Reviewerly/exHarmony huggingface dataset:
Definition of candidate Essential Variables for the monitoring of mineral resource exploitation

The practice of raw material extraction has a high impact on the environment and represents a potential threat to the health and thriving of local communities. The concept of Extractive Essential Variables (EEVs) are explored in order to propose variables that can be used to quantify the environmental footprint of mineral extraction. Considering the interdependence of mining activities with social, economic and environmental issues, the variables target the development of monitoring tools for the implementation of the Sustainable Development Goals (SDGs). The identification of EEVs is based on the use of Earth Observation products in the field of mineral resources exploitation. A list of variables is proposed based on three classes of Essential Variables (EVs): installation and exploration phase, mineral extraction, and ore processing. These variables take into account the impacts of mining on the hydrology, land, water resources and the atmosphere of the area subjected to mineral exploitation. One of the variables is implemented as an operational workflow addressing SDG15, “life on land”. The workflow is intended to assess the area of forest ecosystem lost due to the presence of a mining site. Geospatial data on the extent of mining concessions and forest cover are combined using ArcGISTM. The workflow is successively translated into a Unix script to automatize the process of data treatment. The script is developed using the Geospatial Data Abstraction Library (GDAL). The use of a Virtual Laboratory Platform (VLab), a web-service-based access platform, increases the accessibility of data and resources and the re-use of the script. This work is a first attempt to propose a framework of EEVs, derived data workflows, while the underlying methodology, partially based on scientific publications and on personal reasoning, still needs to be tested and, improved based on expertise in the sector.
A novel outlier removal method for two‐dimensional radarodometry

Autonomous navigation of platforms in complex environments has a key role in many applications. However, environmental conditions could negatively affect the performance of electro-optical sensors. Hence, the idea of using radar odometry (RO) has been recently developed. However, it suffers from the presence of outliers in the scene as its electro-optical counterparts. This work presents a method to classify radar echoes as inliers or outliers for two-dimensional RO, based on their range rate and bearing angle. The range rate and bearing angle are, in fact, combined to give a classification value, different for each target. At each acquisition time, the median of all classification values is computed. Since classification values of stationary targets, i.e. the inliers, cluster around the median, while moving targets, i.e. the outliers, exhibit larger distance from the median, stationary targets and moving targets can be separated. This is also useful for sense-and-avoid purposes. The method has been tested in the simulated scenario to show effectiveness in detecting outliers and in a real-case scenario to demonstrate significant improvement in the reconstruction of the trajectory of the platform, keeping the final error around 10% of the travelled distance. Further improvement is envisaged by integrating the method in the target tracking strategy.
Semi-interactive Attention Network for Answer Understanding in Reverse-QA

Question answering (QA) is an important natural language processing (NLP) task and has received much attention in academic research and industry communities. Existing QA studies assume that questions are raised by humans and answers are generated by machines. Nevertheless, in many real applications, machines are also required to determine human needs or perceive human states. In such scenarios, machines may proactively raise questions and humans supply answers. Subsequently, machines should attempt to understand the true meaning of these answers. This new QA approach is called reverse-QA (rQA) throughout this paper. In this work, the human answer understanding problem is investigated and solved by classifying the answers into predefined answer-label categories (e.g., True, False, Uncertain). To explore the relationships between questions and answers, we use the interactive attention network (IAN) model and propose an improved structure called semi-interactive attention network (Semi-IAN). Two Chinese data sets for rQA are compiled. We evaluate several conventional text classification models for comparison, and experimental results indicate the promising performance of our proposed models.
Quantum Teleportation and Super-Dense Coding in Operator Algebras

Abstract We show for any $d,m\ge 2$ with $(d,m)\neq (2,2)$, the matrix-valued generalization of the (tensor product) quantum correlation set of $d$ inputs and $m$ outputs is not closed. Our argument uses a reformulation of super-dense coding and teleportation in terms of $C^*$-algebra isomorphisms. Namely, we prove that for certain actions of cyclic group ${{\mathbb{Z}}}_d$, $$\begin{equation*}M_d(C^*({{\mathbb{F}}}_{d^2}))\cong{{\mathcal{B}}}_d\rtimes{{\mathbb{Z}}}_d\rtimes{{\mathbb{Z}}}_d , M_d({{\mathcal{B}}}_d)\cong C^*({{\mathbb{F}}}_{d^2})\rtimes{{\mathbb{Z}}}_d\rtimes{{\mathbb{Z}}}_d,\end{equation*}$$where ${{\mathcal{B}}}_d$ is the universal unital $C^*$-algebra generated by the elements $u_{jk}, \, 0 \le i, j \le d-1$, satisfying the relations that $[u_{j,k}]$ is a unitary operator, and $C^*({{\mathbb{F}}}_{d^2})$ is the universal $C^*$-algebra of $d^2$ unitaries. These isomorphisms provide a nice connection between the embezzlement of entanglement and the non-closedness of quantum correlation sets.
Modelling Informational Entropy

