Reviewerly/exHarmony huggingface dataset:
Lead Isotopes in Exploration for Basement-Hosted Structurally Controlled Unconformity-Related Uranium Deposits: Kiggavik Project (Nunavut, Canada)

Pb-isotopes have been proposed as pathfinders for sandstone-hosted unconformity-related U deposits, with isotope ratios providing information on mineralization timing and element remobilization and migration. Pb-isotopes proximal to mineralization display radiogenic signatures, often with ‘excess Pb’ suggestive of derivation from greater U concentrations than are currently present. The U deposits in the Kiggavik project area (west of Baker Lake, NU, Canada) are basement-hosted, contain several generations of pitchblende mineralization, display a strong structural control, and are located in fault-related fracture systems and foliation-parallel veinlets. Drill core samples were analysed by Inductively-Coupled Plasma-Mass Spectrometer (ICP-MS) for Pb isotopes following multi-acid total-digestion, reverse Aqua Regia partial-digestion, and weak-acid-leach attacks, to evaluate the utility of the respective dissolution methods in Pb-isotope pathfinder geochemistry. Partial-digestion results are similar to weak-acid-leach results, indicating that interpretation of Pb-isotope signatures can be carried out from partial-digestion data if weak-acid-leach data are unavailable. Application of this pathfinder method at Kiggavik shows that Pb-isotope ratios display systematic trends useful for exploration vectoring. Uranium-content-adjusted 206Pb/204Pb ratios and 206Pb/204Pb ‘excess-lead’ data highlight anomalous isotopic values. 207Pb/206Pb ratios display downhole trends complementary to location of mineralization. Three-dimensional (3D) distributions of Pb-isotope data at the Contact U prospect show systematic trends and form halos around the mineralization. Isotopic footprints are limited to &lt;50 m from the mineralization outline, reflecting host-rock and structural control, but indicate areas with elevated potential for U mineralization and provide vectoring information within basement lithologies.
Application of machine learning techniques for real-time sign language detection using wearable sensors

Sign language is a method of communication primarily used by the hearing impaired and mute persons. In this method, letters and words are expressed by hand gestures. In fingerspelling, meaningful words are constructed by signaling multiple letters in a sequence. In this paper, a system has been developed to detect fingerspelling in American Sign Language (ASL) and Bengali Sign Language (BdSL) using (data) gloves containing some suitably positioned sensors. The methodologies employed can be used even in resource-constrained environments. The system is capable of accurately detecting both static and dynamic symbols in the alphabets. The system shows a promising accuracy of (up to) 96%. Furthermore, this work presents a novel approach to perform a continuous assessment of symbols from a stream of run-time data.
Estimating a one-class naive Bayes text classifier

Nowadays more and more information extraction projects need to classify large amounts of text data. The common way to classify text is to build a supervised classifier trained on human-labeled positive and negative examples. In many cases, however, it is easy to label positive examples, but hard to label negative examples. In this paper, we address the problem of building a one-class classifier when only the positive examples are labeled. Previous works on building one-class classifier mostly use positive examples and unlabeled data. In this paper, we show that a configurable one-class classifier such as one-class naive Bayes can be optimized by examining the clustering quality of the classification on target data. We propose to use existing and new quality scores for determining clustering quality of the classification. Experimental analysis with real-world data show that our approach generally achieves high classification accuracy, and in some cases improves the accuracy by more than 10% compared to state-of-art baselines.
The Annual Report, Indicator of Library Transparency

Abstract This article addresses the annual reports published by Spanish university libraries on their websites. Report form and content were analyzed, along with availability, timing and position on the library’s website with the concomitant visibility and accessibility. Although some of the items included (collection, services, staff, premises and facilities) were found to be the same year after year, the degree of detail furnished was observed to vary. New subjects were also identified: partnering, cooperation, communication, marketing, dissemination, presence in social networks and libraries’ impact on their surrounds. Annual reports and their dissemination among stakeholders are recommended as an exercise in accountability and management transparency.
SPEAr: Expediting Stream Processing with Accuracy Guarantees

Stream Processing Engines (SPEs) are used for realtime and continuous processing with stateful operations. This type of processing poses numerous challenges due to its associated complexity, unpredictable input, and need for timely results. As a result, users tend to overprovision resources, and online scaling is required in order to overcome overloaded situations. Current attempts for expediting stateful processing are impractical, due to their inability to uphold the quality of results, maintain performance, and reduce memory requirements. In this paper, we present the SPEAr system, which can expedite processing of stateful operations automatically by trading accuracy for performance. SPEAr detects when it can accelerate processing by employing online sampling and accuracy estimation at no additional cost. We built SPEAr on top of Storm and our experiments indicate that it can reduce processing times by more than an order of magnitude, use more than an order of magnitude less memory, and offer accuracy guarantees in real-world benchmarks.
PPNet

