Reviewerly/exHarmony huggingface dataset:
Modeling Neonatal EEG Using Multi-Output Gaussian Processes

Neonatal seizures are sudden events in brain activity with detrimental effects in neurological functions usually related to epileptic fits.Though neonatal seizures can be identified from electroencephalography (EEG), this is a challenging endeavour since expert visual inspection of EEG recordings is time consuming and prone to errors due the data's nonstationarity and low signal-to-noise ratio.Towards the greater aim of automatic clinical decision making and monitoring, we propose a multi-output Gaussian process (MOGP) framework for neonatal EEG modelling.In particular, our work builds on the multi-output spectral mixture (MOSM) covariance kernel and shows that MOSM outperforms other commonly-used covariance functions in the literature when it comes to data imputation and hyperparameter-based seizure detection.To the best of our knowledge, our work is the first attempt at modelling and classifying neonatal EEG using MOGPs.Our main contributions are: i) the development of an MOGP-based framework for neonatal EEG analysis; ii) the experimental validation of the MOSM covariance kernel on real-world neonatal EEG for data imputation; and iii) the design of features for EEG based on MOSM hyperparameters and their validation for seizure detection (classification) in a patient specific approach.
The Influence of Digital Assistants on Search Engine Strategies: Recommendations for Voice Search Optimization

Digital assistants are conquering the world and providing new opportunities and challenges for digital marketing and e-commerce. Apparently, one-third of Internet users in Switzerland already use (digital) voice assistants. One of the advantages of voice search is time as a search query is four times faster spoken than typed. The goal of this study is to determine factors that should be used to gain visibility in search engines and ultimately optimize voice search. To be able to answer the research questions, six Swiss experts were selected and interviewed. Based on the interviews, the five most important factors for VSO (Voice Search Optimization) were determined and tested with 30 everyday questions on Google Home and Siri. The interviews revealed that the search behavior would change in the future, and therefore, websites should consider these recommendations and focus on natural language for their content. The practical test showed that not all of them were that prevalent in the provided search results but might become more important over time.
IPFS-Based Image Sharing Using Perceptual Hash Code with Digital Right Management

In the current scenario, a large amount of information is exchanged over the Internet. An unauthorized user access may cause the information to be modified, disseminated, misused, detected, and used. Digital rights management (DRM) is a method of preserving copyrights in digital media. It often requires more elements than simply restricting or prohibiting copies of the content. This strategy includes the deployment of technologies that limit the copying and use of copyrighted works as well as proprietary software. The proposed approach has two modules. They are hash code generation of an image and Store and retrieve the data from IPFS. First, select the watermarked image. The different types of perceptual hashes are average hash, perceptual hash, difference hash, and wavelet hashing. The perceptual hash codes are generated for the selected watermarked image. Then, the watermarked image and created copyright text files are shared through the IPFS system. The goal of this proposed system is to compare the perceptual hashes by using the hamming distance, size of the image, and calculation time. Based on the evaluation, difference hash algorithm provides a better result. So, it is used to check whether the send and retrieve files of IPFS are similar or not. In the IPFS file system, hash codes are used to find the location of a file effectively and securely. This paper proposes, how IPFS and perceptual hashes are preventing unauthorized user access to the system while sharing the images over the Internet. Genuine image authors’ copyrights are protected by using perceptually identical images on a decentralized blockchain IPFS store.
Correlations in twisted double-layer graphene with virtual photons in a microcavity

We analyze the entanglement generation of a system composed of two decoupled rotated graphene layers inside a planar microcavity. By considering the electromagnetic field of the cavity in the vacuum state and using time-dependent perturbation theory it is possible to obtain the range of geometric parameters at which the quantum states of electrons in different layers are entangled. By employing the negativity measure, correlations between layers are obtained for time scales smaller than the light-crossing time of the layers. It is shown that the negativity measure is modulated by the rotation angle between layers, allowing manipulation ofXstates. Finally, an experimental protocol is analyzed in order to detect non-causal effects between layers, by allowing back-voltage switching functions in the two layers with supports that do not overlap in time. By turning off the second-back voltage at a time smaller than the light-crossing time, it is possible to obtain correlations between layers through the independent interaction with virtual photons. The exchange of virtual photons implies that the propagator can be nonzero outside the light cone and this non-causal propagation can create entangled quantum states.
Construction of Intelligent Experimental Teaching Platform Based on Intelligent Control Algorithm

