Reviewerly/exHarmony huggingface dataset:
Advanced TensorBoard

TensorBoard is an official toolkit of TensorFlow, providing the visualization needed for designing neural networks and machine learning experimentation. The use of the “tf.keras.callbacks.TensorBoard” function is limited in supporting information in various states and statistics of the neural networks during the training process. This chapter introduces how to write summary data and display on TensorBoard with the “tf.summary” module of TensorFlow, providing many application programming interfaces (APIs) such as tf.summary.scalar, tf.summary.image, tf.summary.text, tf.summary.audio, and tf.summary.histogram. To help readers with more in-depth use of TensorBoard, we present two scenarios of visualizing the training results of the neural networks. In the first scenario, we apply the “tf.summary.image” API for observing the image results. In the second scenario, we employ a hyperparameter tuning technique with the HParams dashboard to identify the most suitable hyperparameters (learning rate, optimizer, weight initialization method, and so on) for designing a network model with a specific problem.
On the Complexity of the LWR-Solving BKW Algorithm

The Blum-Kalai-Wasserman algorithm (BKW) is an algorithm for solving the learning parity with noise problem, which was then adapted for solving the learning with errors problem (LWE) by Albrecht et al. Duc et al. applied BKW also to the learning with rounding problem (LWR). The number of blocks is a parameter of BKW. By optimizing the number of blocks, we can minimize the time complexity of BKW. However, Duc et al. did not derive the optimal number of blocks theoretically, but they searched for it numerically. Duc et al. also showed that the required number of samples for BKW for solving LWE can be dramatically decreased using Lyubashevsky's idea. However, it is not shown that his idea is also applicable to LWR. In this paper, we theoretically derive the asymptotically optimal number of blocks, and then analyze the minimum asymptotic time complexity of the algorithm. We also show that Lyubashevsky's idea can be applied to LWR-solving BKW, under a heuristic assumption that is regularly used in the analysis of LPN-solving BKW. Furthermore, we derive an equation that relates the Gaussian parameter σ of LWE and the modulus p of LWR. When σ and p satisfy the equation, the asymptotic time complexity of BKW to solve LWE and LWR are the same.
Stereo Vision Based Robot for Remote Monitoring with VR Support

The machine vision systems have been playing a significant role in visual monitoring systems. With the help of stereovision and machine learning, it will be able to mimic human-like visual system and behaviour towards the environment. In this paper, we present a stereo vision based 3-DOF robot which will be used to monitor places from remote using cloud server and internet devices. The 3-DOF robot will transmit human-like head movements, i.e., yaw, pitch, roll and produce 3D stereoscopic video and stream it in Real-time. This video stream is sent to the user through any generic internet devices with VR box support, i.e., smartphones giving the user a First-person real-time 3D experience and transfers the head motion of the user to the robot also in Real-time. The robot will also be able to track moving objects and faces as a target using deep neural networks which enables it to be a standalone monitoring robot. The user will be able to choose specific subjects to monitor in a space. The stereovision enables us to track the depth information of different objects detected and will be used to track human interest objects with its distances and sent to the cloud. A full working prototype is developed which showcases the capabilities of a monitoring system based on stereo vision, robotics, and machine learning.
Novel Methods for Forensic Multimedia Data Analysis: Part II

We are proposing a new concept for new multimedia data processing techniques for varied multimedia sources. We address our work toward speech, video and images, handwriting and text documents. The methods and techniques will form a toolkit that can be used for different cases in a court of law in order to extract information from different multimedia sources. In addition to the data mentioned above, social media (e.g., Facebook and Twitter) provide multimedia data in new formats. Such data allow the investigator to identify and compare objects, events or persons based on data properties, including biometric features or more symbolic features that point to coincidences and anomalies. We continue our work of novel methods for forensic multimedia data analysis Part I by a description of related work and a proposal of the methods and techniques we are developing beyond the state of the art for handwriting, multimedia feature extraction, novelty detection, legal aspects and cloud computing. Then, we describe the tasks that should be solved by the system and the different multimedia data. We describe expected results of our proposed toolkit. Finally, we summarize the objective of our work.
Detecting Accuracy of Accelerometer and Gyroscope Wearable Shimmer Sensors using Linear Regression

Smart Wearable can measures physical activities by analyzing the user’s body movements which requires existing sensors such as accelerometer and gyroscope in order to secure perilous data so that it should not go missing at the point of high speed rotation or high impact. Accelerometer is a sensor that has been generally recognized as suitable and practical in smart wearable devices to measure and assess human physical activities. Wearable accelerometer sensor affords easily moveable systems that stream real-time data. The gyroscope sensor track human movement and activity and improves its accuracy. In this paper, we are comparative analyzing the commercially available SHIMMER3 wearable 3rd generation sensors raw data of age under 71-80+ people from two different sensors i.e. accelerometer and gyroscope and modeling it with machine learning approach of linear regression.
A Survey on Self-Attention Generative Adversarial Networks Based Tag Recommendations

