Reviewerly/exHarmony huggingface dataset:
Weakly supervised spatial relation extraction from radiology reports

Weak supervision holds significant promise to improve clinical natural language processing by leveraging domain resources and expertise instead of large manually annotated datasets alone. Here, our objective is to evaluate a weak supervision approach to extract spatial information from radiology reports.Our weak supervision approach is based on data programming that uses rules (or labeling functions) relying on domain-specific dictionaries and radiology language characteristics to generate weak labels. The labels correspond to different spatial relations that are critical to understanding radiology reports. These weak labels are then used to fine-tune a pretrained Bidirectional Encoder Representations from Transformers (BERT) model.Our weakly supervised BERT model provided satisfactory results in extracting spatial relations without manual annotations for training (spatial trigger F1: 72.89, relation F1: 52.47). When this model is further fine-tuned on manual annotations (relation F1: 68.76), performance surpasses the fully supervised state-of-the-art.To our knowledge, this is the first work to automatically create detailed weak labels corresponding to radiological information of clinical significance. Our data programming approach is (1) adaptable as the labeling functions can be updated with relatively little manual effort to incorporate more variations in radiology language reporting formats and (2) generalizable as these functions can be applied across multiple radiology subdomains in most cases.We demonstrate a weakly supervision model performs sufficiently well in identifying a variety of relations from radiology text without manual annotations, while exceeding state-of-the-art results when annotated data are available.
Colon Cancer Prediction with Transfer Learning and K-Means Clustering

The automatic diagnosis of colon cancer is important for patients and their prognosis through the analysis of histopathological images. Traditional feature extraction methods extract low-level image features, and prior knowledge is required to choose meaningful features, which may be modified significantly by humans. Hence, unsupervised and supervised deep convolutions neural networks were utilized to analyze histopathological images of colon cancer. To overcome the impact of the unbalanced histopathology images in sub-classes, the balanced sub-classes are turned right and left, up and down, and rotated counter clockwise by 90 $$^\circ $$ and 180 $$^\circ $$ . The proposed experimental findings for supervised histopathological image classification of colon cancer demonstrate that $$Inception\_V3$$ and Inception $$ResNet\_V2$$ outperform current algorithms. These findings suggest that the $$Inception\_ResNet\_V2$$ network is superior deep learning architecture for analyzing histopathological images for diagnosis colon cancers. As a result, in order to perform unsupervised image analysis, $$Inception\_ResNet\_V2$$ is utilized to extract features from colon cancer histopathology images. In addition, a new autoencoder network was created to convert the features collected by $$Inception\_ResNet\_V2$$ to a low-dimensional space for image clustering analysis. The test results demonstrate that the proposed autoencoder network outperforms the $$Inception\_ResNet\_V2$$ network in terms of clustering.
Fall detection based on dynamic key points incorporating preposed attention

Accidental falls pose a significant threat to the elderly population, and accurate fall detection from surveillance videos can significantly reduce the negative impact of falls. Although most fall detection algorithms based on video deep learning focus on training and detecting human posture or key points in pictures or videos, we have found that the human pose-based model and key points-based model can complement each other to improve fall detection accuracy. In this paper, we propose a preposed attention capture mechanism for images that will be fed into the training network, and a fall detection model based on this mechanism. We accomplish this by fusing the human dynamic key point information with the original human posture image. We first propose the concept of dynamic key points to account for incomplete pose key point information in the fall state. We then introduce an attention expectation that predicates the original attention mechanism of the depth model by automatically labeling dynamic key points. Finally, the depth model trained with human dynamic key points is used to correct the detection errors of the depth model with raw human pose images. Our experiments on the Fall Detection Dataset and the UP-Fall Detection Dataset demonstrate that our proposed fall detection algorithm can effectively improve the accuracy of fall detection and provide better support for elderly care.
Implementing Post-quantum Cryptography for Developers

