Reviewerly/exHarmony huggingface dataset:
A Conceptual Framework for Personal Science

This paper introduces a conceptual framework to guide research and education into the practice of personal science, which we define as using empirical methods to pursue personal health questions. Personal science consists of five activities: questioning, designing, observing, reasoning, and discovering. These activities are conceptual abstractions derived from review of self-tracking practices in the Quantified Self community. These practices have been enabled by digital tools to collect personal real-world data. Similarities and differences between personal science, citizen science and single subject (N-of-1) research in medicine are described. Finally, barriers that constrain widespread adoption of personal science and limit the potential benefits to individual wellbeing and clinical and public health discovery are briefly discussed, with perspectives for overcoming these barriers.
Sparse and low-rank regularized deep subspace clustering

Subspace clustering aims at discovering the intrinsic structure of data in unsupervised fashion. As ever in most of approaches, an affinity matrix is constructed by learning from original data or the corresponding hand-crafted feature with some constraints on the self-expressive matrix (SEM), which is then followed by spectral clustering algorithm. Based on successful applications of deep technologies, it has become popular to simultaneously accomplish deep feature and self-representation learning for subspace clustering. However, deep feature and SEM in previous deep methods are lack of precise constraints, which is sub-optimal to conform with the linear subspace model. To address this, we propose an approach, namely sparse and low-rank regularized deep subspace clustering (SLR-DSC). In the proposed SLR-DSC, an end-to-end framework is proposed by introducing sparse and low-rank constraints on deep feature and SEM respectively. The sparse deep feature and low-rank regularized SEM implemented via fully-connected layers are encouraged to facilitate a more informative affinity matrix. In order to solve the nuclear norm minimization problem, a sub-gradient computation strategy is utilized to cater to the chain rule. Experiments on the data sets demonstrate that our method significantly outperforms the competitive unsupervised subspace clustering approaches.
Designing a SHA-256 processor for blockchain-based IoT applications

An increasing number of Internet of Things applications have been recently considering the use of the blockchain technology, calling for dedicated computational capabilities of the underlying devices. This poses an inherent challenge in an application domain notoriously characterised by several, often stringent limitations in terms of available resources. This paper focuses on a key component of the blockchain technology, the SHA-256 hash function, proposing a hardware accelerator which can be adapted to different requirements according to the application context. Experimental results show how the proposed accelerator can be used to satisfy different sets of constraints, effectively providing low-end devices with the computational capabilities required to undertake complex tasks such as blockchain protocols.
A Full-Reference Stereoscopic Image Quality Measurement Via Hierarchical Deep Feature Degradation Fusion

For the problem of stereoscopic image quality measurement (SIQM), it is difficult to design an efficient yet reliable full reference (FR) SIQM method due to our limited knowledge about the properties of human binocular vision. Inspired by the fact that the input visual information is hierarchically processed in our human brain, we consider different levels of distortion in an image cause individual degradations on hierarchical features, and propose to fuse the degradations on hierarchical features to facilitate the task of FR-SIQM. As one of the most classical convolutional neural network (CNN) architectures, the VGG-16 network is first applied to each view of the stereopair to build hierarchical deep feature representations based on which monocular quality estimation (MQE) and binocular quality fusion (BQF) are then performed. Specifically, the MQE stage estimates a set of layer-wise monocular quality scores by measuring the similarity between the hierarchical feature maps of the distorted monocular view and those of the reference monocular view. The BQF stage estimates a set of layer-wise binocular quality scores via a weighted average of the corresponding layer-wise monocular quality scores. The adaptive weights are determined by a modified hierarchical feature energy-based Gain-Control model. Finally, the layer-wise binocular quality scores across all layers are fused into an overall binocular quality score via regression. Experiments on three benchmark databases validate the state-of-the-art performance of our method.
M-GCN: Multi-Branch Graph Convolution Network for 2D Image-based on 3D Model Retrieval

2D image based 3D model retrieval is a challenging research topic in the field of 3D model retrieval. The huge gap between two modalities - 2D image and 3D model, extremely constrains the retrieval performance. In order to handle this problem, we propose a novel multi-branch graph convolution network (M-GCN) to address the 2D image based 3D model retrieval problem. First, we compute the similarity between 2D image and 3D model based on visual information to construct one cross-modalities graph model, which can provide the original relationship between image and 3D model. However, this relationship is not accurate because of the difference of modalities. Thus, the multi-head attention mechanism is employed to generate a set of fully connected edge-weighted graphs, which can predict the hidden relationship between 2D image and 3D model to further strengthen the correlation for the embedding generation of nodes. Finally, we apply the max-pooling operation to fuse the multi-graphs information and generate the fusion embeddings of nodes for retrieval. To validate the performance of our method, we evaluated M-GCN on the MI3DOR dataset, Shrec 2018 track and Shrec 2014 track. The experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.
Understanding More About Human and Machine Attention in Deep Neural Networks

