Reviewerly/exHarmony huggingface dataset:
Contextual-Aware Information Extractor with Adaptive Objective for Chinese Medical Dialogues

Electronic Medical Records ( EMRs ) are the foundation of modern medical information systems. Despite the benefits of EMRs, the exhausting process of constructing EMRs decreases the efficiency of medical consultation. Therefore, it becomes an emerging research field to automatically extract EMRs from medical dialogues. In Chinese medical dialogues, the phenomena of omission and reference are extremely common, leading to strong contextual relevance among utterances. However, recent studies on converting Chinese medical dialogues to EMRs lack a reliable mechanism to effectively exploit the contextual relevance information among utterances. Moreover, they neglect the frequency imbalance of different items and treat these items indiscriminately, which eventually degrade the overall system performance. In this article, we proposed a Contextual-Aware Information Extractor ( CANE ), which employs a local-to-global mechanism over utterances to model the contextual relevance among utterances. Furthermore, an adaptive objective is introduced to alleviate the frequency imbalance of items by dynamically assigning weights to each sample. Experimental results indicate that CANE outperforms previous state-of-the-arts with considerable improvements (+6.11% and +3.39% on F1-score).
An Improved Association Analysis Method Based on Previous and Latter Constraint for Transmission Section Overload

Once the large-capacity ultra-high voltage direct current (UHVDC) transmission system has a blocking failure, a series of control measures are implemented to fill the power shortage of the receiving-end power grid. Focus on the problem of overloaded transmission sections after primary and secondary frequency modulation, this paper proposes an improved association analysis method and adjustment strategy based on previous and latter constraint for transmission section overload. Firstly, Using dynamic power flow algorithm to simulate blocking faults and the transmission sections are adjusted by sensitivity method. Then, the adjustment information is extracted, after data processing, the database of unit, load adjustment strategies and section state change is set up, the algorithm is improved that fore-part item-set is restricted to be the unit and load adjustment strategy and rear-part item-set is restricted to be the change of the section state. The strong association rules between the adjustment strategy and the change of the section state are analyzed. Finally, the results of DC fault simulation and association analysis show that the improved method greatly reduces the generation of invalid rules, and the strong association rules effective for section adjustment are extracted from association analysis, which provides decision-making basis for overloaded transmission section adjustment of the power grid.
The Effect of Facial Attributes in Identifying Gender Using Facial Recognition

Facial Recognition technology has become part of people's daily lives. This technology can be used in various ways such as face filter applications, mobile lock screens, to security cameras. This study aims to experiment in making models that use facial recognition attributes to identify gender by using five types of classification analysis methods. Based on the research that we conducted using several methods and measuring it based on the evaluation parameters, it can be seen that the model that uses Random Forest method has the highest level of accuracy among other methods, with an accuracy of 97.502%. Therefore, the Random Forest is the best model in determining the gender of the sample that we studied.
Design of a Hydrogeological Monitoring System Based on a Computer Network

Previous hydrogeological monitoring methods have low monitoring accuracy. Therefore, a hydrogeological monitoring system based on a computer network is designed. The hardware part of the system mainly includes a control processor module, sensor module, power module, GPRS wireless transmission module, wireless transceiver module, SD storage module and GPS module. In the system software part, the database and data conversion method are designed to realize the monitoring of hydrogeology. The experimental results show that the studied system has little difference from the actual monitoring results in the soil temperature test, soil humidity test and soil liquid level test, which proves that the studied hydrogeological monitoring system improves the accuracy of hydrogeological monitoring.
Studies on Calibration of Blood Coagulation Analyzer

Blood coagulation analyzer is widely used in medical institutions. This article presents a novel design of calibration method for the essential metrological parameters of blood coagulation analyzer, such as "relative indication error of FIB", "repeatability", "concentration carryover rate", "linear correlation coefficient of FIB", and "temperature deviation", by studying the working principle of the device. The procedure as well as the selected reference and quality control materials are proved to be qualified for the calibration requirements of blood coagulation analyzer by performing calibration experiment on 8 typical types of blood coagulation analyzers, which is of great importance for the quality control and establishment of metrological traceability system of the device.
One Shot Verification of Handwritten Signatures using Siamese Networks

