Reviewerly/exHarmony huggingface dataset:
Mineralogical composition and total organic carbon quantification using x-ray fluorescence data from the Upper Cretaceous Eagle Ford Group in southern Texas

Six southern Texas Eagle Ford cores were investigated to quantify mineralogical composition and total organic carbon (TOC). Machine learning of the x-ray fluorescence (XRF) data set was conducted using neural network analysis to predict mineralogies for L1, L2, and L3 and TOC for L1, L2, L3, Iona-1, Innes-1, and well “X.” Inees-1 and well “X” were used as blind tests to check the quality of the developed models. The online Neural Designer software was used to perform the training process and develop models. Quantitative laboratory-measured x-ray diffraction (XRD) mineralogies and TOC were used to conduct the training and develop high-resolution semiquantitative models, and the derived mineralogic and organic matter models were found to be promising. The modeled mineralogy and TOC represent continuous relative abundances, which are far more significant than scattered individual XRD and TOC point measurements. The significance of this study is that it allows for the use of relatively inexpensive and nondestructive XRF analysis that requires minimal sample preparation to construct high-resolution mineral abundances and TOC content. With modern advances in technology, XRF can now be measured on drill cuttings in real time while drilling is occurring, allowing operators to use the proposed method to construct semiquantitative mineralogical and TOC models for evaluating placement of laterals in prospective intervals and designing completion techniques accordingly.
Design of Hardware-Friendly Memory Enhanced Neural Networks

Neural networks with external memories have been proven to minimize catastrophic forgetting, a major problem in applications such as lifelong and few-shot learning. However, such memory enhanced neural networks (MENNs) often require a large number of floating point-based cosine distance metric calculations to perform necessary attentional operations, which greatly increases energy consumption and hardware cost. This paper investigates other distance metrics in such neural networks in order to achieve more efficient hardware implementations in MENNs. We propose using content addressable memories (CAMs) to accelerate and simplify attentional operations. Our hardware friendly approach implements fixed point L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> distance calculations via ternary content addressable memories (TCAM) and fixed point L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> distance calculations on a general purpose graphical processing unit (GPGPU). As a representative example, a 32-bit floating point-based cosine distance MENN with M · D multiplications has a 99.06% accuracy for the Omniglot 5-way 5-shot classification task. Based on our approach, with just 4-bit fixed point precision, a L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> - L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> distance hardware accuracy of 90.35% can be achieved with just 16 TCAM lookups and 16·D addition and subtraction operations. With 4-bit precision and a L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> distance, hardware classification accuracies of 96.00% are possible. Hence, 16 TCAM lookups and 16·D multiplication operations are needed. Assuming the hardware memory has 512 entries, the number of multiplication operations is reduced by 32x versus the cosine distance approach.
Detecting mitotic cells in HEp-2 images as anomalies via one class classifier

We propose a novel framework for classification of mitotic v/s non-mitotic cells in a Computer Aided Diagnosis (CAD) system for Anti-Nuclear Antibodies (ANA) detection. In the proposed work, due to unique characteristics (the rare occurrence) of the mitotic cells, their identification is posed as an anomaly detection approach. This will resolve the issue of data imbalance, which can arise in the traditional binary classification paradigm for mitotic v/s non-mitotic cell image classification. Here, the characteristics of only non-mitotic/interphase cells are captured using a well-defined feature representation to characterize the non-mitotic class distribution well, and the mitotic class is posed as an anomalous class. This framework requires training data only for the majority (non-mitotic) class, to build the classification model. The feature representation of the non-mitotic class includes morphology, texture, and Convolutional Neural Network (CNN) based feature representations, coupled with Bag-of-Words (BoW) and Spatial Pyramid Pooling (SPP) based summarization techniques. For classification, in this work, we employ the One-Class Support Vector Machines (OC-SVM). The proposed classification framework is validated on a publicly available dataset, and across various experiments, we demonstrate comparable or better performance over binary classification, attaining 0.99 (max.) F-Score in one case. The proposed framework proves to be an effective way to solve the mentioned problem statement, where there are less number of samples in one of the classes.
Complete MDP convolutional codes

