Reviewerly/exHarmony huggingface dataset:
Integrated Hyperspectral and Geochemical Study of Sediment-Hosted Disseminated Gold at the Goldstrike District, Utah

The Goldstrike district in southwest Utah is similar to Carlin-type gold deposits in Nevada that are characterized by sediment-hosted disseminated gold. Suitable structural and stratigraphic conditions facilitated precipitation of gold in arsenian pyrite grains from ascending gold-bearing fluids. This study used ground-based hyperspectral imaging to study a core drilled in the Goldstrike district covering the basal Claron Formation and Callville Limestone. Spectral modeling of absorptions at 2340, 2200, and 500 nm allowed the extraction of calcite, clay minerals, and ferric iron abundances and identification of lithology. This study integrated remote sensing and geochemistry data and identified an optimum stratigraphic combination of limestone above and siliciclastic rocks below in the basal Claron Formation, as well as decarbonatization, argillization, and pyrite oxidation in the Callville Limestone, that are related with gold mineralization. This study shows an example of utilizing ground-based hyperspectral imaging in geological characterization, which can be broadly applied in the determination of mining interests and classification of ore grades. The utilization of this new terrestrial remote sensing technique has great potentials in resource exploration and exploitation.
Financial Development, Foreign Banks and Economic Growth in Africa

Abstract This study aims to examine and analyze the impact of financial development and foreign bank penetration on African economies. An empirical study for the period 1995–2015 is conducted using the system GMM estimator. Our empirical results indicate that foreign bank entry has a positive and significant impact on economic growth in the countries of North and Southern Africa, while in the other two regions considered in this study (West and Central Africa, East Africa) the impact is negative and rarely significant. In addition, our results show that the development of financial markets has a positive and significant effect on economic growth only in the Southern African region. The paper concludes that policymakers should focus on long‐term policies to strengthen the financial sector to truly meet the needs of African people.
Towards Unsupervised Single Image Dehazing With Deep Learning

Deep learning computation is often used in single-image de-hazing techniques for outdoor vision systems. Its development is restricted by the difficulties in providing a training set of degraded and ground-truth image pairs. In this paper, we develop a novel model that utilizes cycle generative adversarial network through unsupervised learning to effectively remove the requirement of a haze/depth data set. Qualitative and quantitative experiments demonstrated that the proposed model outperforms existing state-of-the-art dehazing models when tested on both synthetic and real haze images.
Complex neural fuzzy system and its application on multi-class prediction — A novel approach using complex fuzzy sets, IIM and multi-swarm learning

In this paper, we present a novel complex neural fuzzy approach to multi-class prediction. The complex neural fuzzy system (CNFS) is proposed using complex fuzzy sets (CFSs), fuzzy causalities and multi-swarm machine learning. In general, CFSs are regarded as advanced fuzzy sets with membership degrees defined in the unit disk of the complex plane, in contrast to regular fuzzy sets with membership degrees in the real-valued unit interval [0,1]. The proposed model is composed of the premises designed by CFSs, the consequences designed by Takagi–Sugeno linear functions, and a fuzzy causality layer connecting the premises toward the consequences, and it is able to perform prediction of multiple targets. The usage of fuzzy causality in the proposed model makes difference to traditional fuzzy models using If-Then rules, and gives the freedom and flexibility for model construction. To optimize the proposed CNFS, we present a hybrid learning scheme using the particle swarm optimization with multiple swarms (denoted as PSOmsw) and the Kalman filtering algorithm (denoted as KFA). In the hybrid learning method, the KFA updates the consequence parameters while the PSOmsw evolves the rest parameters of the model. The proposed approach has been tested with experiments using several real-world stock market datasets. Compared with other methods, the proposed approach has shown excellent performance.
Emotion Recognition Based on Multi-View Body Gestures

Body gesture, a crucial component of "body language", remains less explored to recognize emotion while face expression-based and speech-based approaches are widely investigated. In this paper, we introduce an exploratory experiment to recognize emotion using deep learning only from body gestures. 43,200 multi-view RGB videos of simplified body gestures and their neutral control groups are captured from 80 humans using Hikvision network cameras to support the experiment. A novel approach is proposed to use deep neural network fuse skeleton and RGB features only using single-modality RGB video data. Experimental results show our approach achieves substantial improvements both in individual categories and overall and is provided with stronger generalization capability as well.
Implementation of Sentiment Classification of Movie Reviews by Supervised Machine Learning Approaches

