Reviewerly/exHarmony huggingface dataset:
Application of Improved YOLOV4 in Intelligent Driving Scenarios

With the development of unmanned technology, the technical innovation of invehicle vision detection system is also getting faster and faster, while the improvement of algorithm accuracy often brings an increase in the number of parameters and poor real-time performance. In his paper, the optimization of the algorithm structure of YoLoV4 target detection is achieved by using MobileNet-v3 instead of the CspDarkNet53 master Network, which has the inverse residual structure of linear bottleneck, while the lightweight attention mechanism is added to the feature extraction process, and the learning degree of feature channels is enhanced; due to the long computation time of sigmoid, it also uses ReLU6(x+3)/6 is used to approximate the original activation function due to the long computation time of sigmoid; the system parameters are reduced by constructing a depth-separable convolution instead of the normal convolution in PaNet. Meanwhile, this paper improves the original upsampling method by using dual cubic interpolation, which makes the image more smooth, less image loss and more accurate feature extraction during he upsampling method. The map% is improved from 79.1% to 81.2% on the voc dataset, reaching 58.14 FPS.
Patterns of basic knowledge

Epistemetrics, a discipline concerning measuring knowledge qualitative and quantitatively, is an under developed area of research. A primary reason for the current status is a perceived lack of a formal structure of knowledge. In particular, a number of fundamental questions about knowledge remain to be answered: What is the basic unit of knowledge? How many types of knowledge are there? How do they relate to each other? And what are the hierarchies of knowledge? To address these questions, we proposed a knowledge categorization system, the EApc framework. The present work collects further evidence, from theories and practices of several diverse research disciplines, to argue for, to refine, and to expand the EApc framework. This framework can provide new perspectives in understanding scientific processes and in assessing values and impacts of research proposals and products. It may also shed light on the mechanisms of human cognition and information processing.
Convergence analysis and Design principle for Federated learning in Wireless network

Recently, federated learning (FL) has been treated as an important and promising learning scheme in IoT, enabling devices to jointly learn a model without sharing their data sets. Different from centralized training on some collected data sets, FL training suffers a lot of constraints from limited resources in the network. Therein, the bandwidth and package loss restrict interactions in training. Meanwhile, the highly distributed data sets and limited computation could also affect its convergence. To figure out the specific impact, we analyze the convergence rate of FL training considering both communication and training. Further taking in training costs in terms of time and power, the closed-form optimal settings for communication networks are proposed with principles to assist the parameter selection. The results build a bridge between AI and communication, giving us an intuitive knowledge of how the background system could influence the distributed training process.
A retrieval model family based on the probability ranking principle for ad hoc retrieval

Abstract Many successful retrieval models are derived based on or conform to the probability ranking principle (PRP). We present a new derivation of a document ranking function given by the probability of relevance of a document, conforming to the PRP. Our formulation yields a family of retrieval models, called probabilistic binary relevance (PBR) models, with various instantiations obtained by different probability estimations. By extensive experiments on a range of TREC collections, improvement of the PBR models over some established baselines with statistical significance is observed, especially in the large Clueweb09 Cat‐B collection.
The Musical Eseniniana by Anatoly Alexandrov

The article presents the information about the contemporary of Esenin — the Russian and Soviet composer A. N. Alexandrov (1888–1982) and his musical Eseniniana. The information was revealed during the author’s search work.
P-Values for one-dimensional multimodal distributions

Simulation methods were developed to find p-values for univariate mixture distributions. Looking for the maximum elbow of the unexplained variance in a kernel density estimation was determined to be insufficient to evidence that data is multimodal. It is shown by theoretical and simulated results that random distributions have similar elbowing behaviour. It is only elbowing, or variance convergence, above the level in random distributions that indicates a mode has been found. However, kernel h-distance and variance by step were together found to be better predictors of confidence intervals of modal data. Confidence intervals of p = 0.1 and p = 0.01 are modelled in equations and can be applied to data with any standard deviation and sample sizes of 12 and over. The methods were tested on data generated from bimodal up to five modes.
Adaptive preprocessing of character recognition image based on neural network

