Reviewerly/exHarmony huggingface dataset:
MORPHOLOGICAL IMAGE ANALYSIS BASED ON ATTRIBUTE AND RELATIONAL REPRESENTATIONS OF MOSAIC IMAGE MODELS

A unified scheme for morphological analysis based on attribute and relational representations of mosaic image models is proposed. We consider 4 main types of model representation: functional-attribute (2D feature map), functional-relational (4D relational map), structure-resource-attribute (an area list with resources and attributes), and structure-resource-relational (a graph, which nodes correspond to regions and edges – to relations and both having resource attributes). In this case, the forms of representation of the model are equivalent to each other, in the sense that they contain the same information, there is a one-to-one correspondence between them, and the formulas for the transition from one representation to another can be written out explicitly. In this scheme, the construction of specific morphological operator for some complete image model presumes the separation of this model into two parts: the guiding (modifying) part, which determines the transformation algorithm, and the guided (modifiable) part to be transformed. These two parts of the model can intersect, therefore cannot be called “variable” and “constant” components. As a basic sample, we consider the halftone Pyt’ev morphology. We explore the specifics of different-sort models, introduce the mutual models and propose different tools for creation of model-based morphological operators. Further, various other morphological systems can be described and explored using the proposed generalized approach.
Towards an Optimized Containerization of HPC Job Schedulers Based on Namespaces

Recently, container technology is gaining increasing attention and has become an alternative to the traditional virtual machines artifact. The technology is used to deploy large-scale applications in several areas such as Big Data, AI, and High-Performance Computing (HPC). In the HPC field, several management tools exist as Slurm in one hand. On the other hand, the literature has considered many container scheduling strategies. The majority of container scheduling strategies don’t think about the amount of data transmitted between containers. This paper presents a new container scheduling strategy that automatically groups containers that belong to the same group (Namespace) on the same node. In brief, the plan is application-aware as long as someone knows which containers should be grouped in the same Namespace. The objective is to compact the nodes with containers of the same group to reduce the number of nodes used, the communication inter-node costs, and improve containerized applications’ overall Quality-of-Service (QoS). Our proposed strategy is implemented under the Kubernetes framework. Experiments demonstrate the potential of our strategy under different scenarios. Most importantly, we show first that cohabitation between our new scheduling strategy and the default Kubernetes strategy is possible and for the benefit of the system. Thanks to Namespaces, cohabitation is not limited to two methods for scheduling either batch jobs or online services. Second, thanks to the deployment automation, we also demonstrate that multiple Slurm clusters can be instantiated from a pool of bare metal nodes. This reality contributes to the concept of “HPC as a Service.”
Advanced Image Encryption &amp; Decryption using Rubik’s Cube Technology

The world changing at a fast pace and more than ever there’s this need to secure data and preserve one’s privacy. Advanced algorithms and technologies that can be used for secure transmission of texts, images and videos are being tried and tested. We have used the Rubik’s Cube Technology for secure encryption and decryption of colored images.
Blockchain Invoices and High-Quality Development of Enterprises

With the continuous development of national tax information construction projects, more and more scholars have begun to study the basic technology of blockchain and the latest impact and application of blockchain technology in the construction of electronic invoice systems. These studies have important reference significance for promoting the development of electronic invoices and promoting blockchain technology in China. The purpose of this article is to study the blockchain invoice cloud platform as the main research object to study its relationship with the high-quality development of enterprises. The blockchain-based invoice system can well solve the problem of lack of credit for SMEs under traditional financing methods and release and use idle bank credit of high-quality core enterprises. Although the blockchain has achieved “revolutionary” technological breakthroughs, there are still some problems and challenges in practical applications. This article focuses on understanding blockchain invoices and understanding blockchain. The influence of various aspects of blockchain invoices on the development of enterprises is discussed with examples. It can be seen from the experimental results that the development of blockchain invoices will affect the high-quality development of enterprises and is directly proportional to the relationship. 69.4% of companies save a lot of time. It provides a reference for the government and industry to make better use of blockchain technology to promote the development of physical enterprises, create new models of blockchain technology integration, and achieve high-quality development of enterprises.
Optimum Management of Energy Exchanges in Microgrid Electricity Market Based on Blockchain Technology