Entertainment is crucial part of human life entertainments like songs, music, drama and movies etc. So for watching good movies most of the peoples generally prefer theater. If movie is not good then we feel nervous and we think wasted our money and time for watching bad movie so people prefer to go to movie by reading reviews and rating for that movie on various apps like IMDb, flixter and voice etc. But by reading one or two reviews we cannot say movie is good or bad because different peoples have different opinions some peoples like action or some like thrill or romance so people gives reviews based on their area of interest. So we cannot predict movie for that we proposed movie reviews based on sentiment analysis and classification algorithms such as Naïve bayes and Random forest (RF). Sentiment analysis generally utilized to identify the sentiment of huge amount of text. We compare naïve bayes and RF machine learning techniques for measuring negative, positive and neutral reviews.
Unsupervised Single Image Underwater Depth Estimation

Depth estimation from a single underwater image is one of the most challenging problems and is highly ill-posed. Due to the absence of large generalized underwater depth datasets and the difficulty in obtaining ground truth depth-maps, supervised learning techniques such as direct depth regression cannot be used. In this paper, we propose an unsupervised method for depth estimation from a single underwater image taken "in the wild" by using haze as a cue for depth. Our approach is based on indirect depth-map estimation where we learn the mapping functions between unpaired RGB-D terrestrial images and arbitrary underwater images to estimate the required depth-map. We propose a method which is based on the principles of cycle-consistent learning and uses dense-block based auto-encoders as generator networks. We evaluate and compare our method both quantitatively and qualitatively on various underwater images with diverse attenuation and scattering conditions and show that our method produces state-of-the-art results for unsupervised depth estimation from a single underwater image.
Sequence to sequence deep learning models for solar irradiation forecasting

The energy output a photo voltaic(PV) panel is a function of solar irradiation and weather parameters like temperature and wind speed etc. A general measure for solar irradiation called Global Horizontal Irradiance (GHI), customarily reported in Watt/meter2, is a generic indicator for this intermittent energy resource. An accurate prediction of GHI is necessary for reliable grid integration of the renewable as well as for power market trading. While some machine learning techniques are well introduced along with the traditional time-series forecasting techniques, deep-learning techniques remains less explored for the task at hand. In this paper we give deep learning models suitable for sequence to sequence prediction of GHI. The deep learning models are reported for shortterm forecasting {1 - 24} hour along with the state-of-the art techniques like Gradient Boosted Regression Trees(GBRT) and Feed Forward Neural Networks(FFNN). We have checked that spatio-temporal features like wind direction, wind speed and GHI of neighboring location improves the prediction accuracy of the deep learning models significantly. Among the various sequence-to-sequence encoder-decoder models LSTM performed superior, handling short-comings of the stateof-the-art techniques.
Seismic signal recognition by unsupervised machine learning

SUMMARY Seismic signal recognition can serve as a powerful auxiliary tool for analysing and processing ever-larger volumes of seismic data. It can facilitate many subsequent procedures such as first-break picking, statics correction, denoising, signal detection, events tracking, structural interpretation, inversion and imaging. In this study, I propose an automatic technique of seismic signal recognition taking advantage of unsupervised machine learning. In the proposed technique, seismic signal recognition is considered as a problem of clustering data points. All the seismic sampling points in time domain are clustered into two clusters, that is, signal or non-signal. The hierarchical clustering algorithm is used to group these sampling points. Four attributes, that is, two short-term-average-to-long-term-average ratios, variance and envelope are investigated in the clustering process. In addition, to quantitatively evaluate the performance of seismic signal recognition properly, I propose two new statistical indicators, namely, the rate between the total energies of original and recognized signals (RTE), and the rate between the average energies of original and recognized signals (RAE). A large number of numerical experiments show that when the signal is slightly corrupted by noise, the proposed technique performs very well, with recognizing accuracy, precision and RTE of nearly 1 (i.e. 100 per cent), recall greater than 0.8 and RAE about 1–1.3. When the signal is moderately corrupted by noise, the proposed technique can hold recognizing accuracy about 0.9, recognizing precision nearly to 1, RTE about 0.9, recall around 0.6 and RAE about 1.5. Applications of the proposed technique to real microseismic data induced from hydraulic fracturing and reflection seismic data demonstrate its feasibility and encouraging prospect.
Insider Attack Protection: Lightweight Password-Based Authentication Techniques Using ECC

