Reviewerly/exHarmony huggingface dataset:
The Importance of Local Labels Distribution and Dominance for Node Classification in Graph Neural Networks

Relational datasets extracted from citation networks, recommender systems, and social networks among others open up a new application domain of Artificial Intelligence (AI) over graph-structured data. However, the irregular structure of graph data hinders the direct application of traditional Deep Learning algorithms such as Convolutional Neural Networks (CNNs). Various Machine Learning algorithms attempt to embed graphs in regular low dimensional space to benefit from existing AI tools. In this work, we propose a comprehensive comparison between state-of-the-art Graph Neural Networks models (GNNs) which defines node embeddings by mimicking convolution on graphs following a recursive neighboring-aggregation schema. For node classification tasks, we prove that the GNN variants which capture local labels' distribution and dominance are more likely to generate effective node representation vectors. We demonstrate that the GNN variant Graph Isomorphism Networks which proved powerful on graph classification tasks, fails to preserve its efficiency when it comes to node classification, because it gives more attention to the structural information in the graph.
Anomaly Detection in Unsupervised Surveillance Setting Using Ensemble of Multimodal Data with Adversarial Defense

Autonomous aerial surveillance using drone feed is an interesting and challenging research domain. Along with surveillance, an autonomous device should be capable of detecting device malfunction or abnormality in real time. However, the inherent uncertainty embedded within the type and level of abnormality makes supervised techniques unsuitable since the adversary may present a unique anomaly for intrusion. To counter this, in this paper, we propose an unsupervised ensemble anomaly detection system to detect device anomaly of an unmanned drone analyzing multimodal data like images and IMU (Inertial Measurement Unit) sensor data synergistically. We have proposed AngleNet and used autoencoder to analyze image and IMU data, respectively and later ensembled the two pipelines for predicting degree of abnormality of the device. Furthermore, we have applied adversarial attack to test the robustness of the proposed approach and integrated defense mechanism. The proposed method performs satisfactorily on the IEEE SP Cup-2020 dataset with an accuracy of 97.8%.
Geo-Indistinguishablility for Crowdsourced-Based Radio Environment Map Construction

The aim of this paper is to preserve location privacy of crowdsourced-based spectrum sensing agents using geo-indistinguishability. We considered database-driven dynamic spectrum access, where a radio environment map provides spectrum availability information for dynamic spectrum access management. Moreover, we assumed crowdsourced-based spectrum sensing, where a pool of allocated mobile users, called crowdsourced-based spectrum sensing agents, sense the spectrum and report their actual location and the received signal strength to the spectrum manager that constructs a radio environment map. This discloses location information of crowdsourced-based spectrum sensing agents and violates their location privacy. Consequently, crowdsourced-based spectrum sensing agents could be discouraged to participate in spectrum sensing. In our paper, to solve the problem of location disclosure, we adopted planar Laplacian mechanism, where each crowdsourced-based spectrum sensing agent reports an obfuscated location instead of its actual location, which achieves geo-indistinguishability. Our simulation results were based on real-world CRAWDAD dataset. Our results showed that with a moderate privacy level, location privacy of crowdsourced-based spectrum sensing agents was preserved while the effect of introduced location noise on the accuracy of radio environment map was insignificant.
Image compact‐resolution and reconstruction using reversible network

The dual problem of image super-resolution (SR), which is referred to as compact-resolution (CR), and the corresponding image reconstruction are studied. These two problems have been studied independently by the researchers. In this study, a novel model for image CR and the corresponding reconstruction using the reversible network has been proposed. The reversible network has two properties, the first property, lossless information forwarding, which makes the compact-resolved image retain more information from the original HR image. The second property, bidirectional mapping, by which the forward and reverse propagation of a reversible network can be utilised to implement image CR and reconstruction, respectively, i.e. using the reverse process of image CR to guide the reconstruction. In addition, the utilisation of a reversible network may reduce the size of the model. The superiority of the proposed model was demonstrated by comparing its performance with the state-of-the-art methods on four well-known benchmark datasets.
Dirichlet process mixture models with shrinkage prior

We propose Dirichlet process mixture (DPM) models for prediction and cluster‐wise variable selection, based on two choices of shrinkage baseline prior distributions for the linear regression coefficients, namely, the Horseshoe prior and the Normal‐Gamma prior. We show in a simulation study that each of the two proposed DPM models tends to outperform the standard DPM model based on the non‐shrinkage normal prior, in terms of predictive, variable selection, and clustering accuracy. This is especially true for the Horseshoe model and when the number of covariates exceeds the within‐cluster sample size. A real data set is analysed to illustrate the proposed modelling methodology, where both proposed DPM models again attained better predictive accuracy.
Spatial Aggregation Analysis and Systemic Risk Prevention of Internet Finance Based on Neural Network Model