Abstract Words in natural scenes contain abundant information. Automatic acquisition of text information in images can help people understand images more effectively and further process images such as storage, compression and retrieval. Pattern recognition technology is to use machines to simulate people’s various recognition abilities. At present, it mainly simulates people’s visual and auditory abilities. Based on BP neural network in deep learning, a text positioning BP neural network is proposed, which automatically extracts the text features in the image and avoids the defect of using manual design features This paper studies the preprocessing algorithm of Chinese character recognition in natural scenes, including binarization of text regions, extraction of character colors, clustering analysis of colors, segmentation of characters, etc., and designs the rules for merging text regions after positioning. Experimental results show that the algorithm can solve the problem of insufficient recognition ability for text images and long-sequence text images with complex background noise between characters when the background and text gray level are similar.
The Design and Implementation of New Chemistry Classroom Instructional Model under the Condition of “Internet Plus”

Under the background of &#x201C;Internet Plus Education&#x201D;, this study designed a new chemistry classroom instructional model based on several theories. Then, an one-semester experiment was carried out on the &#x201C;Internet plus&#x201D; typical school to prove the practical effect of the model, including experimental class (N&#x003D;48) and control class (N&#x003D;48). Results showed that, the new model significantly increased the students&#x0027; problem-solving ability, while there was no significant difference between two classes&#x0027; academic performances. To this end, we made a summary and recommendations about the model.
Camera based real time distress monitoring system for geriatric people

Technological advancement in wireless communications, wireless sensors and emergence of Internet of Things (IOT) has helped in improving the quality of life. Enabling technologies like Bluetooth low energy (BLE) is essential to lodge intelligence in the smart device. By combining BLE, IOT, wireless sensor networks and wireless communication simple and efficient smart devices can be designed and developed. This paper focuses on developing a distress monitoring system for geriatric people based on the stress levels they encounter or on their body posture under unsafe conditions. The unsafe conditions can either be an attack on them or some health-related issues. The system consists of a wrist band with sensors, fall detector, bracelet camera and a smart phone to provide user interface.
SoundexGR: An algorithm for phonetic matching for the Greek language

Abstract Text usually suffers from typos which can negatively affect various Information Retrieval and Natural Language Processing tasks. Although there is a wide variety of choices for tackling this issue in the English language, this is not the case for other languages. For the Greek language, most of the existing phonetic algorithms provide rather insufficient support. For this reason, in this paper, we introduce an algorithm for phonetic matching designed for the Greek language: we start from the original Soundex and we redesign and extend it for accommodating the Greek language’s phonetic rules, ending up to a family of algorithms, that we call ${\tt Soundex}_{GR}$ . Then, we report various experimental results showcasing how the algorithm behaves in different scenarios, and we provide comparative results for various parameters of the algorithm for revealing the trade-off between precision and recall in datasets with different kinds of errors. We also provide comparative results with matching using stemming, full phonemic transcription, and edit distance, that demonstrate that ${\tt Soundex}_{GR}$ performs better (indicatively, it achieves F-Score over 95% in collections of similar-sounded words). The simplicity, efficiency, and effectiveness of the proposed algorithm make it applicable and adaptable to a wide range of tasks.
Scenario-Based Collision Avoidance Control with Deep Q-Networks for Industrial Robot Manipulators

This work proposes a scenario-based Deep Reinforcement Learning (DRL) approach enabling robot manipulators to efficiently execute industrial tasks while avoiding the collision with obstacles. The proposal exploits a DRL-based decision maker trained ad hoc so as to be able to automatically select at any time instant the most appropriate control methodology, in a given set, to execute the required industrial task. The capability of performing the selection automatically is "learnt" by training the system relying on a suitably designed reward function. It takes into account the robot relative distances from the target and the obstacles, the computational cost associated with each methodology, as well as the percentage of task completion obtained by applying the selected methodology. The learning skill is enforced by a properly sized Deep Q-Network (DQN). The proposal is assessed relying on realistic robotic manipulator scenarios reproduced in the CoppeliaSim environment.
A Prediction Scheme For Movie Preference Rating Based on DeepFM Model

Most recommendation systems work based on a useritem matrix with users on the rows and items on the columns. Each cell of the user-item matrix contains a rating value that indicates the user’s preference for an item. However, in reality, there can be many missing rating values in the user-item matrix. So, the rating matrix is not completely filled and becomes very sparse. Such a problem is called a “data sparsity problem”. To alleviate the data sparsity problem, predicting the ratings of unrated items with high accuracy is very important. The previously proposed prediction model is DeepFM, which reflects both low and high-order interaction of input features. But DeepFM is a general prediction model that does not depend on a specific problem domain. In this paper, we customized the DeepFM model to reflect better the low and high-order features interaction of the movie recommendation dataset than the original DeepFM. The evaluation results show that our proposed scheme predicts the rating with higher accuracy than the original DeepFM and other existing methods.
Anonymous Voting System Against Byzantine Attacks

