Reviewerly/exHarmony huggingface dataset:
Semantic Segmentation Based on Depth Background Blur

Deep convolutional neural networks (CNNs) are effective in image classification, and are widely used in image segmentation tasks. Several neural netowrks have achieved high accuracy in segementation on existing semantic datasets, for instance PASCAL VOC, CamVid, and Cityscapes. However, there are nearly no studies on semantic segmentation from the perspective of a dataset itself. In this paper, we analyzed the characteristics of datasets, and proposed a novel experimental strategy based on bokeh to weaken the impact of futile background information. This crucial bokeh module processed each image in the inference phase by selecting an opportune fuzzy factor σ, so that the attention of our network can focus on the categories of interest. Some networks based on fully convolutional networks (FCNs) were utilized to verify the effectiveness of our method. Extensive experiments demonstrate that our approach can generally improve the segmentation results on existing datasets, such as PASCAL VOC 2012 and CamVid.
Design secure multi-level communication system based on duffing chaotic map and steganography

Cryptography and steganography are among the most important sciences that have been properly used to keep confidential data from potential spies and hackers. They can be used separately or together. Encryption involves the basic principle of instantaneous conversion of valuable information into a specific form that unauthorized persons will not understand to decrypt it. While steganography is the science of embedding confidential data inside a cover, in a way that cannot be recognized or seen by the human eye. This paper presents a high-resolution chaotic approach applied to images that hide information. A more secure and reliable system is designed to properly include confidential data transmitted through transmission channels. This is done by working the use of encryption and steganography together. This work proposed a new method that achieves a very high level of hidden information based on non-uniform systems by generating a random index vector (RIV) for hidden data within least significant bit (LSB) image pixels. This method prevents the reduction of image quality. The simulation results also show that the peak signal to noise ratio (PSNR) is up to 74.87 dB and the mean square error (MSE) values is up to 0.0828, which sufficiently indicates the effectiveness of the proposed algorithm.
Database as a Service (DBaaS) Challenges and Solutions

Cloud services have become one of the trends in the IT industry. The real idea of Cloud computing is to satisfy the client without increasing infrastructure operations and costs. It is easy to use, cost-effective, less infrastructure and configuration costs. It has almost zero maintenance, low operational cost and less effort. Using cloud service to deploy Database is known as Database as a service (DBaaS) which become one of the most used cloud services with time. The DBaaS and cloud databases market is one of the fastest-growing Software-as-a-Service (SaaS), expected to grow more and more by next decade. Data warehouse and database vendors have joined established cloud providers in offering hosted versions of their software, enabling customers to leverage the many benefits of cloud computing for their applications’ data storage, search, and access needs. Like any new trend in IT, DBaaS faces many challenges at the forefront, like security, availability, and interoperability. To overcome these challenges, this work discusses them and provides solutions to use DBaaS in an efficient way.
A Comprehensive Framework for Ontology Based Classifier using Unstructured Data

The knowledge contained within the natural language data can be used to build expert systems. Classifying unstructured data using ontology and text classification algorithms to extract information is one way of approaching the problem of building intelligent systems. One major problem with text processing is most data generated is unstructured and ambiguous, as, data with a structure helps to identify meaningful patterns and eventually exhibit the latent knowledge. Ambiguity in natural language affects accuracy of categorization. Also, Natural Language Processing techniques when combined with semantic data modeling through ontological knowledge will also solve the problem of domain knowledge representation thereby enabling improved data classification facilities, particularly in large datasets where number of features scale to unmanageable proportions. In this paper, the domain knowledge is presented as a knowledge graph, derived from the semantic data modeling. Further, to achieve better Multi Class classification, Multinomial Naive Bayes algorithm is applied to categorize items in their respective classes. For the experiments, Data about various news groups were used for testing the accuracy of the model. Experimental results have proved that the proposed classifier performs better compared to existing systems.
Analysis on the Application of Virtual Reality Technology in Operation Skill Teaching

The improvement of the level of science and technology puts forward higher requirements on the operation skill level of various equipment operators. Virtual reality technology can better solve various problems in the operation skill training. This article starts with the advantages of virtual reality technology in the teaching of operating skills, combined with the characteristics of virtual reality technology, and proposes the composition of an operating skills teaching system based on virtual reality technology; finally, several issues that need attention in the use of the virtual training system are put forward.
Search based software engineering

tutorial Share on Search based software engineering: challenges, opportunities and recent applications Authors: Ali Ouni University of Quebec University of QuebecView Profile , Mohamed Wiem Mkaouer Rochester Institute of Technology Rochester Institute of TechnologyView Profile Authors Info & Claims GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2021 Pages 1032–1063https://doi.org/10.1145/3449726.3461425Published:08 July 2021Publication History 0citation77DownloadsMetricsTotal Citations0Total Downloads77Last 12 Months39Last 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Interpretable Design of Reservoir Computing Networks Using Realization Theory

