Reviewerly/exHarmony huggingface dataset:
Crude oils as ore fluids: An experimental in-situ XAS study of gold partitioning between brine and organic fluid from 25 to 250 °C

Organic matter can be associated with mineralization in hydrothermal ore deposits. One hypothesis is that this organic matter represents remnants of organic fluids (crude oils) that were competing with aqueous fluids for metal transport and contributed to metal endowment. We investigated the transport of gold (Au) in model oil compounds (S-free n-dodecane, CH3(CH2)10CH3, DD; and S-bearing 1-dodecanethiol, CH3(CH2)10CH2SH; DDT) from 25 °C to 250 °C using in-situ synchrotron X-ray absorption spectroscopy (XAS) experiments to determine the speciation and the structural properties of gold complexes in the aqueous- and oil-based fluids. For most experiments, DD or DDT were in contact with Au-bearing acidified water, or acidified water plus 10 wt% NaCl (pH25°C = 1.85 in both cases). Gold rapidly partitioned from the aqueous phase into DD and DDT. Below 125 °C, Au(III)Cl is dominant in the DD and the adjacent water with a refined coordination number (CN) of chloride of 4.0(3) and an AuCl bond length of 2.28 Å, consistent with the tetrachloroaurate complex (AuCl4−) being stable in both the aqueous and organic phases. In contrast, Au(III) is rapidly reduced in the presence of DDT and an Au(I) complex dominates in both water and adjacent DDT with a CN of sulfur ∼2.0, suggesting a [RS-Au-SR]− (RS = DDT with deprotonated thiol group) complex with AuS bond lengths ranging from 2.29(1) Å to 2.31(3) Å. In an open system of DDT in contact with water, of which the water and DDT were analyzed separately, AuCl4− was dominant in the water phase, and Au(RS)2− dominant in DDT, possibly due to different equilibration kinetics in the beaker and glassy carbon tube. Since sulfur and organothiol compounds are ubiquitous and abundant components in natural oils, this study demonstrates the potential of natural oils to scavenge and enrich gold from co-existing gold-bearing brines. In particular, Au(I) organothiol complexes may contribute to transport in low-temperature (<125 °C) ore fluids such as those in basinal environments – in both hydrothermal fluids and oils. At temperatures ≥125 °C, gold was reduced to metallic gold in all experiments, suggesting that organo-stabilized nanoparticles may be the major form of gold to be scavenged, concentrated or transported in crude oils at these conditions. The results imply that brine-oil interactions may enrich Au in oils, and that oils may be an effective ore fluid in sedimentary environments.
Exploring C semantics and pointer provenance

The semantics of pointers and memory objects in C has been a vexed question for many years. C values cannot be treated as either purely abstract or purely concrete entities: the language exposes their representations, but compiler optimisations rely on analyses that reason about provenance and initialisation status, not just runtime representations. The ISO WG14 standard leaves much of this unclear, and in some respects differs with de facto standard usage --- which itself is difficult to investigate. In this paper we explore the possible source-language semantics for memory objects and pointers, in ISO C and in C as it is used and implemented in practice, focussing especially on pointer provenance. We aim to, as far as possible, reconcile the ISO C standard, mainstream compiler behaviour, and the semantics relied on by the corpus of existing C code. We present two coherent proposals, tracking provenance via integers and not; both address many design questions. We highlight some pros and cons and open questions, and illustrate the discussion with a library of test cases. We make our semantics executable as a test oracle, integrating it with the Cerberus semantics for much of the rest of C, which we have made substantially more complete and robust, and equipped with a web-interface GUI. This allows us to experimentally assess our proposals on those test cases. To assess their viability with respect to larger bodies of C code, we analyse the changes required and the resulting behaviour for a port of FreeBSD to CHERI, a research architecture supporting hardware capabilities, which (roughly speaking) traps on the memory safety violations which our proposals deem undefined behaviour. We also develop a new runtime instrumentation tool to detect possible provenance violations in normal C code, and apply it to some of the SPEC benchmarks. We compare our proposal with a source-language variant of the twin-allocation LLVM semantics proposal of Lee et al. Finally, we describe ongoing interactions with WG14, exploring how our proposals could be incorporated into the ISO standard.
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions.

Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters. We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters to optimal weights and biases. We show how to construct scalable best-response approximations for neural networks by modeling the best-response as a single network whose hidden units are gated conditionally on the regularizer. We justify this approximation by showing the exact best-response for a shallow linear network with L2-regularized Jacobian can be represented by a similar gating mechanism. We fit this model using a gradient-based hyperparameter optimization algorithm which alternates between approximating the best-response around the current hyperparameters and optimizing the hyperparameters using the approximate best-response function. Unlike other gradient-based approaches, we do not require differentiating the training loss with respect to the hyperparameters, allowing us to tune discrete hyperparameters, data augmentation hyperparameters, and dropout probabilities. Because the hyperparameters are adapted online, our approach discovers hyperparameter schedules that can outperform fixed hyperparameter values. Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems. We call our networks, which update their own hyperparameters online during training, Self-Tuning Networks (STNs).
Remote education vs traditional education based on effectiveness at the micro level and its connection to the level of development of macro-economic systems

Purpose The purpose of this paper is to verify the developed hypothesis on the basis of comparison of remote education and traditional education in terms of its effectiveness at the micro-level, as well as to determine the presence, degree and nature of correlation between the share of remote education and level of development of economic systems of separate states (macro-level). Design/methodology/approach The methodologies of this paper are based on the system approach and include methods of research investigation such as problem, logical, comparative analysis, synthesis and formalization, as well as a purposely designed author’s method of assessment of the effectiveness of remote education and traditional education. In addition to the specified methods, this methodology also includes the methods of correlation and regression analysis which are used by the authors to determine the presence and degree of correlation between the share of remote education and the state of macro-level economic systems. The authors have analyzed the correlation between the share of remote education in the higher education structure according to the summarized data of the ICEF Monitor and the existing studies and publications on this topic ( y ) with indicators of macro-level economic systems such as GDP, billions of dollars ( x 1 ); GDP per capita ( x 2 ); Education Index according to the United Nations Development Program ( x 3 ); Knowledge Economy Index according to The World Bank Group ( x 4 ); and the index of innovative development of socioeconomic systems according to INSEAD, WIPO and Cornell University ( x 5 ). The econometric analysis of the mentioned factors was performed after that. Findings The authors have come to conclusion that remote education is indeed much more effective at the micro-level, since it allows the students to receive similar educational services with greater convenience, a wider choice of higher educational institutions and at a lower cost compared to traditional education. At the same time, no negative influence of remote education on the macro-level economic system has been revealed; on the contrary, a positive, albeit slight, influence similar to traditional education has been found. For this reason, promotion of the formation and development of remote education is recommended instead of limitation, since it allows modernizing the educational system for the benefit of both supply and demand. Originality/value The research contributes to the development of the concept of socioeconomic development of economic systems through clarification of influence of remote education on it.
Adaptive CCR-ELM with variable-length brain storm optimization algorithm for class-imbalance learning

Class-specific cost regulation extreme learning machine (CCR-ELM) can effectively deal with the class imbalance problems. However, its key parameters, including the number of hidden nodes, the input weights, the biases and the tradeoff factors are normally generated randomly or preset by human. Moreover, the number of input weights and biases depend on the size of hidden layer. Inappropriate quantity of hidden nodes may lead to the useless or redundant neuron nodes, and make the whole structure complex, even cause the worse generalization and unstable classification performances. Based on this, an adaptive CCR-ELM with variable-length brain storm optimization algorithm is proposed for the class imbalance learning. Each individual consists of all above parameters of CCR-ELM and its length varies with the number of hidden nodes. A novel mergence operator is presented to incorporate two parent individuals with different length and generate a new individual. The experimental results for nine imbalance datasets show that variable-length brain storm optimization algorithm can find better parameters of CCR-ELM, resulting in the better classification accuracy than other evolutionary optimization algorithms, such as GA, PSO, and VPSO. In addition, the classification performance of the proposed adaptive algorithm is relatively stable under varied imbalance ratios. Applying the proposed algorithm in the fault diagnosis of conveyor belt also proves that ACCR-ELM with VLen-BSO has the better classification performances.
Deep Learning Framework for Cyber Threat Situational Awareness Based on Email and URL Data Analysis

