Reviewerly/exHarmony huggingface dataset:
Pegmatite mapping using spectroradiometry and ASTER data (Zenaga, Central Anti-Atlas, Morocco)

The region of Angarf, located in the southern part of the Zenaga inlier (Central Anti-Atlas, Morocco), is known by the abundance of pegmatite veins, essentially constituted by quartz, muscovite and feldspar. These veins are hosted in the mica schists and granites of the Paleoproterozoic basement. Mapping of pegmatites was approached by using synergetic field data, spectroradiometric measurements, and ASTER data. Analysis and interpretation of the spectroradiometric data allowed the identification of an important sericitic alteration. The measured spectra of the pegmatites detected the presence of absorption bands at wavelengths of 900 nm, 1400 nm, 1900 nm, 2200 nm, 2350 nm, and 2450 nm, which allowed the selection of the most informative ASTER spectral bands for pegmatite mapping. After geometric, radiometric, and atmospheric corrections to ASTER data, some processing techniques were applied, including Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Band Ratios (BR). Then, several RGB compound displays were analyzed using the results of these treatments. Three combinations of RGB were selected because of their potentials to extract the pegmatites outcrops. The first RGB combines the PC2, PC1, and PC3, it's suited to achieve good separation between geological facies allowing the identification of pegmatites. The second considers the MNF2, PC1, and 6/3; it has the same mapping potential as PC combination. The third combines 1/3, 1/4, and 2/5, it has allowed the identification of more pegmatites found in the field compared to the other two combinations. This last combination proved to be the most suitable for simply and rapidly identifying pegmatites. This result was validated using field and spectroradiometric data. The approach presented in this study gave satisfactory results in comparison with field data. It is a low-cost technique that highly applicable for pegmatite mapping and in the exploration of their minerals. The result could assist the development of pegmatite exploration in the other regions.
Content‐based and knowledge graph‐based paper recommendation: Exploring user preferences with the knowledge graphs for scientific paper recommendation

Abstract Researchers usually face difficulties in finding scientific papers relevant to their research interests due to increasing growth. Recommender systems emerge as a leading solution to filter valuable items intelligently. Recently, deep learning algorithms, such as convolutional neural network, improved traditional recommendation technologies, for example, the graph‐based or content‐based methods. However, existing graph‐based methods ignore high‐order association between users and items on graphs, and content‐based methods ignore global features of texts for explicit user preferences. Therefore, this paper proposes a C ontent‐based and knowledge G raph‐based P aper Rec ommendation method ( CGPRec ), which uses a two‐layer self‐attention block to obtain global features of texts for more complete explicit user preferences, and proposes an improved graph convolutional network for modeling high‐order associations on the knowledge graph to mine implicit user preferences. And the knowledge graph in this paper is constructed with concept nodes, user nodes, paper nodes, and other meta‐data nodes. Experimental results on a public dataset, CiteULike‐a, and a real application log dataset, AHData, show that our model outperforms compared with baseline methods.
An Encrypted Traffic Classification Framework Based on Convolutional Neural Networks and Stacked Autoencoders

In recent years, deep learning-based encrypted traffic classification has proven to be effective; especially, using neural networks to extract features from raw traffic to classify encrypted traffic. However, most of the neural networks need a fixed-sized input, so that the raw traffic need to be trimmed. This will cause the loss of some information; for example, we do not know the number of packets in a session. To solve these problems, a framework, which implements both a convolutional neural network (CNN) and a stacked autoencoder (SAE), is proposed in this paper. This framework uses a CNN to extract high-level features from raw network traffic and uses an SAE to encode the 26 statistical features calculated by raw traffic directly. The statistical features can be used to supplement the information loss due to trimming. After that, the outputs from the CNN and the encoder in SAE are combined into new high-level features; these new features include the information from the trimmed raw traffic and statistical features. Finally, these new high-level features are used to classify encrypted traffic. “ISCX VPNnonVPN” traffic dataset is used to demonstrate the feasibility of this framework. The framework proposed in this paper can improve the performance of encrypted traffic classification; it achieves an f1-score of 0.98. Furthermore, new high-level features, which generated by combining the features extracted from a convolutional neural network and a stacked autoencoder, can represent different classes of traffic well. More importantly, this work is unique in the encrypted traffic classification field, for it is the first time to use both raw traffic and statistical features as the input of the model.
Repertoires on the move: exploiting technological affordances and contexts in mobile messaging interactions