The reservoir computing networks (RCNs) have been successfully employed as a tool in learning and complex decision-making tasks. Despite their efficiency and low training cost, practical applications of RCNs rely heavily on empirical design. In this article, we develop an algorithm to design RCNs using the realization theory of linear dynamical systems. In particular, we introduce the notion of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> -stable realization and provide an efficient approach to prune the size of a linear RCN without deteriorating the training accuracy. Furthermore, we derive a necessary and sufficient condition on the irreducibility of the number of hidden nodes in linear RCNs based on the concepts of controllability and observability from systems theory. Leveraging the linear RCN design, we provide a tractable procedure to realize RCNs with nonlinear activation functions. We present numerical experiments on forecasting time-delay systems and chaotic systems to validate the proposed RCN design methods and demonstrate their efficacy.
A Pseudo 943 million Frames Per Rate High-Speed Camera with Asynchronous Double-Frame Exposure for Motion Estimation

This paper presents an asynchronous DFE (A-DFE) method which uses simple dual generic cameras with a dedicated designed hardware architecture for the camera mismatch, which significantly improves the upper limit of the measured velocity. Based on this method, we build up a pseudo-high-speed camera (PHSC) system for motion estimation on FPGA. The system achieves the shortest time interval of 1.06 ns, and the energy efficiency with 3.23 nJ/pixel on the low-cost XILINX Spartan-7 platform.
Crossmodality Person Reidentification Based on Global and Local Alignment

RGB-infrared (RGB-IR) person reidentification is a challenge problem in computer vision due to the large crossmodality difference between RGB and IR images. Most traditional methods only carry out feature alignment, which ignores the uniqueness of modality differences and is difficult to eliminate the huge differences between RGB and IR. In this paper, a novel AGF network is proposed for RGB-IR re-ID task, which is based on the idea of global and local alignment. The AGF network distinguishes pedestrians in different modalities globally by combining pixel alignment and feature alignment and highlights more structure information of person locally by weighting channels with SE-ResNet-50, which has achieved ideal results. It consists of three modules, including alignGAN module ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>A</mi> </math> ), crossmodality paired-images generation module ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>G</mi> </math> ), and feature alignment module ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>F</mi> </math> ). First, at pixel level, the RGB images are converted into IR images through the pixel alignment strategy to directly reduce the crossmodality difference between RGB and IR images. Second, at feature level, crossmodality paired images are generated by exchanging the modality-specific features of RGB and IR images to perform global set-level and fine-grained instance-level alignment. Finally, the SE-ResNet-50 network is used to replace the commonly used ResNet-50 network. By automatically learning the importance of different channel features, it strengthens the ability of the network to extract more fine-grained structural information of person crossmodalities. Extensive experimental results conducted on SYSU-MM01 dataset demonstrate that the proposed method favorably outperforms state-of-the-art methods. In addition, we evaluate the performance of the proposed method on a stronger baseline, and the evaluation results show that a RGB-IR re-ID method will show better performance on a stronger baseline.
Research on Artificial Intelligence Optimization Based on Genetic Algorithm

As an important force for national construction and development, science and technology are also a major factor affecting global economic development. Especially in the rapid development of artificial intelligence technology, genetic algorithm, as one of its optimization methods, has played a positive role in the field of innovation and technological innovation, and has changed people’s life and work in a certain sense. Therefore, on the basis of understanding artificial intelligence and genetic algorithm, this paper further studies how to carry out artificial intelligence optimization based on genetic algorithm.
Introduction

This chapter sets out the aims of the book, the approach, what is covered in the book, and what is not. The chapter introduces the R and RStudio software packages. It also summarizes the changes made to the second edition of the book. The book starts by introducing several different variations of the basic linear-model analysis (analysis of variance, linear regression, analysis of covariance, etc.). The later part of the book covers generalized linear models (for data with non-normal distributions). The book ends by combining these two extensions into generalized linear mixed-effects models. To allow a learning-by-doing approach, the R code necessary to perform the basic analysis is embedded in the text along with the key output from R.
Active deep learning to detect cognitive concerns in electronic health records

