Reviewerly/exHarmony huggingface dataset:
iTurboGraph

With the rise of streaming data for dynamic graphs, large-scale graph analytics meets a new requirement of Incremental Computation because the larger the graph, the higher the cost for updating the analytics results by re-execution. A dynamic graph consists of an initial graph G and graph mutation updates Δ G$ of edge insertions or deletions. Given a query Q, its results $Q(G)$, and updates for Δ G$ to G, incremental graph analytics computes updates Δ Q$ such that Q($G \cup Δ G)$ = $Q(G)$ $\cup$ Δ Q$ where $\cup$ is a union operator. In this paper, we consider the problem of large-scale incremental neighbor-centric graph analytics (\NGA ). We solve the limitations of previous systems: lack of usability due to the difficulties in programming incremental algorithms for \NGA and limited scalability and efficiency due to the overheads in maintaining intermediate results for graph traversals in \NGA. First, we propose a domain-specific language, ŁNGA, and develop its compiler for intuitive programming of \NGA, automatic query incrementalization, and query optimizations. Second, we define Graph Streaming Algebra as a theoretical foundation for scalable processing of incremental \NGA. We introduce a concept of Nested Graph Windows and model graph traversals as the generation of walk streams. Lastly, we present a system \SystemName, which efficiently processes incremental \NGA for large graphs. Comprehensive experiments show that it effectively avoids costly re-executions and efficiently updates the analytics results with reduced IO and computations.
Using blockchain technology to ensure security in the cloud and IoT environment

The Internet of Things (IoT) and cloud technology have been in development for more than a decade. The rapid development of new technology in the modern era has increased the possibility of introducing IoT and cloud technologies in various fields of activity, but at the same time, the dramatic increase in the number of connected devices implementing IoT makes it necessary to take into account the concerns and risks associated with their security. The article investigates the problems that can arise in the cloud and IoT technologies in existing infrastructures, as well as the future development of the cloud and IoT with the use of blockchain technology to solve these problems.
Design of N-Term Scalable High-Performance Modular Multiplication Operator on GF (2<sup>m</sup>)

The higher number of iterations and longer pipeline delays are the major bottlenecks in the performance of Elliptic Curve Cryptography (ECC). Being the core of the arithmetic unit, the design of the high-performance multiplier is an important aspect that directly affects the design of the encryption circuit. In this paper, a full parallel LSB modular multiplication operator for pipelined Karatsuba-Offman Algorithm (KOA) structure is presented. Moreover, an n-term high-performance architecture for scalable modular multiplication operator is also proposed by combining the (b,2)way KA method, which is highly suitable for generalized polynomials. In comparison to existing designs, the proposed multiplier accomplishes lower complexity in time and space that leads to faster encryption with lesser area consumption. The proposed scalable multiplier achieves a 44.73% reduction in time, 36.40% savings in area consumption, and attains 43.98% efficiency in energy consumption.
Efficient Sub-Codeword Key Equation Solver for Generalized Integrated Interleaved BCH Decoder

Generalized integrated interleaved (GII) error-correcting codes nest sub-codewords to form codewords of more powerful codes. They can achieve hyper-speed decoding with good error correction capability. For GII codes built on BCH codes, the first decoding stage is to decode individual BCH sub-words. This stage largely determines the throughput and dominates the area of the overall decoder. Unlike that in traditional BCH decoding, longer polynomials need to be kept in the key-equation solver (KES) step of the first stage in order to continue the KES step in the second-stage nested decoding of GII codes. To take advantage of the very fast storage class memories (SCMs), GII codes with 3-error-correcting BCH sub-codewords can be utilized. This brief proposes a low-complexity and high-speed design for the KES of 3-error-correcting BCH sub-word decoding. Formulas are developed to compute the KES results directly instead of utilizing the traditional iterative process. More importantly, through analyzing the properties of the involved variables, the coefficients are scaled and reformulated to substantially reduce the complexity. Detailed hardware implementation architectures are also developed in this brief. Our design achieves three times throughput with 20.2% smaller area than the best prior design for a code over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$GF(2^{10})$ </tex-math></inline-formula> .
Weight spectra of Gabidulin rank-metric codes and Betti numbers

