Reviewerly/exHarmony huggingface dataset:
Identification of Symptoms Based on Natural Language Processing (NLP) for Disease Diagnosis Based on International Classification of Diseases and Related Health Problems (ICD-11)

The digestive system is a vital organ, considering its function in processing food and drinks that are consumed every day so it is very important to be maintained. But sometimes people have a lack of awareness and knowledge of the initial symptoms of digestive diseases so that being a factor causing the disease to be serious can even cause death. Identification of symptoms as early as possible is very important for the diagnosis process so that immediate control measures can be taken to prevent and overcome the spread of disease. Anamnesis process is needed to get the symptoms of the disease, question and answer process between the patient and medical personnel whose results are stored in the Electronic Medical Record (EMR) in the form of narration to assist in the process of Clinical Decision Support (CDS). EMR is often difficult to do computing processing due to inappropriate grammar. For computers to process natural languages, Natural Language Processing (NLP) is used. In this study, an NLP system was created that can identify symptoms of the digestive disease by using to optimize the CDS process. The method used to identify symptoms of the disease is Named Entity Recognition (NER), which determines which tokens are included in the symptoms of the disease. The model trained with 800 epochs produces f1-score accuracy of 0.79. Experimental results show that the NER process supported by stemming and removing stopwords in pre-processes can improve system accuracy.
BioReddit: Word Embeddings for User-Generated Biomedical NLP

Word embeddings, in their different shapes and evolutions, have changed the natural language processing research landscape in the last years.The biomedical text processing field is no stranger to this revolution; however, researchers in the field largely trained their embeddings on scientific documents, even when working on user-generated data.In this paper we show how training embeddings from a corpus collected from user-generated text from medical forums heavily influences the performance on downstream tasks, outperforming embeddings trained both on general purpose data or on scientific papers when applied to user-generated content.
The growth and challenges of cyber insurance

Cyberattacks have grown in frequency and cost over the past decade, with high-profile cases, such as the 2013 Target data breach, the 2017 Equifax data breach, and the leak of Democratic National Committee emails during the 2016 election making national headlines.Ransomware attacks, intellectual property theft, and fraud cost companies billions in recovery expenses, fines, and lost revenues every year.More firms are purchasing cyber insurance as a way to cover losses and expenses resulting from cyber incidents.However, cyber insurance alone is not a panacea, and even firms that have cyber insurance may not be as protected as they think.Unlike traditional lines of business such as private auto insurance, where standardized policies provide liability or collision coverage, cyber insurance policy language is not standardized.The types of risks covered under cyber insurance vary significantly across policies and businesses, and insurers do not always agree on what loss events are covered under those policies.The features of cyber events, including a limited loss history, the unreliability of past data when predicting future events, and the possibility of a large-scale attack where losses are highly correlated across companies and/or industries, make it difficult to write comprehensive policies.In this Chicago Fed Letter, we examine the extent to which cyber insurance can help protect businesses and the wider economy from the costs of cyberattacks and how institutional factors and legal uncertainties may obstruct the development of this market.
The Potential of Multi-Sensor Remote Sensing Mineral Exploration: Examples from Southern Africa

Traditional exploration techniques usually rely on extensive geological field work complemented by geophysical ground surveying. However, this approach can be limited by field accessibility, financial status, area size and climate and can be confronted with public rebuff. We recommend the use of multi-scale hyperspectral remote sensing to mitigate the limitations of traditional exploration. Multi-scale remote sensing is particularly beneficial, especially in inaccessible and remote areas with little infrastructure, because it allows for a systematic, dense and generally non-invasive surveying. Additionally, Unmanned Aerial Systems (UAS) coupled with various sensors provide an opportunity to conduct noninvasive exploration in socially sensitive areas and in relatively inaccessible locations. The development of operational technologies and the evaluation of appropriate processing techniques in different mineral deposit environments contribute to a rapidly evolving field at the cutting edge of exploration technologies. Ultimately, this study provides the opportunity to advance the discovery of critical raw material deposits.
Why δ is not ‰ and why we should not use ε and μ notations

Geostandards and Geoanalytical ResearchVolume 43, Issue 4 p. 527-528 Editorial Why δ is not ‰ and why we should not use ε and μ notations Thomas C. Meisel, Thomas C. Meisel Chair of the Joint Editors in Chief, Geostandards and Geoanalytical ResearchSearch for more papers by this author Thomas C. Meisel, Thomas C. Meisel Chair of the Joint Editors in Chief, Geostandards and Geoanalytical ResearchSearch for more papers by this author First published: 19 November 2019 https://doi.org/10.1111/ggr.12303Citations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume43, Issue4Special Issue Section: Geoanalysis 2018December 2019Pages 527-528 RelatedInformation
Handling and pushing objects using unmanned guided vehicles

