Reviewerly/exHarmony huggingface dataset:
Automated Quantitative Mineralogy Applied to Metamorphic Rocks

The ability to apply automated quantitative mineralogy (AQM) on metamorphic rocks was investigated on samples from the Fiskenæsset complex, Greenland. AQM provides the possibility to visualize and quantify microstructures, minerals, as well as the morphology and chemistry of the investigated samples. Here, we applied the ZEISS Mineralogic software platform as an AQM tool, which has integrated matrix corrections and full quantification of energy dispersive spectrometry data, and therefore is able to give detailed chemical information on each pixel in the AQM mineral maps. This has been applied to create mineral maps, element concentration maps, element ratio maps, mineral association maps, as well as to morphochemically classify individual minerals for their grain shape, size, and orientation. The visualization of metamorphic textures, while at the same time quantifying their textures, is the great strength of AQM and is an ideal tool to lift microscopy from the qualitative to the quantitative level.
No-Reference Quality Evaluation of Stereoscopic Video Based on Spatio-Temporal Texture

Due to the wide application of stereoscopic display technology, stereoscopic video quality assessment (SVQA) is facing great challenges, but worthwhile. Stereoscopic videos contain a great deal of information, which involves not only the spatial domain but also the spatio-temporal domain. Motion in stereoscopic video plays a critical role in quality perception, while the existing SVQA methods rarely refer to motion factors, and the performance of these methods is restrained. In this article, a novel SVQA based on motion perception is introduced and its performance is superior to that of existing excellent methods. Particularly, to appropriately reduce the amount of data processing, we extract the key-frame sequences according to the influence of movement intensity on binocular visual quality perception. The binocular summation and difference operations are implemented on extracted sequences, and then spatial texture and spatio-temporal texture statistic measurement are extracted simultaneously with local binary patterns from three orthogonal planes (LBP-TOP). Experiments are implemented on two publicly available databases and the results demonstrate the effectiveness and robustness of our algorithm for various categories of distortion stereoscopic video pairs.
3-D Printed Smart Orthotic Insoles: Monitoring a Person's Gait Step by Step

Gait monitoring using a 3D printed smart insole embedded with embedded optical fibre Bragg gratings is reported in this paper.The smart insole combines 3D printing technology and fiber Bragg gratings (FBGs) sensors providing high sensitivity and end-point low cost.Results using pressure points measured by four FBGs are sufficient to differentiate foot loads and gait types.
Development and Analysis of Intelligent Recommendation System Using Machine Learning Approach

Recommendation systems have changed the way inanimate websites interact with their users. Instead of providing static information when users search and possibly buy products, recommendation systems increase the degree of interactivity to expand the possibilities provided to the user. Recommendation systems generate recommendations independently for each specific user based on his past purchases and searches, as well as on the basis of the behavior of other users. This article describes recommender systems and their algorithms. Recommendation system is developed using SVD algorithm. The architectural pattern MVC was used. The implementation represents a three-tier architecture (DAL, BLL, WEB). Program realization of recommendation system was made using .Net language.
The Positive Impact of Push vs Pull Progress Feedback

Lack of physical activity has been shown to increase disease and reduce life expectancy. In response, mobile devices are increasingly being used to support people's health and fitness by tracking physical activity. Prior work shows that the type of feedback, either ambient or via notification, affects users' behavior towards their physical activity. Yet, these phone- and watch-based interactions and notifications have primarily been visual in nature. Inspired by prior research, we explored the impact of feedback modality (visual, tactile, and hybrid: visual/tactile) on 44 participants' behavior and exercise mindset in a 6-week field study. We present the differences between modalities and the notion of push vs. pull for interface feedback and notifications. Across 1,662 days of study data, we found statistically significant impacts of feedback modality and, in particular, the positive effects of push feedback on participants' mindset about the process of exercise. Our results also highlight design guidelines for wearables and multimodal notification systems.
AgentChain: A Decentralized Cross-Chain Exchange System

Blockchain enables fair assets delivery between untrusted peers. However, it's difficult to exchange assets between different blockchain systems. Centralized cryptocurrency exchange platform provides a flexible solution to such problem to some extent, but it contradicts the nature of blockchain, and property losses from it have occurred for many times since then. Currently, there are several cross-chain methods developed, while the implement cannot be compatible with most of the existing blockchain due to its performance, i.e. decentralization, efficiency, security, and algorithm compatibility. In this paper, we propose a new cross-chain system, named AgentChain, based on multi-signature with better performance. This system is open to trading operators, who can be combined as several decentralized trading groups by locking tokens to ensure its credibility. Users can choose a reputable trading group and deposit assets to the trading group's multi-signature address on the existing blockchain. Then the assets will be mapped to AgentChain by the trading group, on which token fair exchange is supported. The proposed AgentChain is compatible with most of the existing blockchain projects, only with the requirement that they should support multi-signature. The comparison between AgentChain and other cross-chain projects shows that the proposed system has a better performance and can be implemented practically.
Byzantine Fault Tolerant Algorithm Based on Vote