By ‘informational entropy’, we understand an inherent boundary to knowability, due e.g. to perceptual, theoretical, evidential or linguistic limits. In this paper, we discuss a logical framework in which this boundary is incorporated into the semantic and deductive machinery, and outline how this framework can be used to model various situations in which informational entropy arises.
RADR: Routing for Autonomous Drones

Path planning is one of the major challenges surrounding the development of autonomous drones. This paper presents an approach which can be utilized by autonomous drones to successfully travel from a source point to a destination point using Dijkstra's shortest path algorithm. This approach involves processing a 2D image of the environment in order to limit the search area, converting the image into a network and then applying Dijkstra's algorithm to determine the shortest path. Tests were conducted to determine if the algorithm correctly detects a nonexistent path. Finally, we discuss future direction for this algorithm to improve its performance and applicability for drone surveillance.
PLCOM: Privacy-preserving outsourcing computation of Legendre circularly orthogonal moment over encrypted image data

In the image outsourcing system, image privacy is still an increasing concern since the cloud service provider and image owners are not in the same trusted domain. The most straightforward method for guaranteeing image privacy is to leverage cryptographic tools, but traditional cryptographic tools make feature extraction algorithms useless. To this end, we propose a privacy-preserving feature extraction scheme for Legendre circularly orthogonal moment, which is a novel global feature descriptor and can be used for image analysis. We first develop a novel feature descriptor, which is one of the circularly orthogonal moments and termed as Legendre Circularly Orthogonal Moment (LCOM). Then, we present a mathematical framework for implementing Privacy-preserving Legendre Circularly Orthogonal Moment (PLCOM) by combining LCOM and somewhat homomorphic encryption, and implement the image reconstruction in the encrypted domain based on PLCOM. Besides, the detailed theoretical analysis of message space and expanding factor generated by the quantitative technology shows that LCOM and image reconstruction in the plaintext domain can be realized with the aid of PLCOM. Finally, experimental results verify that the PLCOM’s performance in terms of image reconstruction capability and image recognition accuracy is acceptable.
Wavelength dependence of intrinsic detection efficiency of NbN superconducting nanowire single-photon detector*

Superconducting nanowire single-photon detectors (SNSPDs) have attracted considerable attention owing to their excellent detection performance; however, the underlying physics of the detection process is still unclear. In this study, we investigate the wavelength dependence of the intrinsic detection efficiency (IDE) for NbN SNSPDs. We fabricate various NbN SNSPDs with linewidths ranging from 30 nm to 140 nm. Then, for each detector, the IDE curves as a function of bias current for different incident photon wavelengths of 510–1700 nm are obtained. From the IDE curves, the relations between photon energy and bias current at a certain IDE are extracted. The results exhibit clear nonlinear energy–current relations for the NbN detectors, indicating that a detection model only considering quasiparticle diffusion is unsuitable for the meander-type NbN-based SNSPDs. Our work provides additional experimental data on SNSPD detection mechanism and may serve as an interesting reference for further investigation.
Measuring similarity between sequential datasets

Similarity measurement is a basic problem in data mining, but little work focuses on the similarity between sequential datasets. We propose the density-emerging pattern. And we propose a novel similarity measurement between sequential datasets based on the quality of shared-density-aware and shared-emerging patterns. Similarity measuring can be divided into three stages, i.e., pattern mining, evaluating the quality of patterns, and evaluating similarity. We performed experiments on real protein sequence datasets to test the effectiveness and efficiency of our method. A case study of sequential data set classification was carried out and high accuracy was obtained. The results show that our method is able to be effectively used in the classification of sequential datasets.
When Not to Classify: Detection of Reverse Engineering Attacks on DNN Image Classifiers

This paper addresses detection of a reverse engineering (RE) attack targeting a deep neural network (DNN) image classifier; by querying, RE's aim is to discover the classifier's decision rule. RE can enable test-time evasion attacks, which require knowledge of the classifier. Recently, we proposed a quite effective approach (ADA) to detect test-time evasion attacks. In this paper, we extend ADA to detect RE attacks (ADA-RE). We demonstrate our method is successful in detecting "stealthy" RE attacks before they learn enough to launch effective test-time evasion attacks.
Learnable Pooling Methods for Video Classification