With the rapid development of the information age, electronic information technology plays an increasingly important role in the historical torrent of social progress. This article mainly studies the construction of an intelligent experimental teaching platform based on intelligent control algorithms. For the test of multi-computer serial communication, a computer PC and two student slave systems are used for testing. The host computer PC and the two slaves are connected through the hardware design of this scheme, that is, the hardware connection of RS485 multi-machine communication. Then try to communicate with a certain slave through the host PC to the network, and then verify the connectivity of the multi-machine serial network. In order to test the software on the host PC, we simulate users to use the software and test its reflection under common errors. After opening the software first, set the port number that does not exist on this machine deliberately, and then click the “connection test” button. After successfully connecting to the corresponding slave, click the “Enable automatic data collection” button, you can see that the value of the current slave is automatically refreshed every 1 second, and the frequency and power sent by the slave are well controlled. When the system is logged in concurrently within 500 users, it can log in normally, and the average login response time is less than 2 seconds, which meets the actual performance requirements. The results show that the intelligent control algorithm can improve the accuracy of the intelligent experimental teaching platform.
Table Detection and Extraction using OpenCV and Novel Optimization Methods

Information Retrieval from scanned documents and images is a challenging task as there can be varying templates or document structures available. The amount of data is thus increasing exponentially leading to a rise in scanned and digital documents like PDFs, resumes, invoices, mark-sheets, medical reports, Demat receipts, etc. One of the major challenges faced is when trying to extract information from the tabular structure as it is can be uniform or non uniform in nature, meaning unequal number of rows and columns. There have been many approaches suggesting to extract tables from a digitized pdf document but none of the existing approaches have a unified approach to extract all tables from any kind of a scanned document. In this paper, the focus is on extracting information from tables with various layouts, maintaining their structure, and processing them into textual data from images. Our algorithm achieved an average accuracy of 95% tested over more than 100 images outperforming many state of the art models.
Dilated CNN for Crowd Count

In the current scenario of the COVID-19 pandemic estimating the count of number of people present in public places at a particular time has become a significant task. Crowd count is attracting a lot of researchers from the computer vision and deep learning field. It has been found that to achieve this objective computer vision techniques such as deep learning, machine learning, etc. outperform traditional ways of estimating crowd count that uses handcrafted features such as Histogram of gradients, Haar, Scale Invariant Feature Transform and gives better results with higher accuracy. The paper studies the effect of dilation on convolution layers in estimating the crowd count. We have also done a comparative analysis of the developed model with different dilation rates on the ShanghaiTech dataset (part A and part B). The model is trained with images containing occluded and restricted visibility of heads. The model outputs the result with substantial accuracy in estimating the headcount in images of the dense crowd in a sensibly less amount of time.
Administrative Management Grid Service Platform Based on Information Processing Technology Study

short-paper Share on Administrative Management Grid Service Platform Based on Information Processing Technology Study Authors: Zijia Yang Harbin University of Commerce, China Harbin University of Commerce, ChinaView Profile , Shunyu Wang Harbin University of Commerce, China Harbin University of Commerce, ChinaView Profile Authors Info & Claims ICASIT 2021: 2021 International Conference on Aviation Safety and Information TechnologyDecember 2021 Pages 31–34https://doi.org/10.1145/3510858.3510872Published:04 March 2022Publication History 0citation11DownloadsMetricsTotal Citations0Total Downloads11Last 12 Months11Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Exploratory Data Analysis for Credit Card Fraud Detection

In this modern era where everything is online, making a transaction is not safe anymore, so it is duty of every bank to check the transaction details and analyse them whether it’s a fraud transaction or not which is not an easy task and very time consuming in the era where time is money here comes Data Science and Machine Learning algorithm to rescue us from this vital problem. This project includes to give a complete view how to solve this problem using very sophisticated Machine Learning algorithm with Credit Card Fraud Detection, which includes modelling with the past records of Credit Card Transactions with the ones who have the Fraud Transactions. Here we want to classify all the correct Fraud Transactions to avoid any misclassifications. In this project, first we have focused in data pre-processing and data transformation, then we have performed various Classification algorithm to get a model having maximum accuracy score
Tipster: A Topic-Guided Language Model for Topic-Aware Text Segmentation

