can be explored with the scaled rewards. The proposed model is evaluated on a simulated dataset and a real-world dataset. The experimental results demonstrate that the proposed model is superior to other methods, giving rise to a new solution for dynamic treatment regimes and beyond.
NEEBS: Nonexpert large‐scale environment building system for deep neural network

Summary Deep neural networks (DNNs) have greatly improved the accuracy of various tasks in areas such as natural language processing (NLP). Obtaining a highly accurate DNN model requires multiple repetitions of training on a huge dataset, which requires a large‐scale cluster the compute nodes of which are tightly connected by high‐speed interconnects to exchange a large amount of intermediate data with very short latency. However, fully using the computational power of a large‐scale cluster for training requires knowledge of its components such as a distributed file system, an interconnection, and optimized high‐performance libraries. We have developed a Non‐Expert large‐scale Environment Building System (NEEBS) that aids a user in building a fast‐running training environment on a large‐scale cluster. It automatically installs and configures the applications and necessary libraries. It also optimally prepares tools to stage both data and executable programs, and launcher scripts suitable for both the applications and job submission systems of the cluster. NEEBS achieves 93.91% throughput scalability in NLP pretraining. We also present an approach to reduce pretraining time of highly accurate DNN model for NLP using a large‐scale computation environment built using NEEBS. We trained a Bidirectional Encoder Representations from Transformers (BERT)‐3.9b and a BERT‐xlarge using a dense masked language model (MLM) on Megatron‐LM framework and evaluated the improvement in learning time and learning efficiency for a Japanese language dataset using 768 graphics processing units (GPUs) on the AI Bridging Cloud Infrastructure (ABCI). Our implementation NEEBS improved learning efficiency per iteration by a factor of 10 and completed the pretraining of BERT‐xlarge in 4.7 h. This pretraining takes 5 months on a single GPU. To determine if the BERT models are correctly pretrained, we evaluated their accuracy in two tasks, Stanford Natural Language Inference Corpus translated into Japanese (JSNLI) and Twitter reputation analysis (TwitterRA). BERT‐3.9b achieved 94.30% accuracy for JSNLI, and BERT‐xlarge achieved 90.63% accuracy for TwitterRA. We constructed pretrained models with comparable accuracy to other Japanese BERT models in a shorter time.
Struct2Hair: A hair shape descriptor for hairstyle modeling

Abstract In recent years, it becomes possible to extract hair information for hair reconstruction from multiple cameras or monocular camera. Using a single image as the input avoids the high cost setups and complex calibration compared to multiviewed reconstruction. Taking advantage of an extendible hairstyle database, this paper introduced Struct2Hair, a novel single‐viewed hair modelling approach by extracting hair shape descriptor (HSD). The HSD is defined as the fundamental structure‐aware feature, which is a combination of critical shapes in a hairstyle. A complete dataset of critical hair shapes is constructed from a known database of three‐dimensional (3D) hair models. We first analyze the input two‐dimensional (2D) image to extract the orientation information and 2D hair sketch automatically. The extracted information is then used to retrieve the corresponding critical shapes with optimization to build the robust HSD. Finally, the HSD constructs a weighted 3D hair orientation field to guide full‐head hair model generation. Our method can preserve local geometric features of hair and retain the whole shape of the hairstyle globally owing to the HSD, which will benefit further hair editing and stylization.
A Bayesian approach to forward and inverse abstract argumentation problems

This paper studies a fundamental mechanism by which conflicts between arguments are drawn from sentiments regarding acceptability of the arguments. Given sets of arguments, an inverse abstract argumentation problem seeks attack relations between arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in each of the attack relations. It is an inverse problem of the traditional problem we refer to as the forward abstract argumentation problem. Given an attack relation, the forward abstract argumentation problem seeks sets of arguments such that acceptability semantics interprets each argument in the sets of arguments as being acceptable in the attack relation. We give a probabilistic model of argumentation-theoretic inference. It is a generative model formalising the process by which acceptability semantics interprets acceptability of arguments in a given attack relation. We show that it gives a broad view of solutions to the forward and inverse abstract argumentation problems. Specifically, solutions to the inverse and forward abstract argumentation problems are shown to be equivalent to a maximum likelihood estimate and maximum likelihood prediction, respectively, which are both available with the generative model. In addition, they are shown to be special cases of the posterior distribution and the evidence, respectively, which are both obtained by probabilistic inference on the generative model. We report an experiment result and application example of the generative model in the inverse problems.
Field Cognitive Styles on Visual Cognition in the Event Structure Design of Bivariate Interactive Dorling Cartogram—The Similarities and Differences of Field-Independent and Field-Dependent Users