Maximum distance profile (MDP) convolutional codes have the property that their column distances are as large as possible. It has been shown that, transmitting over an erasure channel, these codes have optimal recovery rate for windows of a certain length. Reverse MDP convolutional codes have the additional advantage that they are suitable for forward and backward decoding algorithms. Beyond that the subclass of complete MDP convolutional codes has the ability to reduce the waiting time during decoding. The first main result of this paper is to show the existence and genericity of [Formula: see text] complete MDP convolutional codes for all code parameters with [Formula: see text] as well as that complete MDP convolutional codes cannot exist if [Formula: see text]. The second main contribution is the presentation of two concrete construction techniques to obtain complete MDP convolutional codes. These constructions work for all code parameters with [Formula: see text] but require that the size of the underlying base field is (sufficiently) large.
Hybrid fuzzy control for the goethite process in zinc production plant combining type-1 and type-2 fuzzy logics

The goethite process is a complicated reaction system which holds a significant position in zinc hydrometallurgy process. It exhibits nonlinear behavior and time-delay nature because of the chemical reactions. To achieve the stable and real-time control performance, a hybrid fuzzy control strategy integrating a type-1 and a type-2 fuzzy logic controllers is proposed in this paper. According to the on-line measured pH, the type-1 fuzzy controller (T1 FLC) via the Takagi–Sugeno fuzzy control is employed to control the zinc oxide additive rate. A parameter tuning method that does not require the system model is designed for the T1 FLC. Because the ferrous ion concentration cannot be measured on-line, the interval type-2 fuzzy logic controller (IT2 FLC) is utilized to control the oxygen flow rate. According to the feedback information, gradient descent algorithm is used to update the parameters in the IT2 FLC. Because of the unknown system structure, gradient descent information is estimated by the simultaneous perturbation stochastic approximation. Finally, simulations are conducted using the practical production data. The simulation results show the effectiveness of the proposed strategy. The hybrid fuzzy control strategy improves the control performance of the goethite process, compared with PID controller.
Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation

Collaborative Filtering (CF) is among the most successful techniques in recommendation tasks. Recent works have shown a boost of performance of CF when introducing the pairwise relationships between users and items or among items (users) using interaction data. However, these works usually only utilize one kind of information, i.e., user preference in a user-item interaction matrix or item dependency in interaction sequences which can limit the recommendation performance. In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph). We design a novel graph convolutional network (PGCN) to learn shared representations of users and items with the three heterogeneous graphs. In our approach, a neighbor pooling and a convolution operation are designed to aggregate features of neighbors. Extensive experiments on two real-world datasets demonstrate that our graph convolution approaches outperform various competitive methods in terms of two metrics, and the heterogeneous graphs are proved effective for improving recommendation performance.
Efficient solar radiation estimation using cohesive artificial neural network technique with optimal synaptic weights

Estimation of solar potential is vital for renewable energy applications. In several studies, artificial neural networks have been employed to model solar radiation using various meteorological parameters. The collection and availability of the most appropriate input parameters is important for getting an accurate artificial neural network model. The present study aims to estimate the global solar radiation using different meteorological parameters and identify the significant parameters based on the analysis of synaptic weights in an artificial neural network model using the connection weight approach. Initially, artificial neural network and empirical models is applied to estimate the solar radiation in Chamba region. The artificial neural network architecture 5-48-15-1 resulted in minimum mean absolute percentage error of 12.15%. The mean absolute percentage error values for linear models are found to be 18.95%, 15.39%, and 21.62%, respectively. Thereafter, connection weight approach is applied to find significant parameters. The efficacy of the approach has been shown through a case study related to estimation of solar radiations in the Hamirpur region situated in the state of Himachal Pradesh (India). Five input parameters, namely temperature (T), relative humidity (RH), clearness index (K T ), precipitation (P T ), and pressure (P), have been considered to estimate solar radiations using a feed-forward neural network. The proposed approach infers that temperature is the most significant parameter followed by humidity and pressure. The clearness index and precipitation has been found to have the least effect on the estimation of solar radiations. Results also indicate that artificial neural network based technique is more accurate compared to empirical model.
The Visual Task Adaptation Benchmark

Representation learning promises to unlock deep learning for the long tail ofvision tasks without expansive labelled datasets. Yet, the absence of a unifiedyardstick to evaluate general visual representations hinders progress. Manysub-fields promise representations, but each has different evaluation protocolsthat are either too constrained (linear classification), limited in scope(ImageNet, CIFAR, Pascal-VOC), or only loosely related to representationquality (generation). We present the Visual Task Adaptation Benchmark (VTAB): adiverse, realistic, and challenging benchmark to evaluate representations. VTABembodies one principle: good representations adapt to unseen tasks with fewexamples. We run a large VTAB study of popular algorithms, answering questionslike: How effective are ImageNet representation on non-standard datasets? Aregenerative models competitive? Is self-supervision useful if one already haslabels?
A Survey of Recommendation Algorithms Based on Knowledge Graph Embedding