The radical advancement in web services has drawn more attention toward intensifying the security of numerous applications that interact and serve the Internet users. In order to access the different applications on the web, users need to disclose their personal credentials such as username and password to the servers to get authenticated. This private information should be prevented from being exploited due to different security attacks that will result in illegal activities. Securing the systems from several attacks is vital. In comparison to all the security attacks, insider attacks are devastating due to the privilege the insiders of an organization possess to breach the information, which results in irrecoverable damage to both user and the organization. Numerous protocols were proposed to secure the system from insider attacks but proved to be inefficient. This article puts forward a novel authentication protocol for insider attacks based on a robust cryptographic algorithm, ECC. The informal security analysis of the protocol reveals that the proposed protocol is not only immune to insider attacks but also prevents several attacks. It shows that the protocol is relatively better than other existing protocols in terms of computational cost and communication overhead with high level of security.
Distribution Padding in Convolutional Neural Networks

Even though zero padding is usually a staple in convolutional neural networks to maintain the output size, it is highly suspicious because it significantly alters the input distribution around border region. To mitigate this problem, in this paper, we propose a new padding technique termed as distribution padding. The goal of the method is to approximately maintain the statistics of the input border regions. We introduce two different ways to achieve our goal. In both approaches, the padded values are derived from the means of the border patches, but those values are handled in a different way in each variant. Through extensive experiments on image classification and style transfer using different architectures, we demonstrate that the proposed padding technique consistently outperforms the default zero padding, and hence can be a potential candidate for its replacement.
Keypoint based weakly supervised human parsing

Fully convolutional networks (FCN) have achieved great success in human parsing in recent years. In conventional human parsing tasks, pixel-level labeling is required for guiding the training, which usually involves enormous human labeling efforts. To ease the labeling efforts, we propose a novel weakly supervised human parsing method which only requires simple object keypoint annotations for learning. We develop an iterative learning method to generate pseudo part segmentation masks from keypoint labels. With these pseudo masks, we train a FCN network to output pixel-level human parsing predictions. Furthermore, we develop a correlation network to perform joint prediction of part and object segmentation masks and improve the segmentation performance. The experiment results show that our weakly supervised method is able to achieve very competitive human parsing results. Despite that our method only uses simple keypoint annotations for learning, we are able to achieve comparable performance with fully supervised methods which use the expensive pixel-level annotations.
Bolt: Towards a Scalable Docker Registry via Hyperconvergence

Docker container images are typically stored in a centralized registry to allow easy sharing of images. However, with the growing popularity of containerized software, the number of images that a registry needs to store and the rate of requests it needs to serve are increasing rapidly. Current registry design requires hosting registry services across multiple loosely connected servers with different roles such as load balancers, proxies, registry servers, and object storage servers. Due to the various individual components, registries are hard to scale and benefits from optimizations such as caching are limited. In this paper we propose, implement, and evaluate BOLT-a new hyperconverged design for container registries. In BOLT, all registry servers are part of a tightly connected cluster and play the same consolidated role: each registry server caches images in its memory, stores images in its local storage, and provides computational resources to process client requests. The design employs a custom consistent hashing function to take advantage of the layered structure and addressing of images and to load balance requests across different servers. Our evaluation using real production workloads shows that BOLT outperforms the conventional registry design significantly and improves latency by an order of magnitude and throughput by up to 5x. Compared to state-of-the-art, BOLT can utilize cache space more efficiently and serve up to 35% more requests from its cache. Furthermore, BOLT scales linearly and recovers from failure recovery without significant performance degradation.
A Multi-Scale Conditional Generative Adversarial Network for Face Sketch Synthesis

We investigate conditional generative adversarial network (cGAN) as a solution to realize the face-to-sketch translation problems. These networks not only learn the mapping relationships between the face and responding sketch, but also generate a loss function to train the mapping relationships automatically. This makes it possible to regard the transformation problems as minimizing the loss function. In previous works, cGAN employs a single scale to resolve the above problems and lacks multi-scale information. In this work, considering that image multi-scale representation can capture image texture, structure and other important features more effectively, and we construct a three-layer pyramid model to obtain multi-scale information, and employ the proposed multiscale cGAN to train the mapping relationships. With respects to four metrics, our method outperforms previous models.
Near-Field Directionality Beyond the Dipole Approximation: Electric Quadrupole and Higher-Order Multipole Angular Spectra

A paramount example among spin-related optical phenomena is the quantum spin Hall effect of light, by which the direction of propagating guided modes can be controlled by the spin of the source. For unidirectional excitation of guided waves, the focus has been only on dipolar sources, leaving aside higher-order multipoles. Exploiting the angular-spectrum representation, the authors present a general analytical treatment of near-field directionality beyond the dipole approximation. This enables a considerable advance toward full control of spin-dependent directionality at the nanoscale, and should be useful for engineering light-matter coupling in nanophotonics and quantum optics.
Deep Network Pruning for Object Detection

