Reviewerly/exHarmony huggingface dataset:
Gold mineralization related to Proterozoic cover in the Congo craton (Central African Republic): A consequence of Panafrican events

Despite its high endowments, the gold mining potential of Central African greenstone belts is seemingly underrated when compared to equivalent belts in neighbor West Africa. This is probably because, over the past half century, only minor exploration efforts were ever made in this region. In the southwest of the Central African Republic, near the locality of Moboma, gold-bearing quartz veins are hosted in greenschist facies Paleoproterozoic formations that are intruded by numerous dolerite dykes. These rocks occur in a strongly deformed terrane that marks the front of the Panafrican Oubanguides nappe, developed during an E-W regional shortening. Presence of multiple banding indicates repeated reactivation of the quartz veins and circulation of H2O–CO2–NaCl fluids, similar to those characterizing typical orogenic gold-bearing settings. Fluid inclusion petrography and microthermometry permitted to distinguish two different fluids: one, aqueous-carbonic, circulated at relatively high temperature (Th = 250–270 °C) and was responsible for the main stage of Au deposition; a second fluid of low-salinity trapped in microcracks and in a late quartz generation, interpreted as meteoric, precipitated silver-poor native gold. At a later stage, supergene alteration caused the formation of discrete gold nuggets in the upper levels of the mineralization. The competent nature of the dolerite dykes and quartzite intersected by these quartz veins contributed to focus rock fracturing, localizing fluid circulation and the mineralization. The alteration assemblage developed in the veins is equivalent to that found in the dolerite dykes, which was dated at 571 Ma, thus pointing to a Panafrican age for the mineralization at Moboma.
Image Haze Removal Based on Superpixels and Markov Random Field

Image haze removal is critical for autonomous driving. However, it is a challenging task for the existing image dehazing algorithms to eliminate the block effect completely and handle objects similar to light (such as snowy objects and white buildings). To address this problem, we propose a novel single-image dehazing method based on superpixels and Markov random field. We obtain the transmission map in the superpixel domain to eliminate the block/halo effect and introduce Markov random field to revise the transmission map in the superpixel domain. The key idea is that the sparsely distributed, incorrectly estimated transmittances can be corrected by properly characterizing the spatial dependencies between the incorrectly estimated superpixels and the neighbouring well-estimated superpixels. The experimental results demonstrate that the proposed method outperforms state-of-the-art image dehazing methods.
Real-Time Application for Generating Multiple Experiences from 360° Panoramic Video by Tracking Arbitrary Objects and Viewer’s Orientations

We propose a novel authoring and viewing system for generating multiple experiences with a single 360° video and efficiently transferring these experiences to the user. An immersive video contains much more interesting information within the 360° environment than normal videos. There can be multiple interesting areas within a 360° frame at the same time. Due to the narrow field of view in virtual reality head-mounted displays, a user can only view a limited area of a 360° video. Hence, our system is aimed at generating multiple experiences based on interesting information in different regions of a 360° video and efficient transferring of these experiences to prospective users. The proposed system generates experiences by using two approaches: (1) Recording of the user’s experience when the user watches a panoramic video using a virtual reality head-mounted display, and (2) tracking of an arbitrary interesting object in a 360° video selected by the user. For tracking of an arbitrary interesting object, we have developed a pipeline around an existing simple object tracker to adapt it for 360° videos. This tracking algorithm was performed in real time on a CPU with high precision. Moreover, to the best of our knowledge, there is no such existing system that can generate a variety of different experiences from a single 360° video and enable the viewer to watch one 360° visual content from various interesting perspectives in immersive virtual reality. Furthermore, we have provided an adaptive focus assistance technique for efficient transferring of the generated experiences to other users in virtual reality. In this study, technical evaluation of the system along with a detailed user study has been performed to assess the system’s application. Findings from evaluation of the system showed that a single 360° multimedia content has the capability of generating multiple experiences and transfers among users. Moreover, sharing of the 360° experiences enabled viewers to watch multiple interesting contents with less effort.
CLOSURE: Assessing Systematic Generalization of CLEVR Models.