In the real world, banks have always required a handwritten signature while creating a new account, signing cheques or any other official document. But they never really verify if the signature provided is authentic or just a forgery trying to fraud the person and the bank. Not only banks documents but other official documents like those in courtrooms, hospitals, and property documents also need to authenticate the handwritten signatures. Our paper introduces a novel and efficient offline signature verification method that provides a quick, secure and reliable real-time technique to verify and authenticate the handwritten signatures on any official documents. The proposed technique looks to automate the entire system to ensure that proper validation is done and forgery is detected at the source. Currently, some systems have been introduced to automate the process of signature verification but they face multiple drawbacks that our proposed solution manages to overcome in an efficient manner. The existing systems often require large datasets, training and execution time and face accuracy issues. The following algorithm uses a one-shot learning approach to validate signatures. One-shot learning combined with the Siamese network ensures that with the help of only one signature (taken maybe at the time of original account creation), we can train the model and spot the differences to display whether the signature is authentic or forged with high accuracy. Our algorithm improves on the current model by enhancing its speed and usability. Our solution minimizes the time and energy required in the verification process and drastically lowers the human error that may occur. In this paper, we also compare our results with other techniques while providing an improved solution.
Sentiment Analysis for Arabic Language Using Word Embedding

Sentiment analysis is the mission of determining polarity positive, negative or neutral and extracting opinions from a text. Sentiment analysis is frequently used for English. This paper deals with sentiment analysis in Arabic language. Word Embedding or word distributing approach is the numerical representation of a text which captures meaning and semantic relationships. This help improving sentiment classification accuracy. In this paper, Word2Vec models were used to extract the polarity, depending on the meaning and the context. Different machine learning classifiers are used to learn the model. Trained and pre-trained word2vec models with different dimensions have been addressed. Furthermore, a deep learning architecture that combines convolutional neural networks (CNN) with recurrent neural network (RNN) is proposed. The result denotes that CNN-RNN Model Architecture with the highest accuracy of 91.11% outperforms CNN and ML Classifiers.
Parallax attention stereo matching network based on the improved group-wise correlation stereo network

Recent stereo matching methods, especially end-to-end deep stereo matching networks, have achieved remarkable performance in the fields of autonomous driving and depth sensing. However, state-of-the-art stereo algorithms, even with the deep neural network framework, still have difficulties at finding correct correspondences in near-range regions and object edge cues. To reinforce the precision of disparity prediction, in the present study, we propose a parallax attention stereo matching algorithm based on the improved group-wise correlation stereo network to learn the disparity content from a stereo correspondence, and it supports end-to-end predictions of both disparity map and edge map. Particular, we advocate for a parallax attention module in three-dimensional (disparity, height and width) level, which structure ensures high-precision estimation by improving feature expression in near-range regions. This is critical for computer vision tasks and can be utilized in several existing models to enhance their performance. Moreover, in order to making full use of the edge information learned by two-dimensional feature extraction network, we propose a novel edge detection branch and multi-featured integration cost volume. It is demonstrated that based on our model, edge detection project is conducive to improve the accuracy of disparity estimation. Our method achieves better results than previous works on both Scene Flow and KITTI datasets.
A hierarchical sharing mechanism based privacy data protection

The frequent occurrence of privacy data breaches and the subsequent issuance of data security laws and regulations have led to increased attention paid to the security of personal data. Ensuring the safety of personal privacy data while releasing data has become a significant challenge. Most of the existing privacy protection models provide the same query results for all data queries without considering the demand for data accuracy by different users, the privacy differences of data with different attributes, and the risk of privacy leakage caused by multiple queries. Based on this, this paper proposes a centralized differential privacy-based data hierarchical query algorithm from the data querier's demand for data precision in the data publishing scenario. Firstly, the corresponding trust level is calculated based on the three attribute values of the trust value of the data querier, the query authority level and the privacy level of the query data, and the privacy protection parameters corresponding to the trust level are used to realize the grading of the data querier. Secondly, the privacy budget parameters are set to avoid the risk of privacy leakage caused by multiple queries made by attackers. At the same time, the McSherry-Laplace mean algorithm is improved to reduce the error rate while ensuring that it satisfies differential privacy. The experimental results show that the above algorithm processes different datasets to provide accurate and differentiated usable data for various data querier users while satisfying differential privacy.
Bio-Inspired Firefly Algorithm A Methodical Survey – Swarm Intelligence Algorithm