This paper aims handling box-shape objects combining mapping, searching, and path planning techniques. The proposal enables a mobile robot to push objects autonomously from random positions to a final destination. Laser scanner data are used to build up a 2D map, which aids the objects’ identification in the scene. Next, a topological map is created and Bézier curves provide suitable paths taking into account the position of the robot, objects and final destination. Then, Dijkstra’s algorithm finds the optimal route. Finally, simulations are run in V-REP + Matlab, and real experiments validate the proposal, which demonstrates quite efficient for environments without occlusion of the objects to be transported.
Configuration-Making Algorithm for the Smart Machine Controller Based on the Internet of Things Concept

This article focuses on the approaches of selecting cost-effective solutions to configurations of hardware components in digital systems (in particular, smart machines) by the combinatorial optimization method. A search for the solution to the application of cross-functional or special-purpose hardware components for LoT devices is a multi-criteria objective. The proposed selection algorithm for the configuration of hardware components feeds on rigorous formalization and ranking of qualitative indicators, parameters and key features of the components in question. The authors have applied the algorithm in the MatLab environment in order to choose the best configuration of hardware components for the smart machine intended to be a core of an intelligent control unit for an analysis of AC network parameters and control over the actuating electromechanical devices. The results of the analysis have become a ground for the development of the special-purpose controller for an intelligent module of the smart machine. The synthetic solution to a configuration of the smart controller provides an analysis of monitoring data. It can also make it possible for the smart machine to transfer data via a service interface to an external automation controller, which remotely disconnects the machine at the command of a user. The result of the design procedure is the advisable configuration of the special-purpose smart machine controller intended to analyze the AC network parameters obtained using the proposed design methodology.
Sentiment Analysis of Finnish Customer Reviews

This paper presents a hybrid algorithm that takes advantage of polarity lexicons along with state-of-the-art word embeddings with weighted average of reviews and headlines for predicting polarity of reviews. The algorithm gives a significantly better coverage compared to polarity lexicons and a better classification accuracy in comparison to the word embedding models. We implemented Finnish polarity lexicons with different construction techniques, and evaluated the quality of the lexicons with translated polarity lexicons. The polarity lexicon construction and the polarity classification involves a new dataset, which was extracted from online user reviews in Finnish. In general, using headlines instead of reviews as sentiment data source provides better results at the expense of coverage.
Understanding Generalization in Recurrent Neural Networks

In this work, we develop the theory for analyzing the generalization performance of recurrent neural networks. We first present a new generalization bound for recurrent neural networks based on matrix-1 norm and Fisher-Rao norm. The definition of Fisher-Rao norm relies on a structural lemma about the gradient of RNNs. This new generalization bound assumes that the covariance matrix of the input data is positive definite, which might limit its use in practice. To address this issue, we propose to add random noise to the input data and prove a generalization bound for training with random noise, which is an extension of the former one. Compared with existing results, our generalization bounds have no explicit dependency on the size of networks. We also discover that Fisher-Rao norm for RNNs can be interpreted as a measure of gradient, and incorporating this gradient measure not only can tighten the bound, but allows us to build a relationship between generalization and trainability. Based on the bound, we analyze the effect of covariance of features on generalization of RNNs theoretically and discuss how weight decay and gradient clipping in the training can help improve generalization.
Facial Expression Recognition by Jointly Partial Image and Deep Metric Learning

The performance of facial expression recognition (FER) tends to deteriorate due to high intraclass variations and high interclass similarities.To address this problem, an expression recognition model based on a joint partial image and deep metric learning method (PI&DML) is proposed.First, we propose cropping the active units (AU) that are most closely related to the expression to generate a partial image for feature extraction, which is conducive to mitigating the negative impact of the abovementioned problems to some extent.Second, a novel expression metric loss function (EMLF) is suggested to enhance the intraclass similarities and interclass variations.Finally, superior performance is achieved by jointly optimizing the expression metric loss and classification loss.As demonstrated by the visualization results, the proposed EMLF is effective at increasing the distance between various expressions and reducing the distance between the same expressions.The evaluations on three public expression databases have demonstrated that our method is capable of achieving better results than the state-of-the-art methods.
Symbolic Top-k Planning