Abstract Widely used public key cryptography is threatened by the development of quantum computers. Post-quantum algorithms have been designed for the purpose of protecting sensitive data against attacks with quantum computers. National Institute of Standards and Technology has recently reached the end of the third round of post-quantum standardization process and has published three digital signatures and one key encapsulation mechanism for standardization. Three of the chosen algorithms are based on lattices. When implementing complex cryptographic algorithms, developers commonly use cryptographic libraries in their solutions to avoid mistakes. However, most of the open-source cryptography libraries do not yet have post-quantum algorithms integrated in them. We chose a C++ cryptography library, Crypto++, and created a fork where we integrated four lattice-based post-quantum algorithms. We analyzed the challenges in the process as well as the performance, correctness and security of the implemented algorithms. The performance of the integrated algorithms was overall good, but the integration process had its challenges, many of which were caused by the mathematical complexity of lattice-based algorithms. Different open-source implementations of post-quantum algorithms will be essential to their easier use for developers. Usability of the implementations is also important to avoid possible mistakes when using the algorithms.
Graph-based Fusion Modeling and Explanation forDisease Trajectory Prediction.

We propose a relational graph to incorporate clinical similarity between patients while building personalized clinical event predictors with a focus on hospitalized COVID-19 patients. Our graph formation process fuses heterogeneous data, i.e., chest X-rays as node features and non-imaging EHR for edge formation. While node represents a snap-shot in time for a single patient, weighted edge structure encodes complex clinical patterns among patients. While age and gender have been used in the past for patient graph formation, our method incorporates complex clinical history while avoiding manual feature selection. The model learns from the patient's own data as well as patterns among clinically-similar patients. Our visualization study investigates the effects of 'neighborhood' of a node on its predictiveness and showcases the model's tendency to focus on edge-connected patients with highly suggestive clinical features common with the node. The proposed model generalizes well by allowing edge formation process to adapt to an external cohort.
Deep Learning for Epidemiologists: An Introduction to Neural Networks

Abstract Deep learning methods are increasingly being applied to problems in medicine and health care. However, few epidemiologists have received formal training in these methods. To bridge this gap, this article introduces the fundamentals of deep learning from an epidemiologic perspective. Specifically, this article reviews core concepts in machine learning (e.g., overfitting, regularization, and hyperparameters); explains several fundamental deep learning architectures (convolutional neural networks, recurrent neural networks); and summarizes training, evaluation, and deployment of models. Conceptual understanding of supervised learning algorithms is the focus of the article; instructions on the training of deep learning models and applications of deep learning to causal learning are out of this article’s scope. We aim to provide an accessible first step towards enabling the reader to read and assess research on the medical applications of deep learning and to familiarize readers with deep learning terminology and concepts to facilitate communication with computer scientists and machine learning engineers.
Group-Wise Co-Salient Object Detection with Siamese Transformers Via Brownian Distance Covariance Matching

Co-salient object detection (CoSOD) aims to discover and segment foreground targets in a group of images with the same semantic category. Existing mainstream approaches often employ convolutional neural networks (CNNs) to learn the semantic-invariant features from a group of images. Despite demonstrated success, there exist two limitations: 1) The CNNs introduce the inductive bias of locality that are difficult to model long-range dependency, limiting their feature representation capability. 2) Their models lack discriminability to differentiate semantic differences between different groups since only one group of images with the same semantic category has been taken into account for model training. To address these issues, this paper presents a Siamese Transformer architecture for CoSOD that can fully mine the group-wise semantic contrast information for more discriminative feature learning. Specifically, the designed Siamese Transformer takes two groups of images as input for feature contrastive learning. Each group is processed by a Transformer branch with shared weights to capture the long-range interaction information. Besides, to model the complex non-linear interactions between these two branches, we further design a Brownian distance covariance (BDC) module that uses joint distribution to measure the inter- and intra-group semantic similarity. The BDC can be efficiently calculated in closed form that can fully characterize independence for effective feature contrastive learning. Extensive evaluations on the three largest and most challenging benchmark datasets (CoSal2015, CoCA, and CoSOD3k) demonstrate the superiority of our method over a variety of state-of-the-art methods.
Cross-Device Federated Learning for Mobile Health Diagnostics: A First Study on COVID-19 Detection