In the Swarm Intelligence domain, the firefly algorithm(s) is the most significant algorithm applied in most all optimization areas. FA and variants are easily understood and implemented. FA is capable of solving different domain problems. For solving diverse range of engineering problems requires modified FA or Hybrid FA algorithms, but it is possible additional scope of improvement. In recent times swarm intelligence based intelligent optimization algorithms have been used for Research purposes. FA is one of most important intelligence Swarm algorithm that can be applied for the problems of Global optimization. FA algorithm is capable of achieving best results for complicated issues. In this research study we have discussed and different characteristics of FA and presented brief Review of FA. Along with other metahauristic algorithm we have discussed FA algorithm&#x2019;s different variant like multi objective, and hybrid. The applications of firefly algorithm are bestowed. The aim of the paper is to give future direction for research in FA.
Leveraging Syntactic Structures in Natural Language Processing

Natural language processing (NLP) is a cross-discipline of computer science and linguistics. Significant improvements in deep learning algorithms and relevant models promote the advancement in the accuracy of language processing tasks. Deep learning participates as the linguistic character-extractor and learns through a deep neural network to handle greater data and facilitate achievements corresponding to the inputs. However, syntax-related ambiguities that cause uncertainty of outputs are often ignored. Therefore, this article will contrapose the ambiguous issues with discourse; it will introduce some basic syntactical study methods and information and then review the existing problems in natural language processing. Moreover, this work summarizes basic research and indicate prior knowledge of discourse ambiguities in natural language processing. Through the analyzing the distinctions in language processing and recent learning models, it is expected to present an explicit overview of relevant language subjects and potential research insights.
Research on Noise Reduction of Motor Ripple Based on Improved Wavelet Threshold Function

The use of window motor ripple signals for window anti-pinching can effectively reduce costs compared to the use of Hall sensors. However, noise reduction is required since the ripple signal is small in amplitude and contains interference noise that is not conducive to feature detection. The traditional wavelet threshold de-noising method uses soft and hard threshold functions with constant deviation and additional oscillation problems. In this paper, an improved wavelet threshold function is proposed, and the noise reduction effect of the three threshold functions is examined in MATLAB using simulated signals. By analyzing the two data indexes of signal-to-noise ratio and root mean square error, the improved wavelet threshold function proposed in this paper can effectively improve the noise reduction effect of ripple signals.
A Proposed Approach to Check Project Idea Similarity Using Topic Modelling

Project selection in college years is a daunting task as many of the ideas students come up with have been previously implemented and they need to excogitate a new idea. Our system, Project Idea Buddy, aids this problem by comparing the abstract of a project with the previous projects&#x2019; whitebooks to find how they overlap. This saves the students from manual lookup and dependence on faculties. The system aims to make use of topic modelling and semantic understanding of the text to solve this issue. Existing work available for determining the semantic similarity between text is not concrete to deal with the problem. The system will employ various NLP techniques like keyword extraction, language modelling, etc., combined with similarity measures to present the Topic Similarity and suggest related articles using web-scraping. This paper surveys exhaustively the advances that can be utilized to assemble the framework and expound on their benefits. The goal is to make the laborious process of project selection a digital and not a tedious one.
$500M Merger Boosts NeuroRx VIP Guided Missile vs. COVID-19

GEN EdgeVol. 2, No. 1 Virology$500M Merger Boosts NeuroRx VIP Guided Missile vs. COVID-19Alex PhilippidisAlex PhilippidisSearch for more papers by this authorPublished Online:30 Dec 2020https://doi.org/10.1089/genedge.2.1.68AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 2Issue 1Jan 2020 Information© 2020 by GEN PublishingTo cite this article:Alex Philippidis.$500M Merger Boosts NeuroRx VIP Guided Missile vs. COVID-19.GEN Edge.Jan 2020.409-413.http://doi.org/10.1089/genedge.2.1.68Published in Volume: 2 Issue 1: December 30, 2020PDF download
A Study of Integration Path and Application of Active Identification Carriers

In the face of comprehensive connectivity needs brought about by large-scale digital transformation, a clear and accurate consensus on the identification of people, machines and objects is the key to the development of Industrial Internet. The identifier resolution system embodies the consensus and foundation of industrial Internet. Active identification carrier represents the new growth point and large-scale development trend of industrial Internet identifiers in the future. This paper examines the mainstream active identification carriers and the paths of integrating industrial Internet identifier resolution capabilities, enabling industrial terminals to actively connect and share data. This paper also explores the typical application models of active identification carriers that use active identification to address the pain points of industrial enterprises and issues of industry regulation.
End-to-End Acoustic Model Using 1D CNN and BLSTM Networks with Focal CTC Loss