With the increasing success of deep learning in various applications, there is an increasing need to have deep models that can be used for deployment in real-time and/or resource constrained scenarios. In this context, this paper analyzes the pruning of deep models for object detection in order to reduce the number of weights and hence the number of computations. Very deep networks based on ResNet like architectures, like YOLOv3 have unique challenges when attempting to prune them. This paper proposes a network pruning technique based on agglomerative clustering for the feature extractor and using mutual information for the detector. The performance of the proposed techniques is also compared with that of a relatively shallow network, i.e., YOLOv2. A compression percentage of around 30% results in a 10% drop of mean average precision (mAP) in YOLOv3, whereas in YOLOv2 the drop was around 6% on the COCO dataset.
Block Chain-Based Data Audit and Access Control Mechanism in Service Collaboration

In the context of big data, cloud storage services provide users with the ideal data storage service. But external store of data causes cloud storage service providers gain control of the data. Therefore, our work should consider how to ensure the privacy of data and maintain the integrity of data when enjoying convenient services. This paper builds a blockchain-based solution through research on cloud storage service model and blockchain technology. And related protocols are built on the solution-based architecture. In our solution, the decentralized model solves the single point of trust problem in the traditional data auditing service model by collective trust. A public agreement enables auditors to efficiently build proof of data integrity without touching data. The protocol allows users to trace the history of their data, and examine whether the owner of the data guarantees the privacy of the data in an after-the-fact audit. In addition, our work constructed the prototype system of the scheme and revealed the effectiveness of the scheme through system testing.
Grover Search as a Naturally Occurring Phenomenon

We provide first evidence that under certain conditions, 1/2-spin fermions may naturally behave like a Grover search, looking for topological defects in a material. The theoretical framework is that of discrete-time quantum walks (QWs), i.e., local unitary matrices that drive the evolution of a single particle on the lattice. Some QWs are well known to recover the (2+1)–dimensional Dirac equation in continuum limit, i.e., the free propagation of the 1/2-spin fermion. We study two such Dirac QWs, one on the square grid and the other on a triangular grid reminiscent of graphenelike materials. The numerical simulations show that the walker localizes around the defects in O(N) steps with probability O(1/logN), in line with previous QW search on the grid. The main advantage brought by those of this Letter is that they could be implemented as "naturally occurring" freely propagating particles over a surface featuring topological defects—without the need for a specific oracle step. From a quantum computing perspective, however, this hints at novel applications of QW search: instead of using them to look for "good" solutions within the configuration space of a problem, we could use them to look for topological properties of the entire configuration space.Received 2 September 2019Revised 7 March 2020Accepted 16 April 2020DOI:https://doi.org/10.1103/PhysRevLett.124.180501© 2020 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasFermionsQuantum algorithmsQuantum walksPhysical SystemsElectronsGrapheneQuantum InformationCondensed Matter, Materials & Applied Physics
Semi-Supervised Cross-Modality Action Recognition by Latent Tensor Transfer Learning

Microsoft's Kinect sensors are receiving an increasing amount of interests by security researchers since they are cost-effective and can provide both visual and depth modality data at the same time. Unfortunately, depth or RGB modalities are unavailable in training or testing procedures in some realistic scenarios. Therefore, we explore a new problem focusing on the arbitrary absence of modality, which is completely different from the conventional action recognition. The new problem in action recognition aims to deal with cross modality data (e.g., RGB training and depth testing data), “missing” modality data (e.g., RGB training and RGB-D test data), and single-modality data (e.g., RGB/depth in both phases). Accordingly, our method aims to borrow some information (e.g., correlation between two modalities) from the well-established RGB-D dataset and apply it to the existing dataset to recover some latent information to improve the performance of recognition. For instance, a cross-modality regularizer is used to preserve the correlation of RGB and depth modalities. The “missing” knowledge is considered as latent information, which is recovered by low-rank learning in our model. In the real world, the target data are usually sparsely labeled or completely unlabeled; however, we could exploit the pseudolabels of the target as prior knowledge for “supervised” learning in the target domain. Accordingly, we propose a semi-supervised model for transfer learning. The experiments on three widely used RGB-D action datasets show that our method performs better than that of the state-of-the-art transfer learning methods in most cases in terms of accuracy and time efficiency.