Human visual system can selectively attend to parts of a scene for quick perception, a biological mechanism known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Human attention</i> . Inspired by this, recent deep learning models encode attention mechanisms to focus on the most task-relevant parts of the input signal for further processing, which is called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Machine/Neural/Artificial attention</i> . Understanding the relation between human and machine attention is important for interpreting and designing neural networks. Many works claim that the attention mechanism offers an extra dimension of interpretability by explaining where the neural networks look. However, recent studies demonstrate that artificial attention maps do not always coincide with common intuition. In view of these conflicting evidence, here we make a systematic study on using artificial attention and human attention in neural network design. With three example computer vision tasks (i.e., salient object segmentation, video action recognition, and fine-grained image classification), diverse representative backbones (i.e., AlexNet, VGGNet, ResNet) and famous architectures (i.e., Two-stream, FCN), corresponding real human gaze data, and systematically conducted large-scale quantitative studies, we quantify the consistency between artificial attention and human visual attention and offer novel insights into existing artificial attention mechanisms by giving preliminary answers to several key questions related to human and artificial attention mechanisms. Overall results demonstrate that human attention can benchmark the meaningful ‘ground-truth’ in attention-driven tasks, where the more the artificial attention is close to human attention, the better the performance; for higher-level vision tasks, it is case-by-case. It would be advisable for attention-driven tasks to explicitly force a better alignment between artificial and human attention to boost the performance; such alignment would also improve the network explainability for higher-level computer vision tasks.
A path towards quantum advantage in training deep generative models with quantum annealers

The development of quantum-classical hybrid (QCH) algorithms is critical to achieve state-of-the-art computational models. A QCH variational autoencoder (QVAE) was introduced in reference [1] by some of the authors of this paper. QVAE consists of a classical auto-encoding structure realized by traditional deep neural networks to perform inference to and generation from, a discrete latent space. The latent generative process is formalized as thermal sampling from a quantum Boltzmann machine (QBM). This setup allows quantum-assisted training of deep generative models by physically simulating the generative process with quantum annealers. In this paper, we have successfully employed D-Wave quantum annealers as Boltzmann samplers to perform quantum-assisted, end-to-end training of QVAE. The hybrid structure of QVAE allows us to deploy current-generation quantum annealers in QCH generative models to achieve competitive performance on datasets such as MNIST. The results presented in this paper suggest that commercially available quantum annealers can be deployed, in conjunction with well-crafted classical deep neutral networks, to achieve competitive results in unsupervised and semisupervised tasks on large-scale datasets. We also provide evidence that our setup is able to exploit large latent-space QBMs, which develop slowly mixing modes. This expressive latent space results in slow and inefficient classical sampling and paves the way to achieve quantum advantage with quantum annealing in realistic sampling applications.
Pupil Localisation and Eye Centre Estimation Using Machine Learning and Computer Vision

Various methods have been used to estimate the pupil location within an image or a real-time video frame in many fields. However, these methods lack the performance specifically in low-resolution images and varying background conditions. We propose a coarse-to-fine pupil localisation method using a composite of machine learning and image processing algorithms. First, a pre-trained model is employed for the facial landmark identification to extract the desired eye frames within the input image. Then, we use multi-stage convolution to find the optimal horizontal and vertical coordinates of the pupil within the identified eye frames. For this purpose, we define an adaptive kernel to deal with the varying resolution and size of input images. Furthermore, a dynamic threshold is calculated recursively for reliable identification of the best-matched candidate. We evaluated our method using various statistical and standard metrics along with a standardised distance metric that we introduce for the first time in this study. The proposed method outperforms previous works in terms of accuracy and reliability when benchmarked on multiple standard datasets. The work has diverse artificial intelligence and industrial applications including human computer interfaces, emotion recognition, psychological profiling, healthcare, and automated deception detection.
A Traffic Surveillance System for Obtaining Comprehensive Information of the Passing Vehicles Based on Instance Segmentation

