Reviewerly/exHarmony huggingface dataset:
Improving Automated Geological Logging of Drill Holes by Incorporating Multiscale Spatial Methods

Abstract Manually interpreting multivariate drill hole data is very time-consuming, and different geologists will produce different results due to the subjective nature of geological interpretation. Automated or semi-automated interpretation of numerical drill hole data is required to reduce time and subjectivity of this process. However, results from machine learning algorithms applied to drill holes, without reference to spatial information, typically result in numerous small-scale units. These small-scale units result not only from the presence of very small rock units, which may be below the scale of interest, but also from misclassification. A novel method is proposed that uses the continuous wavelet transform to identify geological boundaries and uses wavelet coefficients to indicate boundary strength. The wavelet coefficient is a useful measure of boundary strength because it reflects both wavelength and amplitude of features in the signal. This means that boundary strength is an indicator of the apparent thickness of geological units and the amount of change occurring at each geological boundary. For multivariate data, boundaries from multiple variables are combined and multiscale domains are calculated using the combined boundary strengths. The method is demonstrated using multi-element geochemical data from mineral exploration drill holes. The method is fast, reduces misclassification, provides a choice of scales of interpretation and results in hierarchical classification for large scales where domains may contain more than one rock type.
Hellinger distance-based stable sparse feature selection for high-dimensional class-imbalanced data

Abstract Background Feature selection in class-imbalance learning has gained increasing attention in recent years due to the massive growth of high-dimensional class-imbalanced data across many scientific fields. In addition to reducing model complexity and discovering key biomarkers, feature selection is also an effective method of combating overlapping which may arise in such data and become a crucial aspect for determining classification performance. However, ordinary feature selection techniques for classification can not be simply used for addressing class-imbalanced data without any adjustment. Thus, more efficient feature selection technique must be developed for complicated class-imbalanced data, especially in the context of high-dimensionality. Results We proposed an algorithm called sssHD to achieve stable sparse feature selection applied it to complicated class-imbalanced data. sssHD is based on the Hellinger distance (HD) coupled with sparse regularization techniques. We stated that Hellinger distance is not only class-insensitive but also translation-invariant. Simulation result indicates that HD-based selection algorithm is effective in recognizing key features and control false discoveries for class-imbalance learning. Five gene expression datasets are also employed to test the performance of the sssHD algorithm, and a comparison with several existing selection procedures is performed. The result shows that sssHD is highly competitive in terms of five assessment metrics. In addition, sssHD presents limited differences between performing and not performing re-balance preprocessing. Conclusions sssHD is a practical feature selection method for high-dimensional class-imbalanced data, which is simple and can be an alternative for performing feature selection in class-imbalanced data. sssHD can be easily extended by connecting it with different re-balance preprocessing, different sparse regularization structures as well as different classifiers. As such, the algorithm is extremely general and has a wide range of applicability.
A Blockchain Based Model to Eliminate Drug Counterfeiting

The drug counterfeit problem has become global and so huge that it has drawn significant attention from everyone. The fake drug industry is worth $10B per year as estimated by survey. Also estimated by WHO, about a million die per year due to fake drugs. The distribution of fake drugs is a crucial issue. One of main reasons behind drug counterfeiting is imperfect supply chain. There are many loopholes in our drug supply chain. In the present scenario of supply chain, either the information is not at all shared between the parties during the hand-off process or a little or irrelevant information is shared, which has led to counterfeiting. The counterfeit drug not only affects the health condition of patients but also results in the financial loss of genuine manufacturer. In this paper, the details of the drug counterfeit have been explained along with its impact. Also, various measures to fight the counterfeits using blockchain technology have been discussed. Using blockchain technology traceability, visibility and security can also be incorporated into the drug supply chain. This proposed system will track the drugs from its origin, the manufacturer to the end, the consumer.
Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy

Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, namely COVID-19. Many of these solutions threaten individual rights and privacy. Our goal is to break past the false dichotomy of effective versus privacy-preserving contact tracing. We offer an alternative approach to assess and communicate users' risk of exposure to an infectious disease while preserving individual privacy. Our proposal uses recent GPS location histories, which are transformed and encrypted, and a private set intersection protocol to interface with a semi-trusted authority. There have been other recent proposals for privacy-preserving contact tracing, based on Bluetooth and decentralization, that could further eliminate the need for trust in authority. However, solutions with Bluetooth are currently limited to certain devices and contexts while decentralization adds complexity. The goal of this work is two-fold: we aim to propose a location-based system that is more privacy-preserving than what is currently being adopted by governments around the world, and that is also practical to implement with the immediacy needed to stem a viral outbreak.
Look Globally, Age Locally: Face Aging With an Attention Mechanism