Recommender system is able to realize personalized information filtering, which is a key way for knowledge discovering in information-rich environment. Knowledge graphs contain rich semantic associations between entities, which can be utilized to strengthen relationships between recommended items and bring interpretability for recommendation. With the establishment of knowledge graphs in various fields, the increasing numbers of researchers have carried out studies on recommendation algorithm based on knowledge graphs. Among them, recommendation algorithm based on knowledge graph embedding is a kind of simple and effective way to introduce knowledge entities and their relation into traditional recommender system. In this paper, the study about this kind of studies is summarized by analysis of existing literatures. We discuss and compare several studies, and the key elements of these algorithms are statistically analyzed.
Hindi Visual Genome: A Dataset for Multi-Modal English to Hindi Machine Translation

Visual Genome is a dataset connecting structured image information with English language. We present “Hindi Visual Genome”, a multi-modal dataset consisting of text and images suitable for English-Hindi multi-modal machine translation task and multi-modal research. We have selected short English segments (captions) from Visual Genome along with the associated images and automatically translated them to Hindi. A careful manual post-editing followed which took the associated images into account. Overall, we prepared a set of 31,525 segments (which we conveniently split into training, development and testdata), accompanied by a challenge test set of 1,400 segments. This challenge test set was created by searching for particularly ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolvethe ambiguity. Our dataset is the first manually revised dataset for multi-modal English-Hindi machine translation, freely available for non-commercial research purposes. Our Hindi version of Visual Genome also allows to create Hindi image labelers or other practical tools. Hindi Visual Genome also served in Workshop on Asian Translation (WAT) 2019 Multi-Modal Translation Task.
Genetic Algorithm Based Multi-objective Optimization Framework to Solve Traveling Salesman Problem

The combination of objectives to be optimized is known as Combinatorial Optimization. Traveling Salesman is one of the demanding topics in the field of Combinatorial Optimization whereas in traditional TSP, only unique objectives are optimized. In this research work, the Multi-objective Traveling Salesman Problem is considered. The multi-objective TSP is an expanded instance of TSP. In MOTSP, more than one objective function is determined and optimized to meet the finest solutions. MOTSP is solved by using Genetic Algorithm. MOTSP gave the best possible Pareto optimal solutions for all the data instances that were tested. Genetic Algorithm produces near-optimal solutions in a fair period of time for almost all the objectives. The Traveling Salesman Problem was tested with various instances from TSPLIB dataset which consists of different number of cities in this work. The experimental results showed that the GA gives nearest optimal solutions to the problem instances taken from the TSP Library and considered in this work.
The mediation role of public governance in the relationship between entrepreneurship and economic growth

Purpose The purpose of this paper is to investigate the mediation role of public governance in the relationship between entrepreneurship and economic growth in the United Arab Emirates (UAE). Design/methodology/approach To achieve this aim, the study uses a 20-year time series analysis (1996–2015) and tests the effect of entrepreneurship on economic growth, through public governance, via a mediator model. Findings The study has determined that public governance buoys the positive effect that entrepreneurship activities exert on economic growth in the UAE. Based on this determination, the study posits a set of recommendations that focus on supporting entrepreneurship activities that play a significant role in economic growth. Originality/value The study adds to the literature on the impact of entrepreneurship on economies dependent on oil revenues vis-à-vis a public policy perspective. The study provides insights into the type of entrepreneurship that most efficaciously suits the Emirati social and cultural milieu in terms of fostering national economic growth. In addition, the study limns a vision of the role of public governance in creating an enabling environment that stimulates entrepreneurial activity and, in turn, increases economic growth in the Emirates.
Optical image encryption based on biometric keys and singular value decomposition

We propose an asymmetric optical image cryptosystem based on biometric keys and singular value decomposition (SVD) in the Fresnel transform domain. In the proposed cryptosystem, the biometric keys are palmprint phase mask generated by a palmprint, a chaotic phase mask, and an amplitude truncated Fourier transform, which can provide the cryptosystem with more data security due to the uniqueness of the palmprint. Two images are first encoded into a complex function, which then is modulated by the palmprint phase mask. A Fresnel transform and then an SVD operation are performed on the modulated result. The SVD operation is used to generate private secret keys, which makes the encryption secret keys and decryption secret keys different, and thus the encryption process and decryption process are different. In addition, multiple images are encrypted into a real-valued ciphertext, making it convenient to transport and record. Numerical simulation results have demonstrated that our proposed encryption system has robustness against statistical, occlusion, noise, and chosen-plaintext attacks.
Wearable-based mobility monitoring: the long road ahead