Blockchain is an emerging technology that due to its unique features, such as decentralization, elimination of intermediaries, immutability and increased security, accuracy and transparency has been highly regarded in various industries, including the smart grid. Creating an electricity market for energy exchanges between producers and consumers in a microgrid is important because of the increasing tendency to use renewable energy such as solar cells. This paper presents two pricing mechanisms based on the Mid-Market Rate and the auction to find the optimal price of energy exchanges, which increases the profit from sales for producers and reduces the cost of purchase for consumers. This paper also proposes three smart contracts for peer-to-peer energy trading which are responsible for executing energy exchanges and enabling the recording of transaction information on the Ethereum network in an encrypted manner with great precision and transparency.
Using Blockchain-Based Attestation Architecture for Securing IoT

When the internet first came along, humans had no idea if it really will disrupt their life and the way they interact with almost everything from healthcare, education, financial services to their day-to-day lifestyle. They had no clue that it would ever have this impact. IoT and edge security is a serious challenge for all the current deployed implementations. Gartner predicts by the end of 2020 that there will be 20.4 billion IoT devices, of which 7.5 billion devices will be for business purposes and 12.8 for consumers. All these devices will be heavily interacting with many endpoints, most of which with a dynamically changing security profile. Organizations around the world are still looking for better solutions and ways to harden their systems while consistently adding new security controls. We are currently in the early phases of blockchain and there is much potential yet to come and be unlocked. In this book chapter, we are going to give an overview of the blockchain and its pillars, and we will offer a new example of integrating a blockchain with IoT to offer a robust IoT security model. In the details of the book chapter, we will show up the mainstream technologies used to accomplish the model.
Fast Extraction Algorithm for Local Edge Features of Super-Resolution Image

Image super-resolution is getting popularity these days in diverse fields, such as medical applications and industrial applications. The accuracy is imperative on image super-resolution. The traditional approaches for local edge feature point extraction algorithms are merely based on edge points for super-resolution images. The traditional algorithms are used to calculate the geometric center of gravity of the edge line when it is near, resulting in a low feature recall rate and unreliable results. In order to overcome these problems of lower accuracy in the existing system, an attempt is made in this research work to propose a new fast extraction algorithm for local edge features of super-resolution images. This paper primarily focuses on the super-resolution image reconstruction model, which is utilized to extract the super-resolution image. The edge contour of the super-resolution image feature is extracted based on the Chamfer distance function. Then, the geometric center of gravity of the closed edge line and the nonclosed edge line are calculated. The algorithm emphasizes on polarizing the edge points with the center of gravity to determine the local extreme points of the upper edge of the amplitude-diameter curve and to determine the feature points of the edges of the super-resolution image. The experimental results show that the proposed algorithm consumes 0.02 seconds to extract the local edge features of super-resolution images with an accuracy of up to 96.3%. The experimental results show that our proposed algorithm is an efficient method for the extraction of local edge features from the super-resolution images.
Review on Mobile Application Development Based on Flutter Platform

Abstract: Covid-19 pandemic has made mobile applications very popular. There is a huge competition between many mobile application development frameworks available in the market. In today’s world ease of development is the thing all developers are seeing for and flutter has come with the required platform to support development of applications for both iOS and Android platforms, consequently decreasing the cost and complexity of application creation across both platforms. Flutter is the crossplatform mobile application development open-source SDK tool for developing high-performance and more reliable mobile applications for iOS and Android operating systems. This paper has been undertaken to indicate the benefits of using flutter over other app development platforms. Keywords: Cross-Platform Mobile Application, Flutter, Dart, GetX State Management, Firebase
A Novel Area-Efficient Fast CORDIC for Energy-efficient Adaptive Exponential Integrate and Fire Neuron Design

The realization of bionic neural models has great challenges on the biological characteristics and hardware efficiency of neurons. This paper proposes an energy efficient Adaptive Exponential Integrate and Fire (AdEx) neuron based on a novel Area-Efficient Fast COordinate Rotation Digital Computer (AEF-CORDIC). The novel AEF-CORDIC has a higher energy efficiency and shorter latency by reducing the iteration times with negligible hardware overhead. FPGA implementation results show that the proposed AEF-CORDIC based AdEx Neuron has good biological characteristics. Compared with conventional CORDIC based design, our design has 48.4% and 52.5% improvement on energy efficiency and processing speed, respectively.
A Tool to Extract Onion Links from Tor Hidden Services and Identify Illegal Activities