Software-defined networking introduces opportunities to optimize the Internet Service Provider’s network and to improve client experience for the Video-on-Demand applications. Recent studies on SDN frameworks show that traffic engineering methods allow a fair share of bandwidth between adaptive video streaming clients. Additionally, ISPs can make better estimations of bandwidth and contribute to the bitrate selection for the clients. This study focuses on another aspect of network assistance in video delivery: CDN server selection. In a typical framework where the ISP contributes to the CDN selection, the video provider and the network provider interfaces are merged together. Clients connect to the ISP to get the best CDN server candidate for a given video. This exposes client requests to the ISP. However, video providers have been investing large resources for encrypted video provisioning to preserve their client’s information from third parties, especially network providers. The typical approach is not practical due to privacy concerns. In this study, we present a framework called PPNet to allow CDN-ISP collaboration while preventing the ISP’s access to the video request and availability information. Our framework introduces an isolation between the video provider’s and the ISP’s web interfaces. Clients connect to both of the interfaces and deliver information on a need-to-know basis. As a second contribution, PPNet introduces a practical optimization method for CDN selection. Real-time data collection capabilities of a typical OpenFlow network is used as the input for optimization. Congestion-awareness has been the priority. To adapt for changing network conditions, capability of utilizing multiple servers simultaneously for a single video is introduced. Instead of directing each video client into a CDN node, the proposed system performs request routing per video segment. Finally, we present a system prototype of PPNet and show that our multiple-host adaptive streaming method introduces a significant improvement in quality of experience when compared to the state of the art.
Data in Context: Engaging Audiences with 3D Physical Geo-Visualization

Geo-visualizations are invaluable aids for communicating complex scenarios that place data in spatial location and across time. With the advancement of 3D fabrication (and future potential for actuated displays,) more installations opt to project geo-visualization data on physical objects to provide an additional layer of context. In this paper, we set out to compare the effectiveness and user engagement of geo-visualizations data projected on either a flat 2D surface or a 3D representation of the terrain. We present our methodology and observations from this study. Our results so far are inconclusive, which leads us the propose that physical geo-visualization is a thriving field for further research.
Bearing Fault Diagnosis with Denoising Autoencoders in Few Labeled Sample Case

The application of transfer learning in the fields of computer vision and natural language processing has been successful. It turns out that pre-training some parameters in the network with unsupervised tasks related to downstream tasks is very useful for subsequent task model training. This approach can reduce the dependence of model training on labeled data to a certain extent, which is beneficial to the application of deep learning algorithms. This paper mainly proposes a bearing fault diagnosis model with a small number of labeled samples. The model is trained through pre-training and fine-tuning. It can use a large amount of unlabeled data to obtain sufficient data representation. Finally, fine-tuning is achieved on small sample fault sets Accuracy. At the same time, several optimizations are proposed on the dassic AE fault diagnosis method, and the effectiveness of the optimization content is verifiedin the experimental stage.
A new non-divergence diffusion equation with variable exponent for multiplicative noise removal

A new kind of model based on double degenerate parabolic equation in nondivergence form is proposed for multiplicative noise removal. We first utilize the regularization method to illustrate the existence of the weak solution for the problem and the non-expansion of support of the solution is also provided. In the numerical aspect, experiments and comparisons with other denoising models are presented in order to state the denoising capability of our model.
Wibson: A Case Study of a Decentralized, Privacy-Preserving Data Marketplace

The Internet offers unprecedented opportunities to collect large amounts of personal data at low cost. Typically, it is not only the data collection process but also their further use which is opaque to the individuals. Blockchain technology promises to return Internet users control over their personal data. In this chapter we present and discuss Wibson, a decentralized data marketplace based on the blockchain that provides a way for individuals to control and monetize their personal information in a trusted environment. By using a token and blockchain-enabled smart contracts Wibson allows data sellers and buyers to interact while allowing them to keep their desired level of anonymity. This chapter is based on qualitative interviews and the thorough analysis of the technical documentation. We describe the underlying rationale and functioning of Wibson and provide suggestions for future research at the intersection of blockchain and privacy.
Deep Learning-Based Image Compression with Trellis Coded Quantization