Spamming and Phishing attacks are the most common security challenges we face in today’s cyber world. The existing methods for the Spam and Phishing detection are based on blacklisting and heuristics technique. These methods require human intervention to update if any new Spam and Phishing activity occurs. Moreover, these are completely inefficient in detecting new Spam and Phishing activities. These techniques can detect malicious activity only after the attack has occurred. Machine learning has the capability to detect new Spam and Phishing activities. This requires extensive domain knowledge for feature learning and feature representation. Deep learning is a method of machine learning which has the capability to extract optimal feature representation from various samples of benign, Spam and Phishing activities by itself. To leverage, this work uses various deep learning architectures for both Spam and Phishing detection with electronic mail (Email) and uniform resource locator (URL) data sources. Because in recent years both Email and URL resources are the most commonly used by the attackers to spread malware. Various datasets are used for conducting experiments with deep learning architectures. For comparative study, classical machine learning algorithms are used. These datasets are collected using public and private data sources. All experiments are run till 1,000 epochs with varied learning rate 0.01–0.5. For comparative study various classical machine learning classifiers are used with domain level feature extraction. For deep learning architectures and classical machine learning algorithms to convert text data into numeric representation various natural language processing text representation methods are used. As far as anyone is concerned, this is the first attempt, a framework that can examine and connect the occasions of Spam and Phishing activities from Email and URL sources at scale to give cyber threat situational awareness. The created framework is exceptionally versatile and fit for distinguishing the malicious activities in close constant. In addition, the framework can be effectively reached out to deal with vast volume of other cyber security events by including extra resources. These qualities have made the proposed framework emerge from some other arrangement of comparative kind.
Can You Hear Me Now? Social Listening as a Strategy for Understanding User Needs

Maintaining regular engagement with audiences through various forms of social media is becoming more important as library users and the general public increasingly rely on social media for news, updates, and feedback. While large corporations have been monitoring social media for more than a decade, libraries have recently begun to use social listening to stay abreast of trends and assess user needs as voiced directly by their communities. This column will explain what social listening is, explore concerns over its widespread application, consider examples of social media listening software currently available, and offer an overview of its application in library settings.
A Multi-Stage Convolution Machine with Scaling and Dilation for Human Pose Estimation

Vision-based Human Pose Estimation has been considered as one of challenging research subjects due to problems including confounding background clutter, diversity of human appearances and illumination changes in scenes.To tackle these problems, we propose to use a new multi-stage convolution machine for estimating human pose.To provide better heatmap prediction of body joints, the proposed machine repeatedly produces multiple predictions according to stages with receptive field large enough for learning the long-range spatial relationship.And stages are composed of various modules according to their strategic purposes.Pyramid stacking module and dilation module are used to handle problem of human pose at multiple scales.Their multi-scale information from different receptive fields are fused with concatenation, which can catch more contextual information from different features.And spatial and channel information of a given input are converted to gating factors by squeezing the feature maps to a single numeric value based on its importance in order to give each of the network channels different weights.Compared with other ConvNet-based architectures, we demonstrated that our proposed architecture achieved higher accuracy on experiments using standard benchmarks of LSP and MPII pose datasets.
Generating Multiple Objects at Spatially Distinct Locations

Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as image captions. Furthermore, conditional GANs allow us to control the image generation process through labels or even natural language descriptions. However, fine-grained control of the image layout, i.e. where in the image specific objects should be located, is still difficult to achieve. This is especially true for images that should contain multiple distinct objects at different spatial locations. We introduce a new approach which allows us to control the location of arbitrarily many objects within an image by adding an object pathway to both the generator and the discriminator. Our approach does not need a detailed semantic layout but only bounding boxes and the respective labels of the desired objects are needed. The object pathway focuses solely on the individual objects and is iteratively applied at the locations specified by the bounding boxes. The global pathway focuses on the image background and the general image layout. We perform experiments on the Multi-MNIST, CLEVR, and the more complex MS-COCO data set. Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations. We further show that the object pathway focuses on the individual objects and learns features relevant for these, while the global pathway focuses on global image characteristics and the image background.
A blockchain‐based architecture for securing electronic health record systems

Summary This paper presents a blockchain‐based architecture for our current electronic health record (EHR) systems. Being built on top of existing databases maintained by health providers, the architecture implements a blockchain solution to ensure the integrity of data records and improve interoperability of the systems through tracking all events that happen to the data in the databases. In this proposed architecture, we also introduce a new incentive mechanism for the creation of new blocks on the blockchain. The architecture is independent of any specific blockchain platforms and open to further extensions; hence, it potentially fits in with other electronic record systems that require protection against data misuse.
Multitask learning for biomedical named entity recognition with cross-sharing structure

