Reviewerly/exHarmony huggingface dataset:
Micro-Nanoscale Characteristics of Pyrite and Its Implications for Gold Mineralization: Two Cases of Gold Deposits in the Youjiang Basin and Southwestern Tianshan Mountains

The mineralogical and compositional characteristics of gold-bearing minerals and the occurrence of gold are not only of great significance to exploring the sources of ore-forming materials and their formation mechanisms but also helpful for designing reasonable beneficiations and smelting schemes and achieving remarkable economic benefits. This paper presents an integrated study on the crystal characteristics, elemental composition and distribution of pyrite (the main gold-bearing minerals), on the basis of electron probe microanalysis (EPMA), scanning electron microscopy (SEM), laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) and nano-secondary ion mass spectrometry (NanoSIMS). The occurrence of gold in the Shuiyindong gold deposit and Ashawayi gold deposit has been studied by means of microscopy, SEM, and EPMA images, elemental correlations, S–Fe–As ternary diagrams, logAs–logAu diagrams and Au/As ratios. The gold in pyrite of the Shuiyindong deposit is in the form of nano gold inclusions and lattice gold. The gold in pyrite of the Ashawayi deposit dominantly exists in the form of nano gold inclusions or is present as micro-nano gold particles in the cracks or edges of pyrite, some of which can exist as lattice gold. The ore-forming hydrothermal solution of the Shuiyindong gold deposit is mainly underground hot brine, but it may be reformed by a deep magmatic hydrothermal solution or volcanic-subvolcanic hydrothermal solution. The ore-forming hydrothermal solution of the Ashawayi gold deposit is mainly derived from the metamorphic hydrothermal solution formed during the orogenic process, and the ore-forming process or post-mineralization process may be reformed by the leaching of underground hot brine. Finally, the characteristics of ore-forming fluids and evolution of the two types of deposits are determined via pyrite element surface scanning. This paper shows that micro-nanoscale study of gold-bearing pyrite is of great significance to understanding the gold mineralization process and is worth further study.
On Making Factories Smarter through Actionable Predictions based on Time-Series Data

The emergence of the Industrial Internet of Things paves the way for enhancing the real-time monitoring capabilities of contemporary factories. This can be materialised through sensors collecting data which can be further analysed. This paradigm enables the detection of indicators concerning systems’ degradation and facilitates corrective actions to be performed ahead of time. In this paper, we propose a damage prediction framework exploiting data that are coming from the core IT system in a steel industry use case. The novelty of our approach lies in the exploitation of Deep Learning techniques over streaming operational sensor data. To evaluate the framework, real-life data are collected and analysed based on daily operational activities enriched with the Remaining Useful Life (RUL). The Remaining Useful Life is automatically computed. We also periodically align the damage attribute, which is recorded in the planned activities and is performed by the maintenance engineers. We demonstrate the framework’s potential by conducting a quantitative and qualitative analysis to timely identify rare events based on historical data and predict dynamic behavioural changes in the manufacturing settings.
Towards Transforming Tabular Datasets into Knowledge Graphs

Many applications rely on the existence of reusable data. The FAIR principles identify rich descriptions of data and metadata as the key ingredients for achieving reusability. However, creating descriptive data requires massive manual effort. One way to ensure that data is reusable is by integrating it into a Knowledge Graph (KG). The semantic foundation of these graphs provides the necessary description for reuse. In this paper, we focus on tabular data and how that can be integrated into a KG. Besides the tabular data itself, we leverage existing metadata and publications describing the datasets for the KG construction. To tackle this task, we introduce a machine-learning based framework. Our framework consists of three core modules. The first module predicts the concepts of the KG from various data sources. In the second module, we extract possible relations among these concepts. Afterwards, we will integrate the two modules to build the final KG. As an example domain to develop and evaluate our approach, we focus on Biodiversity research. This is a data-rich domain with a particularly high need for data reuse. We present preliminary results in the context of building a KG schema given table headers. We cluster these headers using two types of representations, word embeddings, and syntactic representation. Our results show that embeddings can catch high-level semantics of headers; thus, they are better descriptors.
Research on observation data compression of hydraulic engineering based on swinging door trending algorithm

Abstract With the wide application of big data technology, there is a geometric series increase of the real-time data collection of water conservancy engineering, which causes a large number of storage units and memory. In order to make the huge amount of data easy to collect and process, an improved Swinging Door Trending algorithm is proposed to compress the data of water conservancy project based on the deep research of data compression algorithm. This work firstly analyzed the collected abnormal points, which identified and processed the points in time to realize the timely alarm of abnormal conditions according to the characteristics of water conservancy engineering data and acquisition requirements; then discussed the dynamic adjustment of tolerance, which is carried out according to the fluctuating state of water conservancy engineering data to store less real-time data; and finally put forward that the improved SDT algorithm has higher compression ratio and better compression efficiency than the traditional SDT method does by experiments.
The Information Literacy Mapping on Community Empowerment Program in the Coastal Community in Semarang