Recently many works attempt to develop image compression models based on deep learning architectures, where the uniform scalar quantizer (SQ) is commonly applied to the feature maps between the encoder and decoder. In this paper, we propose to incorporate trellis coded quantizer (TCQ) into a deep learning based image compression framework. A soft-to-hard strategy is applied to allow for back propagation during training. We develop a simple image compression model that consists of three subnetworks (encoder, decoder and entropy estimation), and optimize all of the components in an end-to-end manner. We experiment on two high resolution image datasets and both show that our model can achieve superior performance at low bit rates. We also show the comparisons between TCQ and SQ based on our proposed baseline model and demonstrate the advantage of TCQ.
Modulation Order Reduction Method for Improving the Performance of AMC Algorithm Based on Sixth–Order Cumulants

For the last two decades a large number of different automatic modulation classification (AMC) algorithms were developed, and many improvements in classification performance are reported. This was commonly achieved by engaging complex structures of neural networks, or other adaptable mechanisms for achieving better precision, when it comes to decision-making. Still, from practical implementation point of view, low algorithm complexity, economical usage of resources and fast execution remain to represent very desirable properties of an AMC algorithm. These properties are recognized in AMC algorithms based on higher - order cumulants as classification features, so their further improvement is of interest. Previous performance analysis of an algorithm based on sixth - order cumulants, in scenarios with complex valued signals' classification, showed that improvements are possible in the context of resources engaged and speed of execution. In this paper a novel approach is presented, for improving the correctness of classification process with sixth-order cumulants and simple two-step feature extraction structure, by engaging a new method for reduction of observed signal's modulation order which directly improves the classification performance. While tested with sixth-order cumulants, proposed method preserves good statistical properties of signal's higher - order cumulants in general, so it can be adopted in other AMC algorithms as well. Proposed modulation order reduction method is described in details, tested through computer simulations within the sixth-order cumulant AMC algorithm, and achieved improvements in performance are presented and explained.
Evaluation of Gaze Depth Estimation from Eye Tracking in Augmented Reality

Gaze tracking in 3D has the potential to improve interaction with objects and visualizations in augmented reality. However, previous research showed that subjective perception of distance varies between real and virtual surroundings. We wanted to determine whether objectively measured 3D gaze depth through eye tracking also exhibits differences between entirely real and augmented environments. To this end, we conducted an experiment (N = 25) in which we used Microsoft HoloLens with a binocular eye tracking add-on from Pupil Labs. Participants performed a task that required them to look at stationary real and virtual objects while wearing a HoloLens device. We were not able to find significant differences in the gaze depth measured by eye tracking. Finally, we discuss our findings and their implications for gaze interaction in immersive analytics, and the quality of the collected gaze data.
Multi-sensual Augmented Reality in Interactive Accessible Math Tutoring System for Flipped Classroom

Evermore widespread “flipped classroom” learning model is associated with increased independence of learning. The problem is the independence of learning math by students with visual impairments, especially the blind. Mathematical content includes spatial objects such as formulas and graphics, inaccessible to blind students and hardly accessible to low vision students. They prevent independent learning. The article presents a method that increases students’ independence in recognising mathematical content in textbooks and worksheets. The method consists in introducing into the document elements of Augmented Reality (AR), that is texts and sounds extending information about the mathematical objects encountered in the content, beyond the information provided by WCAG guidelines and recommendations of the WAI-ARIA standard under development by the W3C consortium. Access to AR elements is gained through multi-sensual User Interface - hearing, the touch of a braille display, touch screen and touch gestures. The method was developed in cooperation with students with visual impairment and math teachers. It is currently undergoing valorisation in Poland, the Netherlands and Ireland.
VersaTouch

We present VersaTouch, a portable, plug-and-play system that uses active acoustic sensing to track fine-grained touch locations as well as touch force of multiple fingers on everyday surfaces without having to permanently instrument them or do extensive calibration. Our system is versatile in multiple aspects. First, with simple calibration, VersaTouch can be arranged in arbitrary layouts in order to fit into crowded surfaces while retaining its accuracy. Second, various modalities of touch input, such as distance and position, can be supported depending on the number of sensors used to suit the interaction scenario. Third, VersaTouch can sense multi-finger touch, touch force, as well as identify the touch source. Last, VersaTouch is capable of providing vibrotactile feedback to fingertips through the same actuators used for touch sensing. We conducted a series of studies and demonstrated that VersaTouch was able to track finger touch using various layouts with average error from 9.62mm to 14.25mm on different surfaces within a circular area of 400mm diameter centred around the sensors, as well as detect touch force. Finally, we discuss the interaction design space and interaction techniques enabled by VersaTouch.
Manifold-Regularized Regression Network