Based on the analysis of previous research, this article deeply discusses the current tagbased recommendation methods from the above two aspects, briefly explains the current common tag recommendation methods and existing problems, and proposes a self-attention-based GAN (called SAGAN) tag recommendation method.In this paper, the performance index of the comparison experiment of the label recommendation approach is designed, our method is to implement self-attention mechanism to generative adversarial networks.The method of generating negative samples by the generator and identifying the samples by the discriminator enables the SAGAN to learn the pros and cons of the samples, and then use the method of generating an adversarial network to find and extract user and resource label data from the existing tags of the recommended object Characteristics, so as to better implement the recommendation that the user matches the resource tags.. Experimental results show that for different types of resources, SAGAN tag recommendation approach have achieved certain performance improvements.
Influence of Discovery Search Tools on Science and Engineering e-books Usage

Abstract Does the introduction of a discovery search engine correlate with an increased use of science and engineering electronic books?Academic libraries are moving toward the use of ebooks at an unprecedented rate and nowhere isthe shift more evident than in science and engineering libraries. Collections such as Knovel,Books 24x7, Springer and Synthesis have been providing online access to science andengineering literature for several years and are leading the way for other disciplines now movingto this format.More recently, new tools that attempt to better expose all types of library formats (e.g. printbooks, online journal articles, etc.) have made their appearance with the promise of renderinglibrary collections easier to discover by replicating the one-stop shopping experience that usersof the Google era have come to know and expect. New discovery search tools such as Summon,Primo, WorldCat Local and others now offer a greater depth (through table of contents, chapter-level and even full-text searching) and breadth (covering ebooks, ebook chapters, journal articles,data, and other resources) than ever before. What implications do these new discovery tools havefor access to existing ebook collections in science and engineering? With the costs of both ebookpackages and discovery tools making up an ever-growing portion of library budgets, do thesetools provide better access to collections than do traditional online catalogues?In this paper, we examine the use of science and engineering ebook collections in three leadingCanadian academic libraries: XXXXX, XXXXX and XXXXX to test the hypothesis thatintroducing a latest generation discovery search tool, such as Summon or WorldCat Local, in alarge academic library correlates with increased usage of major science and engineeringelectronic resources. The study will compare usage of ebook collections such as Books 24x7,Knovel Academic ebook collection, Springer ebook collection, Synthesis ebook collection, andothers that all three institutions have in common prior to and after implementation of a discoverytool. The study will also compare two specific discovery tools: Summon and WorldCat Local todetermine whether there are differences in ebook collection usage based on choice of discoverytool.
Correction to: <i>Artificial Intelligence, Brains, and Beyond: Imperial College London Neurotechnology Symposium, 2020</i>, by Tarasenko A, et al. BIOE 2020;2:310-313.

BioelectricityVol. 2, No. 4 CorrigendumOpen AccessCorrection to: Artificial Intelligence, Brains, and Beyond: Imperial College London Neurotechnology Symposium, 2020, by Tarasenko A, et al. BIOE 2020;2:310-313.Published Online:16 Dec 2020https://doi.org/10.1089/bioe.2020.0040.correxAboutSectionsView articleView PDFView PDF Plus ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleIn the September 2020 issue of Bioelectricity (vol. 2, no. 3; 310-313), the article entitled “Artificial Intelligence, Brains, and Beyond: Imperial College London Neurotechnology Symposium, 2020” by Tarasenko et al. requires correction.In the author byline on page 310, author Daryna Ivaskevych's name is misspelled.The author byline originally states: “Anna Tarasenko, MS,1 Mikheil Oganesyan, MS,1 Daryna Ivashkevych, MA,2 Sergii Tukaiev, PhD,2 Dauren Toleukhanov,1 and Nickolai Vysokov, PhD1”The author byline should now read: “Anna Tarasenko, MS,1 Mikheil Oganesyan, MS,1 DarynaIvaskevych, MA,2 Sergii Tukaiev, PhD,2 Dauren Toleukhanov,1 and Nickolai Vysokov, PhD1”The online version of the article has been corrected to reflect this.FiguresReferencesRelatedDetails Volume 2Issue 4Dec 2020 InformationCopyright 2020, Mary Ann Liebert, Inc., publishersTo cite this article:Bioelectricity.Dec 2020.426-426.http://doi.org/10.1089/bioe.2020.0040.correxPublished in Volume: 2 Issue 4: December 16, 2020
Elliptic Curve Elgamal Encryption Scheme using Higher-Order Golden Matrices

In this paper, we proposed ElGamal encryption scheme of elliptic curves based on the golden matrices. This algorithm works with a bijective function identified as characters of ASCII from the elliptic curve points and the matrix produced the additional private key, which was obtained from golden matrices defined by A.P Stakhov.
Motion artifact correction of MRI based on a generative adversarial network and domain consistency