Wearable devices (or wearables) combine technology-based objective measures with unsupervised patient monitoring, which is particularly appealing considering the restricted amount of time that physicians can spend with their patients. Unsupervised patient monitoring applications range from those tracking patient behaviour (for treatment decisions or endpoints in clinical trials) to the detection of early warning signals or telediagnosis. Long-term unsupervised mobility assessment in movement disordersMobile health technologies (wearable, portable, body-fixed sensors, or domestic-integrated devices) that quantify mobility in unsupervised, daily living environments are emerging as complementary clinical assessments. Data collected in these ecologically valid, patient-relevant settings can overcome limitations of conventional clinical assessments, as they capture fluctuating and rare events. These data could support clinical decision making and could also serve as outcomes in clinical trials. However, studies that directly compared assessments made in unsupervised and supervised (eg, in the laboratory or hospital) settings point to large disparities, even in the same parameters of mobility. Full-Text PDF
GAN-Based Image Super-Resolution with a Novel Quality Loss

Single image super-resolution (SISR) has been a very attractive research topic in recent years. Breakthroughs in SISR have been achieved due to deep learning and generative adversarial networks (GANs). However, the generated image still suffers from undesired artifacts. In this paper, we propose a new method named GMGAN for SISR tasks. In this method, to generate images more in line with human vision system (HVS), we design a quality loss by integrating an image quality assessment (IQA) metric named gradient magnitude similarity deviation (GMSD). To our knowledge, it is the first time to truly integrate an IQA metric into SISR. Moreover, to overcome the instability of the original GAN, we use a variant of GANs named improved training of Wasserstein GANs (WGAN-GP). Besides GMGAN, we highlight the importance of training datasets. Experiments show that GMGAN with quality loss and WGAN-GP can generate visually appealing results and set a new state of the art. In addition, large quantity of high-quality training images with rich textures can benefit the results.
Deep learning-based liver segmentation for fusion-guided intervention

Tumors often have different imaging properties, and there is no single imaging modality that can visualize all tumors. In CT-guided needle placement procedures, image fusion (e.g. with MRI, PET, or contrast CT) is often used as image guidance when the tumor is not directly visible in CT. In order to achieve image fusion, interventional CT image needs to be registered to an imaging modality, in which the tumor is visible. However, multi-modality image registration is a very challenging problem. In this work, we develop a deep learning-based liver segmentation algorithm and use the segmented surfaces to assist image fusion with the applications in guided needle placement procedures for diagnosing and treating liver tumors. The developed segmentation method integrates multi-scale input and multi-scale output features in one single network for context information abstraction. The automatic segmentation results are used to register an interventional CT with a diagnostic image. The registration helps visualize the target and guide the interventional operation. The segmentation results demonstrated that the developed segmentation method is highly accurate with Dice of 96.1% on 70 CT scans provided by LiTS challenge. The segmentation algorithm is then applied to a set of images acquired for liver tumor intervention for surface-based image fusion. The effectiveness of the proposed methods is demonstrated through a number of clinical cases. Our study shows that deep learning-based image segmentation can obtain useful results to help image fusion for interventional guidance. Such a technique may lead to a number of other potential applications.
Least squares projection twin support vector clustering (LSPTSVC)

Clustering is a prominent unsupervised learning technique. In the literature, many plane based clustering algorithms are proposed, such as the twin support vector clustering (TWSVC) algorithm. In this work, we propose an alternative algorithm based on projection axes termed as least squares projection twin support vector clustering (LSPTSVC). The proposed LSPTSVC finds projection axis for every cluster in a manner that minimizes the within class scatter, and keeps the clusters of other classes far away. To solve the optimization problem, the concave-convex procedure (CCCP) is utilized in the proposed method. Moreover, the solution of proposed LSPTSVC involves a set of linear equations leading to very less training time. To verify the performance of the proposed algorithm, several experiments are performed on synthetic and real world benchmark datasets. Experimental results and statistical analysis show that the proposed LSPTSVC performs better than existing algorithms w.r.t. clustering accuracy as well as training time. Moreover, a comparison of the proposed method with existing algorithms is presented on biometric and biomedical applications. Better generalization performance is achieved by proposed LSPTSVC on clustering of facial images, and Alzheimer’s disease data.