Practical Byzantine Fault Tolerance (PBFT) algorithm is a popular solution for establishing consensus in blockchain systems. However, there are some issues in the PBFT algorithm, such as high energy consumption, low efficiency, and poor scalability. These problems are not solved even in some of its improved algorithms, such as Byzantine consensus algorithm based on Gossip protocol (GBC) and Credit Practical Byzantine Fault Tolerance (CBPFT) algorithms. To address these issues, this paper proposes a Byzantine Fault Tolerant algorithm based on Vote algorithm (vBFT). The nodes in the network are divided into three types of with different responsibilities by the vBFT algorithm, which are client, slave, and master nodes. The state between nodes could be dynamically adjusted at any time. As shown our simulations and analysis, the proposed algorithm has significantly improved performance in terms of dynamics, energy consumption, fault tolerance and low-latency compared with consensus algorithms such as PBFT, GBC and CBPFT algorithm.
Powering Virtual Try-On via Auxiliary Human Segmentation Learning

Image-based virtual try-on for fashion has gained considerable attention recently. This task requires to fit an in-shop cloth image on a target model image. An efficient framework for this is composed of two stages: (1) warping the try-on cloth to align with the body shape and pose of the target model, and (2) an image composition module to seamlessly integrate the warped try-on cloth onto the target model image. Existing methods suffer from artifacts and distortions in their try-on output. In this work, we propose to use auxiliary learning to power an existing state-of-the-art virtual try-on network. We leverage prediction of human semantic segmentation (of the target model wearing the try-on cloth) as an auxiliary task and show that it allows the network to better model the bounds of the clothing item and human skin, thereby producing a better fit. Using exhaustive qualitative and quantitative evaluation we show that there is a significant improvement in the preservation of characteristics of the cloth and person in the final try-on result, thereby outperforming the existing state-of-the-art virtual try-on framework.
Improving the Estimation of Tail Ratings in Recommender System with Multi-Latent Representations

The importance of the distribution of ratings on recommender systems (RS) is well-recognized. And yet, recommendation approaches based on latent factor models and recently introduced neural variants (e.g., NCF) optimize for the head of these distributions, potentially leading to large estimation errors for tail ratings. These errors in tail ratings that are far from the mean predicted rating fall out of a uni-modal assumption underlying these popular models, as we show in this paper. We propose to improve the estimation of tail ratings by extending traditional single latent representations (e.g., an item is represented by a single latent vector) with new multi-latent representations for better modeling these tail ratings. We show how to incorporate these multi-latent representations in an end-to-end neural prediction model that is designed to better reflect the underlying ratings distributions of items. Through experiments over six datasets, we find the proposed model leads to a significant improvement in RMSE versus a suite of benchmark methods. We also find that the predictions for the most polarized items are improved by more than 15%.
Scaling TensorFlow, PyTorch, and MXNet using MVAPICH2 for High-Performance Deep Learning on Frontera

Frontera is the largest NSF-funded cluster in the US and comprises of 8,008 nodes equipped with the latest Intel Xeon processors (Cascade-Lake). In this paper, we explore the potential of Frontera for training state-of-the-art Deep Learning (DL) models at scale. Most DL studies present performance data from large-scale GPU clusters that are equipped with NVIDIA GPUs. However, our earlier performance characterization studies have helped us achieve comparable performance with CPU-only clusters as well. Based on this, we configure three important DL frameworks; 1) TensorFlow, 2) PyTorch, and 3) MXNet, using Horovod and two Message Passing Interface (MPI) libraries on Frontera: 1) MVAPICH2 and 2) Intel MPI. We provide a systematic performance comparison for TensorFlow using MVAPICH2 and Intel MPI on 2,048 Frontera nodes. Using a four process per-node configuration, we observe near-linear scaling for ResNet-50 training for TensorFlow up to 8,192 MPI processes (on 2,048 nodes) offering a sustained performance of 250,000 images/second. In addition, we provide insights into process per node and batch size configurations for TensorFlow as well as for PyTorch and MXNet. Based on single-node performance behavior, we scale all three DL frameworks up to 1,024 processes (256 nodes) for various models like ResNet-50/101/152 and Inception-v3/v4.
Fusion of complex networks and randomized neural networks for texture analysis