With the advancement of modern technologies, the human-machine interaction conveyed towards a more natural means of communication. The frequent example used in the devices is speech, where an Automatic Speech Recognition (ASR) system is prominent to convert the uttered word to text. In this paper, we focus on the acoustic model which translate the relationship between the acoustic features and the phonemes to a probability distribution. To directly output the spoken phonemes from the input features, we introduce an End-to-End acoustic model. The proposed model is built with the combination of the one-dimensional Convolutional Neural Networks (1D CNNs) and Bidirectional Long Short-Term Memory (BLSTM) while using Focal Connectionist Temporal Classification (CTC) loss in the training phase. Experimental results show that the proposed acoustic model reaches up to 20.3% in terms of Phone Error Rate (PER).
AI and The Cardiologist-When Mind, Heart and Machine Unite

Artificial Intelligence (AI) and Deep Learning have made much headway in the consumer and advertising sector, not only affecting how and what people purchase these days, but also affecting behaviour and cultural attitudes. It is poised to influence nearly every aspect of our being, and the field of cardiology is not an exception. This paper aims to brief the clinician on the advances in AI and machine learning in the field of Cardiology, it’s applications, while also recognising the potential for future development in these two mammoth fields. With the advent of big data, new opportunities are emerging to build AI tools, with better accuracy, that will directly aid not only the clinician but also allow nations to provide better healthcare to its citizens.
A Practical Experience Applying Security Audit Techniques in An Industrial Healthcare System

Healthcare institutions are an ever-innovative field, where modernization is advancing by leaps and bounds. This modernization, called “digitization”, brings with it some concerns that need to be taken into account. The aim of this work is to present a way to combine the advantages of cybersecurity and artificial intelligence to protect what is of most concern: electronic medical records and the privacy of patient data. Health-related data in healthcare systems are subject to strict regulations, such as the EU’s General Data Protection Regulation (GDPR), non-compliance with which imposes huge penalties and fines. Healthcare cybersecurity plays an important role in protecting this sensitive data, which is highly valuable to criminals. The methodology used to perform a security assessment process has been orchestrated with frameworks that make the audit process as comprehensive and organized as possible. The results of this study include a security audit on an industrial scenario currently in production. An exploitation and vulnerability analysis has been performed, and more that 450 vulnerabilities has been found. This chapter outlines a systematic approach using artificial intelligence to enable the system security team to facilitate the process of conducting a security audit taking into account the sensitivity of their systems.
Multi-Agent Feedback Enabled Neural Networks for Intelligent Communications

In the intelligent communication field, deep learning (DL) has attracted much attention due to its strong fitting ability and data-driven learning capability. Compared with the typical DL feedforward network structures, an enhancement structure with direct data feedback have been studied and proved to have better performance than the feedfoward networks. However, due to the above simple feedback methods lack sufficient analysis and learning ability on the feedback data, it is inadequate to deal with more complicated nonlinear systems and therefore the performance is limited for further improvement. In this paper, a novel multi-agent feedback enabled neural network (MAFENN) framework is proposed, consisting of three fully cooperative intelligent agents, which make the framework have stronger feedback learning capabilities and more intelligence on feature abstraction, denoising or generation, etc. Furthermore, the MAFENN frame work is theoretically formulated into a three-player Feedback Stackelberg game, and the game is proved to converge to the Feedback Stackelberg equilibrium. The design of MAFENN framework and algorithm are dedicated to enhance the learning capability of the feedfoward DL networks or their variations with the simple data feedback. To verify the MAFENN framework’s feasibility in wireless communications, a multi-agent MAFENN based equalizer (MAFENN-E) is developed for wireless fading channels with inter-symbol interference (ISI). Experimental results show that when the quadrature phase-shift keying (QPSK) modulation scheme is adopted, the SER performance of our proposed method outperforms that of the traditional equalizers by about 2 dB in linear channels. When in nonlinear channels, the SER performance of our proposed method outperforms that of either traditional or DL based equalizers more significantly, which shows the effectiveness and robustness of our proposal in the complex channel environment.
A Symbolic-domain Music Generation Method Based on Leak-GAN

To efficiently train deep generative neural works, generative adversarial networks have been applied in plenty of fields including music generation. We introduce a novel way of generating symbolic-domain music via adversarial training, which allows the discriminator to leak its information to the generator for better guidance. To prove good quality of the generated pieces, we conduct comparative test on LSTM model and evaluate the randomly-sampled music on five metrics in the light of statistics and music theory. The results shows that our model is more capable of generating coherent, natural and realistic music works.