The accurate segmentation and structural topics of plain documents not only meet people’s reading habit, but also facilitate various downstream tasks. Recently, some works have consistently given positive hints that text segmentation and segment topic labeling could be regarded as a mutual task, and cooperating with word distributions has the potential to model latent topics in a certain document better. To this end, we present a novel model namely Tipster to solve text segmentation and segment topic labeling collaboratively. We first utilize a neural topic model to infer latent topic distributions of sentences considering word distributions. Then, our model divides the document into topically coherent segments based on the topic-guided contextual sentence representations of the pre-trained language model and assign relevant topic labels to each segment. Finally, we conduct extensive experiments which demonstrate that Tipster achieves the state-of-the-art performance in both text segmentation and segment topic labeling tasks.
A Reliable Digital Watermarking Algorithm Based On DCT-SVD Algorithm

Internet image works often use DCT and SVD based image digital watermarking techniques to indicate the attribution right of the work in the process of dissemination.Although this technique has good concealment, the false positive leads to the ambiguity of the attribution right of the work. This paper addresses this problem by proposing a method that uses the principal component of the watermarked image instead of the original singular value, and the experimental results show that the algorithm has strong robustness and security while also solving the problem of false positives leading to blurred attribution rights of works.
Bringing Coordination Languages Back to the Future Using Blockchain Smart Contracts

This paper presents a blockchain extension of the run-time Sensing as a Service SOA (3SOA) approach presented in [5]. 3SOA defines a practical approach for implementing service-oriented Internet of Things (IoT) using coordination languages to integrate and program individual IoT objects to compose into full IoT system. We believe that the modularity, reuse, interoperability and portability of this model has much to offer, but that there exist some challenges in overcoming the performance issues inherent in the approach, and extending the range of applications to which it is suited. We are particularly interested in applying the coordination languages to decentralized systems. To this end, blockchain smart contracts are proposed to offer a decentralized trustable method to automatically verify compliance with pre-defined conditions before executing a transaction involving multiple parties. To validate our proposal, we demonstrate a healthcare functional prototypes as a proof of concept.
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems

Deep learning recommendation systems at scale have provided remarkable gains through increasing model capacity (i.e. wider and deeper neural networks), but it comes at significant training cost and infrastructure cost. Model pruning is an effective technique to reduce computation overhead for deep neural networks by removing redundant parameters. However, modern recommendation systems are still thirsty for model capacity due to the demand for handling big data. Thus, pruning a recommendation model at scale results in a smaller model capacity and consequently lower accuracy. To reduce computation cost without sacrificing model capacity, we propose a dynamic training scheme, namely alternate model growth and pruning, to alternatively construct and prune weights in the course of training. Our method leverages structured sparsification to reduce computational cost without hurting the model capacity at the end of offline training so that a full-size model is available in the recurring training stage to learn new data in real time. To the best of our knowledge, this is the first work to provide in-depth experiments and discussion of applying structural dynamics to recommendation systems at scale to reduce training cost. The proposed method is validated with an open-source deep learning recommendation model (DLRM) and state-of-the-art industrial-scale production models.
Detecting Twitter Hate Speech Using Sentiment Analysis

In current era due to the rapid increase in the consumption of the Internet by people of different cultural backgrounds, malicious content on the Internet has become an endless problem. In the automatic detection of malicious text content, distinguishing between hate speech and profanity will be a major issue. The police investigating strategies for positive or negative emotions in the text. Companies use it extensively to perceive emotions in social data, measure overall reputation, and understand customers. For example, if you use sentiment analysis to mechanically analyze more than 4000 reviews related to your products, you can more easily determine whether customers are satisfied with your customer service and pricing plans. The Twitter information set is usually used for comparative analysis of the model. Information science provides useful data based on large amounts of complex information or big data. Information science/engineering combines different fields of statistics and computer science to interpret information for decision-making purposes. The purpose of this article is to provide the method for detecting twitter the speech using Support vector machine and Machine Learning algorithm.
Evaluation of Augmented Reality and Consumer Perceptions in Traditional Dishes

The concept of augmented reality (AR) has made significant advances in the last two decades. This technology that is used in every field can also be used in preserving and archiving traditional dishes in a digital environment. Therefore, the purpose of this research is to develop an AR supported mobile application and to try to reveal how the application is perceived by making the consumers who eat at traditional restaurants experience the application. In the first phase, a mobile application was developed for this purpose. In the second phase, the qualitative research method was used and the mobile application was tested on 30 customers who eat in traditional restaurants and their reviews about the mobile application are considered. At the end of the study, most of the participants expressed that they liked the application and find it interesting and surprising to see 3d images inside the application.
The Roles of Information Presentation on User Performance in Mobile Augmented Reality Application