The vehicle information acquired by current video-based traffic surveillance methods is usually onefold. In this paper, a traffic surveillance system for obtaining comprehensive vehicle information, including type, number of axles, 3D bounding box, speed, length, current driving lane, and traffic volume, is proposed based on instance segmentation which is realized by Mask R-CNN. The work mainly contains: 1) An annotated image dataset which contains car, coach, truck, and wheel is established for training Mask R-CNN; 2) The method for identifying the number of axles based on MaskIoU is proposed; 3) The 3D bounding box is generated based on the high-quality vehicle segmentation, which is more accurate than that generated by background subtraction methods; 4) For calculating the vehicle speed and length, the reference points on the road plane are determined by the lane with dashed line and one vanishing point to calculate the homography matrix; 5) In order to keep the obtained vehicle information reliability, the results identified from multi-frames are obtained by means of the tracking method SORT for processing. The proposed system is verified in different scenes. The average recognition accuracies of the vehicle types and number of axles are more than 97% and 88% in three testing videos. The speed errors of more than 90% vehicles are less than 4%. And the ratios of both missing and repetition in vehicle counting are low in general.
Identification of multi-element geochemical anomalies using unsupervised machine learning algorithms: A case study from Ag–Pb–Zn deposits in north-western Zhejiang, China

Identifying significant geochemical anomalies is a fundamental task in mineral exploration. In terms of stochastic process, a specific ore-forming process can be regarded as a small probability event since the deposit type sought rarely occurs in the crust of the earth. Therefore, machine learning algorithms for anomaly detection are suitable for identification of mineralization-related geochemical anomalies. However, identification of significant geochemical anomaly signatures is often hindered by irrelevant and/or redundant variables in a geochemical dataset. It is then necessary to extract significant features by means of dimensional reduction before applying unsupervised machine learning algorithms for anomaly detection. In this study, six unsupervised machine learning methods including isolation forest (IF) and five hybrid models that combined IF with dimensional reduction techniques were implemented and evaluated in identifying Ag–Pb–Zn polymetallic mineralization-related geochemical anomalies using stream sediment geochemical data from north-western Zhejiang Province, China. The five hybrid models involved combining IF with hierarchical clustering (HC-IF), principal component analysis (PCA-IF), stacked denoising autoencoder (SDAE-IF), both HC and PCA (HC-PCA-IF) and both HC and SDAE (HC-SDAE-IF). Anomalies identified by these six methods were evaluated by the receiver operating characteristic (ROC) curve method. The spatial overlay of known mineral deposits and the identified geochemical anomaly maps showed that a strong spatial association existed between the locations of discovered mineral deposits and high geochemical anomaly areas. Furthermore, the area under the curve (AUC) values suggested that the HC-SDAE-IF hybrid model performed best and the HC-IF and HC-PCA-IF models performed better than the IF, PCA-IF and SDAE-IF models. This study demonstrated that: (i) IF is a useful and efficient unsupervised machine learning algorithm for detection of Ag-Pb-Zn polymetallic mineralization-related geochemical anomalies in the study area; and, (ii) the proper manipulation of dimensional reduction and extraction of mineralization-related geochemical variables can not only significantly improve the performance of unsupervised machine learning algorithms including deep learning methods in geochemical anomaly detection, but also be instrumental in clarifying the geological meaning of the identified geochemical anomaly. In this regard, reducing dimensionality and extracting mineralization-related variables (i.e. elements) from raw geochemical data is strongly recommended before applying unsupervised machine learning methods, particularly the sophisticated deep learning algorithms, in geochemical anomaly detection.
Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems

Providing useful information to the users by recommending highly demanded products and services is a fundamental part of the business of many top tier companies. Recommender Systems make use of many sources of information to provide users with accurate predictions and novel recommendations of items. Here we propose, DeepMF, a novel collaborative filtering method that combines the Deep Learning paradigm with Matrix Factorization (MF) to improve the quality of both predictions and recommendations made to the user. Specifically, DeepMF performs successive refinements of a MF model with a layered architecture that uses the acquired knowledge in a layer as input for subsequent layers. Experimental results showed that the quality of both the predictions and recommendations of DeepMF overcome the baselines.
Discretely-constrained deep network for weakly supervised segmentation

An efficient strategy for weakly-supervised segmentation is to impose constraints or regularization priors on target regions. Recent efforts have focused on incorporating such constraints in the training of convolutional neural networks (CNN), however this has so far been done within a continuous optimization framework. Yet, various segmentation constraints and regularization priors can be modeled and optimized more efficiently in a discrete formulation. This paper proposes a method, based on the alternating direction method of multipliers (ADMM) algorithm, to train a CNN with discrete constraints and regularization priors. This method is applied to the segmentation of medical images with weak annotations, where both size constraints and boundary length regularization are enforced. Experiments on two benchmark datasets for medical image segmentation show our method to provide significant improvements compared to existing approaches in terms of segmentation accuracy, constraint satisfaction and convergence speed.
HARMer: Cyber-Attacks Automation and Evaluation