Abstract We consider q -matroids and their associated classical matroids derived from Gabidulin rank-metric codes. We express the generalized rank weights of a Gabidulin rank-metric code in terms of Betti numbers of the dual classical matroid associated to the q -matroid corresponding to the code. In our main result, we show how these Betti numbers and their elongations determine the generalized weight polynomials for q -matroids, in particular, for the Gabidulin rank-metric codes. In addition, we demonstrate how the weight distribution and higher weight spectra of such codes can be determined directly from the associated q -matroids by using Möbius functions of its lattice of q -flats.
Estimating the Effect of COVID-19 Contact Tracing Application Using Agent-Based Simulation

In this paper, we estimate the effectiveness of COVID-19 contact tracing applications using agent-based simulations.We develop a simulation model and see how many infected patients can be reduced using the applications.In order to investigate the effectiveness of the applications, we enlarge the tracing area from direct contacts to indirect contacts.The results of our agent-based simulation show that detecting indirect contacts can reduce the number of infected patients.
An Analysis on Blockchain Consensus Protocols for Fault Tolerance

The revolution with technologies happens very often, here is one such technology called “Blockchain” which will be a game-changer in many functional sectors of the society. Initially, this technology was developed to support cryptocurrencies (such as Bitcoin, Ethereum, and Litecoin etc).Now a days it is gaining the attention of the researchers in almost the entire field. The consensus protocol in the cryptography and peer to peer architecture is one of the essential parts of the blockchain technology. The best consensus protocol in a blockchain system provides the fault tolerance. These censuses in blockchain are broadly classified in to two types. The absolute-finality consensus protocol and probabilistic-finality consensus protocols. In this paper, the different types of censuses protocols are discussed along with their working, strength and weakness. It also gives the overview on the different types of blockchain technology with their advantages and disadvantages. At the last we also presented a comparison of these consensus protocols by considering different qualitative parameters.
ECC Image Encryption Using Matlab Simulink Blockset

To implement hardware operations on various Xilinx FPGAs, we provide a method of software simulation, providing models (set of Simulink blocks) and presenting the concept of software simulation using Matleb Simulink for processing and encryption of images. This work presents an efficient architecture of various image algorithms, en-cryption, decryption and Keys generate using an elliptic curve, for all kinds of color and grayscale images with a minimum number of generator blocks possible. Example and illustration of these architecture implemented were presented.
An Industrial Assistance System with Manual Assembly Step Recognition in Virtual Reality

In the era of Industry 4.0, worker assistance systems are becoming more and more important. In order to assist shop floor workers in manual assembly tasks, we implemented an assistance system in virtual reality. A deep neural network was trained to recognize the current work step in real-time during an assembly process, thus giving the assistance system context-awareness. We defined the problem of assembly step recognition as a multivariate time series classification using the poses of the workers head, both hands and all relevant tools and objects. With this definition, the VR environment's output can also be replaced with data from the real world. For our proof-of-concept assembly step recognition system, we created an assembly process consisting of six different work steps, five movable assembly parts and one tool. We showed that we can train an activity recognition model for assembly steps with only 10 assembly recordings. To achieve this, we used multiple data augmentation techniques and proposed a novel method of synthesizing new training data, which we call Path Joining. With only 10 training recordings, we attain a categorical classification accuracy of 81 percent and with 60 recordings we achieve an accuracy of 89 percent.
A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup

Failure time data with a cured subgroup are frequently confronted in various scientific fields and many methods have been proposed for their analysis under right or interval censoring. However, a cure model approach does not seem to exist in the analysis of partly interval-censored data, which consist of both exactly observed and interval-censored observations on the failure time of interest. In this article, we propose a two-component mixture cure model approach for analyzing such type of data. We employ a logistic model to describe the cured probability and a proportional hazards model to model the latent failure time distribution for uncured subjects. We consider maximum likelihood estimation and develop a new expectation-maximization algorithm for its implementation. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed method is examined through simulation studies. An application to a set of real data on childhood mortality in Nigeria is provided.
Semi-supervised Text Classification Based On Graph Attention Neural Networks

The text classification task is the most basic and essential in natural language processing. It can find the rules about documents and distinguish the categories of different papers. Nowadays, graph convolutional neural network is widely used in text classification task because of its scalability and high efficiency. However, the GCN model has the best effect at layer 2. As the number of network layers increases, problems such as over smoothing will occur. This paper proposes a semi-supervised text classification model based on a graph attention neural network, which makes full use of the characteristics of information transmission along with the graph by adding residual connections to deepen the number of network layers. Simultaneously, the attention mechanism can be used to give different weights according to the importance of neighboring nodes to target nodes. A dual-layer attention mechanism at the topic level and node level is proposed to learn the importance of different neighboring nodes and extra neighboring node information to target nodes. Experimental results show that this model is superior to other methods on four benchmark datasets.
Video Recognition Techniques for Indian Sign Language in Healthcare Domain