Abstract With the development of science and technology, the application of neural network model is more and more extensive. In order to avoid the outbreak of financial crisis, economists and policy makers around the world pay special attention to the timely warning of financial crisis. If we can make an accurate early warning of the economic crisis, the government and regulatory agencies will have more time to deal with the possible risks and avoid major interference to economic growth. Based on this, this paper studies the spatial aggregation analysis and systemic risk prevention of Internet Finance Based on neural network model. Based on BP neural network, this paper constructs a spatial econometric model by using spatial econometric analysis method, and analyzes the empirical results of financial spatial aggregation. The results show that the Moran’s I value of China’s Internet finance index in December 2019 is 0.372, P value is 0.001, Moran’s I value of Internet payment is 0.395, P value is 0.002, Moran’s I value of Internet monetary fund is 0.357, P value is 0.001, Moran’s I value of Internet investment and financing is 0.324, P value is 0.002, Moran’s I of Internet insurance is 0.313, P value is 0.001. The results show that there is spatial agglomeration in the development of Internet finance in China. In addition, this paper also studies the systemic risk of Internet finance, analyzes its causes and puts forward corresponding preventive measures.
Distributed-Memory Parallel Symmetric Nonnegative Matrix Factorization

We develop the first distributed-memory parallel implementation of Symmetric Nonnegative Matrix Factorization (SymNMF), a key data analytics kernel for clustering and dimensionality reduction. Our implementation includes two different algorithms for SymNMF, which give comparable results in terms of time and accuracy. The first algorithm is a parallelization of an existing sequential approach that uses solvers for non symmetric NMF. The second algorithm is a novel approach based on the Gauss-Newton method. It exploits second-order information without incurring large computational and memory costs. We evaluate the scalability of our algorithms on the Summit system at Oak Ridge National Laboratory, scaling up to 128 nodes (4,096 cores) with 70% efficiency. Additionally, we demonstrate our software on an image segmentation task.
Comparative Survey on Single Password Authentication Techniques

Passwords are the most common way of authentication. Generally, people tend to use the same password for numerous online servers. However, passwords have various vulnerabilities, including offline dictionary attacks, phishing, honeypots, keyloggers, and man-in-the-middle attacks. Single Password Authentication (SPA) techniques enable users to be secure with a single password. Throughout this article, SPA protocol, and the attacks that password are vulnerable to are introduced. Then, a detailed survey of existing SPA systems is proposed. The existing SPA schemes are compared, and we conclude with future research directions.
Construction and Application of Smart Education Teaching Platform

Abstract With the continuous development of science and technology, my country has gradually entered the era of big data. In this case, various Internet technologies and teaching software continue to appear and are widely used in classrooms. Therefore, this article discusses how to make better use of intelligent learning platforms in terms of big data to improve the quality of education and serve platform users. This system divides teaching work into basic modules and provides a network application platform for smart classrooms. Through the connection with the education cloud platform, it can identify the sharing of high-quality educational resources, expand its implementation and promotion, and use intelligent learning. The platform is the test group, and traditional education is the control group for all research fields and research. The research results show that the intelligent learning platform is a new type of service platform that has a certain impact on the traditional education model. However, from another perspective, the technology has improved the traditional education model in an innovative way. This model can be used to realize intelligent learning through the use of Internet technology, and can solve various problems in the field of education, increase the allocation of reasonable educational resources and significantly improve the quality of education.
Robust Incremental Logistic Regression for Detection of Anomaly Using Big Data

Nowadays a lot of data are being continuously or incrementally collected at various fields through IoT (Internet of Things) devices and sensors. To extract useful patterns from these huge volumes of data, pattern recognition and machine learning techniques are applied, which build models by extracting patterns from data at once, and the models are not updated until the performances of the models deteriorate significantly. This traditional approach of learning a model using all the data at once may not be feasible in many applications because it may require a huge communication cost and storage to collect the data and take a very long time to build a model, and the onetime built model may not be able to learn the changed patterns automatically over time. To overcome some of the limitations of the traditional approach, we propose a new method called Robust Incremental Logistic Regression (RILR), which learns and updates model parameters as new batches of training data arrive. We show the effectiveness of the proposed method by performing experiments with 10 publicly available data sets and evaluating it in terms of AUC (Area Under the receiver operating characteristic Curve) on test data, robustness, execution time, and storage requirement. Experimental results suggest that our proposed method is able to achieve the desired performance on most of the data sets.
Finite‐time distributed block‐decomposed information filter for nonlinear systems with colored measurement noise

