Reviewerly/exHarmony huggingface dataset:
Comparison of Named Entity Recognition models based on Neural Network in Biomedical

The need of automated information extraction increases with the increase in biomedical text. Named entity recognition is one of the core tasks in automatic information extraction. Performing named entity recognition tasks are quite challenging in biomedical field. These challenges include limited availability of annotated datasets and misclassification of entities having multiple meanings. Many Neural Network and deep learning-based models are developed for overcoming these challenges and for increasing the performance of named entity recognition tasks. This paper compares different models based on neural network architecture. The performance of these models is compared on JNLPBA dataset. The results show that Long short-term memory - conditional random field model with Wiki PubMed-PMC embeddings has outperformed other models by achieving highest precision and F1-score. CollaboNet model achieves the highest recall. Further analysis is needed to explore and compare the tools for performing named entity tasks in biomedical field.
Privacy Budgeting for Growing Machine Learning Datasets

The wide deployment of machine learning (ML) models and service APIs exposes the sensitive training data to untrusted and unknown parties, such as end-users and corporations. It is important to preserve data privacy in the released ML models. An essential issue with today's privacy-preserving ML platforms is a lack of concern on the tradeoff between data privacy and model utility: a private datablock can only be accessed a finite number of times as each access is privacy-leaking. However, it has never been interrogated whether such privacy leaked in the training brings good utility. We propose a differentially-private access control mechanism on the ML platform to assign datablocks to queries. Each datablock arrives at the platform with a privacy budget, which would be consumed at each query access. We aim to make the most use of the data under the privacy budget constraints. In practice, both datablocks and queries arrive continuously so that each access decision has to be made without knowledge about the future. Hence we propose online algorithms with a worst-case performance guarantee. Experiments on a variety of settings show our privacy budgeting scheme yields high utility on ML platforms.
Structured Fine-Tuning of Contextual Embeddings for Effective Biomedical Retrieval

Biomedical literature retrieval has greatly benefited from recent advances in neural language modeling. In particular, fine-tuning pretrained contextual language models has shown impressive results in recent biomedical retrieval evaluation campaigns. Nevertheless, current approaches neglect the inherent structure available from biomedical abstracts, which are (often explicitly) organised into semantically coherent sections such as background, methods, results, and conclusions. In this paper, we investigate the suitability of leveraging biomedical abstract sections for fine-tuning pretrained contextual language models at a finer granularity. Our results on two TREC biomedical test collections demonstrate the effectiveness of the proposed structured fine-tuning regime in contrast to a standard fine-tuning that does not leverage structure. Through an ablation study, we show that models fine-tuned on individual sections are able to capture potentially useful word contexts that may be otherwise ignored by structure-agnostic models.
The construction of mobile education in cloud computing

Mobile education has made great progress. Currently, there are a lot of elements to curb its development. The existence of cloud computing provides new era for the development of mobile of mobile education. This article studies and elaborates cloud computing. After summarizing its features, the article analyzes the possibility of combining it with mobile education and puts forward a new mobile education mode based on cloud computing in order to carrying out explorative research on the development of mobile education.
Research on Music Assisted Teaching System Based on Artificial Intelligence Technology

Abstract With the advent of the information age, computer technology has been greatly developed, especially the development of Artificial Intelligence(AI). And with the passage of time, AI began to involve various fields, music education is no exception. In this paper, after a detailed understanding of some research results of AI on music assisted instruction system, we mainly analyze the students’ video, audio and other related information, and save it in the database. This paper first introduces the evaluation process by using AI technology. In fact, it is necessary to find out the relationship between the influencing factors and evaluation of music assisted teaching system. Neural network(NN) is actually a model proposed by simulating the way people think in the brain. It has no strict requirements for data distribution. In terms of nonlinear data processing method, robustness and dynamics, it is very suitable to be used as a model for evaluating music assisted instruction system. Then each factor is taken as the input parameter of the NN. According to the evaluation index of music teaching, a special modeling system is designed. With the help of technical personnel, we obtained the sample data of music performance and completed the neural training. The experimental results show that the development of AI technology has broken the original situation of traditional teaching, especially the application of music system and intelligent music software based on AI in music teaching.
Appearance-based gaze estimation using separable convolution neural networks