Biomedical named entity recognition (BioNER) is a fundamental and essential task for biomedical literature mining, which affects the performance of downstream tasks. Most BioNER models rely on domain-specific features or hand-crafted rules, but extracting features from massive data requires much time and human efforts. To solve this, neural network models are used to automatically learn features. Recently, multi-task learning has been applied successfully to neural network models of biomedical literature mining. For BioNER models, using multi-task learning makes use of features from multiple datasets and improves the performance of models. In experiments, we compared our proposed model with other multi-task models and found our model outperformed the others on datasets of gene, protein, disease categories. We also tested the performance of different dataset pairs to find out the best partners of datasets. Besides, we explored and analyzed the influence of different entity types by using sub-datasets. When dataset size was reduced, our model still produced positive results. We propose a novel multi-task model for BioNER with the cross-sharing structure to improve the performance of multi-task models. The cross-sharing structure in our model makes use of features from both datasets in the training procedure. Detailed analysis about best partners of datasets and influence between entity categories can provide guidance of choosing proper dataset pairs for multi-task training. Our implementation is available at https://github.com/JogleLew/bioner-cross-sharing .
Recognition of Facial Expressions under Varying Conditions Using Dual-Feature Fusion

Facial expression recognition plays an important role in communicating the emotions and intentions of human beings. Facial expression recognition in uncontrolled environment is more difficult as compared to that in controlled environment due to change in occlusion, illumination, and noise. In this paper, we present a new framework for effective facial expression recognition from real-time facial images. Unlike other methods which spend much time by dividing the image into blocks or whole face image, our method extracts the discriminative feature from salient face regions and then combine with texture and orientation features for better representation. Furthermore, we reduce the data dimension by selecting the highly discriminative features. The proposed framework is capable of providing high recognition accuracy rate even in the presence of occlusions, illumination, and noise. To show the robustness of the proposed framework, we used three publicly available challenging datasets. The experimental results show that the performance of the proposed framework is better than existing techniques, which indicate the considerable potential of combining geometric features with appearance-based features.
Multi-Agent Common Knowledge Reinforcement Learning

Cooperative multi-agent reinforcement learning often requires decentralised policies, which severely limit the agents' ability to coordinate their behaviour. In this paper, we show that common knowledge between agents allows for complex decentralised coordination. Common knowledge arises naturally in a large number of decentralised cooperative multi-agent tasks, for example, when agents can reconstruct parts of each others' observations. Since agents can independently agree on their common knowledge, they can execute complex coordinated policies that condition on this knowledge in a fully decentralised fashion. We propose multi-agent common knowledge reinforcement learning (MACKRL), a novel stochastic actor-critic algorithm that learns a hierarchical policy tree. Higher levels in the hierarchy coordinate groups of agents by conditioning on their common knowledge, or delegate to lower levels with smaller subgroups but potentially richer common knowledge. The entire policy tree can be executed in a fully decentralised fashion. As the lowest policy tree level consists of independent policies for each agent, MACKRL reduces to independently learnt decentralised policies as a special case. We demonstrate that our method can exploit common knowledge for superior performance on complex decentralised coordination tasks, including a stochastic matrix game and challenging problems in StarCraft II unit micromanagement.
An Investigation for Enhancing Registration Performance with Brain Atlas by Novel Image Inpainting Technique using Dice and Jaccard Score on Multiple Sclerosis (MS) Tissue

This paper presents a novel approach of investigating registration performance of different Multiple Sclerosis (MS) affected brain tissues with brain atlas by image inpainting technique. MS is an immune-mediated disorder that develops from an interaction of the individual's genetics and unidentified environmental causes. In order to carry out medical diagnosis in proper time image registration plays a vital role. In this work, different MS lesions are introduced in healthy brain tissue in order to analyse and characterize according to their characteristics (T1 weighted image, T2 weighted image) because MS lesions create a strong bias in image registration process. Thus, image inpainting technique appears to be a handy tool to reduce the bias efficiently. It is evident that the proposed inpainting algorithm performs satisfactorily with a view to reducing the bias in the registration step. The overall performance of the technique is evaluated by utilizing Dice and Jaccard scores. MATLAB and FSL software are used to perform the simulation.