Abstract This paper considers the distributed filtering problem for discrete‐time nonlinear systems with colored measurement noise obeying a nonlinear autoregressive process in sensor networks. A novel block‐decomposed information‐type filter for such systems is proposed in a centralized fusion structure, by using the statistical linear regression to deal with model nonlinearities and the measurement difference approach to overcome the noise correlation caused by colored measurement noises. Meanwhile, with the help of block matrix inverse operation to realize the high‐dimensional block matrix decomposition, the information vector and information matrix of the original system state in the designed information filter is directly estimated recursively, so as to own good numerical stability. Then, the finite‐time distributed implementation of the proposed filter is put forward, where the final filtering estimate in each sensor node is consistent with the centralized filtering result, by ensuring that each sensor node directly obtains the average values of shared variables in the sensor network through finite iterations of average consensus. Finally, the posterior Cramér–Rao lower bound is derived to show the proposed filter reaches the optimal theoretical performance bound in the premise of statistical linear regression and Gaussian posterior probability density approximation. A target tracking example with colored measurement noise obeying a linear and a nonlinear autoregressive processes validates the proposed method.
WITHDRAWN: Robust frequency affinity-based high utility itemset mining approach using multiple minimum utility

High-Utility-Itemset Mining (HUIM) is intended to discover exceedingly significant patterns by considering buy amounts and unit benefits of things. The greater part of the HUIM calculations is intended for static databases. In the present genuine applications, for example, market basket examination, business decision making and association data of web administrations, extensive volumes of databases are expanding slowly through embedding new information. The customary mining calculations are not reasonable for handling these dynamic databases and extricating helpful data. In this paper, a novel mining approach is introduced for incremental transactional databases to alter the discovered high utility itemsets. Instead of high utility values in each transaction, an affinity utility value is employed for discovering highly profitable itemsets with strong frequency affinity. Along with this, an upper bound knowledge-weighted utilization (KWU) is utilized to maintain the downward closure property. Wide spread experimental analyses showed that the suggested Itemset Mining approach is very effective and scalable for mining fascinating HUIP mining.
A versatile offset operator for the discrete observation of objects

The possibility of reconstructing the response of a system, starting from the response of a reference one, has always been a point of great interest in structural dynamics. This is proved by the increasing attention about the similitude and scaling methods, although the associated predictions are still not fully reliable. Therefore, the aim of this paper is to present a method aimed at defining a transformation matrix which links the outputs between two sets of points belonging to different linear systems. This matrix used for the transformation can be invoked for both deterministic and stochastic loads. As preliminary and validating test case, the reconstruction of the displacement responses of two plates subjected to deterministic load is presented. The results show that an exact estimation of the frequency response is achieved at the degrees of freedom used for defining the transformation, as expected.
MPI Meets Cloud: Case Study with Amazon EC2 and Microsoft Azure

Scientists have traditionally employed High Performance Computing (HPC) systems to scale parallel scientific applications. Over the last decade, cloud based systems have been trying to meet the performance of bare-metal hardware while keeping the benefits of computing-as-service model intact. Today's HPC cloud offerings such as Microsoft Azure H-series and Amazon EC2 provide near native performance, high cus-tomizability, resource provisioning and isolation, and elasticity to dynamically scale applications. MPI is the defacto programming model used to scale these scientific applications on to large-scale supercomputers. While several studies have been carried out to understand the performance of MPI libraries and applications on various HPC systems, understanding the characteristics of them on the state-of-the-art HPC clouds is still lacking in the literature. In this paper, we provide a systematic study of HPC applications and benchmarks on two popular HPC clouds. The overarching goal is to understand the performance characteristics of HPC clouds and study the impact of customizability on the usage of MPI. We do a case study of performance optimization on two popular HPC clouds, and obtain up to 150% improvement in point-to-point communication bandwidth. We also compare the performance of several different MPI libraries for various micro-benchmarks and scientific applications (WRF) on two HPC clouds and one native supercomputer (Frontera). Based on our insights gained from this study, we provide a set of guidelines for MPI application developers to scale their applications on clouds and achieve the best performance.
Data Interactive Access Gateway for Internal and External Networks in Dual Network Isolation Environment