Gaze estimation is one of the current important research contents of computer vision. For the current situation where the gaze estimation neural network has a large amount of parameters but the accuracy is not greatly improved and the head pose is difficult to handle, this paper proposes a simplified gaze estimation network model SLeNet based on the LeNet neural network. The deep separable convolution in the Xception network is used to reduce the amount of parameters in the convolution part and improve the computational performance of the network model. The method of splicing head posture features is retained, but another branch neural network is designed to learn head posture based on eye image and mouth corner information, and no additional module is required to obtain head posture separately. The improved network model is used to compare experiments with the original network and VGG-16 on the MPIIGaze dataset. The results show that the improved SLeNet network model performs better on the MPIIGaze dataset than LeNet and VGG-16 and has fewer parameters.
Application of a Hyperbolic Tangent Chaotic Map to Random Bit Generation and Image Encryption

In this paper, a two-parameter one-dimensional chaotic map with hyperbolic tangent and two nested sinusoidal terms is proposed. The reported map exhibits rich chaotic behavior including such phenomena as the period-doubling route to chaos, crisis, and antimonotonicity appearing. The proposed map is applied for the pseudo-random bit generation and image encryption. To generate bit sequences, a simple rule is used. The generated sequences are verified using NIST statistical tests and cross-correlation analysis. Image encryption is performed using two rounds of shuffling of image pixels and XOR operation. We explicitly show the suitability of the proposed algorithm through histogram, correlation, and entropy analysis for the sample grayscale image.
Fusion based Image Retrieval using Haralick Moments and TSBTC Features

Due to the advancements in image acquisition devices, images have become the resourceful foundation of information representation. An enormous amount of image data is generated and shared due to the improvements in data storage and communication technologies. Searching and retrieving similar content images from these huge repositories has become a key issue. This article introduces a fusion of Haralick features and Thepade's Sorted Block Truncation Coding (TSBTC) n-ary features for image retrieval. The proposed technique is tested on modified COIL and augmented Wang datasets. The similarity between dataset images and query images is measured using Mean Squared Error (MSE). The performance of the proposed method is tested using average retrieval accuracy (ARA). The experimental results show that the fusion of Haralick features and 8-ary TSBTC features gives an ARA of 44.20% for the augmented Wang dataset and the feature fusion of Haralick and 4-ary TSBTC gives an ARA of 74.08% for the modified COIL dataset. The proposed technique performs better as compared to the existing techniques in terms of the ARA performance metric with statistical significance.
A lightweight network with bidirectional constraints for single image super-resolution

The existing methods based on the convolutional neural network (CNN) show excellent performance in single image super-resolution (SISR). Massive convolutional layers can enhance these CNN-based methods, but they increase memory consumption due to a large number of parameters. In addition, the unilateral constraint on SISR only focuses on the up-sampling process and ignores image degradation, limiting the accuracy of convergence in network training. In response to these problems, we propose a lightweight network with bidirectional constraints (LNBC) for SISR. We present an extended layer named enhanced cycle residual block (CRB), then develop a lightweight network with CRB as the feature inference layer. CRB improves feature expression ability by alternating and multiplexing convolutional layers without increasing parameters. In addition, unlike existing methods that only constrain the up-sampling process, we propose bidirectional constraints on SISR named cycle consistency verification (CCV). In CCV, the introduced degradation network simulates image degradation, and provides constraints on image degradation for the up-sampling network in joint training. Image up-sampling and degradation constitute bidirectional constraints to tighten the convergence of the up-sampling network. Experiments show that LNBC advances comparison methods in the compromise of image super-resolution performance, memory overhead and running time.
Hand-Drawn Emoji Recognition using Convolutional Neural Network

Emojis are like small icons or images used to express our sentiments or feelings via text messages. They are extensively used in different social media platforms like Facebook, Twitter, Instagram etc. We considered hand-drawn emojis to classify them into 8 classes in this research paper. Hand-drawn emojis are the emojis drawn in any digital platform or in just a paper with a pen. This paper will enable the users to classify the hand-drawn emojis so that they could use them in any social media without any confusion. We made a local dataset of 500 images for each class summing a total of 4000 images of hand-drawn emojis. We presented a system which could recognise and classify the emojis into 8 classes with a convolutional neural network model. The model could favorably recognise as well as classify the hand-drawn emojis with an accuracy of 97%. Some pre-trained CNN models like VGG16, VGG19, ResNet50, MobileNetV2, InceptionV3 and Xception are also trained on the dataset to compare the accuracy and check whether they are better than the proposed one. On the other hand, machine learning models like SVM, Random Forest, Adaboost, Decision Tree and XGboost are also implemented on the dataset.
Time Series Analysis of Cryptocurrencies Using Deep Learning &amp; Fbprophet

