Reviewerly/exHarmony huggingface dataset:
Impact of heterogeneities and surface roughness on pXRF, pIR, XRD and Raman analyses: Challenges for on-line, real-time combined mineralogical and chemical analyses on drill cores and implication for “high speed” Ni-laterite exploration

On-line, real-time chemical and mineralogical analyses on drill cores are highly demanded by mining companies. However, they are a challenge because of drill core surface state and sample heterogeneities. We selected four rock samples: highly porous, siliceous breccia and serpentinized harzburgite coming from the base of a nickel laterite profile in New Caledonia which were sonic drilled, and fine grained, homogeneous sandstone and coarse grained granite which were diamond drilled and provided by Eijkelkamp Sonic Drill with unknown origin. The samples were analysed at five surface states (diamond or sonic drilled, cut as squares, polished at 6 and 0.25 μm, powdered <80 μm) by portable XRF spectroscopy (pXRF) in mining and soil modes and portable infrared spectroscopy (pIR, Visible and Near Infrared-Short Wave Infrared range (VNIR-SWIR)). A total of 52 pXRF and 200 pIR analyses were performed per sample at each surface state. This study shows that the surface state has minor influence on the results of the portable instruments. By comparing pIR and pXRF results with laboratory devices (Raman spectroscopy, XRD with Rietveld refinement, XRF spectroscopy and ICP-AES), we evidence the lower and less accurate information obtained from handheld instruments in terms of chemistry and mineralogy. The porosity and grain size effect on the measurement need to be taken into consideration for on-line drill core analyses. We show that the combination of complementary analytical techniques helps to overcome the drawbacks of the core texture and of the precision of portable instruments in order to define the regions of interest (ROI) for mining companies. We also demonstrate that a precise pXRF calibration is mandatory and that the concentration of light elements (Si, Mg), even if not accurate, shows sufficient contrast along the lateritic profile for ROI definition.
A novel hybrid approach for solving the multiple traveling salesmen problem

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ELIFAN, an algorithm for the estimation of cloud cover from sky imagers

Abstract. In the context of a network of sky cameras installed on atmospheric multi-instrumented sites, we present an algorithm named ELIFAN, which aims to estimate the cloud cover amount from full-sky visible daytime images with a common principle and procedure. ELIFAN was initially developed for a self-made full-sky image system presented in this article and adapted to a set of other systems in the network. It is based on red-to-blue ratio thresholding for the distinction of cloudy and cloud-free pixels of the image and on the use of a cloud-free sky library, without taking account of aerosol loading. Both an absolute (without the use of a cloud-free reference image) and a differential (based on a cloud-free reference image) red-to-blue ratio thresholding are used. An evaluation of the algorithm based on a 1-year-long series of images shows that the proposed algorithm is very convincing for most of the images, with about 97 % of relevance in the process, outside the sunrise and sunset transitions. During those latter periods, however, ELIFAN has large difficulties in appropriately processing the image due to a large difference in color composition and potential confusion between cloud-free and cloudy sky at that time. This issue also impacts the library of cloud-free images. Beside this, the library also reveals some limitations during daytime, with the possible presence of very small and/or thin clouds. However, the latter have only a small impact on the cloud cover estimate. The two thresholding methodologies, the absolute and the differential red-to-blue ratio thresholding processes, agree very well, with departure usually below 8 % except in sunrise–sunset periods and in some specific conditions. The use of the cloud-free image library gives generally better results than the absolute process. It particularly better detects thin cirrus clouds. But the absolute thresholding process turns out to be better sometimes, for example in some cases in which the sun is hidden by a cloud.
Accurate Hierarchical Human Actions Recognition From Kinect Skeleton Data

Human action recognition has become one of the most active research topics in natural human interaction and artificial intelligence, and has attracted much attention. Human movement ranges from simple to complex, from low-level to advanced, with an increasing degree of complexity and data noise. In other words, there is a complicated hierarchy in movement actions. Hierarchy theory can efficiently describe these complicated hierarchical relationships of human actions. Accordingly, a hierarchical framework for human-action recognition is designed in this paper. Different features are selected according to the level of action, and specific classifiers are selected for different features. In particular, a two-level hierarchical recognition framework is constructed and tested on Kinect skeleton data. At the first level, we use support vector machine for a coarse-grained classification, while at the second level we use a combination of support vector machine and a hidden Markov model for a fine-grained classification. Ten-fold cross-validations are used in our performance evaluation on public and self-built datasets, achieving average recognition rates of 95.69% and 97.64%, respectively. These outstanding results imply that the hierarchical step-wise precise classification can well reflect the inherent process of human action.
Efficient Clustering Method Based on Density Peaks With Symmetric Neighborhood Relationship