The aims of this research is to mapping the information literacy of the coastal community in Semarang. The mapping consists of the availability and absorption of sources of information, the pattern of society in using information and access to sources of information, and regulatory support for the city government. The second objective is to conduct information literacy mapping on women's groups in coastal communities, related to information on women's empowerment programs from the Semarang city government. In this study, there were four aspects that were mapped. The first aspect is planning activities, implementation activities, benefit taking, and evaluation. The research design used in this study is sequential exploratory, which is collecting and analyzing qualitative data then collecting and analyzing quantitative data. The research design used in this study is sequential exploratory, which consist of collecting and analyzing qualitative data followed by collecting and analyzing quantitative data. Data for this research collected in Mangunharjo Coastal Village, in Tugu City District, Semarang. Data collection is done through a focus group discussion and questionnaires, random sampling interview, and observation. The data analysis technique is done by combining quantitative analysis of data obtained from questionnaires and qualitative data analysis for data obtained from observations and, library study for mapping models. The results showed that the culture of information literacy in coastal communities in the study locations was good. In the first aspect, the availability and the use of information sources at the research location, it shows low score. Whereas in the second aspect, community activities in using information and access to sources of information, shows a bad condition. And in the third aspect, Semarang government regulation support for information literacy programs shows a good condition. Furthermore, the results of research information literacy in terms of women's empowerment programs, starting from the planning, implementation, benefit taking, and program evaluation phases, are categorized as positive.
PACELC: Enchantment multi-dimension TensorFlow for value creation through Big Data

New online mode learns more about different kinetic models. Frequency algorithm reduces the loss function, which directly compensates for the error between the required and the actual acceleration. It allows the use of green acceleration principles such as speed accelerators and TensorFlow as a helper function of the robot mode. The use of direct loss eliminates the problem of learning outside the scope of indirect loss programs, usually in their current state. The power of re-learning creates a trend online tip according to standard non-linear parameters updating and updating online can correct frequency varied operating error during big data real-world generation. JEDEC reduced the machine learning sequence by a combined multi-dimension robust management robust study is planned for future tasks. This paper describes the operation and control of the controller for analytical, there is a clear link between the size of the compressor, the vibration level and the lens pool, learning new machine learning tools. In particular, JEDEC(Joint Electron Device Engineering Council) would like to use relational PACELC(Partition exists for Availability/consistency Else Latency/consistency) theoretical analysis to obtain the same summary and intensity The results of frequency case will also focus on increasing demand Important task balance, especially Restore model other types of contractions, as well as the connection between this reduced style adapter control and the learned control style multi-dimensions in sushisen algorithm using reduces these updates Deep Lanning Networks. Therefore, BDA data Partitioning helps to reduce the complexity of the calculation in the learning process and classification of data storage.
A Optimization Algorithm for Association Rule Based on Spark Platform

Compared with the limitations of hardware resources for the serial algorithm of association rule, the parallel algorithm has obvious advantages in large data processing and operation efficiency. This paper tries to design parallel Apriori algorithm and FP-Growth algorithm by the Spark platform. Through experiment contrast, FP-Growth parallel algorithm (PS-FP-Growth algorithm) is better. The PS-FP-Growth algorithm deals with unbalanced nodes by grouping them and FP Tree structure is complex. According to these problems, this paper presents the optimization and improvement of the balanced grouping strategy of node tasks and the non-frequent item merging pruning strategy. By the final analysis, the improved algorithm (PS-MP-FP-Growth) is not only more efficient, but also more stable.
Vocal wow in an adapted reflex resonance model

Vocal wow and tremor are slow modulations of the voice presumed to result from integration of auditory and somatosensory feedback, respectively. This distinction has important implications for diagnosis and treatment of neurological disorders that may differentially impact these systems, but the underlying mechanisms remain poorly understood. An important contribution on this matter is the reflex resonance model [Titze et al. (2002). J. Acoust. Soc. Am. 111(5), 2272–2282], which demonstrates that a 4–7 Hz vibrato (or tremor) can indeed be elicited by adjusting feedback parameters in a simple model of laryngeal muscle activation, mediated by time-delayed somatosensory feedback. This paper expands on this model by incorporating an auditory feedback loop and shows that wow emerges as feedback parameters exceed critical values described by a Hopf bifurcation. The wow period increases with delay and is almost invariant with respect to gain for delays above 200 ms. Parametric formulas for recovering feedback parameters from the acoustic signal are presented. With both feedback loops in place, auditory and somatosensory parameters interact and alter vocal modulations. Model predictions are illustrated in two subjects, one with a diagnosis of multiple sclerosis and intermittent tremor. Findings suggest that phonatory instabilities provide considerable insight into normal and pathogenic changes to the sensorimotor control of voice.
Correlation Filter Network Model Performance Analysis