Timely diagnosis of dementia is important to patients and their caregivers for advanced planning, yet dementia is under-diagnosed by healthcare professionals and under-coded in claims data. Sensitive and specific tools to detect cognitive concerns in diverse clinical settings could prompt referral for cognitive evaluation and specialist care.We developed a deep learning natural language processing (NLP) method to detect cognitive concerns in unstructured clinician notes from electronic health records (EHR). We leveraged a gold-standard set of ∼1000 patients sampled randomly from three strata: patients with diagnosis codes, patients with specialist visits but no code, or patients with neither. The physician performed a detailed chart review and adjudication of cognitive status, noting "cognitive concern" (i.e., any evidence of cognitive difficulties) and rating patients on a 5-point scale: normal, normal vs. MCI, MCI, MCI vs. dementia, and dementia. We used 10% of the labeled data as a test set and the remaining 90% for training and validation of the model to classify patients with any cognitive concerns (normal vs. other). We also built a web-based chart review annotation tool that facilitates labeling and enables an active learning loop to scale up labeling to thousands of charts.In a random sample from the gold-standard dataset, 30 out of 80 patients with cognitive concerns had no diagnosis code or medication related to dementia; we hypothesized that our deep learning tool could leverage clinical text to improve detection of cognitive concerns. Indeed, a model with codes and medications had an area under the receiver operating characteristic (AUROC) curve of 0.79, sensitivity of 0.59, and specificity of 1.00 for the binary classification task. The deep learning model improved the AUROC to 0.90, increased sensitivity to 0.79, and maintained specificity of 0.98. Notes from primary care, specialties such as neurology and psychiatry, and social workers had the highest likelihood of containing information.The deep learning model was successful in detecting cases without a dementia-related diagnosis code or medication. Automatic processing of electronic medical records with a deep learning tool can be used for early detection of cognitive concern to optimize patient care and predict hospital readmission.
Wavelet Based Selective Image Encryption Scheme Using Tinkerbell Chaotic Map

In this era of information technology, the exchange of information is taking place globally. In order to facilitate it, special attention is being given to network policies as well as security. The digital images contain a significant amount of information; therefore, an encryption module should be fast and processed in minimum time without facing any complexity in a shared network environment. This paper presents a novel symmetric image encryption algorithm in the wavelet transform domain based upon the properties of the Tinkerbell chaotic system along with Arnold's cat map to support confusion and diffusion. Two-Dimensional Discrete wavelet transform (2D-DWT) is used to decompose an image into its wavelet transform domain. The shuffling module is applied to the least significant coefficients using Arnold's cat map, whereas the encryption module deals with the most significant part of the image using Tinkerbell chaotic map based on the stream cipher. All the suitable parameters measure the algorithm's overall performance, and it is evident through results that it can be used for satellite and medical images to provide confidentiality.
Medical Service Chatbot for Basic Medication

The main motto of the health care chatbot is to avoid unnecessary point of contact with the doctors, surgeons, and medical advisors. The chatbots can connect people with text or through voice interface which will make it as an application of artificial conversation and machine learning. As the chatbots are fully operational and an easy tool to communicate, it is fully accessible at all times as there is no requirement of any human for operating. For a basic medication arriving at the hospitals after traveling, a long distance from source to destination is not suggested. Therefore, as a replacement chatbots can fill that requirement in an effective manner in dealing with the complex problems. The whole application is developed using the python web framework Django and the database used is postgreSql. The underlying principle for predicting the disease is decision tree.
Sentiment Analysis of the Indonesian Health Ministry Performance in Covid-19 Crisis using Support Vector Machine (SVM)

Corona Virus Disease or COVID 19 is a new virus disease that originated in 2019 [6], Indonesia has reported first COVID-19 In 2nd March 2020. Various attempts have been made by the government, such as taking strict measures by temporal lockdown or cordoning off the areas that were suspected of having risks of community spread. As a source of information, the internet has changed substantially,. for example, social media. social media is a communication tool that is very popular among internet users today, From social media, users can update status, send messages, even, become a platform for exchanging socio-economic opinions and political views both in their place of residence or their country. This paper deals with the sentiment analysis of Indonesian after the peformance of Indonesian Ministry Of Health. We used the social media platform Twitter for our analysis. Tweets were studied to gauge the opinion of Indonesian towards peformance of Indonesian Ministry Of Health. Tweets were extracted using the two prominent keywords used namely: “terawan ”and “menkes” from June 15th to September 19th 2020. A total of 200 tweets were considered for the analysis. This study has successfully implemented the SVM algorithm for sentiment analysis on tweet data about peformance of Indonesian Ministry Of Health during COVID-19 Crisis. This is shown by the accuracy of using tweet data as much as 200 data, which is 172 data are training data and 28 are testing data. Besides the amount of data that affects accuracy, there are also other factors, namely the use of the kernel and the number of classes used. The results show that the Linear Kernel has the best accuracy, precision and recall rate compared to other kernels, respectively 75% for accuracy, 78.4% for precision and a recall value of 75%. for polynomial kernels, Gaussian and Sigmoid have the same accuracy, precision, and recall rates, namely, respectively. 60.71% for accuracy, 36.86% for precision and 60.71% recall value.
BGC: Multi-agent Group Belief with Graph Clustering