The density peaks clustering (DPC) is a clustering method proposed by Rodriguez and Laio (Science, 2014), which sets up a decision graph to identify the cluster centers of data points.Because the improper selection of its parameter cut-off distance will lead to the wrong selection of initial cluster centers with no corrective actions in the subsequent assignment process, DPC may not identify cluster centers with different densities accurately.Especially, all cluster centers are settled as soon as they are detected, after which the DPC simply assigns each point to the same cluster as its nearest neighbor of higher density.This tends to cause the erroneous assignments of data and thus degrade the efficiency of clustering.In this paper, we propose a robust clustering method which establishes a symmetric neighborhood graph over all data points, based on the k-nearest neighbors and reverse k-nearest neighbors of each point.In order to distinguish the density peaks from all data points, local densities of each point are calculated using the reverse k-nearest neighbors.After that, initial centers for clusters are estimated over the peaks and similar clusters are aggregated on the symmetric neighborhood graph, which ends up with every point being successfully assigned to a cluster.To testify the efficiency of the new clustering method, numerical experiments and comparison works have been done on a variety of artificial and real data sets for clustering.
Learning to Learn: Hierarchical Meta-Critic Networks

In recent years, deep reinforcement learning methods have achieved impressive performance in many different fields, including playing games, robotics, and dialogue systems. However, there are still a lot of restrictions here, one of which is the demand for massive amounts of sampled data. In this paper, a hierarchical meta-learning method based on the actor-critic algorithm is proposed for sample efficient learning. This method provides the transferable knowledge that can efficiently train an actor on a new task with a few trials. Specifically, a global basic critic, meta critic, and task specified network are shared within a distribution of tasks and are capable of criticizing any actor trying to solve any specified task. The hierarchical framework is applied to a critic network in the actor-critic algorithm for distilling meta-knowledge above the task level and addressing distinct tasks. The proposed method is evaluated on multiple classic control tasks with reinforcement learning algorithms, including the start-of-the-art meta-learning methods. The experimental results statistically demonstrate that the proposed method achieves state-of-the-art performance and attains better results with more depth of meta critic network.
Simulation analysis of key technology optimization of 5G mobile communication network based on Internet of Things technology

With the rapid development of mobile Internet and Internet of Things business, the 5G mobile communication process has been accelerated. This article summarizes the Internet technology under 5G mobile communication, analyses the connotation of Internet of Things, and studies the integration of 5G mobile communications and Internet of Things technology. Through the analysis and discussion of the new possibilities in the Internet of Things era under 5G mobile communication technology, the research shows that the simulation analysis combined with the cell breathing technology and the base station dormancy technology aims to decrease the energy consumption of the base station under the premise of ensuring the communication quality, thereby improving the network energy efficiency. Research shows that in the dense and uniform user distribution scenario, the power consumption has a small decrease due to the increase of power consumption, but the system capacity is significantly improved and the communication quality is improved. It can ensure the user’s fairness and system capacity to achieve, and meet the rate requirements of different users. The analog results verify the accuracy of the theoretical analysis.
Large-alphabet encoding for higher-rate quantum key distribution

The manipulation of high-dimensional degrees of freedom provides new opportunities for more efficient quantum information processing.It has recently been shown that highdimensional encoded states can provide significant advantages over binary quantum states in applications of quantum computation and quantum communication.In particular, highdimensional quantum key distribution enables higher secret-key generation rates under practical limitations of detectors or light sources, as well as greater error tolerance.Here, we demonstrate high-dimensional quantum key distribution capabilities both in the laboratory and over a deployed fiber, using photons encoded in a high-dimensional alphabet to increase the secure information yield per detected photon.By adjusting the alphabet size, it is possible to mitigate the effects of receiver bottlenecks and optimize the secret-key rates for different channel losses.This work presents a strategy for achieving higher secret-key rates in receiver-limited scenarios and marks an important step toward high-dimensional quantum communication in deployed fiber networks.
IoT for Water Management: Towards Intelligent Anomaly Detection