The most significant problem within current voting systems is that all the voting content should be explicitly gathered to a central database, which can be easily be tampered with and retraced. Moreover, the distributed nature of Internet is prone to Sybil and DoS (Denial of Service) attacks implying that malicious nodes may brush tickets or refrain valid users from voting. Due to these reasons, we present a decentralized anonymous voting system against byzantine attacks. The main idea of our system is inspired by a consensus algorithm called Paxos which is proven to be effective for blockchain consistency. In terms of the optimization of Paxos algorithm combined with a semantic secure cryptosystem, we solved the problems such as large data throughput, extendibility over the massive network, overheads taken by nodes, and the tolerance of Byzantine fault On these bases, we not only actualized our scheme as a whole but also testified its capability of data integrity and anonymity.
Regarding approaches to simulating random‐effects

Research Synthesis MethodsVolume 13, Issue 3 p. 294-294 LETTER TO THE EDITOR Regarding approaches to simulating random-effects Paul Bramley, Corresponding Author Paul Bramley paul.e.bramley@gmail.com orcid.org/0000-0001-6209-6640 Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK University of Sheffield, Sheffield, UK Correspondence Paul Bramley, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK. Email: paul.e.bramley@gmail.comSearch for more papers by this authorJosé Antonio López-López, José Antonio López-López Department of Basic Psychology & Methodology, University of Murcia, Murcia, SpainSearch for more papers by this author Paul Bramley, Corresponding Author Paul Bramley paul.e.bramley@gmail.com orcid.org/0000-0001-6209-6640 Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK University of Sheffield, Sheffield, UK Correspondence Paul Bramley, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, UK. Email: paul.e.bramley@gmail.comSearch for more papers by this authorJosé Antonio López-López, José Antonio López-López Department of Basic Psychology & Methodology, University of Murcia, Murcia, SpainSearch for more papers by this author First published: 02 February 2022 https://doi.org/10.1002/jrsm.1549Read 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 onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume13, Issue3May 2022Pages 294-294 RelatedInformation
Evaluation Method of Immersive Drama Stage Presentation Effect Based on Data Mining

When the existing evaluation methods evaluate the presentation effect of immersion drama stage, due to the incomplete data in the evaluation, there is a large error between the effect evaluation and the actual evaluation results. Therefore, an evaluation method of immersive drama stage presentation effect based on data mining is designed. Clean the data with incomplete, inconsistent and noisy presentation effect on the immersive drama stage, and merge the cleaning results into a unified data set. Regression technology is used to fill in the missing data through data conversion, reduction and other methods. The multi-dimensional spatial covariance matrix is used to compress the data to determine the evaluation factor set. When the stage presentation mode is unified with the mining goal, the evaluation factor set needs to be re selected to determine the effect evaluation level. From the practical content of effect evaluation, it can be seen that the error between this method and the actual evaluation is small, and it has accurate evaluation effect.
Friday: Smart Assistant Helmet

This paper discusses a project that aims at making the driving experience of two wheeler vehicles more seamless. Friday - Smart Assistant Helmet makes the driving experience seamless by including features such as making voice calls, sending text messages, alerting when receiving call and many more. It also has alert mechanism for the vehicles overtaking from behind. All this features are having voice interface, which makes sure that the driving is uninterrupted as well as ensures the safety of the driver and the vehicle. The name Friday is a reference to the assistant's name of the character, Tony Stark played by the actor Robert Downey Junior in the famous Marvel's Avengers series.
Are You Dictating to Me? Detecting Embedded Dictations in Doctor-Patient Conversations

Medical scribes chart doctor-patient conversations in real time or by listening to an audio recording afterwards. Doctors sometimes dictate during a patient encounter, a highly informative part for a scribe. We introduced a light-weight annotation schema and ana-lyzed recordings of 105 randomly selected doctor-patient encounters from 21 physicians to quantify the frequency and automatically de-tect dictated regions. Dictation behavior of individual doctors was consistent but varied among them. A linguistic analysis is provided to describe differences of doctors speech when talking to a patient or dictating. A description of the data is given, highlighting challenges of segmenting audio into conversation and dictation regions. We in-vestigate different features and methods to segment conversations including keyword spotting, acoustic features and class-conditioned language models. Results are anchored to a majority class base-line. Using only acoustic features allows to predict dictated speech without the need of a speech recognition system performing com-parable to a rule-based approach using lexical features derived from a speech recognition system. Performance is assessed using leave-one-physician-out cross validation and an analysis using a random forest classifier indicates that language model derived features are most useful, and that a combination of acoustic and lexical features performed best.
Sparse Generalized Dirichlet Prior Based Bayesian Multinomial Estimation