Object tracking is an significant field of computer vision, which is extensive used in the fields of automatic driving and intelligent monitoring. However, target tracking still has difficulties in tracking caused by factors such as occlusion, illumination, and scale changes. In order to accurately track the target and increase the tracking performance of the model, the paper reproduces the two goals of the full convolution twin neural network and the correlation filtering neural network. The tracking algorithm is tested using the OTB100 data set. The target tracking overlap rate differs by 1.3%, and the success rate differs by 1.5%. Experiments show that, in the face of complex environments, the relevant filtering neural network updates the model in time and shows excellent tracking results.
Maximal unbordered factors of random strings

A border of a string is a non-empty prefix of the string that is also a suffix of the string, and a string is unbordered if it has no border other than itself. Loptev, Kucherov, and Starikovskaya [CPM'15] conjectured the following: If we pick a string of length n from a fixed non-unary alphabet uniformly at random, then the expected maximum length of its unbordered factors is n−O(1). We confirm this conjecture by proving that the expected value is, in fact, n−O(σ−1), where σ is the size of the alphabet. This immediately implies that we can find such a maximal unbordered factor in linear time on average. However, we go further and show that the optimum average-case running time is in Ω(n)∩O(nlogσ⁡n) due to analogous bounds by Czumaj and Gąsieniec [CPM'00] for the problem of computing the shortest period of a uniformly random string.
The Video Stabilization Based on Improved Smoothing Filtering and Curve Fitting

The existing electronic video image stabilization algorithm is not good enough in real time or in stabilization performance. To improve this method, we propose a method of video image stabilization based on Multi-filter compensation. Firstly, It uses corner detection after canny process, and then picks up feature points according to partical filter, calculates the average offset as overall motionvector. Secondly, in order to achieve the same performance as Kalman filter, we use multiple-filter to remove high frequency jitter. Finally, so as to make the movement smoother and closer to the real situation, curve fitting is used to remove low frequency. Experimental results show that compared to traditional Kalman filter, this method has higher real-time, can remove high frequency and low frequency effective.
Eye in-painting using WGAN-GP for face images with mosaic

In order to protect personal privacy, news reports often use the mosaics upon the face of the protagonist in the photo. However, readers will feel uncomfortable and awkward to this kind of photos. In this research, we detect the eye mosaic and try to use eye complementing which is not the same with original picture but matches the nearby texture. It can arouse readers' interest in reading. Traditional in-painting research is not suitable for filling special or large objects, such as eyes or boxes on the ground. They only can fill a small area of missing parts or a single background refer to nearby textures, such as landscape photos. We use WGAN-GP that can refer to nearby textures to generate special objects for in-painting eyes. We also divide the training set into male and female according to gender to avoid eye makeup appearing in all pictures. The experiment result shows our method get higher score in blind testing.
Realtime Semantic Similarity Analysis of Bulk Outlook Emails Using BERT

Semantic similarity (SS) analysis is a technique for finding similarities between words/sentences/documents based on their meaning. In natural language processing (NLP), SS is an important element to find a suitable mail from a bulk inbox. As the number of mails and mail content increases, it becomes difficult to get the matches with keywords and nearly impossible for many cases. This paper presents a method to find SS between query statements and mail content using BERT (Bidirectional Encoder Representations from Transformers). BERT is a pre-trained unsupervised NLP model developed by Google. The results are presented and compared with the existing keyword-based search to prove the efficiency of the proposed approach.
Detecting Duplicate Bi-Lingual Mash-up Question Pairs Using Siamese Multi-layer Perceptron Network

Online Question & Answering (Q&A) platforms facilitate instant information, but with the influx of questions and answers, the response time is high and the quality of answer is compromised too. Duplicate content further corrupts the filtering mechanism. Concurrently, bi-lingual mash-up, specifically Anglicization of language, that is, to make or become English in sound, appearance, or character, is a commonly observed phenomenon on social media. This research put forwards a model for semantic matching of duplicate question pairs, where one question is in the source language (English) and the other is a mash-up (Hindi + English = Hinglish). In the proposed model, firstly language transformation is done to translate the mash-up question into the source language text and then a Siamese artificial neural network (multi-layer perceptron) is implemented to detect semantically similar question pairs using Manhattan distance function for similarity measure. The encoder vector representations and their distance are given as input to the logit (logistic regression) model for binary classification as duplicate or not-duplicate. The model achieves an accuracy of 70.09%.
Bagging Trees and Random Forests