Sign Language is an important mode of communication for the deaf-mute community. Sign language to text conversion models is currently in development and is getting popular day by day with the advancement of deep learning. With this paper, the plan is to create a system that can identify healthcare domain related basic words like doctor, blood, appointment, etc in real time using a webcam. In this paper, 3 different methods are compared for action recognition which gives an estimate of the output. Based on the comparative results, OpenPose + LSTM method is found to be the most effective. This will help in bridging the gap between the normal community and deaf-mute community.
COLPOUSIT: A Hybrid Model for Tourist Place Recommendation based on Machine Learning Algorithms

Tourism is an important sector for a country's economic growth. The travel recommendations should be made focused on better growth and attract more travelers. There is a huge amount of travel information and ideas available on the web that allows the users to make poor travel decisions. This paper focuses on building a hybrid travel recommender system by implementing collaborative-based, popularity-based, and nearby place weighted recommender system. The proposed system recommends the travel spots to the users based upon their interests and other criteria specified. In order to implement these methods, we applied a comparative study on different machine learning algorithms for collaborative-based approach and have performed weighted hybridization. These methods provide a personalized and customized list of similar places with respect to places of interest to the users. Thus, a hybrid system built using these methods provides a better recommendation of places with the advantages of these methods. The obtained results confirm that the hybrid method better than other recommender approaches when used separately.
Augmented Reality and New Opportunities for Cultural Heritage

This chapter considers the potential for Augmented Reality (AR) in cultural heritage, based on a literature review and authors' previous work. Firstly, AR technology is briefly presented and explained. Then, the cultural heritage applications of AR are examined in both the Visualization and Gamification sections. Visualization includes applications that enhance the visitor's experience by blending it with text, sound, or 3D models in a museum or heritage site. Gamification engages AR games about cultural heritage and is aimed at attracting the visitor's attention to inform the visitor about the site in a more entertaining way. Finally, future work is discussed within the context of the Total Augmentation Paradigm.
Hierarchical Lovász Embeddings for Proposal-free Panoptic Segmentation

Panoptic segmentation brings together two separate tasks: instance and semantic segmentation. Although they are related, unifying them faces an apparent paradox: how to learn simultaneously instance-specific and category-specific (i.e. instance-agnostic) representations jointly. Hence, state-of-the-art panoptic segmentation methods use complex models with a distinct stream for each task. In contrast, we propose Hierarchical Lovász Embeddings, per pixel feature vectors that simultaneously encode instance- and categorylevel discriminative information. We use a hierarchical Lovász hinge loss to learn a low-dimensional embedding space structured into a unified semantic and instance hierarchy without requiring separate network branches or object proposals. Besides modeling instances precisely in a proposal-free manner, our Hierarchical Lovász Embeddings generalize to categories by using a simple Nearest-Class-Mean classifier, including for non-instance "stuff" classes where instance segmentation methods are not applicable. Our simple model achieves state-of-the-art results compared to existing proposal-free panoptic segmentation methods on Cityscapes, COCO, and Mapillary Vistas. Furthermore, our model demonstrates temporal stability between video frames.
The Dynamics of Social Engineering and Cybercrime in the Digital Age

Social engineering attacks have emerged to become one of the most problematic tactics used against businesses today. Social engineers employ both human-based and computer-based tactics to successfully compromise their targeted networks. This chapter will discuss the basics of social engineering and what it means today. It will explain some common attack methods like baiting, phishing, pretexting, quid pro quo, tailgating, and dumpster diving. It will then highlight the impact social engineering has had on the rise in cybercrime and why threat actors have grown more innovative. Finally, this chapter will discuss what multi-layer defense or defense in depth is and offer countermeasures that can be enforced to defend against social engineering attacks.
Significant Cloud Computing Service for Secured Heterogeneous Data Storing and its Managing by Cloud Users