The popularity of count data is accompanied by its challenging nature such as high-dimensionality and sparsity. Multinomial distribution and extensions are widely applied for modeling data with multivariate count sequences. This paper considers the strength of estimating multinomial parameters based on Bayesian methodology taken into account the sparsity property. Respect of this, we propose a probabilistic approach using a hierarchical generalized Dirichlet prior for sparse multinomial distributions. Our technique builds up on Bayesian knowledge over large discrete domains represented by subsets of feasible outcomes: observed and unobserved. This model allows us to predict the new outcomes based on the preceding data generated from a multinomial distribution. Evaluation over large benchmarks associated with emotion prediction through two different experiments results in competitive performance. The first experiment reveals predicting emotions in poetry context from English and German dictionaries. The second experiment concerns analyzing flow of emotions related to natural disasters.
Temporal ASP: From Logical Foundations to Practical Use with telingo

This document contains some lecture notes for a seminar on Temporal Equilibrium Logic (TEL) and its application to Answer Set Programming (ASP) inside the 17th Reasoning Web Summer School (RW 2021). TEL is a temporal extension of ASP that introduces temporal modal operators as those from Linear-Time Temporal Logic. We present the basic definitions and intuitions for Equilibrium Logic and then extend these notions to the temporal case. We also introduce several examples using the temporal ASP tool telingo.
Simultaneous Imputation and Prediction with High-dimensional Data (SIP-HD): A Deep Learning Model for Disease Diagnosis

Accurate diagnosis directly impacts service quality and resource allocation in healthcare operations. Advanced medical tests can improve doctors’ diagnostic accuracy but are invasive, costly, and sometimes infeasible to conduct in certain geographic locations and health conditions. We find that machine learning models can better utilize advanced medical test results for accurate disease prediction compared with doctors. To achieve decent diagnostic performance when advanced medical test results are missing, our research proposes a deep learning diagnostic model, SIP-HD, that simultaneously performs imputation and prediction with high-dimensional data. Our model performs better in accuracy than traditional two-step machine learning models that first impute missing data using mean or K-nearest neighbors (KNN) imputation and then apply machine learning models such as Logistic regression (LR) and light gradient boosting machine (LGB). Moreover, our model performs better than doctors’ preliminary diagnoses that utilize limited advanced medical test results. Compared to the usage of advanced medical tests, our model is a low-cost and non-invasive alternative for delivering a high-quality diagnosis for patients, especially for those living in rural areas. Moreover, our model is valuable to assist doctors’ diagnostic practices for patients via telehealth.
Development of Museum Informatics in Russia and Abroad

In the era of the global Internet, the global informatization of services and the digitalization of the economy, the tourism and hospitality industry is rapidly changing, so the main purpose of this contribution is to study the development of museum informatics in Russia and abroad. The research objectives are to study the existing experience of using modern information and communication technologies in the museum space, as well as to analyze the positive and negative aspects. Based on the study of the practical experience of such organizations as the Museum Computer Network Association MCN (Museum Computer Network), the Information Network for the National Heritage of Canada (Canadian Heritage Information Network), the Main Computing Center of the Ministry of Culture of the Russian Federation, it is concluded that it is necessary to use information and communication technologies in the museum space to improve the quality of service for services consumers. The problems of the introduction of information and communication technologies in the museum space are identified. Their elimination will help make the world’s cultural heritage accessible to the general population.
Disease Prediction Based on Individual’s Medical History Using CNN

Analyzing health records to avert future complications and provide the right treatment plays an important role in medical diagnostics. This paper introduces a method to use real-life ICD-coded Electronic Medical Records (EMR), clinical data collected between 2018-2019 in Africa, to create a prediction model. The prediction model uses recent advances in machine learning, specifically Convolutional Neural Networks (CNN), to provide an alternative and a powerful way for disease risk prediction. An algorithm is first designed to process the real-life EMR data into multi-step time-series forecasting data. A CNN model is then developed, to predict the individual’s future disease class (ICD-10 CM chapters) risk based on their demographic and medical history. The experimental results show that the model predicts future disease risk, for an individual, with an accuracy of 80.73%. We conclude that such a model can prove immensely useful in cost savings for individuals and hospitals by performing preemptive corrective action, provide additional guidelines for hospitals’ capacity planning, when the model is applied for demography, and, most importantly, peace of mind on personal health.