Biomedical image analysis by machine learning enables accurate cell counting and localization, which are essential to clinic diagnosis and research. However, high-density population of the cells poses great challenges. The variability and intricacy in the shapes and colours of cells further jeopardize the performance of existing models. Aiming to overcome the difficulties, we propose an end-to-end manifold-regularized regression network (MRRN) for cell counting and localization. The backbone is a U-Net tweaked for regression and trained by density maps to preserve the spatial distribution of the cells. Based on graph Laplacian, the patch-wise manifold regularizer imposed on the U-Net can represent the intrinsically nonlinear and complex features of the images, thus increasing the generalizability of the network and lowering the risk of overfitting. Experimental results show that MRRN outperforms several state-of-the-art algorithms on a widely-adopted synthetic data set and a real-world carcinoma data set, which verifies the efficacy and applicability of the proposed model.
Evaluation of deep learning models for Urdu handwritten characters recognition

Abstract As a classical and significant problem, handwritten character recognition has been widely used in our daily lives. With recent deep learning methods, previous studies have achieved a great improvement for this problem in the past few years. However, the handwritten character recognition for Urdu, which is one of the largest languages of the world, is less studied in the existing literature. In this paper, we fill in this gap and evaluate different deep learning models on the problem of Urdu handwritten characters recognition based on a newly released dataset. Combined with data augmentation and transfer learning techniques, we achieve the state-of-the-art results by recognizing digits and characters with an accuracy of 98.94% and 99.08%, respectively, which greatly improves the baselines of 97% and 86.5%.
Integrative blockwise sparse analysis for tissue characterization and classification

The topic of sparse representation of samples in high dimensional spaces has attracted growing interest during the past decade. In this work, we develop sparse representation-based methods for classification of clinical imaging patterns into healthy and diseased states. We propose a spatial block decomposition method to address irregularities of the approximation problem and to build an ensemble of classifiers that we expect to yield more accurate numerical solutions than conventional sparse analyses of the complete spatial domain of the images. We introduce two classification decision strategies based on maximum a posteriori probability (BBMAP), or a log likelihood function (BBLL) and an approach to adjusting the classification decision criteria. To evaluate the performance of the proposed approach we used cross-validation techniques on imaging datasets with disease class labels. We first applied the proposed approach to diagnosis of osteoporosis using bone radiographs. In this problem we assume that changes in trabecular bone connectivity can be captured by intensity patterns. The second application domain is separation of breast lesions into benign and malignant categories in mammograms. The object classes in both of these applications are not linearly separable, and the classification accuracy may depend on the lesion size in the second application. Our results indicate that the proposed integrative sparse analysis addresses the ill-posedness of the approximation problem and produces very good class separation for trabecular bone characterization and for breast lesion characterization. Our approach yields higher classification rates than conventional sparse classification and previously published convolutional neural networks (CNNs) that we fine-tuned for our datasets, or utilized for feature extraction. The BBLL technique also produced higher classification rates than learners using hand-crafted texture features, and the Bag of Keypoints, which is a sophisticated patch-based method. Furthermore, our comparative experiments showed that the BBLL function may yield more accurate classification than BBMAP, because BBLL accounts for possible estimation bias.
Multi-Source Neural Model for Machine Translation of Agglutinative Language

Benefitting from the rapid development of artificial intelligence (AI) and deep learning, the machine translation task based on neural networks has achieved impressive performance in many high-resource language pairs. However, the neural machine translation (NMT) models still struggle in the translation task on agglutinative languages with complex morphology and limited resources. Inspired by the finding that utilizing the source-side linguistic knowledge can further improve the NMT performance, we propose a multi-source neural model that employs two separate encoders to encode the source word sequence and the linguistic feature sequences. Compared with the standard NMT model, we utilize an additional encoder to incorporate the linguistic features of lemma, part-of-speech (POS) tag, and morphological tag by extending the input embedding layer of the encoder. Moreover, we use a serial combination method to integrate the conditional information from the encoders with the outputs of the decoder, which aims to enhance the neural model to learn a high-quality context representation of the source sentence. Experimental results show that our approach is effective for the agglutinative language translation, which achieves the highest improvements of +2.4 BLEU points on Turkish–English translation task and +0.6 BLEU points on Uyghur–Chinese translation task.