This paper consists of cryptocurrency prediction and analysis using different algorithms, the major cryptocurrency took into account for analysis and prediction are Bitcoin (BTC), Ethereum (ETH), Chainlink (LINK), Bitcoin Cash (BTC), XRP (XRP). Nowadays, investing in cryptocurrency has become a major deal, with huge cash flow and billions of industries which has taken over the small industry that was over the past. With this investment, it is important to understand the high & low of a particular cryptocurrency and what output will be generated with such decisions. Prediction of cryptocurrencies is tangible and requires lots of understanding regarding the flow of money on daily basis. The machine learning industry has advanced to a great extent and it would further do, this advancement has led us to a bigger problem-solving technique, that is prediction of data or analysis of trend which can be in any format. The format in this paper is a time series analysis of the daily high-low-close of digital currency. The algorithms used for such analysis is LSTM (Long Short-Term Memory) which is part of Deep Learning and further Fbprophet which is an Auto Machine Learning for prediction is used. The metric used for the analysis of the algorithm is MAE (Mean Absolute Error). The programming language used is Python, which solves the majority of use cases.
The Information Retrieval Anthology

We present the IR Anthology, a corpus of information retrieval publications accessible via a metadata browser and a full-text search engine. Following the example of the well-known ACL Anthology, the IR Anthology serves as a hub for researchers interested in information retrieval. Our search engine ChatNoir indexes the publications' full texts, enabling a focused search and linking users to the respective publisher's site for personal access. Listing more than 40,000 publications at the time of writing, the IR Anthology can be freely accessed at https://IR.webis.de.
The Application of Yolov4 And A New Pedestrian Clustering Algorithm to Implement Social Distance Monitoring During The COVID-19 Pandemic

Abstract Based on the global spread of COVID-19 epidemic, this paper implements an application of social distance monitoring in public places, aiming to control the spread of virus by controlling the social distance of pedestrians. The application mainly uses YOLOv4 object detection algorithm and DeepSORT multiple objects tracking algorithm to detect and label pedestrians, and uses affine transformation to calibrate the scene to a more intuitive bird’s-eye view. Based on the analysis of pedestrian movement, the author proposes a novel pedestrian clustering algorithm to avoid the impact of peers on the monitoring results. Finally, three indicators are selected to classify the pedestrian to analyse and evaluate the risk of virus infection in a certain place. In this paper, comparing the yolov4 algorithm with the most suitable performance of other research results, the author indirectly concludes that yolov4 is the most suitable method in the application of social distance monitoring. And the new pedestrian clustering algorithm plays a role in the application, which improves the practicability of the application. Through the experiment, it is found that the social distance in the scene can be monitored in real time and accurately, and there is a correlation between the average distance of pedestrian clusters, the density of pedestrian clusters and the infection index, which can be used to evaluate the safety of places and prevent the spread of virus.
A Survey of Watermarking Technique using Deep Neural Network Architecture

A computerized watermark is a sort of marker secretly introduced in a noise accepting signals, let us say, video, audio or image. It is normally accustomed to recognize proprietorship for the copyright of such signals. "Watermarking" is the way toward covering computerized bits in a conveyor signal. The shrouded data need not have to contain a connection to the transporter signal. Computerized watermarks might be applied to verify the genuineness or uprightness of the conveyor signal or to demonstrate the uniqueness of its proprietors. This paper explores a new family of transformation dependent on Deep Learning systems. We survey and present a relative investigation of the different profound learning procedures that target installing watermark that can be worked upon to accomplish substantial security level for the data being transmitted, and robustness over a few attacks particularly when transmitted over the loud medium. Our paper centers on how deep learning-based watermarking approaches show their prevalence because of the level of imperceptibility and robustness presented by adjustments in recurrence coefficients. We likewise feature the parts of the current methodologies that can be worked upon to accomplish images that are exceptionally indistinct from one another in the future.
Towards Independent Run-Time Cloud Monitoring

Cloud computing services are integral to the digital transformation. They deliver greater connectivity, tremendous savings, and lower total cost of ownership. Despite such benefits and benchmarking advances, costs are still quite unpredictable, performance is unclear, security is inconsistent, and there is minimal control over aspects like data and service locality. Estimating performance of cloud environments is very hard for cloud consumers. They would like to make informed decisions about which provider better suits their needs using specialized evaluation mechanisms. Providers have their own tools reporting specific metrics, but they are potentially biased and often incomparable across providers. Current benchmarking tools allow comparison but consumers need more flexibility to evaluate environments under actual operating conditions for specialized applications. Ours is early stage work and a step towards a monitoring solution that enables independent evaluation of clouds for very specific application needs. In this paper, we present our initial architecture of the Cloud Monitor that aims to integrate existing and new benchmarks in a flexible and extensible way. By way of a simplistic demonstrator, we illustrate the concept. We report some preliminary monitoring results after a brief time of monitoring and are able to observe unexpected anomalies. The results suggest an independent monitoring solution is a powerful enabler of next generation cloud computing, not only for the consumer but potentially the whole ecosystem.
Scalable Auto-weighted Discrete Multi-view Clustering