Federated learning (FL) aided health diagnostic models can incorporate data from a large number of personal edge devices (e.g., mobile phones) while keeping the data local to the originating devices, largely ensuring privacy. However, such a cross-device FL approach for health diagnostics still imposes many challenges due to both local data imbalance (as extreme as local data consists of a single disease class) and global data imbalance (the disease prevalence is generally low in a population). Since the federated server has no access to data distribution information, it is not trivial to solve the imbalance issue towards an unbiased model. In this paper, we propose FedLoss, a novel cross-device FL framework for health diagnostics. Here the federated server averages the models trained on edge devices according to the predictive loss on the local data, rather than using only the number of samples as weights. As the predictive loss better quantifies the data distribution at a device, FedLoss alleviates the impact of data imbalance. Through a real-world dataset on respiratory sound and symptom-based COVID-19 detection task, we validate the superiority of FedLoss. It achieves competitive COVID-19 detection performance compared to a centralised model with an AUC-ROC of 79%. It also outperforms the state-of-the-art FL baselines in sensitivity and convergence speed. Our work not only demonstrates the promise of federated COVID-19 detection but also paves the way to a plethora of mobile health model development in a privacy-preserving fashion.
Learnable Distribution Calibration for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) faces the challenges of memorizing old class distributions and estimating new class distributions given few training samples. In this study, we propose a learnable distribution calibration (LDC) approach, to systematically solve these two challenges using a unified framework. LDC is built upon a parameterized calibration unit (PCU), which initializes biased distributions for all classes based on classifier vectors (memory-free) and a single covariance matrix. The covariance matrix is shared by all classes, so that the memory costs are fixed. During base training, PCU is endowed with the ability to calibrate biased distributions by recurrently updating sampled features under supervision of real distributions. During incremental learning, PCU recovers distributions for old classes to avoid ‘forgetting’, as well as estimating distributions and augmenting samples for new classes to alleviate ‘over-fitting’ caused by the biased distributions of few-shot samples. LDC is theoretically plausible by formatting a variational inference procedure. It improves FSCIL’s flexibility as the training procedure requires no class similarity priori. Experiments on CUB200, CIFAR100, and mini-ImageNet datasets show that LDC respectively outperforms the state-of-the-arts by 4.64%, 1.98%, and 3.97%. LDC’s effectiveness is also validated on few-shot learning scenarios. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Bibikiller/LDC</uri> .
Off-Policy Evaluation With Online Adaptation for Robot Exploration in Challenging Environments

Autonomous exploration has many important applications. However, classic information gain-based or frontier-based exploration only relies on the robot current state to determine the immediate exploration goal, which lacks the capability of predicting the value of future states and thus leads to inefficient exploration decisions. This letter presents a method to learn how “good” states are, measured by the state value function, to provide a guidance for robot exploration in real-world challenging environments. We formulate our work as an off-policy evaluation (OPE) problem for robot exploration (OPERE). It consists of offline Monte-Carlo training on real-world data and performs Temporal Difference (TD) online adaptation to optimize the trained value estimator. We also design an intrinsic reward function based on sensor information coverage to enable the robot to gain more information with sparse extrinsic rewards. Results show that our method enables the robot to predict the value of future states so as to better guide robot exploration. The proposed algorithm achieves better prediction and exploration performance compared with the state-of-the-arts. To the best of our knowledge, this work for the first time demonstrates value function prediction on real-world dataset for robot exploration in challenging subterranean and urban environments.
A spectral graph convolution for signed directed graphs via magnetic Laplacian

Signed directed graphs contain both sign and direction information on their edges, providing richer information about real-world phenomena compared to unsigned or undirected graphs. However, analyzing such graphs is more challenging due to their complexity, and the limited availability of existing methods. Consequently, despite their potential uses, signed directed graphs have received less research attention. In this paper, we propose a novel spectral graph convolution model that effectively captures the underlying patterns in signed directed graphs. To this end, we introduce a complex Hermitian adjacency matrix that can represent both sign and direction of edges using complex numbers. We then define a magnetic Laplacian matrix based on the adjacency matrix, which we use to perform spectral convolution. We demonstrate that the magnetic Laplacian matrix is positive semi-definite (PSD), which guarantees its applicability to spectral methods. Compared to traditional Laplacians, the magnetic Laplacian captures additional edge information, which makes it a more informative tool for graph analysis. By leveraging the information of signed directed edges, our method generates embeddings that are more representative of the underlying graph structure. Furthermore, we showed that the proposed method has wide applicability for various graph types and is the most generalized Laplacian form. We evaluate the effectiveness of the proposed model through extensive experiments on several real-world datasets. The results demonstrate that our method outperforms state-of-the-art techniques in signed directed graph embedding.
Quantum operation of affective artificial intelligence