Recent advances have witnessed that value decomposed-based multi-agent reinforcement learning methods make an efficient performance in coordination tasks. Most current methods assume that agents can communicate to assist decisions, which is impractical in some real situations. In this paper, we propose an observation-to-cognition method to enable agents to realize high efficient coordination without communication. Inspired by the neighborhood cognitive consistency (NCC), we introduce the group concept to help agents learn a belief, a type of consensus, to realize that adjacent agents tend to accomplish similar sub-tasks to achieve cooperation. We propose a novel agent structure named Belief in Graph Clustering (BGC) via Graph Attention Network (GAT) to generate agent group belief. In this module, we further utilize an MLP-based module to characterize special agent features to express the unique characteristics of each agent. Besides, to overcome the consistent agent problem of NCC, a split loss is introduced to distinguish different agents and reduce the number of groups. Results reveal that the proposed method makes excellent coordination and achieves a significant improvement in the SMAC benchmark. Due to the group concept, our approach maintains excellent performance with an increase in the number of agents.
The Digitalization of Learning Assessment

Assessment is recognized as significant in the world of education. The field of assessment is seen as one of the most difficult areas to reform due to the various challenges faced by teachers and students during the process of learning. Changes to digital technology in teaching and learning activities have influenced the assessment process in all educational institutions. Current assessment method that is mainly summative is often described as outdated compared to the development of the current world of education which focuses on outcome-based education (OBE). In general, the development of digital technology has changed the implementation of teaching and learning activities which also involves learning assessment. New technologies and tools have long been seen as a new possibility for assessment due to their features or capabilities that are potentially beneficial as they offer a more holistic, immediate and engaging assessment experience. Nevertheless, past studies have concluded that the use of digital technology is not yet ‘transformative’ due to its limited innovation and because its use is still linked to traditional assessment methods. In general, this paper will address the digital technology in teaching practice and the assessment of learning. Keywords: Digitalization, Learning, Assessment, Formative, Summative
Sentiment Analysis of Twitter for Election Prediction

Elections are considered to be the most important feature of a democracy. In the past few years, election analysis and predictions have become very important for political parties and news organizations. The influx of various social media platforms such as twitter, Facebook and YouTube have drawn a large number of people that share their ideological and political thoughts and hence, it’s become important to analyse them in a much more sophisticated manner. Various data mining algorithms have been used to extract tweets and perform sentiment analysis pertaining to a related topic. Sentiment analysis refers to the technique to identify positive, negative or neutral opinions from a text. Though the use of sentiment analysis we will analyse the sentiment score for the two main political parties of India. The paper will brief on various techniques that have been used for election predictions. Various results from different methods have been included in this paper along with precision, accuracy and validity of the final outcome. The main aim of this paper is to create a model for the better prediction that will help in the analysis of voting choices of users. To increase the validity of the final results, various refining techniques have been used so that only relevant tweets are analysed.
A Hybrid ANN with Rao Algorithm Based Optimization (RA + ANN) for Short Term Forecasting of Crypto Currencies

In current days crypto currencies are considered as good investment assets, therefore effective prediction of crypto currencies become a trending area of research in economic domain. Capturing the random movement of such currencies with statistical methods is a challenging task. Nonlinear procedures like artificial neural networks (ANNs) are found suitable for modeling the volatile time series data. However, fixing optimal parameters of ANN through proper training is a tedious job and influences the performance of ANN a lot. This article proposes and analyzes a hybrid ANN, through a recently developed Rao algorithm (RA) based optimization (RA + ANN) for effective prediction of four popular and fast-growing crypto currencies such as Bitcoin, Litecoin, Ethereum, and Ripple. Few other comparative models such as GA + ANN, PSO + ANN, MLP, SVM, LSE, and ARIMA are developed and trained in similar way. All the models are evaluated through mean absolute percentage of error (MAPE) and average relative variance (ARV) metrics. It is found that the proposed RA + ANN generated lowest MAPE and ARV values compared to others and hence can be suggested as a promising financial tool for crypto currencies prediction.