This paper designs and provides an internal and external network data interactive access gateway for dual-network isolation architecture. Based on asynchronous communication and advanced queue technology, the external mobile applications can efficiently access the intranet business service data by transforming and reconstructing the transmission content of the entire internal and external network interaction link. It can not only reuse the existing intranet service data, but also ensure data security and reduce the difficulty of development and deployment. The response time is shortened by request concurrent monitoring and advanced queue technology, which can meet the control requirements of the production environment for response time and server pressure during high concurrency. The interaction of TCP long connections between internal and external networks is realized through the sorting and assembly of data packets and the one-to-one matching of internal and external network sessions. At the same time, it also provides security control functions such as service registration and identity authentication to ensure the safety and credibility of the transmitted data.
Entity Relation Extraction Method based on Fusion of Multiple Information and Attention Mechanism

Entity relation extraction, as the core task and important link in the fields of information extraction, natural language understanding and information retrieval, aims to extract structured entities from a large amount of unstructured text and to extract semantic relations between entities. The entity relation extraction technology based on deep learning has gradually surpassed the traditional method based on features and kernel functions in terms of the depth of feature extraction and the accuracy of model. This paper proposes a multiple model fusion strategy, adds the Convolutional Neural Networks (CNN) on the basis of the original model of Bi-directional Long Short-Term Memory (Bi-LSTM), uses the syntactic structure information of syntactic information and the shortest dependent path separately as input of the experiment, and introduces the attention mechanism to study the dependency relations between the words to capture the internal semantic information of a sentence. The experiment shows that the effect of entity relation extraction based on the fusion of multiple information and attention mechanism is significantly improved than that of a single information model.
E-fraud economy as an emergent perspective towards the corpus of African hustler narratives

The different categories of African hustler narratives represent particular ways of addressing the shortcomings of the African postcolonial economy from the perspective of the ultimately self-defeating responses of individual characters. This article examines the narrative significance of e-fraud literature as a subset of African hustler narratives. As an e-fraud novel, Adaobi Tricia Nwaubani’s I Do Not Come to You by Chance follows the lives of e-scammers for whom e-fraud practice is an alternative to the exclusionary Nigerian postcolonial economy. However, in embracing e-fraud as an alternative to their economic exclusion, e-fraudsters in Nwaubani’s novel appropriate a deceptive digital geography that is in dialogue with the same exploitative and extractive Nigerian postcolonial economic landscape which they seek to circumvent. In this way, the novel articulates the extent to which the performance of e-fraud economy as a hustle economy intersects with the arbitrariness and decline of Nigerian postcolonial economy.
WITHDRAWN: Real time analysis of unmask face detection in human skin using tensor flow package and IoT algorithm

In covid19, security provided by Indian government, due to severe virus speed to sudden death. This proposed method solved face detection, mask detection and thermal value monitoring for security purpose. We need amount of testing and need tests 50000/day to measure count of infection. Government produces a report that maintain social distancing, wearing mask when in outside and proper testing. Most people do not serious to wearing mask. We focused the counting of Unmask-persons using opencv and MTCNN algorithm. Faces conditions are changing due to various atmospheric season and light vision. The MTCNN algorithm adjusted video streaming data with Mask detection accuracy at >90%. A database stored in Excel file or send Emergency message to email or buzzer kept on, when unmask visit at any places like that shopping complex, hotel, hospital, Traffic areas and temple. MTCNN- Multi-Task Convolutional Neural Network algorithm solved facial recognition problems. We have proposed the algorithm for Mask detection and Thermals value detection using Pycharm-python and implemented in Raspberry pi4. Support Vector Machine (SVM) supported tasks of MTCNN and stored in database. The outcome of the system is Alert through monitor and SMS to officials Government persons, when capturing unmasked persons visit in live streaming video using Opencv – Python.
Evaluation and Recommendation for Scrum Implementation Improvement with Hybrid Scrum Maturity Model: A Case Study of A New Telco Product

In 2019, a telco company launched a new product for the youth. It has the tagline “Everything You Want” that gave young customers the freedom to bundle their mobile communication packages. The Blue team developed the product guided by Scrum, However, there were troubles during software development, such as missing the targeted deliverables. A measurement of the Scrum process maturity level was conducted to address the problems. It followed the modified Scrum Maturity Model, which was updated from the 2017 Scrum Guide and Scrum Body of Knowledge version 3. The assessment showed that the Blue team achieved maturity level 1 (Initial). It was attained with 16 “Fully Achieved” practices, 49 “Largely Achieved” practices, and 14 “Partially Achieved” practices. Once the maturity level and existing problems were identified, recommendations based on Scrum components (Scrum roles, Scrum artifacts, and Scrum events) were proposed. It aimed to improve Scrum practices that had not been fully achieved by the Blue team.