Abstract The review analyses the fundamental principles which Artificial Intelligence should be based on in order to imitate the realistic process of taking decisions by humans experiencing emotions. Two approaches are considered, one based on quantum theory and the other employing classical terms. Both these approaches have a number of similarities, being principally probabilistic. The analogies between quantum measurements under intrinsic noise and affective decision making are elucidated. It is shown that cognitive processes have many features that are formally similar to quantum measurements. This, however, in no way means that for the imitation of human decision making Affective Artificial Intelligence has necessarily to rely on the functioning of quantum systems. The analogies between human decision making and quantum measurements merely demonstrate formal common properties in their functioning. It is in this sense that one has to understand quantum operation of Artificial Intelligence. Appreciating the common features between quantum measurements and decision making helps for the formulation of an axiomatic approach employing only classical notions. Artificial Intelligence, following this approach, operates similarly to humans, by taking into account the utility of the considered alternatives as well as their emotional attractiveness. Affective Artificial Intelligence, whose operation takes account of the cognition-emotion duality, avoids numerous behavioural paradoxes of traditional decision making. A society of intelligent agents, interacting through the repeated multistep exchange of information, forms a network accomplishing dynamic decision making based on the evaluation of utility and affected by the emotional attractiveness of alternatives. The considered intelligent networks can characterize the operation of either a human society of affective decision makers, or the brain composed of neurons, or a typical probabilistic network of an artificial intelligence.
Federated Learning for Online Resource Allocation in Mobile Edge Computing: A Deep Reinforcement Learning Approach

Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems.Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use.Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a reduction in energy consumption.A trade-off between the energy consumption of edge devices and the learning accuracy of FL is formulated in this proposed work.The FL-enabled twin-delayed deep deterministic policy gradient (FL-TD3) framework is proposed as a solution to the formulated problem because its state and action spaces are large in a continuous domain.This framework provides the maximum accuracy ratio of FL divided by the device's energy consumption.A comparison of the numerical results with the state-of-the-art demonstrates that the ratio has been improved significantly.
CHALLENGING ISSUES IN CANTONESE-ENGLISH LEXICOGRAPHY

At the outset of writing a bilingual Cantonese-English dictionary that is comprehensive in scope, the lexicographer confronts a series of challenging issues that are fundamental to the successful creation of such an ambitious dictionary. Among the crucial questions to be satisfactorily resolved are at least the following eight: 1) What are the principal criteria that guide the lexicographer’s selection of the Cantonese lexical items that form the contents of the bilingual dictionary? Further, what is the scope of the lexical contents? Broad, by encompassing all lexical items that occur in the speech of Hong Kong Cantonese speakers, even those items that overlap with standard Chinese? Or, narrow, by focusing only on the uniquely Hong Kong Cantonese lexicon? 2) What categories of information about the lexical items should form the structural contents of lexical entries? These could include parts of speech, speech registers, cross-referencing of related lexical items, synonyms, alternative pronunciations and variant written forms, example sentences, etc. 3) How detailed should the equivalent English translations of the Cantonese lexical items be? 4) Should lexical entries include information on their social, cultural, historical, and political associations, usage, etymology (origin and historical development), etc.? 5) What is the organizing principle by which the Cantonese lexical entries are listed in the dictionary, as there are at least four? 6) As for Cantonese pronunciation, which register of the language should be romanized? The conservative, literary, standard pronunciation? The colloquial, informal, commonly-used 懶 音 laan5 jam1 “lazy” pronunciation? Or both of these? 7) Several Cantonese romanization systems are currently in use, so which one is best for representing Hong Kong’s contemporary Cantonese pronunciation? 8) Given that the written form of the Cantonese language has never been formally or officially standardized, so that some lexical items are typically written in two or more different ways, what criteria should guide the lexicographer in deciding how Cantonese words are graphically transcribed in a Cantonese-English dictionary? In writing his ABC Cantonese-English Comprehensive Dictionary 粵語英語大詞典 (Bauer 2020) over the past decade and a half, the author had to face and then answer appropriately each of these important questions. This article has described how he addressed and practically resolved these and other related problems.