This article advances thinking around the semiotic repertoire by exploring how resources are negotiated in the course of interactions mediated by mobile messaging apps, and how this process of co-constructing repertoires is shaped by the affordances and constraints of the virtual spaces in which these interactions take place. Drawing on micro-analysis of ethnographic data from a large funded project, we focus on the closed online networks of two women of Polish origin living and working in the UK, looking specifically at their uses of mobile messaging apps including WhatsApp and Viber. We show how resources come into being through talk and how emergent semiotic repertoires take shape in processes of repertoire assemblage. In particular, we explore the ways in which technological affordances and pre-programmed signs are harnessed and exploited by mobile interlocutors for making meaning in the course of an immediate interaction, often with longer term impact within a network’s emergent repertoire. The study highlights the importance of a dynamic conceptualisation of the semiotic repertoire.
Team performance in a series of regional and national US cybersecurity defense competitions: Generalizable effects of training and functional role specialization

A critical component to any modern cybersecurity endeavor is effective use of its human resources to secure networks, maintain services and mitigate adversarial events. Despite the importance of the human cyber- analyst and operator to cybersecurity, there has not been a corresponding rise in data-driven analytical approaches for understanding, evaluating, and improving the effectiveness of cybersecurity teams as a whole. Fortunately, cyber defense competitions are well-established and provide a critical window into what makes a cybersecurity team more or less effective. We examined data collected at the national finals and four regional events of the Collegiate Cyber Defense Competition and posited that experience, access to simulation-based training, and functional role composition by the teams would predict team performance on four scoring dimensions relevant to the application of information assurance skills and defensive cyber operations: (a) maintaining services, (b) help-desk customer support, (c) handling scenario injects, and (d) mitigating red team attacks. Bayesian analysis highlighted that experience was a strong predictor of service availability, scenario injects, and red team defense. Simulation training was also associated with good performance along these scoring dimensions. High-performing and experienced teams clustered with one another based on the functional role composition of team skills. These results are discussed within the context of stages of team development, the efficacy of challenge-based learning events, and reinforce previous analytical results from cyber competitions.
Knowledge Graph Embedding by Double Limit Scoring Loss

Knowledge graph embedding is an effective way to represent knowledge graph, which greatly enhance the performances on knowledge graph completion tasks, e.g., entity or relation prediction. For knowledge graph embedding models, designing a powerful loss framework is crucial to the discrimination between correct and incorrect triplets. Margin-based ranking loss is a commonly used negative sampling framework to make a suitable margin between the scores of positive and negative triples. However, this loss can not ensure ideal low scores for the positive triplets and high scores for the negative triplets, which is not beneficial for knowledge completion tasks. In this paper, we present a double limit scoring loss to separately set upper bound for correct triplets and lower bound for incorrect triplets, which provides more effective and flexible optimization for knowledge graph embedding. Upon the presented loss framework, we present several knowledge graph embedding models including TransE-SS, TransH-SS, TransD-SS, ProjE-SS and ComplEx-SS. The experimental results on link prediction and triplet classification show that our proposed models have the significant improvement compared to state-of-the-art baselines.
Image super-resolution based on residually dense distilled attention network

Deep convolutional neural networks (CNNs) have been playing an increasingly important role in image super-resolution (SR). However, if we just deepen or widen the networks, it could result in the excess of parameters and the increase of training difficulty. In this paper, we propose a residually dense distilled attention network (RDDAN) to address the problems in SR. Residual networks could make full use of the information of previous layers. In RDDAN we propose a connection block group (CBG), which is stacked in the feature extraction module of the network. CBG consists of two parts, dense enhancement network (DEN) and channel attention producing (CAP) module. First, instead of simply stacking residual blocks, DEN utilizes feature distillation with both dense concatenation and skip connection to extract deep and shallow features, which could enhance the representation ability. Second, with attention mechanism, CAP pays attention to the channel-wise association to adjust channel-wise features and restore high-frequency feature information. By evaluating the performance of results based on benchmark methods, our method achieves a more desirable performance than state-of-the-art methods.
Stamantic clustering: Combining statistical and semantic features for clustering of large text datasets