SAAS (Storage as a service) is referred as a significant cloud computing (CC) services that support cloud users (CU) to overcome the bottleneck on constrained assets and extent storage without updating their gadgets. To guarantee the protection & privacy of CU, the information is generally outsourced in encrypted format. Nevertheless, encrypted information might produce more storage waste in cloud and complicates the data exchange among authorized users. Even now, some problems exist with encrypted data and deduplication in cloud and storage management. Traditional deduplication methods focus on specific application scenarios, whereas in the cloud, reduplication will be completely controlled by servers or data owners. They might not flexibly fulfill the separate requests obtained from data owners, who rely on sensitivity level of data. In this manuscript, the proposed method flexibly provides the similar time through numerous Cloud service providers [CSPs]. The researchers assess the execution with security examination, comparison &execution.
A Proposal of Interactive Tabu Search for Creating Beverage by Blending Source Juices

Obtaining media content suited to user's feelings is one of the essential targets of engineering. However, it is still difficult because feelings are different between users and are hard to be shown as a certain equation. As a method of a beverage, this study proposed Interactive Tabu Search (ITS) that blends source juices for creating new beverages suited to each user's feelings. Tabu Search is one of stochastic local searches, and its properties are a continuous neighborhood search and a tabu list prohibiting cycling. A target of optimization was the ratio of the source juices. A concrete system based on the proposed ITS was constructed with the computer, Arduino, and peristaltic pumps. A tasting experiment composed of two steps was conducted. The target was delicious blended beverage. As a result, continuous increases in the fitness values related to deliciousness were observed, and a significant increase was observed in the maximum fitness. In the progress of the ratios, both different and common trends between the subjects were observed.
Ready, trainer … one*! discovering the entanglement of adaptive learning with virtual reality in industrial training: a case study

In response to the industrial world becoming more complex and the recent social distancing caused by the COVID-19 outbreak, emerging technologies such as Virtual Reality (VR) are gaining importance for industrial training. The novelty, however, does not lie in the sole use of VR-based technology but rather in its adaptive and flexible nature. This paper explores the synergy between VR environments and adaptive-based learning and tests the proposed approach in the railway sector. An iterative co-design approach is adopted to develop a VR-based training system for educating train conductors. The developed system is validated with ten conductors, where their learning preference, learning outcome, and learning performance were assessed. The results show an increase in individual motivation and perceived usefulness for the developed VR-based training system, demonstrating a higher learning preference, in comparison to the currently used physical simulator. Similarly, the results outline a knowledge gain in two out of six key knowledge subjects for conductor education and indicate that time improvements per exercise are needed to meet organizational performance goals. Consequently, this research presents an attractive alternative to costly and product-specific physical simulators and offers three strategic managerial implications opening new opportunities for using VR in developing learning training programmes.
Towards Source-Based Classification of Image Inpainting Techniques: A Survey

Image inpainting is a process of reconstructing an incomplete image from the available information in a visually plausible way. In the proposed framework, existing image inpainting methods are classified in a new perspective. The information which is referred to, while reconstructing an image, is a critical factor of inpainting algorithms. Source of this information can be host image itself or an external source. The proposed framework broadly classifies inpainting algorithms into introspective and extrospective categories based on the source of information. Various parameters influencing the algorithms under these categories are identified in the proposed framework. A comprehensive list of all publicly available datasets along with the references are also summarized. Additionally, an in-depth analysis of the results obtained with the surveyed techniques is performed based on quantitative and qualitative parameters. The proposed framework aids the user in identifying the most suitable algorithm for various inpainting scenarios.
Improving reliability of manipuri offline signature verification using writer independent paradigms

A person's signature is a significant biometric trait that can be used to identify the person, often required in financial transactions and insurance-related activities. It is well-known that during such transactions, handwritten forgery signatures made on behalf of a genuine user results stealing the money or other means of wealth which was safely kept in the bank or other institutions. Therefore, it is necessary to have a vital safeguard like an automated signature verification system against malicious offenders who threaten society. This paper reports a writer independent offline signature verification system that makes use of genuine and forged signatures written in Manipuri script. Further, a combination of handcrafted geometric features and the features extracted using Convolutional Neural Network (CNN) is used. Then, the combined features' feature space is made optimal using the Genetic Algorithm (GA). This system has achieved a very high-level performance using an ensemble of four pattern classifiers, Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Naive Bayes learning, and Decision Tree Learning. Ensembling of the classifiers is done using logical OR rule and Majority Voting. Experiments are conducted on an original database consisting of Manipuri signatures of 81 individuals. Experimental results are compelling, while the proposed offline signature verification system is compared with the existing system.