Face aging is of great importance for cross-age recognition and entertainment-related applications. Recently, conditional generative adversarial networks (cGANs) have achieved impressive results for face aging. Existing cGANs-based methods usually require a pixel-wise loss to keep the identity and background consistent. However, minimizing the pixel-wise loss between the input and synthesized images likely resulting in a ghosted or blurry face. To address this deficiency, this paper introduces an Attention Conditional GANs (AcGANs) approach for face aging, which utilizes attention mechanism to only alert the regions relevant to face aging. In doing so, the synthesized face can well preserve the background information and personal identity without using the pixel-wise loss, and the ghost artifacts and blurriness can be significantly reduced. Based on the benchmarked dataset Morph, both qualitative and quantitative experiment results demonstrate superior performance over existing algorithms in terms of image quality, personal identity, and age accuracy.
Diagnosing BERT with Retrieval Heuristics

Word embeddings, made widely popular in 2013 with the release of word2vec, have become a mainstay of NLP engineering pipelines. Recently, with the release of BERT, word embeddings have moved from the term-based embedding space to the contextual embedding space -- each term is no longer represented by a single low-dimensional vector but instead each term and \emph{its context} determine the vector weights. BERT's setup and architecture have been shown to be general enough to be applicable to many natural language tasks. Importantly for Information Retrieval (IR), in contrast to prior deep learning solutions to IR problems which required significant tuning of neural net architectures and training regimes, "vanilla BERT" has been shown to outperform existing retrieval algorithms by a wide margin, including on tasks and corpora that have long resisted retrieval effectiveness gains over traditional IR baselines (such as Robust04). In this paper, we employ the recently proposed axiomatic dataset analysis technique -- that is, we create diagnostic datasets that each fulfil a retrieval heuristic (both term matching and semantic-based) -- to explore what BERT is able to learn. In contrast to our expectations, we find BERT, when applied to a recently released large-scale web corpus with ad-hoc topics, to \emph{not} adhere to any of the explored axioms. At the same time, BERT outperforms the traditional query likelihood retrieval model by 40\%. This means that the axiomatic approach to IR (and its extension of diagnostic datasets created for retrieval heuristics) may in its current form not be applicable to large-scale corpora. Additional -- different -- axioms are needed.
Explaining Question Answering Models through Text Generation

Large pre-trained language models (LMs) have been shown to perform surprisingly well when fine-tuned on tasks that require commonsense and world knowledge. However, in end-to-end architectures, it is difficult to explain what is the knowledge in the LM that allows it to make a correct prediction. In this work, we propose a model for multi-choice question answering, where a LM-based generator generates a textual hypothesis that is later used by a classifier to answer the question. The hypothesis provides a window into the information used by the fine-tuned LM that can be inspected by humans. A key challenge in this setup is how to constrain the model to generate hypotheses that are meaningful to humans. We tackle this by (a) joint training with a simple similarity classifier that encourages meaningful hypotheses, and (b) by adding loss functions that encourage natural text without repetitions. We show on several tasks that our model reaches performance that is comparable to end-to-end architectures, while producing hypotheses that elucidate the knowledge used by the LM for answering the question.
Quaternion Discrete Fourier Transform-Based Color Image Watermarking Method Using Quaternion QR Decomposition

In this paper, a new Quaternion Discrete Fourier Transform (QDFT)-based digital color image watermarking method is presented. In addition, the Quaternion QR (QQR) decomposition is applied in digital watermarking technology for the first time. First of all, the QDFT and QQR decomposition are performed on the host image, respectively, to acquire the scalar part of the quaternion matrix for watermark information embedding. After that, we divide the scalar part of the quaternion matrix generated by the QQR decomposition into blocks and calculate the entropy. The block with high entropy is selected to embed the watermark information. Then the watermark information is embedded into the extracted block using the quantization index modulation method. We conducted a large number of tests and experimental results indicate that the presented approach obtains excellent robustness against Scaling, Rotation, Median filtering, `Salt & Pepper' noise, and JPEG Compression. Compared with the existing methods, the presented method achieves better performance.
The (1 + ( <i>λ,λ</i> )) GA is even faster on multimodal problems

For the (1 + (λ, λ)) genetic algorithm rigorous runtime analyses on unimodal fitness functions have shown that it can be faster than classical evolutionary algorithms, though on these simple problems the gains are only moderate.
Unconstrained and constrained face recognition using dense local descriptor with ensemble framework