This paper presents a high discriminative texture analysis method based on the fusion of complex networks and randomized neural networks. In this approach, the input image is modeled as a complex network and its topological properties as well as the image pixels are used to train randomized neural networks to create a signature that represents the deep characteristics of the texture. The results obtained surpassed the accuracy of many methods available in the literature. This performance demonstrates that our proposed approach opens a promising source of research, which consists of exploring the synergy of neural networks and complex networks in the texture analysis field.
Yugala: Blockchain Based Encrypted Cloud Storage for IoT Data

Cloud storage enables users to upload, store and download their data. However, with the rapid growth of the Internet of Things (IoT) and edge devices, such as camera, the cloud not only manages the user generate data but also devices generated data. This increment in the volume of outsourced data has raised two important issues in big data management for cloud storage servers, namely, optimal data storage and data security. In order to solve the former issue, cloud storage employs a deduplication technique to avoids storing duplicate data; and uses encryption to solve the later issue. However, both these issues are orthogonal in nature. Thanks to convergent encryption that addresses both the issues simultaneously. Nevertheless, the existing approaches relies on proxy servers and put all the trust on third party, which is vulnerable to single point failure. In this paper, we propose a novel lightweight decentralized encrypted cloud storage architecture called, Yugula, which maintains file confidentiality, removes the centralized data deduplication and increases file integrity by using blockchain. In particular, we discuss two approaches for file confidentiality with data deduplication: one uses double hashing and the other symmetric encryption. In order to show the efficiency and usability, we implemented the proposed architecture and showed its results in terms of high transaction throughput requirement for IoT data management in cloud storage.
Improving consensus clustering with noise-induced ensemble generation

Because of the negative perception towards noise, it is commonly eliminated in the process of data cleansing prior to the analysis process. Some studies attempt to employ tolerant or robust algorithms to achieve a reliable outcome. One way or another, the impact of noise might be minimized, thus preserving the integrity of discovered knowledge. On the other hand, making good use of noise has recently been investigated and exploited in different contexts, such as in privacy-preserving data mining, single clustering and consensus clustering. Given our initial study of employing uniform random noise in the process of ensemble generation as a way to increase diversity within an ensemble, improved clustering goodness can be obtained at specific levels of noise. To consolidate the aforementioned finding, this paper investigates a rich collection of random noise functions, which can be used to form perturbed data variation within the framework of noise-induced ensemble generation. The effectiveness of this approach which uses different cases for random noise is demonstrated over benchmark datasets from the UCI repository. The results suggest that the noise-induced strategy is generally better than the baseline counterpart, whilst showing uneven improvement with different data patterns. As such, a guideline is provided to make the best use of the proposed method with any new set of data.
Movie Tags Prediction and Segmentation Using Deep Learning

The sheer volume of movies generated these days requires an automated analytics for efficient classification, query-based search, and extraction of desired information.These tasks can only be efficiently performed by a machine learning based algorithm.We address the same issue in this paper by proposing a deep learning based technique for predicting the relevant tags for a movie and segmenting the movie with respect to the predicted tags.We construct a tag vocabulary and create the corresponding dataset in order to train a deep learning model.Subsequently, we propose an efficient shot detection algorithm to find the key frames in the movie.The extracted key frames are analyzed by the deep learning model to predict the top three tags for each frame.The tags are then assigned weighted scores and are filtered to generate a compact set of most relevant tags.This process also generates a corpus which is further used to segment a movie based on a selected tag.We present a rigorous analysis of the segmentation quality with respect to the number of tags selected for the segmentation.Our detailed experiments demonstrate that the proposed technique is not only efficacious in predicting the most relevant tags for a movie, but also in segmenting the movie with respect to the selected tags with a high accuracy.
Multi-view RBM with posterior consistency and domain adaptation

The restricted Boltzmann machine (RBM) and extensions are rarely used in the field of multi-view learning. In this paper, we first present a multi-view RBM model for classification, which is named RBM with posterior consistency (PCRBM). PCRBM computes multiple representations by regularizing the marginal likelihood function with the consistency among representations from multiple views. However, PCRBM ignores the specific information of each view, where the learned representations just contain the consistency information among multiple views. To address this problem, we propose a novel multi-view RBM model and name it as RBM with posterior consistency and domain adaptation (PDRBM). PDRBM divides the hidden units of each view into two groups: the one is the view-consistency group that contains the consistency information among multiple views, and the other is the view-specific group that contains the information unique to this separate view. In addition, PDRBM balances the relationship among view-consistency hidden representations for multi-view classification. Contrasting with existing multi-view classification methods, PDRBM has achieved satisfactory results on two-class and multi-class classification multi-view datasets.
Hybrid Blind Audio Watermarking for Proprietary Protection, Tamper Proofing, and Self-Recovery