Nair, Varun Kannimoola, Jinesh M.The dark web is a covered segment of the Internet that provides privacy-protected network access. Tor is a volunteer run prominent dark web network that becomes heaven for criminals to conduct illegal activities. The use of multilayer encryption to achieve anonymity poses a significant hurdle for the law enforcement agency to monitor illicit activities inside the hidden Network. Our study investigates an alternative method to extract the hidden service descriptor from the network. These descriptors also called onion links open a door to hidden services inside dark web. We use a flaw in the v2 protocol to collect the address of hidden service from the memory of a Tor Hidden Service Directory. Automated data extraction and analyzes module provide more insight into contents propagating in Tor network. Using our experiment setup, 4000 onion links are collected and examined. Our analysis shows that socially unjust materials form significant portions of the Tor network.
Logistic Regression Based on t-Distribution Butterfly Optimization Algorithm

In the field of machine learning (ML) and big data analysis, sample data often needs to be classified. Traditional logistic regression can predict the sample set, and it is also the main method to solve the problem of data classification. Compared with the traditional multiple linear regression, logistic regression uses Sigmoid function to replace the original linear discriminant function, and classifies the data with 0 and 1, which greatly reduces the influence of outliers on the discriminant function. In logistic regression, gradient descent (GD) method is often used to solve the parameters of loss function, however, in such optimal parameter solving process GD has the limitation that the objective function can only be a convex one, and the parameter solved is easy to fall into the trap of local extremum, which cannot ensure the global convergence. In this paper, t-distribution butterfly optimization algorithm (TBOA) is proposed to solve the problem of solving the optimal parameters in the loss function of logistic regression. Compared with the traditional butterfly optimization algorithm (BOA), TBOA uses limit threshold, simplex method and sine cosine algorithm to avoid falling into local extremum in the iterative process; and via numerical experiment, it has been verified that TBOA has better convergence and can better obtain the global optimal solution.
The Connection between Secondary Vocational Education and Applied Undergraduate Education Research on Students’ University Learning Investment

The paradigm of higher education quality evaluation has changed from the traditional focus on resource input to the focus on learners’ learning process. Through the comparative analysis of students majoring in connectivity and ordinary majors, this study finds that students majoring in connectivity have low academic challenge level; Connection majors are better than ordinary majors in teacher-student interaction. Communication and interaction opportunities between students majoring in connection with other majors are small. After entering university, students majoring in connectivity are satisfied with their own development in all aspects and have higher development needs.
Bayesian Network

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an online resource, this textbook is an invaluable tool for the upper undergraduate and graduate student.
Streaming Submodular Maximization Under Differential Privacy Noise

The era of big data has brought the need of fast data stream analysis. Recently the problem of streaming submodular optimization has attracted much attention due to the importance of both submodular functions and streaming analytics. However, in real practical setting, streaming data often comes with noise which causes difficulties in analysing and optimizing submodular functions. In this paper, we study the problem of submodular maximization with cardinality constraint under a noisy streaming model, where the impact of noise is assumed to be small as inspired by the framework of differential privacy (so we also call it DP noise). For this problem, we eventually give a worst-case approximation ratio of \(\frac{1}{\left( 2+\left( 1+\frac{1}{k}\right) ^{2}\right) \left( 1+\frac{1}{k}\right) }-\delta \) in one pass. To complement the theoretical analysis, we also conduct experiments across real datasets to show our algorithm outperforms the baseline streaming methods.
MULTI LABEL CLASSIFICATION FOR AN IMAGE USING CONVOLUTIONAL NEURAL NETWORKS﻿

The machine learning has many capabilities one of them is classification. Classification employed in many contexts like telling hotel reviews good / bad, or inferring the image consists of dog, cat etc. As the data increases there is a need to organize it, for that purpose classification can be helpful. Modern classification problems involve the prediction of multiple labels simultaneously associated with a single instance known as "Multi Label Classification". In multi-label classification, each of the input data samples belongs to one or more than one classes or labels. The traditional binary and multi-class classification problems are the subset of the multi-label classification problem. In this paper we are implementing the multi label classification using CNN framework with keras libraries. Classification can be applied to different domain such as text, audio etc. In this paper we are applying classification for an image dataset.
Video Captioning with External Knowledge Assistance and Multi-feature Fusion