The CLEVR dataset of natural-looking questions about 3D-rendered scenes has recently received much attention from the research community. A number of models have been proposed for this task, many of which achieved very high accuracies of around 97-99%. In this work, we study how systematic the generalization of such models is, that is to which extent they are capable of handling novel combinations of known linguistic constructs. To this end, we test models' understanding of referring expressions based on matching object properties (such as e.g. another cube that is the same size as the brown cube) in novel contexts. Our experiments on the thereby constructed CLOSURE benchmark show that state-of-the-art models often do not exhibit systematicity after being trained on CLEVR. Surprisingly, we find that an explicitly compositional Neural Module Network model also generalizes badly on CLOSURE, even when it has access to the ground-truth programs at test time. We improve the NMN's systematic generalization by developing a novel Vector-NMN module architecture with vector-valued inputs and outputs. Lastly, we investigate how much few-shot transfer learning can help models that are pretrained on CLEVR to adapt to CLOSURE. Our few-shot learning experiments contrast the adaptation behavior of the models with intermediate discrete programs with that of the end-to-end continuous models.
The concept of unavoidable features in fuzzy relational compositions

Fuzzy relational calculus, and especially, the compositions of fuzzy relations, had a great impact on many theoretical as well as practical areas of fuzzy modeling and it attracted numerous researchers. Recently, the compositions had been extended in distinct directions including the incorporation of excluding features or the direct use of generalized quantifiers instead of the standard ones. This article provides an investigation of an extension that is mathematically similarly constructed to the concept of excluding features although from the semantic point of view it is opposite. In particular, we introduce the concept of unavoidable features. Formally, the concept is again constructed as conjunctive fusion of an existing fuzzy relational composition with another composition, namely the Bandler-Kohout superproduct, employing a newly defined fuzzy relation of unavoidable features for particular classes. We provide an investigation of mathematical properties of the new concept and we demonstrate the appropriateness of the proposed concept on the classification task. This is provided with help of a real biological classification problems running on a data-sets of dragonflies and amphibians. A significant improvement of the results is emphasizing how strong influence may be provided with simple ideas and concepts fused appropriately together to a more complicated yet still explainable concept.
Feature channel enhancement for crowd counting

Crowd counting, i.e. count the number of people in a crowded visual space, is emerging as an essential research problem with public security. A key in the design of the crowd counting system is to create a stable and accurate robust model, which requires to process on the feature channels of the counting network. In this study, the authors present a featured channel enhancement (FCE) block for crowd counting. First, they use a feature extraction unit to obtain the information of each channel and encodes the information of each channel. Then use a non-linear variation unit to deal with the encoded channel information, finally, normalise the data and affixed to each channel separately. With the use of the FCE, the positive characteristic channel can be enhanced and weak or negative channel information can be suppressed. The authors successfully incorporate the FCE with two compact networks on the standard benchmarks and prove that the proposed FCE achieves promising results.
Session Type Systems based on Linear Logic: Classical versus Intuitionistic

Session type systems have been given logical foundations via Curry-Howard correspondences based on both intuitionistic and classical linear logic.The type systems derived from the two logics enforce communication correctness on the same class of π-calculus processes, but they are significantly different.Caires, Pfenning, and Toninho informally observed that, unlike the classical type system, the intuitionistic type system enforces locality for shared channels, i.e. received channels cannot be used for replicated input.In this paper, we revisit this observation from a formal standpoint.We develop United Linear Logic (ULL), a logic encompassing both classical and intuitionistic linear logic.Then, following the Curry-Howard correspondences for session types, we define πULL, a session type system for the π-calculus based on ULL.Using πULL we can formally assess the difference between the intuitionistic and classical type systems, and justify the role of locality and symmetry therein.
A semi-automated annotation algorithm based on weakly supervised learning for medical images

For medical image recognition, deep learning requires a massive training set, while annotation work is a tedious and time-consuming process because of the high technical threshold. Furthermore, it is difficult to guarantee annotation accuracy due to the knowledge, skills, and status of the annotator. In this research, we propose a semi-automated annotation model based on weakly supervised learning. Moreover, a target-level annotation method is proposed based on weakly supervised learning that is guided by machine learning. The machine learning method is used to screen the regions of interest (RoIs), whose semantic feature vectors are extracted by the deep learning method. Then, the machine learning method is used to cluster them, and the RoIs are finally classified and labeled by a distance comparison. Therefore, this model achieves target-level semi-automated annotation by only using image-level annotations. We applied this method to ultrasound imaging of thyroid papillary carcinoma. The experiments demonstrate the potential of this new methodology to reduce the workload for pathologists and increase the objectivity of diagnoses. We find that 89.8% of papillary thyroid carcinoma regions can be detected automatically, while 82.6% of benign and normal tissue can be excluded without the use of any additional immunohistochemical markers or human intervention.
An Improved Deep Neural Network for Modeling Speaker Characteristics at Different Temporal Scales