This paper presents an ensemble face recognition system which makes use of the novel local descriptor called Dense Local Graph Structure (D-LGS) which is exploited from symmetric LGS that and it uses additional graph structure in addition to its own local graph structure. This additional local graph structure is generated by finding additional corner pixel points through bilinear interpolation of neighborhood pixels. These corner pixels lead to most stable features and information related to local deformation of the image. In this proposed ensemble system, three classifiers, namely K-nearest neighbor, Chi-square and correlation coefficient are used. Further the proposed approach fuses the decisions obtained from individual classifiers through OR rule, majority voting and AND rule. To evaluate the performance of proposed ensemble system, the experiment is conducted with three face databases viz. AT&T (formerly The ORL Database of Faces), UFI and LFW face database. The ensemble face recognition system on the use of novel dense local graph structure has reached the accuracy of 100% on AT&T, 99.3488% on UFI and 87.3372% on LFW face database. Further, the templates of D-LGS are optimized using Genetic algorithm (GA) as part of ‘curse-of-dimensionality’ and the reduced number of templates give accuracies of 100% on AT&T and 99.2165% on LFW face database.
Effects of synchronised engine sound and vibration presentation on visually induced motion sickness

Abstract Driving simulator usage is often accompanied by motion sickness, and techniques for its prevention are not yet established. To reduce visually induced motion sickness (VIMS), we investigated the effects of synchronised presentation of engine sounds and motorcycle vibration on VIMS. A total of 80 participants experienced a driving scene with a head-mounted display for 5 minutes with or without synchronised presentation of engine sound and vibration. The results showed that VIMS scores, as measured by the Fast Motion Sickness scale, were significantly lower in participants who experienced the driving scene with sounds and vibration than in those who experienced the scene with sounds only, vibration only, or neither. Multiple regression analyses revealed that susceptibility to VIMS consistently explained the severity of VIMS to some extent but not with perceived realism of the virtual reality (VR) scene, sex, and experiences about VR devices and vehicles. This study demonstrated that simultaneous presentation of engine sounds and vibration, which were synchronous to each other and tightly coupled with the visual flow speed, effectively reduces VIMS while experiencing motorcycling simulators. The findings not only improve practical knowledge for reducing VIMS in driving simulators but also provide evidence for understanding the mechanisms of VIMS.
Prognostics and health management of FAST cable-net structure based on digital twin technology

Abstract The Five-hundred-meter Aperture Spherical radio Telescope (FAST) will be fully commissioned later in 2019. Once commissioned, operation and maintenance of FAST will be the most prominent task. The unique working mode of active shape-changing of FAST cable-net structure makes the traditional maintenance way, which combines routine inspection with preventive maintenances not only expensive, but also unable to effectively avoid potential risks in operations. Therefore, it is necessary to develop an economical and reliable operation/maintenance technology for FAST cable-net structure. In this paper, a Prognostics and Health Management (PHM) system is proposed based on the advanced Digital Twin (DT) technology. Through the finite element analysis of DT model, the current safety status of FAST cable-net is evaluated, and the fatigue life of components in the cable-net is predicted. Hence Condition-Based Maintenance (CBM) of FAST cable-net structure can be realized. The PHM system described in this paper can effectively guarantee the healthy and safe operation of the FAST cable-net structure, greatly improve the maintenance efficiency and reduce the cost for maintenance works.
Continuous Gesture Recognition Based on Time Sequence Fusion Using MIMO Radar Sensor and Deep Learning

Gesture recognition that is based on high-resolution radar has progressively developed in human-computer interaction field. In a radar recognition-based system, it is challenging to recognize various gesture types because of the lacking of gesture transversal feature. In this paper, we propose an integrated gesture recognition system that is based on frequency modulated continuous wave MIMO radar combined with deep learning network for gesture recognition. First, a pre-processing algorithm, which consists of the windowed fast Fourier transform and the intermediate-frequency signal band-pass-filter (IF-BPF), is applied to obtain improved Range Doppler Map. A range FFT based MUSIC (RFBM) two-dimensional (2D) joint super-resolution estimation algorithm is proposed to obtain a Range Azimuth Map to obtain gesture transversal feature. Range Doppler Map and Range Azimuth Map then respectively form a Range Doppler Map Time Sequence (RDMTS) and a Range Azimuth Map Time Sequence (RAMTS) in gesture recording duration. Finally, a Dual stream three-dimensional (3D) Convolution Neural Network combined with Long Short Term Memory (DS-3DCNN-LSTM) network is designed to extract and fuse features from both RDMTS and RAMTS, and then classify gestures with radial and transversal change. The experimental results show that the proposed system could distinguish 10 types of gestures containing transversal and radial motions with an average accuracy of 97.66%.
On Mutual Information in Contrastive Learning for Visual Representations