The objective of top-k planning is to determine a set of k different plans with lowest cost for a given planning task. In practice, such a set of best plans can be preferred to a single best plan generated by ordinary optimal planners, as it allows the user to choose between different alternatives and thus take into account preferences that may be difficult to model. In this paper we show that, in general, the decision problem version of top-k planning is PSPACE-complete, as is the decision problem version of ordinary classical planning. This does not hold for polynomially bounded plans for which the decision problem turns out to be PP-hard, while the ordinary case is NP-hard. We present a novel approach to top-k planning, called sym-k, which is based on symbolic search, and prove that sym-k is sound and complete. Our empirical analysis shows that sym-k exceeds the current state of the art for both small and large k.
One-Shot Template Matching for Automatic Document Data Capture

In this paper, we propose a novel one-shot template-matching algorithm to automatically capture data from business documents with an aim to minimize manual data entry. Given one annotated document, our algorithm can automatically extract similar data from other documents having the same format. Based on a set of engineered visual and textual features, our method is invariant to changes in position and value. Experiments on a dataset of 595 real invoices demonstrate 86.4% accuracy.
Distributed and Lazy Auditing of Outsourced Data

Data outsourcing is useful for ICT organizations who need cost-effective management of resources. Auditing of outsourced data is an essential requirement for the data owner who outsources it to the untrusted third party storage service provider. It is a posterior mechanism for data verification. In this work, we propose a distributed auditing scheme for verifying the integrity of the outsourced data as compare to the existing centralized auditing schemes. To the best of our knowledge, this is the first such scheme uses distributed auditing. The proposed scheme avoids intense computing requirement at the data owner or the auditor. We classify the existing auditing schemes into generic classes, analyze them, and compare them with the proposed scheme. The performance evaluation of the auditing operation in the proposed scheme is carried out and verified with the analytical results.
Deep Learning Methods for Facial Expression Recognition

Deep learning is very popular methods for facial expression recognition (FER) and classification.Different types of deep learning algorithms have been used for FER such as deep belief network (DBN) and convolutional neural network (CNN).In this paper, we analyze various deep learning methods and their results.We have chosen Deep convolutional neural network as the best algorithms for facial expression detection and classification.In our study, we have tested the algorithm using Japanese Female facial expressions database (JAFFE) datasets by anaconda software.The deep convolution neural networks with JAFFE datasets accuracy rate around 97.01%.
The use of lithogeochemistry in delineating hydrothermal fluid pathways and vectoring towards gold mineralization in the Malartic district, Québec

The world-class, oxidized intrusion-related Canadian Malartic gold deposit, with reserves estimated at 5.56 Moz Au grading 1.10 g/t Au, and a total geological endowment of 16.3 Moz Au, is one of the largest gold deposits in the Archean Superior Province of Canada. The gold mineralization is hosted predominantly by Pontiac Group metasedimentary rocks, Piché Group metavolcanic rocks, and quartz monzodiorite to granodiorite porphyritic intrusions. The ore takes the form of a low-grade envelope of disseminated pyrite (0.35 to 1 g/t Au) grading inwards into higher grade (>1 g/t Au) stockwork and breccia zones. Hydrothermal alteration in the metasedimentary rocks is zonally distributed around the fluid pathways. Proximal alteration is characterized by a microcline±albite-quartz replacement-type assemblage, with lesser phlogopite, calcite±Fe-dolomite, pyrite and rutile. The distal alteration assemblage comprises biotite, microcline±albite, phengite, quartz, calcite, pyrite and rutile. In this study, we assess the magnitude and distribution of fluid-rock interaction in the metasedimentary rocks of the Malartic district. The metaturbidites are separated into four lithotypes based on grain-size to reduce the effects of primary depositional processes on mass change calculations. Despite the variability in protolith composition, the metasedimentary rocks define a geochemically consistent, cogenetic sequence. The results of the mass transfer calculations indicate progressive gains in CO2, S, K2O and LOI, as well as Au, Te, W, Ag, As, Be, Sb, Bi, Mo and Pb, from background, to distal and proximal alteration zones (relative to the least-altered samples). Molar element ratio analysis (alkali/aluminum) indicates an increase in alkali metasomatism (K and Na) adjacent to the main hydrothermal fluid pathways, which is manifested by the progressive stabilization of microcline and albite at the expense of oligoclase, biotite and white mica. Ore-associated pathfinder elements delineate broad enrichment patterns around the deposit, and are used to understand hydrothermal fluid circulation in the Malartic district. A statistical approach based on a comparison of the mass change results with the background composition provides robust constraints on the magnitude and extent of the lithogeochemical haloes. Generally, the alteration forms envelopes that extend along the S2 fabric, with the largest lithogeochemical anomalies (e.g., Au, W, Te and Ag) reaching up to 10 km in length, and 2 km in width. The results of this study demonstrate that whole-rock lithogeochemistry can provide a valuable tool with which to define vectors toward gold mineralization in a regional exploration context.
Blind Panoramic Image Quality Assessment via the Asymmetric Mechanism of Human Brain