This paper presents an improved deep embedding learning method based on a convolutional neural network (CNN) for text-independent speaker verification. Two improvements are proposed for x-vector embedding learning: (1) a multiscale convolution (MSCNN) is adopted in the frame-level layers to capture the complementary speaker information in different receptive fields; (2) a Baum-Welch statistics attention (BWSA) mechanism is applied in the pooling layer, which can integrate more useful long-term speaker characteristics in the temporal pooling layer. Experiments are carried out on the NIST SRE16 evaluation set. The results demonstrate the effectiveness of the MSCNN and show that the proposed BWSA can further improve the performance of the DNN embedding system.
Global trends in apps for agriculture

Although applications (apps) for mobile devices show increasing popularity in the agricultural sector, studies regarding their availability are still incipient. In the present study, a survey was conducted to assess global trends of app availability for agriculture. This survey was conducted in 2015 and 2018 in two app stores with free access (Google Play and Windows Phone); searches for the following keywords were included in the survey: agriculture, agri, and farming. In 2015 and 2018, these searches found 244 and 599 apps from 33 and 61 countries, respectively. Countries with the largest number of apps (i.e., USA, Brazil, and India) accounted for ~64% of all surveyed apps. However, diversity and availability of paid apps was greater in USA and Brazil compared to India. Although many apps were related to magazines and market information, numerous other apps were available on farm management, pests and diseases, precision agriculture, and technical assistance. Related apps that used photographic input and citizen science data were also found. Our study indicates that the global development of apps for agriculture is growing rapidly, with most serving informative purposes at no cost to users.
Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

Autoscaling methods are employed to ensure the scalability of cloud-hosted applications. The public-facing applications are prone to receive sudden workload bursts, and the existing autoscaling methods do not handle the bursty workloads gracefully. It is challenging to detect the burst online from the incoming dynamic workload traffic, and then identifying appropriate resources to address the burst without overprovisioning is even harder. In this paper, we address this challenge by investigating the appropriate method for online burst detection and then proposed a novel predictive autoscaling method to use burst detection for satisfying specific response time requirements. We compared the proposed method with multiple state-of-the-art baseline autoscaling methods under multiple realistic and synthetic bursty workloads for a benchmark application. Our experimental results show a 60.8% average decrease in response time violations as compared to the baseline method.
Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification

Fine-grained vehicle type recognition is one of the main challenges in machine vision. Almost all of the ways presented so far have identified the type of vehicle with the help of feature extraction and classifiers. Because of the apparent similarity between car classes, these methods may produce erroneous results. This paper presents a methodology that uses two criteria to identify common vehicle types. The first criterion is feature extraction and classification and the second criterion is to use the dimensions of car for classification. This method consists of three phases. In the first phase, the coordinates of the vanishing points are obtained. In the second phase, the bounding box and dimensions are calculated for each passing vehicle. Finally, in the third phase, the exact vehicle type is determined by combining the results of the first and second criteria. To evaluate the proposed method, a dataset of images and videos, prepared by the authors, has been used. This dataset is recorded from places similar to those of a roadside camera. Most existing methods use high-quality images for evaluation and are not applicable in the real world, but in the proposed method real-world video frames are used to determine the exact type of vehicle, and the accuracy of 89.5% is achieved, which represents a good performance.
Improving the performance of video Collaborative Filtering Recommender Systems using Optimization Algorithm

The growth amount of information on the Web makes it difficult for many web users to make decision and choose either information or goods. Thus, a recommender system is an approach that helps users to obtain their needs according to her/his preference within a massive amount of information rapidly without waste of time. The main advantage of using a recommender system in any online shopping or social media like Amazon, Netflix and Facebook is to increase the percentage of overall profits, customer satisfaction and retention. In this paper, we introduce an approach to increase the accuracy and to improve the performance of collaborative filtering recommender system. In this paper a hybrid approach is proposed to improve the performance of video collaborative filtering recommender system based on clustering and evolutionary algorithm. Proposed approach combines k-means clustering algorithm and two different evolutionary algorithms which are Accelerated Particle Swarm Optimization Algorithm (APSO) and Forest Optimization Algorithm (FOA). The main aim of this paper is to increase the accuracy of recommendation of user-based collaborative filtering video recommender system. Evaluation and computational results on the MovieLens dataset show that the proposed method has a better performance than the other related methods.
Convergent Dispersal: Toward Storage-Efficient Security in a Cloud-of-Clouds

In the existing system, if the owner wants to share the data from one user to another user there are chances for some security issues to be present.The data which is being transferred from user to user can be hacked.To overcome the security issues, we propose a system where the data transformation is done with high security.If the uploaded data is stored directly then there are chances for data leak.We provide a solution where the uploaded data is getting split into four parts and each key will be generated for the four parts while storing it in the database and it will be encrypted.If the user wants the file access which is uploaded by the owner then the user can send the request.
DeltaPy: A Framework for Tabular Data Augmentation in Python