In clinical diagnosis, magnetic resonance imaging (MRI) motion artifact is a common problem that will affect the doctor's diagnosis.Although the reacquisition of MRI can avoid this problem, it will bring extra economic and time cost to hospitals and patients.Therefore, the correction of motion artifacts has a practical research value.Existing studies mainly focus on correcting motion artifacts from the spatial domain or K-space, and ignore the data consistency between them.In order to solve this problem, a motion artifact correction model based on a generative adversarial network is proposed to maintain the data consistency between the K-space and spatial domain.In this model, K-space data are initially corrected by a frequency domain generator, and then spatial domain data are fine corrected by a spatial domain generator.In optimization phase, the data consistency loss between the K-space and spatial domain is used to maintain data consistency.In four public MRI datasets, ADNI, ABIDE, OASIS and Brain, the experimental results show that the performance of the proposed model has increased by 3.4%, 3.07% and 15.57% on PSNR, SSIM and RMSE, respectively.
Origins of Saline Minerals in Lower Permian Alkaline Lacustrine Deposits in Northwestern Junggar Basin, China: Impact on Hydrocarbon Accumulation

Mahu sag, northwestern Junggar basin, is one of the most prolific oil-gas sags in North Xinjiang, China.Lower Permian Fengcheng formation is considered to be the oldest alkaline lacustrine strata, which provides the resource foundation for the formation of hundreds-of-miles oil province.Saline minerals in the Lower Permian Fengcheng Formation play a significant role in the recovery of palaeoenvironment, emplacement of tight oil and enrichment of organic matters, but have been relatively poorly understood.Based on detailed analyses from optical microscopy, SEM, electronic probe, Raman spectroscopy, XRD and fluid inclusions, saline minerals including mainly sodium carbonates and sodium silicates were identified, and their paragenetic sequence have been well demonstrated.The major sodium carbonates include wegscheiderite, shortite, eitelite, northupite and burbankite and the sodium silicates are reedmergnerite, searlesite, leucosphenite and aegirine.Primary aqueous fluid inclusions in eitelite, shortite and reedmergnerite show homogenization temperatures ranging from 70 °C to 80 °C, 80 °C to 90 °C and 95 °C to 110 °C respectively.All the saline minerals are investigated to have a mesodiagenetic origin.Eitelite, shortite, northupite and wegscheiderite formed successively through recrystallization and replacement from synsedimentary carbonate muds.Subsequently searlesite and reedmergnerite formed as transformation products of volcanic ashes.The composite sodium-silicates veins finally crystalized from hydrothermal fluid.Primary heavy oil fluid inclusions are present in eitelite, northupite, reedmergnerite and shortite, and bitumen is also found to fill crystal growth planes in eitelite.Therefore, the emplacement of hydrocarbon occurred simultaneously with the dissolution, replacement, transformation and recrystallization of most saline minerals and consequently, diagenesis of alkaline sediments from Fengcheng Formation has not been inhibited by hydrocarbon emplacement.
Qualitative Risks

Blockchain shifts the trust in governments and centralized systems to trust in computers, cryptography, software and protocols. However, it is not because mathematics and cryptography are behind cryptocurrencies and blockchain, it necessarily means that all of the operational risks are eliminated and the system is more secure. For example, storing digital currency on an online wallet makes it prone to hacks and there is no way to retrieve money which has been stolen. Moreover, if the owner of the wallet for some reason loses the password, then it is also impossible to retrieve the lost funds. This next chapter is dedicated to explaining some new risks in cryptocurrencies and the important underlying characteristics one should bear in mind. We start by exploring the cryptocurrency market and regulatory issues. Afterwards, we will elaborate on design risks, like the 51% attack, selfish mining and ICOs. To finish this chapter, we will go through various operational risks.
Application of grammar-based codes for lossless compression of digital mammograms

A newly developed grammar-based lossless source coding theory and its implementation was proposed in 1999 and 2000, respectively, by Yang and Kieffer. The code first transforms the original data sequence into an irreducible context-free grammar, which is then compressed using arithmetic coding. In the study of grammar-based coding for mammography applications, we encountered two issues: processing time and limited number of single-character grammar G variables. For the first issue, we discover a feature that can simplify the matching subsequence search in the irreducible grammar transform process. Using this discovery, an extended grammar code technique is proposed and the processing time of the grammar code can be significantly reduced. For the second issue, we propose to use double-character symbols to increase the number of grammar variables. Under the condition that all the G variables have the same probability of being used, our analysis shows that the double- and single-character approaches have the same compression rates. By using the methods proposed, we show that the grammar code can outperform three other schemes: Lempel-Ziv-Welch (LZW), arithmetic, and Huffman on compression ratio, and has similar error tolerance capabilities as LZW coding under similar circumstances.
Explainable model of deep learning for outcomes prediction of in-hospital cardiac arrest patients