In recent years, several unsupervised, "contrastive" learning algorithms in vision have been shown to learn representations that perform remarkably well on transfer tasks. We show that this family of algorithms maximizes a lower bound on the mutual information between two or more "views" of an image where typical views come from a composition of image augmentations. Our bound generalizes the InfoNCE objective to support negative sampling from a restricted region of "difficult" contrasts. We find that the choice of negative samples and views are critical to the success of these algorithms. Reformulating previous learning objectives in terms of mutual information also simplifies and stabilizes them. In practice, our new objectives yield representations that outperform those learned with previous approaches for transfer to classification, bounding box detection, instance segmentation, and keypoint detection. % experiments show that choosing more difficult negative samples results in a stronger representation, outperforming those learned with IR, LA, and CMC in classification, bounding box detection, instance segmentation, and keypoint detection. The mutual information framework provides a unifying comparison of approaches to contrastive learning and uncovers the choices that impact representation learning.
Development of an openEHR Template for COVID-19 Based on Clinical Guidelines

Background The coronavirus disease (COVID-19) was discovered in China in December 2019. It has developed into a threatening international public health emergency. With the exception of China, the number of cases continues to increase worldwide. A number of studies about disease diagnosis and treatment have been carried out, and many clinically proven effective results have been achieved. Although information technology can improve the transferring of such knowledge to clinical practice rapidly, data interoperability is still a challenge due to the heterogeneous nature of hospital information systems. This issue becomes even more serious if the knowledge for diagnosis and treatment is updated rapidly as is the case for COVID-19. An open, semantic-sharing, and collaborative-information modeling framework is needed to rapidly develop a shared data model for exchanging data among systems. openEHR is such a framework and is supported by many open software packages that help to promote information sharing and interoperability. Objective This study aims to develop a shared data model based on the openEHR modeling approach to improve the interoperability among systems for the diagnosis and treatment of COVID-19. Methods The latest Guideline of COVID-19 Diagnosis and Treatment in China was selected as the knowledge source for modeling. First, the guideline was analyzed and the data items used for diagnosis and treatment, and management were extracted. Second, the data items were classified and further organized into domain concepts with a mind map. Third, searching was executed in the international openEHR Clinical Knowledge Manager (CKM) to find the existing archetypes that could represent the concepts. New archetypes were developed for those concepts that could not be found. Fourth, these archetypes were further organized into a template using Ocean Template Editor. Fifth, a test case of data exchanging between the clinical data repository and clinical decision support system based on the template was conducted to verify the feasibility of the study. Results A total of 203 data items were extracted from the guideline in China, and 16 domain concepts (16 leaf nodes in the mind map) were organized. There were 22 archetypes used to develop the template for all data items extracted from the guideline. All of them could be found in the CKM and reused directly. The archetypes and templates were reviewed and finally released in a public project within the CKM. The test case showed that the template can facilitate the data exchange and meet the requirements of decision support. Conclusions This study has developed the openEHR template for COVID-19 based on the latest guideline from China using openEHR modeling methodology. It represented the capability of the methodology for rapidly modeling and sharing knowledge through reusing the existing archetypes, which is especially useful in a new and fast-changing area such as with COVID-19.
Infinite attention: NNGP and NTK for deep attention networks

There is a growing amount of literature on the relationship between wide neural networks (NNs) and Gaussian processes (GPs), identifying an equivalence between the two for a variety of NN architectures. This equivalence enables, for instance, accurate approximation of the behaviour of wide Bayesian NNs without MCMC or variational approximations, or characterisation of the distribution of randomly initialised wide NNs optimised by gradient descent without ever running an optimiser. We provide a rigorous extension of these results to NNs involving attention layers, showing that unlike single-head attention, which induces non-Gaussian behaviour, multi-head attention architectures behave as GPs as the number of heads tends to infinity. We further discuss the effects of positional encodings and layer normalisation, and propose modifications of the attention mechanism which lead to improved results for both finite and infinitely wide NNs. We evaluate attention kernels empirically, leading to a moderate improvement upon the previous state-of-the-art on CIFAR-10 for GPs without trainable kernels and advanced data preprocessing. Finally, we introduce new features to the Neural Tangents library (Novak et al., 2020) allowing applications of NNGP/NTK models, with and without attention, to variable-length sequences, with an example on the IMDb reviews dataset.