We introduce modifications to state-of-the-art approaches to aggregating local video descriptors by using attention mechanisms and function approximations. Rather than using ensembles of existing architectures, we provide an insight on creating new architectures. We demonstrate our solutions in the "The 2nd YouTube-8M Video Understanding Challenge", by using frame-level video and audio descriptors. We obtain testing accuracy similar to the state of the art, while meeting budget constraints, and touch upon strategies to improve the state of the art. Model implementations are available in https://github.com/pomonam/LearnablePoolingMethods .
Content Based Video Retrieval Using SURF, BRISK and HARRIS Features for Query-by-image

Content Based Video Retrieval (CBVR) is an approach for retrieving most relevant videos from the video database. Applications of CBVR are increasing day by day. This paper uses Speeded up Robust Feature (SURF), Binary Robust Invariant Scalable Key Points (BRISK) and HARRIS corner Detector to retrieve the similar videos. Our proposed system firstly identifies the key frames from the video using color Histogram method. In this method the color component is used to identify the key Frame. Next, above said three features are derived from all the videos in database. The three features are also calculated for the query image. By using similarity matching techniques, all the features are jointly used to assign rankings to the videos in the database based on the features of query image. The videos having ranks below threshold can be retrieved as most relevant videos to the query image given.
An Experimental Investigation on Uncertainty in Measuring Vibration Deflection Shapes With Digital Image Correlation

3-D digital image correlation (DIC) is a widespread full-field displacement measurement technique based on stereo vision. It was formulated to deal with static problems in experimental mechanics in the early seventies, and since then, the applicability of the technique increased due to increasing frame rates and dropping prices of machine vision cameras. As a consequence, DIC became a feasible solution for general-purpose vibration testing. This paper, however, does not offer a comprehensive analysis of DIC accuracy when applied to vibration testing, and therefore, this paper offers a “Type A” evaluation of uncertainty when measuring vibration deflection shapes with DIC. Uncertainty is evaluated for different experimental conditions on a stepped sine test. Data show that the average value of uncertainty normally lies below 0.02 mm, but in resonant conditions, it can increase up to 0.05 mm (considering a field of view of about half a meter). This demonstrates a strong correlation between the deflection amplitude and the random uncertainty due to motion blur.
Numerical Verification of the Birch and Swinnerton-Dyer Conjecture for Hyperelliptic Curves of Higher Genus over ℚ up to Squares

The Birch and Swinnerton-Dyer conjecture has been numerically verified for the Jacobians of 32 modular hyperelliptic curves of genus 2 by Flynn, Leprévost, Schaefer, Stein, Stoll and Wetherell, using modular methods. In the calculation of the real period, there is a slight inaccuracy, which might give problems for curves with non-reduced components in the special fiber of their Néron model. In this present article, we explain how the real period can be computed, and how the verification has been extended to many more hyperelliptic curves, some of genus 3, 4, and 5, without using modular methods.
A practical searchable symmetric encryption scheme for smart grid data

Outsourcing data storage to the remote cloud can be an economical solution to enhance data management in the smart grid ecosystem. To protect the privacy of data, the utility company may choose to encrypt the data before uploading them to the cloud. However, while encryption provides confidentiality to data, it also sacrifices the data owners' ability to query a special segment in their data. Searchable symmetric encryption is a technology that enables users to store documents in ciphertext form while keeping the functionality to search keywords in the documents. However, most state-of-the-art SSE algorithms are only focusing on general document storage, which may become unsuitable for smart grid applications. In this paper, we propose a simple, practical SSE scheme that aims to protect the privacy of data generated in the smart grid. Our scheme achieves high space complexity with small information disclosure that was acceptable for practical smart grid application. We also implement a prototype over the statistical data of advanced meter infrastructure to show the effectiveness of our approach.
Natural Scene Nutrition Information Acquisition and Analysis Based on Deep Learning

Background: In the field of personalized health, it is often difficult for individuals to obtain professional knowledge to solve their practical problems timely and accurately. While there are some applications that can get targeted information, they often fail to function properly in nonideal environments, and they cannot achieve precise answers to individual users. Therefore, how to establish an information capture model based on big data and combine it with intelligent search is an important issue in the field of personalized health. Objective: This paper starts with the information acquisition and intelligent recommendation in the field of personalized health, and proposes a natural scene information acquisition and analysis model based on deep learning, focusing on improving the recognition rate of text in natural scenes and achieving targeted smart search to allow users to get more accurate personalized health advice. Methods: In this model, natural scene information is processed from four aspects: targeted big data collection and search, connected text proposal network text detection algorithm and projectionbased text segmentation, capsule network text recognition and result analysis. The model reduces recognition bias due to problems such as special filming conditions and photographic techniques by using deep learning algorithms. At the same time, the data mining has also improved the pertinence of the results analysis. Conclusion: This model combines deep learning and data mining methods to obtain intelligent solutions at a professional level by uploading target information images in non-ideal environments, and is suitable for accurate analysis of problems in personalized health area. Results: The proposed model is applied to analyze the user's nutrient intake requirements. The results show that the method achieves 83% prediction accuracy on the nutrient composition table dataset, and its performance is better than current convolutional neural network applications. And the model can get accurate personalized data to provide users with dietary advice.
Systems theoretic process analysis of information security: the case of aadhaar