Video captioning aims to describe the main content of a given video in natural language, which has become a research hotspot because of its wide potential application prospect. Semantic information, as a priori knowledge, is often applied to improve the caption quality, but the scope of these semantic information is relatively small, resulting in insufficient coverage of video attributes. In this paper, we introduce external knowledge from ConceptNet to expand the semantic coverage, so that the model can refer to more semantic information. In addition, a multi-feature fusion is proposed to obtain more informative video features and higher quality semantic features. Experimental results on the MSVD and MSRVTT datasets show that the proposed method can greatly improve the caption diversity and model performance, surpass all previous models in all evaluation metrics, and achieve the new state-of-the-art results.
Prediction of Local Government Revenue using Data Mining Method

Local Government Revenue or commonly abbreviated as PAD is part of regional income which is a source of regional financing used to finance the running of government in a regional government. Each local government must plan Local Government Revenue for the coming year so that a forecasting method is needed to determine the Local Government Revenue value for the coming year. This study discusses several methods for predicting Local Government Revenue by using data on the realization of Local Government Revenue in the previous years. This study proposes three methods for forecasting local Government revenue. The three methods used in this research are Multiple Linear Regression, Artificial Neural Network, and Deep Learning. In this study, the data used is Local Revenue data from 2010 to 2020. The research was conducted using RapidMiner software and the CRISP-DM framework. The tests carried out showed an RMSE value of 97 billion when using the Multiple Linear Regression method and R2 of 0,942, the ANN method shows an RMSE value of 135 billion and R2 of 0.911, and the Deep Learning method shows the RMSE value of 104 billion and R2 of 0.846. This study shows that for the prediction of Local Government Revenue, the Multiple Linear Regression method is better than the ANN or Deep Learning method. Keywords— Local Government Revenue, Multiple Linear Regression, Artificial Neural Network, Deep Learning, Coefficient of Determination
Reliability of Segmenting Brain Tumor and Finding Optimal Volume Estimator for MR Images of Patients with Glioma’s

Tumor volume estimation is a significant prognostic part of the Glioma tumor detection. Reliable assessment of Glioma tumor segmentation and volume estimation is a common problem in clinical aspects. We aim to propose a tumor segmentation method by suggesting suitable estimator for MR brain tumor volume construction. Run length algorithm is used to automatic initialize the seed point to the region growing algorithm. Region growing algorithm works with a threshold value using 8 × 8 patches. In this experiment includes thirty BraTS2013 high-grade and low-grade Glioma datasets. Proposed method yield 80.12% of Dice similarity with 6.8% of deviation and 84% of accuracy with 10% of deviation. The proposed work uses six state-of-the-art volume detectors to estimate the size of tumor volume. From the results, Cavalieri’s estimator gives more accurate results with less deviation.
Algorithms for Extracting various Local Texture Features

Abstract In the machine learning and computer vision domain, images are represented using their features. Color, shape, and texture are some of the prominent types of features. Over time, the local features of an image have gained importance over the global features due to their high discerning ability in localized regions. The texture features are widely used in image indexing and content-based image retrieval. In the last two decades, various local texture features have been formulated. For a complete description of images, effective and efficient features are necessary. In this paper, we provide algorithms for 10 local texture feature extraction. These texture descriptors have been formulated since the year 2015. We have designed algorithms so that they are time efficient and memory space-efficient. We have implemented these algorithms and verified their output correctness.
Characterizing Myocardial Ischemia and Reperfusion Patterns with Hierarchical Manifold Learning

We aim at better understanding the mechanisms of ischemia and reperfusion, in the context of acute myocardial infarction. For this purpose, imaging and in particular magnetic resonance imaging are of great value in the clinic, but the richness of the images is currently under exploited. In this paper, we propose to characterize myocardial ischemia and reperfusion patterns across a population beyond the scalar measurements used in the clinic. Specifically, we adapted representation learning techniques to not only characterize the population distribution in terms of scar and microvascular obstruction patterns, but also regarding the appearance of late gadolinium images which reflects tissue heterogeneity. To do so, we implemented a hierarchical manifold learning approach where the embedding from a higher-level content (the images) is guided by one from a lower-level content (the infarct and microvascular obstruction segmentations). We demonstrate its relevance on 1711 late gadolinium enhancement slices from 123 patients with acute ST-elevation myocardial infarction. We designed ways to balance the contribution of each level in the hierarchy, and quantify its impact on the overall distribution and on sample neighborhoods. We notably observe that the obtained latent space is a balanced contribution between the two levels of the hierarchy, and is more robust to challenging images subjected to artifacts or specific lesion patterns.