The paper presents a lifting wavelet transform (LWT)-based framework for multi-purpose blind audio watermarking. The proposed schemes can be used to carry out robust watermarking for intellectual property protection as well as fragile watermarking for tamper detection and signal recovery. Following 3-level LWT decomposition of the host audio, the coefficients in selected subbands are partitioned into frames for watermarking. To expand applicability, the robust watermark comprising proprietary information, synchronization code, and frame-related data was particularly embedded in the approximation subband using perceptual-based rational dither modulation (RDM) and adaptive quantization index modulation (AQIM) at a payload capacity of 1523.9 bits per second. The fragile watermark is a highly compressed version of the audio embedded within the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> - and 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> -level detail subbands using 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</sup> - ary AQIM. Hashing comparison and source-channel coding make it possible to identify tampered frames and restore affected regions. Experiment results indicate that the embedded robust watermark can withstand commonly-encountered attacks and the fragile watermark is highly effective in tamper detection and self-recovery. More importantly, the incorporation of a frame synchronization mechanism makes the proposed system resistant to cropping and replacement attacks, all of which were unsolvable using previous watermarking schemes. The perceptual evaluation revealed that the watermark caused only minor degradation. The proposed watermarking scheme is suitable for a wide range of ownership protection and content authentication applications.
Joint dimensionality reduction and metric learning for image set classification

Compared with the traditional classification task based on a single image, an image set contains more complementary information, which is of great benefit to correctly classify a query subject. Thus, image set classification has attracted much attention from researchers. However, the main challenge is how to effectively represent an image set to fully exploit the latent discriminative feature. Unlike in previous works where an image set was represented by a single or a hybrid mode, in this paper, we propose a novel multi-model fusion method across the Euclidean space to the Riemannian manifold to jointly accomplish dimensionality reduction and metric learning. To achieve the goal of our framework, we first introduce three distance metric learning models, namely, Euclidean-Euclidean, Riemannian-Riemannian and Euclidean-Riemannian to better exploit the complementary information of an image set. Then, we aim to simultaneously learn two mappings performing dimensionality reduction and a metric matrix by integrating the two heterogeneous spaces (i.e., the Euclidean space and the Riemannian manifold space) into the common induced Mahalanobis space in which the within-class data sets are close and the between-class data sets are separated. This strategy can effectively handle the severe drawback of not considering the distance metric learning when performing dimensionality reduction in the existing set based methods. Furthermore, to learn a complete Mahalanobis metric, we adopt the L2,1 regularized metric matrix for optimal feature selection and classification. The results of extensive experiments on face recognition, object classification, gesture recognition and handwritten classification demonstrated well the effectiveness of the proposed method compared with other image set based algorithms.
Using Conceptual Models to Relate Multiparameter Satellite Data to Subsurface Volcanic Processes in Latin America

Abstract Satellite data have been extensively used to identify volcanic behavior. However, the physical subsurface processes causing any individual manifestation of activity can be ambiguous. We propose a classification scheme for the cause of unrest that simultaneously considers three multiparameter satellite observations. The scheme is based on characteristics of the volcanic system (open, closed, and eruptive) and unrest mechanisms (intrusion, evolution, and withdrawal) occurring at shallow depths in the volcanic system. We applied these models to satellite observations acquired at 47 of the most active volcanoes in Latin America. Of the volcanoes studied, 44 had a robust enough dataset for classification and were clustered into 4 groups and 10 subgroups with common behavioral characteristics. By identifying that these volcanoes can be clustered into a number of groupings significantly less than the number of volcanoes, we have demonstrated that commonalities in behavior patterns exist among diverse volcanic systems. Identifying volcanoes with similar characteristics underpins the use of past observations at one volcano to forecast activity at another and diverges from typical volcanic groupings, which are focused on geologic parameters (i.e., composition, volcano type, and tectonic setting). Based on satellite data alone, we have identified preeruptive intrusion prior to 15 eruptions at 12 different volcanoes, magma evolution prior to 18 eruptions at 13 volcanoes, and magma withdrawal at 3 eruptions and 3 volcanoes. Improvements to the spatial and temporal resolution are needed to make these relations robust. This classification scheme provides a framework for future automated clustering of volcanoes.