Most existing objective quality evaluation metrics for panoramic images are typically derived from peak signal to noise ratio (PSNR) or structural similarity (SSIM). However, they need pristine panoramic images and are not highly consistent with human perception. To address the problem, we propose a novel blind panoramic image quality assessment (PIQA) method to predict the visual quality of panoramic images based on asymmetric mechanism of human brain. In the proposed method, the high-frequency feature is extracted by panoramic-weighted local binary pattern and relative total variation is used to clip the high-frequency information in panoramic images, then panoramic-weighted statistic feature can represent the lowfrequency feature. Finally, support vector regression (SVR) is adopted to build a quality predictor from feature space to quality score space. The experimental results on the public subjective dataset demonstrate the superiority of our proposed metric compared with state-of-the-art objective PIQA methods.
Optimal Design of Practical Quantum Key Distribution Backbones for Securing CoreTransport Networks

We describe two mixed-integer linear programming formulations, one a faster version of a previous proposal, the other a slower but better performing new model, for the design of Quantum Key Distribution (QKD) sub-networks dimensioned to secure existing core fiber plants. We exploit existing technologies, including non-quantum repeater nodes and multiple disjoint QKD paths to overcome reach limitations while maintaining security guarantees. We examine the models’ performance using simulations on both synthetic and real topologies, quantifying their time and resulting QKD network cost compared to our previous proposal.
Acoustic Pornography Recognition Using Recurrent Neural Network

This research proposes a pornography recognition model using audio features utilizing recurrent neural network. The proposed work is totally different from most of the previous attempts on pornography recognition using visual contents of the nudity images or videos. The importance of using sounds for pornography recognition arises in the cases in which the visual features are not adequately informative of the nudity contents such as dark scenes, and scenes with the covered bodies. Acoustic recognition model selects the finest signal representation by feature extraction of Mel-Frequency Cepstrum Coefficients (MFCCs) after been pre-processed for noise reduction. Extracted features are fed into a network with long short-term memory (LSTM) cells. The LSTM cells have the capability to solve problems associated with temporal dependencies and require learning long-term and solve the vanishing gradient problems associated with Recurrent Neural Network (RNN). A dataset of porno-sound samples has been created based on an existing pornography video dataset. 800 data samples were used including positive and negative samples. The experimental results confirm the feasibility of the proposed acoustic-driven approach by demonstrating an accuracy of 86.50%, and F-score of 86.83%, in the task of pornography recognition.
Fast approach for QR code localization on images using Viola-Jones method

In this paper, a method for QR Code localization on images obtained under uncontrolled environment is presented. The proposed method is a modified Viola-Jones object detection method in which features are calculated over the directional edge image, and a tree classifier is used instead of cascade classifier. The experiments show that the use of the QR Code localization method described in the paper can significantly improve the quality of the existing decoding algorithms. The high performance of the developed method makes it possible to use it in various real-time recognition systems.
Modalities of Expression

When cooking we negotiate between instructions in recipes and personal preferences to make in-the-moment creative decisions. This process represents moments of creativity that utilise and reveal our embodied knowledge. This paper focuses on the capture of expressions of embodied knowledge by digitally-networked utensils. We present a design process investigating the design of tangible interfaces to capture and communicate embodied knowledge as a proposition for recipe authoring tools for open innovation in food. We reflect upon this process to discuss lessons about the individual nature of embodied knowledge and its expression, and the context of capturing it to make design recommendations.
UAV Online Path Planning Based On Improved Genetic Algorithm with Optimized Search Region

When performing online path planning for unmanned aerial vehicle(UAV), the planning algorithm needs to have high search efficiency. In this case, the weakness of poor local search ability and low planning efficiency of the traditional Genetic Algorithm(GA) will be reflected. In addition, the population information is not used sufficiently in GA. To address these shortcomings, this paper proposes an improved genetic algorithm. Before gene manipulation of each generation, some individuals in the population are analyzed to judge the searching value of different regions in the planning space, then the generating regions of evolution operator is reasonably restricted. The improved algorithm is used for UAV online path planning. The simulation results show that the method strengthens the local search ability of the genetic algorithm and improves the planning efficiency, and can complete UAV online path planning for tracking moving targets in the face of sudden threats.