Given that the global water system is deteriorating and the supply and demand are very dynamic, smart ways to improve the water management system are needed so that it becomes more efficient and to extend the services provided to the citizens leading to smart cities. One of many water related problems that can be addressed by the Internet of Things is anomaly detection in water consumption. The analysis of data collected by smart meters will help to personalize the feedback to customers, prevent water waste and detect alarming situations. Water consumption data can be considered as a time series. Time series anomaly detection is an old topic but in this work we attempt to examine which techniques suits better for water consumption. We examine two very well-known methods for time series anomaly detection: an ARIMA-based framework anomaly detection technique which selects as outliers those points no fitting an ARIMA process and also a technique named HOT-SAX which represents windows of data in a discrete way and then discriminates them using a heuristic. They are both very different in nature but the true positive analysis is excellent. The challenge remains in removing the false positive from the picture.
A complete hand-drawn sketch vectorization framework

Vectorizing hand-drawn sketches is an important but challenging task. Many businesses rely on fashion, mechanical or structural designs which, sooner or later, need to be converted in vectorial form. For most, this is still a task done manually. This paper proposes a complete framework that automatically transforms noisy and complex hand-drawn sketches with different stroke types in a precise, reliable and highly-simplified vectorized model. The proposed framework includes a novel line extraction algorithm based on a multi-resolution application of Pearson’s cross correlation and a new unbiased thinning algorithm that can get rid of scribbles and variable-width strokes to obtain clean 1-pixel lines. Other contributions include variants of pruning, merging and edge linking procedures to post-process the obtained paths. Finally, a modification of the original Schneider’s vectorization algorithm is designed to obtain fewer control points in the resulting Bézier splines. All the steps presented in this framework have been extensively tested and compared with state-of-the-art algorithms, showing (both qualitatively and quantitatively) their outperformance. Moreover they exhibit fast real-time performance, making them suitable for integration in any computer graphics toolset.
Face/On: Multi-Modal Haptic Feedback for Head-Mounted Displays in Virtual Reality

While the real world provides humans with a huge variety of sensory stimuli, virtual worlds most of all communicate their properties by visual and auditory feedback due to the design of current head mounted displays (HMDs). Since HMDs offer sufficient contact area to integrate additional actuators, prior works utilised a limited amount of haptic actuators to integrate respective information about the virtual world. With the Face/On prototype complex feedback patterns are introduced that combine a high number of vibration motors with additional thermal sources to transport multi-modal and spatial information. A pre-study determining the boundaries of the feedbacks' intensities as well as a user study showing a significant increase of presence and enjoyment validate Face/On's approach.
Breaking the MDS-PIR Capacity Barrier via Joint Storage Coding

The capacity of private information retrieval (PIR) from databases coded using maximum distance separable (MDS) codes was previously characterized by Banawan and Ulukus, where it was assumed that the messages are encoded and stored separably in the databases. This assumption was also usually made in other related works in the literature, and this capacity is usually referred to as the MDS-PIR capacity colloquially. In this work, we considered the question of if and when this capacity barrier can be broken through joint encoding and storing of the messages. Our main results are two classes of novel code constructions, which allow joint encoding, as well as the corresponding PIR protocols, which indeed outperformed the separate MDS-coded systems. Moreover, we show that a simple, but novel expansion technique allows us to generalize these two classes of codes, resulting in a wider range of the cases where this capacity barrier can be broken.
Transfer Learning for Causal Sentence Detection

We consider the task of detecting sentences that express causality, as a step towards mining causal relations from texts.To bypass the scarcity of causal instances in relation extraction datasets, we exploit transfer learning, namely ELMO and BERT, using a bidirectional GRU with self-attention (BIGRUATT) as a baseline.We experiment with both generic public relation extraction datasets and a new biomedical causal sentence detection dataset, a subset of which we make publicly available.We find that transfer learning helps only in very small datasets.With larger datasets, BIGRU-ATT reaches a performance plateau, then larger datasets and transfer learning do not help.
On Adversarial Mixup Resynthesis

In this paper, we explore new approaches to combining information encoded within the learned representations of auto-encoders. We explore models that are capable of combining the attributes of multiple inputs such that a resynthesised output is trained to fool an adversarial discriminator for real versus synthesised data. Furthermore, we explore the use of such an architecture in the context of semi-supervised learning, where we learn a mixing function whose objective is to produce interpolations of hidden states, or masked combinations of latent representations that are consistent with a conditioned class label. We show quantitative and qualitative evidence that such a formulation is an interesting avenue of research.
Yugong