A new way of thinking about cybersecurity is much needed to deal with the complex and dynamic cyber-ecosystem. In this paper, we introduce a systems thinking based approach for solving problems related to cybersecurity. We adapt the powerful safety-hazard analysis method, Systems Theoretic Process Analysis (STPA) based on systems theory to analyze the cybersecurity related features of India’s massive digital identity program, Aadhaar. Our findings produce important insights. On one hand, it helps identify the security gaps of the Aadhaar system, and on the other hand, it provides controls using systems thinking to overcome these gaps. We contribute to understanding the world of cybersecurity practices and develop risk mitigation strategies that can benefit the Aadhaar.
Convolutional Neural Network-Based Image Distortion Classification

The growing popularity of neural networks encourages their use in various applications. This paper presents the classification results of distortions in the image using convolutional neural networks (CNN). This is one of the steps in various approaches to Non-Reference linage Quality Assessment, where first the distortion type is detected and then, its measured intensity is mapped to the mean opinion score. Accordingly, the score can be used as an optimization parameter for designing image processing or compression algorithms. Thus, being able to easily and reliably detect the distortion type is a very important task. The paper shows two architectures of CNN and compares the obtained results with another solution. The proposed solutions outperform other solutions, based on support vector machines, by over 10% in terms of accuracy.
Multi-stage programs in context

Cross-stage persistence is an essential aspect of multi-stage programming that allows a value defined in one stage to be available in another. However, difficulty arises when implicit information held in types, type classes and implicit parameters needs to be persisted. Without a careful treatment of such implicit information---which are pervasive in Haskell---subtle yet avoidable bugs lurk beneath the surface.
Mobile Conversational Agents for Stroke Rehabilitation Therapy

Mobile health (m-Health) has emerged as a rapidly developing area that is transforming clinical research and health care on a global scale. In this paper, we describe a conversational app for the therapy of stroke rehabilitation. The main objective of the conversational app is to help recovering cognitive abilities of patients by means of a set of proposed exercises, which are divided into 8 categories focused on specific abilities. These categories have been defined after a detailed review of the guidelines for rehabilitation and training therapies. In addition, the application integrates a multimodal conversational interface to facilitate human-computer interaction, which has been specially designed for the elderly and patients with motor or visual or disabilities. The exercises provided by the application can be easily adapted to the specific users' requirements and preferences by means of the incorporation, deletion or modification of routines stored into a specific database isolated from the logic of the application.
Spatio-Temporal Multi-scale Soft Quantization Learning for Skeleton-Based Human Action Recognition

Effective feature representation is important for action recognition. In this paper, a novel soft quantization learning method is proposed to represent visual features for action recognition. Specifically, we propose a dual multi-scale soft-quantization network, which is a trainable quantizer using RBF neurons. The RBF layer includes dual multi-scale structure, namely a three-level hierarchical skeleton structure in space, and a temporal-pyramid based multi-scale time structure. Different spatial levels in the RBF layer have respective RBF neurons for hierarchical spatial information, while the temporal scales share them to reduce the number of parameters in the network. An accumulation layer following the RBF layer summarizes the RBF output as a histogram representation for classification task. The proposed method is end-to-end differentiable that can be trained using regular back-propagation. The conducted experiments on benchmark datasets verify that the proposed method outperforms state-of-the-art methods.
Unsupervised Monocular Depth Estimation Based on Dual Attention Mechanism and Depth-Aware Loss

Most existing monocular depth estimation approaches are su- pervised, but enough quantities of ground truth depth data are required during training. To cope with this, recent techniques deal with the depth estimation task in an unsupervised man- ner, i.e., replacing the use of depth data with easily obtained stereo images for training. Based on this, we propose a nov- el unsupervised learning architecture, which integrates dual attention mechanism into the framework and designs a depth- aware loss for better depth estimation. Specifically, to en- hance the ability of feature representations, we introduce a d- ual attention module to capture global feature dependencies in spatial and channel dimensions for scene understanding and depth estimation. Meanwhile, we propose a depth-aware loss that fully addresses the occlusion problem in brightness con- stancy assumption, the intrinsic characteristics of depth map, and the left-right consistency problem, respectively. Besides, an adversarial loss is employed to discriminate synthetic or realistic depth maps by training a discriminator so as to pro- duce better results. Extensive experiments on KITTI dataset show that our approach achieves state-of-the-art performance compared with other monocular depth estimation methods.