A range of data abstractions have come to the fore since the re-emergence of machine learning. This includes procedures like feature engineering, extraction, transformation, and selection, as well as data pre-processing, generation, synthesisation, and augmentation. This report attempts to unify some of this terminology with the development of a bare-bones Python package, DeltaPy.
Geostatistical and geospatial assessment of soil pollution with heavy metals in Pavlograd city (Ukraine)

This paper presents the result of studies of the geostatistical and geospatial assessment heavy metal pollution in soils caused by various technogenic sources to assess the environmental impact of industrial agglomeration activitiy in the Pavlograd city. The main sources of technogenic pollution in the Pavlograd city and suburban areas are industrial enterprises, coal-fired plants, mine dumps and other. The vast number of analyzed soil samples with weakly acidic pH values from 6.3 to 6.8 is characterized as loam (63 %), the rest are sand (33 %) and clay soils (4 %). The correlation matrix of metals data indicates positive correlation with correlation coefficient r 2 &gt; 0.5 among texture – Ni (0.705) and Pb – Zn (0.695) within the Pavlograd city area. In the case of Cu (2.73) and Cd (4.27), the geoaccumulation index indicated “moderately to strongly” and “strongly to extremely” polluted soils accordingly. Means of pollution index (PI) for heavy metals were between uncontaminated ( PI ≤ 1) and of moderate contaminated soils ( PI ≥ 1).The results of this study show that industrial enterprises’ activities lead to heavy metal contamination in the topsoil of the studied sites and close to the background values. Similar properties were confirmed by principal component analysis (PCA) and cluster analysis. The PCA and cluster analysis results indicate that Cd, Ni, Pb, Cu, Zn and Mn in topsoil were affected by the technogenic activity. The spatial distribution characteristics of Cu, Pb, Cd and Zn in the geochemical maps are also similar. Highlighted are some of the halos in Pavlograd of high density of total soil contamination with heavy metals. The metals come from anthropogenic sources, mainly produced with four industrial enterprises in Pavlograd city. Mostly they are concentrated in the Southeast and West of the city and suburban areas of the Pavlograd district. Therefore, it is necessary arrangements to predict of the development of ecologically dangerous state of environmental pollution within the area of industrial enterprises of city.
A Deep Learning-Based Model for the Automated Assessment of the Activity of a Single Worker

Nowadays, it is necessary to verify the accuracy of servicing work, undertaken by new employees, within a manufacturing company. A gap in the research has been observed in effective methods to automatically evaluate the work of a newly employed worker. The main purpose of the study is to build a new, deep learning model, in order to automatically assess the activity of the single worker. The proposed approach integrates the methods known as CNN, CNN + SVM, CNN + R-CNN, four new algorithms and a piece of work from a selected company, using this as an own-created dataset, in order to create a solution enabling assessment of the activity of single workers. Data were collected from an operational manufacturing cell without any guided or scripted work. The results reveal that the model developed is able to accurately detect the correctness of the work process. The model’s accuracy mostly exceeds current state-of-the-art methods for detecting work activities in manufacturing. The proposed two-stage approach, firstly, assigning the appropriate graphic instruction to a given employee’s activity using CNN and then using R-CNN to isolate the object from the reference frames, yields 94.01% and 73.15% accuracy of identification, respectively.
Integrating lexical and prosodic features for automatic paragraph segmentation

Spoken documents, such as podcasts or lectures, are a growing presence in everyday life. Being able to automatically identify their discourse structure is an important step to understanding what a spoken document is about. Moreover, finer-grained units, such as paragraphs, are highly desirable for presenting and analyzing spoken content. However, little work has been done on discourse based speech segmentation below the level of broad topics. In order to examine how discourse transitions are cued in speech, we investigate automatic paragraph segmentation of TED talks using lexical and prosodic features. Experiments using Support Vector Machines, AdaBoost, and Neural Networks show that models using supra-sentential prosodic features and induced cue words perform better than those based on the type of lexical cohesion measures often used in broad topic segmentation. Moreover, combining a wide range of individually weak lexical and prosodic predictors improves performance, and modelling contextual information using recurrent neural networks outperforms other approaches by a large margin. Our best results come from using late fusion methods that integrate representations generated by separate lexical and prosodic models while allowing interactions between these features streams rather than treating them as independent information sources. Application to ASR outputs shows that adding prosodic features, particularly using late fusion, can significantly ameliorate decreases in performance due to transcription errors.