As a simple, discontinuous, surface deformation statistical map, Dorling cartograms are effective means with which to characterize the geographic distribution of event data attributes. According to existing research, behavioral differences exist in the visual cognition of individuals with different cognitive field styles in the spatial task of switching layers in a two-dimensional electronic map. However, there are few studies that compare the visual cognitive ability of individuals with different cognitive field styles in the cross-layer structure design of Dorling cartogram event information. This paper uses the visual behavior measurement method to analyze the similarities and differences in the visual cognitive ability of two types of individuals, namely, field-independent and field-dependent individuals, in the cross-layer event structure design of Dorling cartograms. We recruited 40 subjects to perform visualization tasks on Dorling cartograms designed with two event structures, and we recorded the visual cognition data for the two types of subjects in both tasks. The results show that the subjects with the field-independent style perform better in the cognition of the Dorling cartogram event structure than the subjects with the field-dependent style, and the “S-T” event structure design is generally more user-friendly than the “T-S” event structure design. Our findings help to provide some references for the event structure design of human-centered Dorling cartograms.
A Modified Wei-Hua-He Digital Signature Scheme Based on Factoring and Discrete Logarithm

A symmetric cipher such as AES in cryptography is much faster than an asymmetric cipher but digital signatures often use asymmetric key ciphers because they provides the sender’s identity and data integrity. In this paper, a modified-He digital signature scheme is proposed using a one-way hash function. The proposed scheme, unlike the He signature technique, employs Euclid’s Division Lemma with large prime moduli p. Its security is built on large integer factoring, discrete logarithms and expanded root problems. The time complexity of the proposed scheme is O(log3p). The proposed modified-He scheme is efficient, as evidenced by the analytical results with key lengths greater than 512 bits.
Semi-supervised LDA pedestrian re-identification algorithm based on K-nearest neighbor resampling

Person re-identification identify a specific person in surveillance network by similarity measurement between images of different camera views. However, existing metric learning based methods suffer from over-fitting problem. To solve this problem, a resampled linear discriminant analysis (LDA) method was proposed based on the statistical and topological characteristics of pedestrian images. This method utilized the k-nearest neighbours to form potential positive sample pairs. The potential positive pairs are used to improve the metric model and generalize the metric model to the test data. By minimizing the inter-class divergence of potential positive sample pairs, a semi-supervised re-sampling LDA person re-identification algorithm was established. It was then tested on the VIPeR, CUHK01 and Market 1501datasets. The results show that the proposed method achieves the best performance compared to some available methods. Especially, the proposed method outplays the best comparison method by 0.6% and 5.76% at rank-1 identification rate on the VIPeR and CUHK01 datasets respectively. At the same time, the improved LDA algorithm has improved the rank-1 identification accuracy of traditional LDA method by 9.36% and 32.11% on these two datasets respectively. Besides, the proposed method is limited to Market-1501 dataset when the test data is of large size.
Discourses on the Social Accommodations of Code-Switching in Computer-Mediated Communication

Code-Switching is a prevalent phenomenon that may be observed in verbal or non-verbal communication of bilinguals who shift between two languages in a conversation. This paper concentrates on the sociolinguistic factors that influence Filipino-English bilinguals to engage in Code-Switching in an online written discourse. Data were gathered by means of transcribing Facebook posts which were analyzed using Appel and Muysken’s (2006) and Malik’s (1994) framework. The findings revealed that the communicative functions comprised in Appel and Muysken’s (2006) framework were predominant in computer-mediated communication between Filipino-English bilinguals. Specifically, 3 functions known as referential, expressive, and directive had the most number of occurrences, which generated 3 prime factors why bilinguals tend to switch between codes: (1) it provides convenience in communication; (2) bilinguals are accustomed to engaging in Code-Switching when communicating feelings, emotions, requests and words of gratitude; and (3) bilinguals switch between codes to directly address the receiver of the message.
An Effective Approach to Scramble Multiple Diagnostic Imageries Using Chaos-Based Cryptography

Medical image encryption could aid in preserving patient privacy. In this article, we provide a chaotic system-based medical picture encryption method. The diffusion and permutation architecture was used. The permutation based on plain image and chaotic keys is offered to shuffle the plain picture's pixels to other rows and columns, successfully weakening the strong connections between neighboring pixels. Diffusion is suggested to spread small changes of plain images to all of the pixels in cipher images to enhance the encryption effect. We analyze the chaotic behavior of the proposed system using various techniques and tests such as bifurcation plots, Lyapunov exponents, MSE, PSNR tests, and histogram analysis.