Mobile Augmented Reality (AR) has an interesting and interactive method of presenting information because it is the combination of real and virtual environments. Thus, a large amount of information is required and some of the necessary information is not fit to the screen due to the small screen display. Hence, lead to user dissatisfaction and not getting the right and accurate information. This study herein aims to examine the roles and optimal combination of information presentation to improve user performance in mobile AR applications. A possible combination of information modalities is created and a study was conducted by comparing two different conditions:1) without sound and haptic feedback; 2) with sound and haptic feedback. Results showed the optimal combination of information modalities is with sound feedback and haptic feedback based on statistical analysis and user preferences. Moreover, the roles of information presentation indeed able to support visual information modality in the mobile AR application.
Monocular vision-based time-to-collision estimation for small drones by domain adaptation of simulated images

Recently, there is an increasing demand for small drones owing to their small size and agility in complex indoor environments. Accordingly, safety issues for navigating small drones become of significant importance. For drones to be able to navigate safely through complex environments, it would be useful to estimate accurate time-to-collision (TTC) to obstacles. To this end, in this paper, we propose a deep learning-based TTC estimation algorithm. To train generalizable neural networks for TTC estimation, large datasets including collision cases are needed. However, in real-world environments, it is impractical and infeasible to collide drones with obstacles to collect a significant amount of data. Simulation environments could facilitate the data acquisition procedure, but the data from simulated environments could be quite different from those of real environments, which is commonly termed as reality-gap. In this study, to reduce this reality-gap, sim-to-real methods based on a variant of the generative adversarial network are used to convert simulated images into real world-like synthetic images. Besides, to consider the uncertainties that come from using the synthetic dataset, the aleatoric loss function and Monte Carlo dropout method are employed. Furthermore, we improve the performance of the deep learning-based TTC estimation algorithm by replacing conventional convolutional neural networks (CNNs) with convolutional long short-term memory (ConvLSTM) layers which are known to be better at handling time-series data than CNNs. To validate the performance of the proposed framework, real flight experiments have been carried out in various indoor environments. Our proposed framework decreases the average TTC estimation error by 0.21 s compared with the baseline approach with CNNs.
Raspberry Pi Based Crowd and Facemask Detection with Email and Message Alert

The wide spread Covid infection 2019 (COVID-19) has conducted global disasters using its unsafe propagate to at excess of a hundred and eighty states, also approximately 136,772,601 confirmed cases along side 2,951,864 passings globally according to April 12, 2021. Really, the immunization is reachable, standard society aren’t ready to select the anti-body due to lack of advice in this, as well as fear. Ergo, it's basic to maintain up helpful getting rid of also to wear cap to crack up the string of crowd molding. Lots of public pro associations expect customers to maintain up favorable dividing also to dress in facemask depending on certain distributing regulations (e.g., covering both the nose and mouth) whenever using general administrations. These developments enlivened evaluation into abbreviated (computer system eyesight based) processes to get face cap comprehension and category analysis which may aid with assessing general behavior and add to convincing the COVID-19 pandemic. Albeit present research inside this room attracted about skillful approaches for face-cover comprehension and category investigation, these commonly do the job under the presumption that complex face indexes provide excellent discovery implementation (such as hidden faces) and establish humans they truly have been underneath 3/4members, also the essential aim of these plans is always to tell apart those who're not donning facemask and maybe perhaps not keeping upward favorable space. The suggested arrangement employs the Open CV object comprehension version to isolate folks from your base along with deep sort solution to address monitor the distinguished people who have the aid of rebounding bins and manufactured IDs. The implications of this Open CV version are in addition invisibly and also other properly called cutting-edge versions so much as suggest standard precision (MAP), summarizes each moment (FPS) and hardship esteems seen as a thing structure and confinement. Subsequently the pairwise vectorized l-2 conventional is processed determined upon the three dimensional part room received by utilising the centroid arranges and dimensions of this jump box. Catching bicycle is done making use of Pi digicam and also sending prompt messages from GSM messages alert in just a little quantity of minutes. The suggested frame is accepted using Python 3 programming.