Document clustering in text mining is a problem that is heavily researched upon. It is observed that individual approaches based on statistical features and semantic features have been extensively used to solve this problem. However, techniques combining the advantages of both types of features have not been frequently researched upon. Specifically, when the growth in the size of textual data is immense, there is a need for such an approach that combines the advantages of both types of features to give more accurate results within an acceptable range of time. In this paper, a document clustering technique is proposed that combines the effectiveness of the statistical features (using TF-IDF) and semantic features (using lexical chains). It is designed to use a fewer number of features while maintaining a comparable and even better accuracy for the task of document clustering.
Magnetic resonance images denoising using a wavelet solution to laplace equation associated with a new variational model

In this paper by exploiting the theory of wavelet transform, a solution of Laplace equation, after changing certain initial conditions in terms of wavelet transformation is obtained. We have further applied this solution of Laplace equation to denoise magnetic resonance (MR) images from brain web dataset at different noise levels. The denoised MR images are again denoised with the help of a new proposed variational model with certain wavelet. We compared our results with the reported results of Yadav et al along with some other recently reported results. It was found that the proposed method not only outperforms the reported methods of Yadav et al but some other recently reported results also.
Anonymization as homeomorphic data space transformation for privacy-preserving deep learning

Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation), which guarantees privacy of the data and allows neural networks to learn successfully. We made several experiments to show how much the performance of the trained neural nets will suffer from the deepening of the anonymization power.
Higher Education and the Challenges of Network Society: Philosophical Themes

The article presents a philosophical analysis of the humanitarian impact of educational reform due to the influence of new digital technologies. The post-industrial development of society has been characterized as a network society. Specifically, the revolution in media today defines the technological and substantive changes in the educational sector. Its modernization in the 1990s in Russia began through the discussion of the humanitarian mission of the classical university model. However, the process of globalization and the mobility of education demanded the alignment and unification of national educational programs. The Bologna Process was a response. It was implemented in Russia as a two-tiered system, although the society still needed specialists and engineers rather than bachelors and masters. The restructuring of the educational process to comply with international standards then led to the necessity of economic reform. Society needed educated specialists but could not support a large number of educational institutions with a sizable staff. In the manufacturing sector, automatization leads to a growth in workforce productivity whereas in education the traditional pedagogy is mainly practiced. Digital technologies have opened up new opportunities for the increased economic efficiency of centers of education. However, the faculty body, especially in the Humanities sphere, expressed strong criticism. The argument is that digital technologies do not solve pedagogic problems. While discussing the economic efficiency, the main, substantive issue of the meaning and purpose of education has been left aside. Hence the question of how an individual’s education can be embedded into the overall educational process continues to be of high relevance. Resorting to philosophy may be appropriate and reasonable inasmuch as it has accumulated a range of caring and self-preservation practices aimed at the development of social skills of an individuum. Philosophy can also provide an anthropological expertise of on-going reforms, identifying their social and cultural implications.
Predicting co‐change probability in software applications using historical metadata

It is quite challenging to track the after-effects of changes with increased dependencies among classes while making a change in software applications. Software change impact analysis aims to identify classes affected by a change in software applications. In recent years, researchers have found that revision history has great potential for identifying evolutionary coupling. The two main factors affecting the prediction results are the length and the age of change history considered for deriving dependencies. The effect of age of change history on co-change prediction results in software applications is empirically studied by varying the weightage of change commits. The results indicate that the older commits have less influence in deriving dependent classes than the newer ones. The proposed approach is useful for software effort estimation and identifying dependencies during the software development, testing, and maintenance phase.
Conceptualising Augmented Reality: From virtual divides to mediated dynamics

Although Augmented Reality (AR) scholarship is largely defined through technocentric boundary work that delineates the virtual from the real, it is nevertheless vital to consider experiential conceptualisations of AR as mediating the human–physical environment, as this makes visible for analysis specific properties that afford specific dynamics of augmented publics. We consider how AR mediates the environment in ways previous media could not, identifying four affordances of note. We name visual (dis)integrity, environmental activation, contextual pointalisation and four-dimensional place(ment), as well as reliance on digital infrastructures as sets of properties and dynamics that speak to what AR affords its users. The article first traces how the conceptualisation of AR in scholarship has yet to move past technocentric metaphors of description, adopted from the Virtuality Continuum (Milgram et al., 1995) that separates reality from mediating technologies. It then pushes media critique conceptualising AR in ways that more accurately account for lived experiences of perception. Doing so updates the metaphors used to understand AR and exposes first- and second-order affordances of AR media, which define but are not definitive of the constraints and potentials present in how the properties and dynamics of AR mediate perceptions of life. The article concludes by noting how future research can now consider definitions of AR media that centre on how perceptible spatial computation augments relations between objects, whether these are conjured from electrons, atoms or humans.
robo-gym – An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots

Applying Deep Reinforcement Learning (DRL) to complex tasks in the field of robotics has proven to be very successful in the recent years. However, most of the publications focus either on applying it to a task in simulation or to a task in a real world setup. Although there are great examples of combining the two worlds with the help of transfer learning, it often requires a lot of additional work and fine-tuning to make the setup work effectively. In order to increase the use of DRL with real robots and reduce the gap between simulation and real world robotics, we propose an open source toolkit: robo-gym <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . We demonstrate a unified setup for simulation and real environments which enables a seamless transfer from training in simulation to application on the robot. We showcase the capabilities and the effectiveness of the framework with two real world applications featuring industrial robots: a mobile robot and a robot arm. The distributed capabilities of the framework enable several advantages like using distributed algorithms, separating the workload of simulation and training on different physical machines as well as enabling the future opportunity to train in simulation and real world at the same time. Finally, we offer an overview and comparison of robo-gym with other frequently used state-of-the-art DRL frameworks.
Super-Resolution of Large Volumes of Sentinel-2 Images with High Performance Distributed Deep Learning

This work proposes a novel distributed deep learning model for Remote Sensing (RS) images super-resolution. High Performance Computing (HPC) systems with GPUs are used to accelerate the learning of the unknown low to high resolution mapping from large volumes of Sentinel-2 data. The proposed deep learning model is based on self-attention mechanism and residual learning. The results demonstrate that state-of-the-art performance can be achieved by keeping the size of the model relatively small. Synchronous data parallelism is applied to scale up the training process without severe performance loss. Distributed training is thus shown to speed up learning substantially while keeping performance intact.
A simple digital image analysis system for automated Ki67 assessment in primary breast cancer

Ki67 is a well-established immunohistochemical marker associated with cell proliferation that has prognostic and predictive value in breast cancer. Quantitative evaluation of Ki67 is traditionally performed by assessing stained tissue slides with light microscopy. Automated image analysis systems have become available and, if validated, could provide greater standardisation and improved precision of Ki67 scoring. Here, we aimed to evaluate the use of the Cognition Master Professional Suite (CogM) image analysis software, which is a simple system for scoring Ki67 in primary breast cancer samples.Sections from 94 core-cut biopsies, 20 excision specimens and 29 pairs of core-cut biopsies and excision specimens were stained for Ki67 with MIB1 antibody and the Dako EnVision FLEX Detection System. Stained slides were scanned to convert them to digital data. Computer-based Ki67 scoring was performed with CogM. Manual Ki67 scoring assessment was conducted on previously stained sections from the same biopsies with a clinically validated system that had been calibrated against the risk of recurrence. A high correlation between manual and digital scores was observed [rCores = 0.92, 95% confidence interval (CI) 0.87-0.94, P < 0.0001; rExcisions = 0.95, 95% CI 0.86-0.98, P < 0.0001] and there was no significant bias between them (P = 0.45). There was also a high correlation of Ki67 scores between paired core-cut biopsies and excision specimens when CogM was used (r = 0.78, 95% CI 0.59-0.89, P < 0.0001).CogM image analysis allows for standardised automated Ki67 scoring that accurately replicates previously clinically validated and calibrated manual scores.
Learning to locate for fine-grained image recognition

In this paper, we propose an end-to-end weakly supervised method for fine-grained image recognition called bounding box-part location method(BBPL), which can locate the object and part precisely without part annotations. The proposed method includes three modules: object detection, ObjectMask, and classification. Firstly, the object detection module predicts the bounding boxes, and the predicted bounding boxes are employed to generate a mask through ObjectMask module. The generated mask can suppress the background interference during recognition. Secondly, the classification module can be further divided into two branches, which are global feature classification and local feature classification. In global feature classification branch, global feature is extracted to get global classification result. While in local feature classification branch, salient point is first detected through our novel salient point detection module, which can greatly reduce the consuming-time compared with the most existing local feature extraction methods. Further, the local feature is extracted in these detected salient points, and local classification result is obtained by local feature classification branch. Finally, we get the final result by fusing the results of two classification branches together. With experiments on three widely used fine-grained image recognition datasets (CUB-200-2011, Stanford Cars, Stanford Dogs), our method can achieve the state-of-the-art performance.