Multi-view clustering has been widely studied in machine learning, which uses complementary information to improve clustering performance. However, challenges remain when handling large-scale multi-view data due to the traditional approaches' high time complexity. Besides, the existing approaches suffer from parameter selection. Due to the lack of labeled data, parameter selection in practical clustering applications is difficult, especially in big data. In this paper, we propose a novel approach for large-scale multi-view clustering to overcome the above challenges. Our approach focuses on learning the low-dimensional binary embedding of multi-view data, preserving the samples' local structure during binary embedding, and optimizing the embedding and clustering in a unified framework. Furthermore, we proposed to learn the parameters using a combination of data-driven and heuristic approaches. Experiments on five large-scale multi-view datasets show that the proposed method is superior to the state-of-the-art in terms of clustering quality and running time.
The COVID-19 Pandemic and the New Poor in Africa: the Straw that Broke the Camel’s Back

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Lab Conditions for Research on Explainable Automated Decisions

Artificial neural networks are being proposed for automated decision making under uncertainty in many visionary contexts, including high-stake tasks such as navigating autonomous cars through dense traffic. Against this background, it is imperative that the decision making entities meet central societal desiderata regarding dependability, perspicuity, explainability, and robustness. Decision making problems under uncertainty are typically captured formally as variations of Markov decision processes (MDPs). This paper discusses a set of natural and easy-to-control abstractions, based on the Racetrack benchmarks and extensions thereof, that altogether connect the autonomous driving challenge to the modelling world of MDPs. This is then used to study the dependability and robustness of NN-based decision entities, which in turn are based on state-of-the-art NN learning techniques. We argue that this approach can be regarded as providing laboratory conditions for a systematic, structured and extensible comparative analysis of NN behavior, of NN learning performance, as well as of NN verification and analysis techniques.
Performance Analysis of Neo4j, MongoDB, and PostgreSQL on 2019 National Election Big Data Management Database

Data has now become a major commodity and asset for any organization. There's also a phenomenon called Big Data where it's a term to describes a large volume of data, both structured and unstructured. With big data, any organization can analyze the data and its results are used for decision making and business strategy. Political parties, for example, use it to analyze the likelihood of their candidate winning in the election based on voter data who votes for their candidate. In Indonesia itself, the use of big data has not been utilized because of limited database infrastructure. Therefore, Indonesia needs a good database that can utilize big data. In this study, the author examines the performance of PostgreSQL, MongoDB, and Neo4J by analyzing each of its complexity using computational complexity theory with Big O notation as its tool. With Big O, the author measures the complexity of each execution time. The conclusion from this study is that MongoDB has excellent performance because it has an O(1) complexity, PostgreSQL has good performance because it has an O(n) and O(1) complexities, and Neo4j has worse performance than MongoDB and PostgreSQL because it has an O(nlog n) complexity.
The Wheel of Rational Numbers as an Abstract Data Type

In an arithmetical structure one can make division a total function by defining 1/0 to be an element of the structure, or by adding a new element such as infinity \(\infty \) or error element \(\perp \). A wheel is an algebra in which division is totalised by setting \(1/ 0 = \infty \) but which also contains an error element \(\perp \) to help control its use. We construct the wheel of rational numbers as an abstract data type \(\mathbb {Q}_w\) and give it an equational specification without auxiliary operators under initial algebra semantics.
DeepNAG: Deep Non-Adversarial Gesture Generation

Synthetic data generation to improve classification performance (data augmentation) is a well-studied problem. Recently, generative adversarial networks (GAN) have shown superior image data augmentation performance, but their suitability in gesture synthesis has received inadequate attention. Further, GANs prohibitively require simultaneous generator and discriminator network training. We tackle both issues in this work. We first discuss a novel, device-agnostic GAN model for gesture synthesis called DeepGAN. Thereafter, we formulate DeepNAG by introducing a new differentiable loss function based on dynamic time warping and the average Hausdorff distance, which allows us to train DeepGAN's generator without requiring a discriminator. Through evaluations, we compare the utility of DeepGAN and DeepNAG against two alternative techniques for training five recognizers using data augmentation over six datasets. We further investigate the perceived quality of synthesized samples via an Amazon Mechanical Turk user study based on the HYPE∞ benchmark. We find that DeepNAG outperforms DeepGAN in accuracy, training time (up to 17 × faster), and realism, thereby opening the door to a new line of research in generator network design and training for gesture synthesis. Our source code is available at https://www.deepnag.com.