Companies like Alibaba operate tens of data centers (DCs) across geographically distributed locations. These DCs collectively provide the storage space and computing power for the company, storing EBs of data and serving millions of batch analytics jobs every day. In Alibaba, as our businesses grow, there are more and more cross-DC dependencies caused by jobs reading data from remote DCs. Consequently, the precious wide area network bandwidth becomes a major bottleneck for operating geo-distributed DCs at scale. In this paper, we present Yugong --- a system that manages data placement and job placement in Alibaba's geo-distributed DCs, with the objective to minimize cross-DC bandwidth usage. Yugong uses three methods, namely project placement, table replication, and job outsourcing, to address the issues of high bandwidth consumption across the DCs. We give the details of Yugong's design and implementation for the three methods, and describe how it cooperates with other systems (e.g., Alibaba's big data analytics platform and cluster scheduler) to improve the productivity of the DCs. We also report comprehensive performance evaluation results, which validate the design of Yugong and show that significant reduction in cross-DC bandwidth usage has been achieved.
ARIES: Evaluation of a reliable and privacy-preserving European identity management framework

Despite several efforts in the last years to make Identity Management Systems (IdMs) reliable, secured and privacy-respectful, identity-related cybercrimes are still continuously expanding. Current IdMs lack of proper security and privacy mechanisms that can holistically manage user's privacy, strong authentication and ID-proofing mechanisms based on biometrics, usage of breeder documents, while maintaining usability for mobile, online or face-to-face scenarios. To fill this gap, the ARIES EU project aims to set up a reliable identity ecosystem, combining mature technologies for meet highest level of assurance, such as biometrics or use of secure elements, with innovative credential derivation mechanisms. ARIES has devised and implemented a privacy-preserving and user-centric Identity Management framework as well as associated management practices that ensure usability and flexibility for identity management processes. This paper presents ARIES results obtained after the successful development and validation of the ARIES IdM System in the associated use cases.
Implementing Any Nonlinear Quantum Neuron

The ability of artificial neural networks (ANNs) to adapt to input data and perform generalizations is intimately connected to the use of nonlinear activation and propagation functions. Quantum versions of ANN have been proposed to take advantage of the possible supremacy of quantum over classical computing. To date, all proposals faced the difficulty of implementing nonlinear activation functions since quantum operators are linear. This brief presents an architecture to simulate the computation of an arbitrary nonlinear function as a quantum circuit. This computation is performed on the phase of an adequately designed quantum state, and quantum phase estimation recovers the result, given a fixed precision, in a circuit with linear complexity in function of ANN input size.
Multi-anchor spatial phase unwrapping for fringe projection profilometry

Phase unwrapping is a necessary step in fringe-projection profilometry that produces accurate depth maps. However, the original wrapped phase is often corrupted by errors, and thus conventional spatial unwrapping suffers from error propagation, such as scanline-based unwrapping, and high complexity, such as quality-guided methods. In this paper, we propose a fast and robust spatial unwrapping method called multi-anchor scanline unwrapping (MASU). Different from previous work, when unwrapping each pixel, MASU refers to multiple anchors in the scanline, where each anchor has a threshold adapting to its location. In such a manner, a set of fringe order candidates are predicted by the anchors according to phase smoothness assumption, and the one with the highest number of votes is chosen. After that, with the obtained fringe order, the absolute phase and depth are computed. Simulation and experiments have shown that even corrupted by severe phase errors, the proposed MASU can still produce robust unwrapped results. In addition, MASU is thousands of times faster than quality-guided unwrapping with comparative or even superior depth accuracy.
Automated Pyramid Summarization Evaluation

Pyramid evaluation was developed to assess the content of paragraph length summaries of source texts.A pyramid lists the distinct units of content found in several reference summaries, weights content units by how many reference summaries they occur in, and produces three scores based on the weighted content of new summaries.We present an automated method that is more efficient, more transparent, and more complete than previous automated pyramid methods.It is tested on a new dataset of student summaries, and historical NIST data from extractive summarizers.
Convolutional Conditional Neural Processes

We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data. Translation equivariance is an important inductive bias for many learning problems including time series modelling, spatial data, and images. The model embeds data sets into an infinite-dimensional function space, as opposed to finite-dimensional vector spaces. To formalize this notion, we extend the theory of neural representations of sets to include functional representations, and demonstrate that any translation-equivariant embedding can be represented using a convolutional deep-set. We evaluate ConvCNPs in several settings, demonstrating that they achieve state-of-the-art performance compared to existing NPs. We demonstrate that building in translation equivariance enables zero-shot generalization to challenging, out-of-domain tasks.