Two ensemble methods are considered in this chapter, namely bagging trees and random forests. One issue with regression trees is their high variance. There is a high variability of the prediction $$\widehat{\mu }_\mathcal {D}(\textit{\textbf{x}})$$ over the trees trained from all possible training sets $$\mathcal {D}$$ . Bagging trees and random forests aim to reduce the variance without too much altering bias.
A Discriminative STGCN for Skeleton Oriented Action Recognition

Action recognition plays a fundamental role in many applications and researches, including man-machine interaction, medical rehabilitation and physical training. However, existing methods realize action recognition mainly relies on the background. This paper attempts to recognize the actions only through the motions. Hence, skeleton information is utilized to realize action recognition. To fully utilize the skeleton information, this paper proposes a discriminative spatio-temporal graph convolutional network (DSTGCN) for background independent action recognition. DSTGCN not only pays attention to the spatio-temporal properties of the motions, but focuses on the inner-class distributions of the actions. Experiments result on two motion oriented datasets validate the effectiveness of the proposed method.
Groups of points on abelian threefolds over finite fields

In this paper we provide an algorithm to classify groups of points on abelian threefolds over finite fields. The classification is given in terms of the Weil polynomial of abelian varieties in a given $\mathbb{F}_q$-isogeny class. This work completes partial classification given by S.Rybakov.
Lightweight Fault Detection and Management for Image Restoration

Image restoration is generally employed to recover an image that has been blurred, for example, for noise suppression purposes.The Richardson-Lucy (RL) algorithm is a widely used iterative approach for image restoration.In this paper we propose a lightweight application-specific fault detection and management scheme for RL that exploits two specific characteristics of such algorithm: i) there is a strong correlation between the input and output images of each iteration, and ii) the algorithm is often able to produce a final output that is very similar to the expected one although the output of an intermediate iteration has been corrupted by a fault.The proposed scheme exploits these characteristics to detect the occurrence of a fault without requiring duplication and to determine whether the error in the output of an intermediate iteration of the algorithm would be absorbed (thus avoiding image dropping and algorithm reexecution) or whether the image has to be discarded and the overall elaboration to be re-executed.An experimental campaign demonstrated that our scheme allows for an execution time reduction of about 54% w.r.t. the classical Duplication with Comparison (DWC), still providing about 99% fault detection.
Enhancing developer interactions with programming screencasts through accurate code extraction

Programming screencasts have become a pervasive resource on the Internet, which is favoured by many developers for learning new programming skills. For developers, the source code in screencasts is valuable and important. However, the streaming nature of screencasts limits the choice that they have for interacting with the code. Many studies apply the Optical Character Recognition (OCR) technique to convert screen images into text, which can be easily searched and indexed. However, we observe that the noise in the screen images significantly affects the quality of OCRed code.
Altering Gaussian process to Student-<i>t</i> process for maximum distribution construction

Gaussian process (GP) regression is widely used to find the extreme of a black-box function by iteratively approximating an objective function when new evaluation obtained. Such evaluation is usually made by optimising a given acquisition function. However, for non-parametric Bayesian optimisation, the extreme of the objective function is not a deterministic value, but a random variant with distribution. We call such distribution the maximum distribution which is generally non-analytical. To construct such maximum distribution, traditional GP regression method by optimising an acquisition function is computational cost as the GP model has a cubic computation of training data. Moreover, the introduction of acquisition function brings extra hyperparameters which made the optimisation more complicated. Recently, inspired by the idea of Sequential Monte Carlo method and its application in Bayesian optimisation, a Monte Carlo alike method is proposed to approximate the maximum distribution with weighted samples. Alternative to the method on GP model, we construct the maximum distribution within the framework of Student-t process (TP) which considers more uncertainties from the training data. Toy examples and real data experiment show the TP-based Monte Carlo maximum distribution has a competitive performance to the GP-based method.
K-NN Classification of Very Large Dataset in Spark Framework

The amount of data is increasing every day through the sources of social media, IoT devices, day-to-day bank transaction, and many other sources. To handle such kind of data, it is typical task and it comes in big data domain. Big data has characterized 5-Vs known as velocity, volume, value, veracity, and last one veracity. The use of machine learning techniques to handle this complexity is becoming a very sought after research area. Classification is another challenging task in big data, and this is the problem that we are aiming to solve using K-NN approach under spark framework. We compared the result of K-NN with hadoop and spark with and without (HPC) and found that training and testing time drastically decrease in spark as compared to hadoop and accuracy also has been increased.