Introduction: Deep learning has outperformed traditional methods in predicting healthcare outcomes. However, deep learning models struggle with explainability and are considered a black box. This article demonstrated the output from Shapley additive explanations (SHAP) analysis can provide meaningful insight into a model's predictions.
Semantic Hyperlapse: a Sparse Coding-based and Multi-Importance Approach for First-Person Videos

The availability of low-cost and high-quality wearable cameras combined with the unlimited storage capacity of video-sharing websites have evoked a growing interest in First-Person Videos. Such videos are usually composed of long-running unedited streams captured by a device attached to the user body, which makes them tedious and visually unpleasant to watch. Consequently, it raises the need to provide quick access to the information therein. We propose a Sparse Coding based methodology to fast-forward First-Person Videos adaptively. Experimental evaluations show that the shorter version video resulting from the proposed method is more stable and retain more semantic information than the state-of-the-art. Visual results and graphical explanation of the methodology can be visualized through the link: https://youtu.be/rTEZurH64ME
Digital Competencies of Teachers in The Context Of Professional Development

In order to point out the importance and significance of studying the problem of digital competencies of teachers in the context of professional development, this paper discusses innovations in XI century education, distance education and teaching media, new roles of teachers, their competencies, and the way of improving competencies. The research included 124 teachers throughout Serbia. The instrument used in this paper is an instrument of the Likert-type assessment scale that was constructed for this research and is called the Digital Teacher Competence and Author Researcher Initiative (DTCPRI). The assessment scale contains 42 claims.
Resonance Image Enhancement using Hybrid Center Weighted Median Filter and Bacteria Foraging Optimization

Magnetic Resonance Image (MRI) is the best imaging technique employed nowadays for diagnosing brain tumour in the initial stage. This paper recommends an unique method for the brain MR image enhancement, that is centred on the Hybrid Center Weighted Median (HCWM) filter and Bacteria Foraging Optimization (BFO). The MR image for this research is obtained from the online and it is pre-processed to remove all the film artifacts. After that the high frequency components are eliminated from the MR brain image by means of a newly proposed HCWM filter. HCWM Filter is the hybrid filter derived by combining the Center Weighted Median Filter and the Weiner Filter. The swarm-based intelligence algorithm called the bacteria foraging optimization is used to predict the weights of the filter dynamically. The performance of the proposed filtering approach is evaluated with the other available filtering methods.
Discovery of Incomplete Diagnostic Model based on Learning

The model-based diagnosis uses the common reasoning of offline model and online observation to obtain whether and why faults occur. However, the diagnosis is based on the premise of complete model. Once there are unknown behaviors in the diagnosis process, the diagnosis results will not be obtained. In this paper, a method of incomplete model discovery based on online diagnosis process is proposed: In the online diagnosis process, the data of the complete model are learned and the model is trained and adjusted. When the incomplete behavior is found, the nature of the incomplete behavior is determined according to the historical diagnostic data and online observation data, and the corresponding transition/state/event is generated and added to the model to further obtain the definite diagnosis results.
Breast cancer detection and classification approach based on ensemble learning

The application of machine learning is constantly increasing especially in the field of automated disease diagnose and prediction such as breast cancer, which is very common in females as it can cause too many death but machine learning can increase the chances of survival by early prognosis and diagnosis if it diagnosed properly and accurately.In this paper we propose an automated breast cancer prediction approach based on the XgBoost random forest classifier(XGBRF) algorithm.In order to prove the effectiveness and accurateness of the proposed approach, Wisconsin diagnose breast cancer dataset is usedover which various classification rates like precision, recall , F1score and confusion matrix are generated.The testing accuracy of our proposed approach is 99%.Apart from that the proposed approach is being compared with various other approaches based on other machine learning classifiers like support vector machine, K-nearest neighbor, Naïve Bayes etc. .
Applications o f Data Mining in Hydrocarbon Exploration , Constraints on Geology and Petroleum Reservoir

In the present scenario, low oil prices and explorations shave triggered energy industries to look into cost depression of supply chains vibrantly. Advanced and new technology had been identified and experimentally considered for new hydrocarbon exploration prevailing data mining. Geoscience and geophysical problems are dominated as data obtained for mining process is enforced for geology and reservoir issues and properties. Our present research deals about understanding the geological problems clearly analysed in the literature and with few experiments, a variety of data mining methods for the outcome has been concluded, which provides solution for a betterment understanding about gas and oil exploration with that of data miners and the geoscientists. Collection of data for various hydrocarbon wells has been addressed with seismic surveys, for identification of source, segregating and forecasting using iteration methods and neural networks had been discussed for betterment exploration of new wells without any constraints.