With the increasing growth of cyber-attack incidences, it is important to develop innovative and effective techniques to assess and defend networked systems against cyber attacks. One of the well-known techniques for this is performing penetration testing which is carried by a group of security professionals (i.e, red team). Penetration testing is also known to be effective to find existing and new vulnerabilities, however, the quality of security assessment can be depending on the quality of the red team members and their time and devotion to the penetration testing. In this paper, we propose a novel automation framework for cyber-attacks generation named `HARMer' to address the challenges with respect to manual attack execution by the red team. Our novel proposed framework, design, and implementation is based on a scalable graphical security model called Hierarchical Attack Representation Model (HARM). (1) We propose the requirements and the key phases for the automation framework. (2) We propose security metrics-based attack planning strategies along with their algorithms. (3) We conduct experiments in a real enterprise network and Amazon Web Services. The results show how the different phases of the framework interact to model the attackers' operations. This framework will allow security administrators to automatically assess the impact of various threats and attacks in an automated manner.
Enterprise financial management information system based on cloud computing in big data environment

Cloud computing technology has the characteristics of low investment costs, strong reliability, flexible expansion, and on-demand services, which can greatly reduce the application threshold of enterprise financial informatization construction, improve the return on investment of informationization, and flexibly adapt to the needs of different stages of business. To solve the problem of enterprise financial management information system based on cloud computing in big data environment. This article proposes the management concept of “business-driven value” as an expense management system. Through the investigation of the company in this article, after 9 years of construction, the number of property in each subsidiary has dropped from an average of 23 people per company to 3.5 people per subsidiary before. Reduced by about 84.7%. Contracted human capital for the company. Compared with the 23 billion yuan in 2010, after the implementation of financial shared services, after 9 years of development, it has now reached 68.55 billion yuan, nearly three times more than before.
Extraction of temporal relations from clinical free text: A systematic review of current approaches

Background: Temporal relations between clinical events play an important role in clinical assessment and decision making.Extracting such relations from free text data is a challenging task because it lies on between medical natural language processing, temporal representation and temporal reasoning.Objectives: To survey existing methods for extracting temporal relations (TLINKs) between events from clinical free text in English; to establish the state-of-the-art in this field; and to identify outstanding methodological challenges.Methods: A systematic search in PubMed and the DBLP computer science bibliography was conducted for studies published between January 2006 and December 2018.The relevant studies were identified by examining the titles and abstracts.Then, the full text of selected studies was analyzed in depth and information were collected on TLINK tasks, TLINK types, data sources, features selection, methods used, and reported performance.Results: A total of 2834 publications were identified for title and abstract screening.Of these publications, 51 studies were selected.Thirty-two studies used machine learning approaches, 15 studies used a hybrid approaches, and only four studies used a rule-based approach.The majority of studies use publicly available corpora: THYME (28 studies) and the i2b2 corpus (17 studies). Conclusion:The performance of TLINK extraction methods ranges widely depending on relation types and events (e.g. from 32% to 87% F-score for identifying relations between clinical events and document creation time).A small set of TLINKs (before, after, overlap and contains) has been widely studied with relatively good performance, whereas other types of TLINK (e.g., started by, finished by, precedes) are rarely studied and remain challenging.Machine learning classifiers (such as Support Vector Machine and Conditional Random Fields) and Deep Neural Networks were among the best performing methods for extracting TLINKs, but nearly all the work has been carried out and tested on two publicly available corpora only.The field would benefit from the availability of more publicly available, high-quality, annotated clinical text corpora.
Modeling Deep Learning Neural Networks With Denotational Mathematics in UbiHealth Environment

Ubiquitous computing environments that are involved in healthcare applications are typically characterized by dynamically changing contexts. The contextual information must be efficiently processed in order to support medical decision making. The ubiquitous computing healthcare ecosystem must be capable of extracting medically valuable characteristics, making precise decisions, and taking medically appropriate actions. In this framework, deep learning networks can be used for data fusion of large and complex sets of information in order to make the appropriate medical diagnoses. The quality of decisions depends on the selection of appropriate network weights, which define a transformation of the given input into a diagnosis. Denotational mathematics provide a promising framework for modeling deep learning networks and adjusting their behavior by adapting their weights for the given input. Furthermore, the fidelity of the network's output can be controlled by applying a regulator to the weights values. The authors show that Denotational Mathematics can serve as a rigorous framework for modeling and controlling deep learning networks, thereby enhancing the quality of medical decision making.