Reviewerly/exHarmony huggingface dataset:
Power up! Robust Graph Convolutional Network via Graph Powering

Graph convolutional networks (GCNs) are powerful tools for graph-structured data. However, they have been recently shown to be vulnerable to topological attacks. To enhance adversarial robustness, we go beyond spectral graph theory to robust graph theory. By challenging the classical graph Laplacian, we propose a new convolution operator that is provably robust in the spectral domain and is incorporated in the GCN architecture to improve expressivity and interpretability. By extending the original graph to a sequence of graphs, we also propose a robust training paradigm that encourages transferability across graphs that span a range of spatial and spectral characteristics. The proposed approaches are demonstrated in extensive experiments to simultaneously improve performance in both benign and adversarial situations.
Multiscale Vision Transformers

We present Multiscale Vision Transformers (MViT) for video and image recognition, by connecting the seminal idea of multiscale feature hierarchies with transformer models. Multiscale Transformers have several channel-resolution scale stages. Starting from the input resolution and a small channel dimension, the stages hierarchically expand the channel capacity while reducing the spatial resolution. This creates a multiscale pyramid of features with early layers operating at high spatial resolution to model simple low-level visual information, and deeper layers at spatially coarse, but complex, high-dimensional features. We evaluate this fundamental architectural prior for modeling the dense nature of visual signals for a variety of video recognition tasks where it outperforms concurrent vision transformers that rely on large scale external pre-training and are 5-10x more costly in computation and parameters. We further remove the temporal dimension and apply our model for image classification where it outperforms prior work on vision transformers. Code is available at: this https URL
Information Security Assessment in Big Data Environment using Fuzzy Logic

In recent years, it has been observed that disclosure of information leads to the risk. Without restrict the accessibility of information providing security is difficult. So, there is a demand of time to fill the gap between security and accessibility of information. In fact, security tools should be usable for improving the security as well as the accessibility of information. Though security and accessibility are not related directly, but some of their factors indirectly affect each other. Attributes play an important role in connecting the gap among security and accessibility. In this paper, finds the main attributes of security and accessibility that impact directly and indirectly each other such as confidentiality, integrity and availability and severity. The significance of every attribute in terms of their weight is important for their effect on the overall security during the big data security life cycle process. To calculate proposed work, researchers used the Fuzzy Analytic Hierarchy Process (Fuzzy AHP).
An Optimal Teaching and Learning based Optimization with Multi-Key Homomorphic Encryption for Image Security

Due to the drastic rise in multimedia content, digital images have become a major carrier of data. Generally, images are communicated or archived via wireless communication changes, and the significance of data security gets increased. In order to accomplish security, encryption is an effective technique which is used to encrypt the images using secret keys in such a way that it is not readable by the hacker. In this view, this study focuses on the design of Teaching and Learning based Optimization (TLBO) with Multi-Key Homomorphic Encryption (MHE) technique, called MHE-TLBO algorithm. The goal of the MHE-TLBO algorithm is to optimally select multiple keys using TLBO algorithm for encryption and decryption processes. In addition, the MHE-TLBO algorithm has derived a fitness function involving peak signal to noise ratio (PSNR) and thereby ensures the superior quality of the reconstructed image. For validating the security performance of the MHE-TLBO algorithm, a comprehensive result analysis is made and the simulation results ensured the betterment of the MHE-TLBO algorithm interms of different aspects.
Polarization descattering imaging: a solution for nonuniform polarization characteristics of a target surface

This paper presents a polarization descattering imaging method for underwater detection in which the targets have nonuniform polarization characteristics. The core of this method takes the nonuniform distribution of the polarization information of the target-reflected light into account and expands the application field of underwater polarization imaging. Independent component analysis was used to separate the target light and backscattered light. Theoretical analysis and proof-of-concept experiments were employed to demonstrate the effectiveness of the proposed method in estimating target information. The proposed method showed superiority in accurately estimating the target information compared with other polarization imaging methods.
Knowledge-based systems for blockchain-based cognitive cloud computing model for security purposes

Today, artificial intelligence (AI) can use the most powerful edge computing systems in the Internet of Things (IoT) for finding the information extracted from vast sensory data such as cyber effects or models in physical environments for classification, identification, and prediction. Heterogeneous IoT devices produce isolated and dispersed information parts, and knowledge sharing and exchange in IoT intelligent applications with several selfish nodes are necessary for complex tasks. In both academia and business, IoT is driving a digital revolution. However, protection and IoT privacy problems are challenged. It offers comfort for everyday lives. Blockchain, a shared cryptographic database, is a promising IoT encryption solution for several manufacturing, finance, and trade sectors. The IoT-based blockchain architecture is an interesting contrast to the conventional, centralized paradigm that struggles to fulfill specific IoT requirements. New concepts for applying data and resources management protection procedures in distributed networks and cloud computing are introduced. Cloud management services can be linked to the application through blockchain technology and distributed leader, a stable cognitive information system that facilitates management operations and securing data. This document provides many ideas for applying personal and behavioral characteristics to security and cryptography protocols, blockchain based on the cognitive cloud computing (BC-CCC) pattern. The simulation result shows that the proposed strategy can significantly enhance data transmission rate (96.2%), security ratio (94.5%), throughput ratio (92.4%), scalability ratio (91.5%), trust rate (93.8%), data trading ratio (96.2%), and reduce storage cost rate (25.1%) compared to other existing methods.
Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer

Embedding learning for categorical features is crucial for the deep learning-based recommendation models (DLRMs). Each feature value is mapped to an embedding vector via an embedding learning process. Conventional methods configure a fixed and uniform embedding size to all feature values from the same feature field. However, such a configuration is not only sub-optimal for embedding learning but also memory costly. Existing methods that attempt to resolve these problems, either rule-based or neural architecture search (NAS)-based, need extensive efforts on the human design or network training. They are also not flexible in embedding size selection or in warm-start-based applications. In this paper, we propose a novel and effective embedding size selection scheme. Specifically, we design an Adaptively-Masked Twins-based Layer (AMTL) behind the standard embedding layer. AMTL generates a mask vector to mask the undesired dimensions for each embedding vector. The mask vector brings flexibility in selecting the dimensions and the proposed layer can be easily added to either untrained or trained DLRMs. Extensive experimental evaluations show that the proposed scheme outperforms competitive baselines on all the benchmark tasks, and is also memory-efficient, saving 60% memory usage without compromising any performance metrics.
New Model of Sustainable Supply Chain Finance Based on Blockchain Technology

Blockchain technology has received more and more attention from all walks of life and has also been sought after by the market and financial institutions. More and more financial institutions and Internet companies have begun to participate in the research of blockchain technology. The application scenarios are also constantly expanding, and the landing in the financial field is the focus of all social concerns. This article takes Agricultural Bank as an example, analyzes its current advantages and disadvantages in using blockchain technology, and proposes policy recommendations based on this to provide a reference for joint-stock commercial banks to better use blockchain technology to improve business performance. Based on the theoretical analysis, this article studies the use of blockchain technology by joint-stock commercial banks, and selects Agricultural Bank, which has a well-developed financial technology in joint-stock commercial banks, as a case study. From the operating background of the agricultural bank and the cross-border payment and settlement business, the background begins with the agricultural bank's own business practices in blockchain technology and the traditional cross-border business and the innovative development of agricultural bank's blockchain technology, and then the application of event analysis to the application effects of agricultural bank's blockchain technology analysis. This paper analyzes the SWOT-PEST of the agricultural bank's use of blockchain technology, combined with the theoretical analysis of blockchain technology in this article, comprehensively analyzes the agricultural bank's use of blockchain technology in four aspects: policy, economy, society, and technology. Strengths, weaknesses, opportunities, and challenges. Through analysis, we can have a comprehensive view on the application of blockchain technology by Agricultural Bank, and based on this, we can summarize the problems that Agricultural Bank may have in applying blockchain technology. Through the analysis of the blockchain, we can see that the growth rate of agricultural bank's net profit was higher in 2013 and 2014. Finally, the article puts forward suggestions for the development of agricultural bank under the blockchain technology.
TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network

To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities.Although the human effort is reduced, the generated incomplete and noisy annotations pose new challenges for learning effective neural models.In this paper, we propose a novel dictionary extension method which extracts new entities through the type expanded model.Moreover, we design a multi-granularity boundaryaware network which detects entity boundaries from both local and global perspectives.We conduct experiments on different types of datasets, the results show that our model outperforms previous state-of-the-art distantly supervised systems and even surpasses the supervised models.
Optimizing Fixation Filters for Eye-Tracking on Small Screens

The study of consumer responses to advertising has recently expanded to include the use of eye-tracking to track the gaze of consumers. The calibration and validation of eye-gaze have typically been measured on large screens in static, controlled settings. However, little is known about how precise gaze localizations and eye fixations are on smaller screens, such as smartphones, and in moving feed-based conditions, such as those found on social media websites. We tested the precision of eye-tracking fixation detection algorithms relative to raw gaze mapping in natural scrolling conditions. Our results demonstrate that default fixation detection algorithms normally employed by hardware providers exhibit suboptimal performance on mobile phones. In this paper, we provide a detailed account of how different parameters in eye-tracking software can affect the validity and reliability of critical metrics, such as Percent Seen and Total Fixation Duration. We provide recommendations for producing improved eye-tracking metrics for content on small screens, such as smartphones, and vertically moving environments, such as a social media feed. The adjustments to the fixation detection algorithm we propose improves the accuracy of Percent Seen by 19% compared to a leading eye-tracking provider's default fixation filter settings. The methodological approach provided in this paper could additionally serve as a framework for assessing the validity of applied neuroscience methods and metrics beyond mobile eye-tracking.
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction

Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English.While the advance of pretrained multilingual encoders suggests an easy optimism of "train on English, run on any language", we find through a thorough exploration and extension of techniques that a combination of approaches, both new and old, leads to better performance than any one crosslingual strategy in particular.We explore techniques including data projection and selftraining, and how different pretrained encoders impact them.We use English-to-Arabic IE as our initial example, demonstrating strong performance in this setting for event extraction, named entity recognition, part-of-speech tagging, and dependency parsing.We then apply data projection and self-training to three tasks across eight target languages.Because no single set of techniques performs the best across all tasks, we encourage practitioners to explore various configurations of the techniques described in this work when seeking to improve on zero-shot training.
Towards a Zero-Trust Micro-segmentation Network Security Strategy: An Evaluation Framework

Micro-segmentation is an emerging security technique that separates physical networks into isolated logical micro-segments (workloads). By tying fine-grained security policies to individual workloads, it limits the attacker’s ability to move laterally through the network, even after infiltrating the perimeter defences. While micro-segmentation is proved to be effective for shrinking enterprise networks attack surface, its impact assessment is almost absent in the literature. This research is dedicated to developing an analytical framework to characterise and quantify the effectiveness of micro-segmentation on enhancing networks security. We rely on a twofold graph-feature-based framework of the network connectivity and attack graphs to evaluate the network exposure and robustness, respectively. Tracking the variations of formulated metrics values post the deployment of micro-segmentation reveals exposure reduction and robustness improvement in the range of 60% – 90%.
Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma

Background An accurate pathological diagnosis of hepatocellular carcinoma (HCC), one of the malignant tumors with the highest mortality rate, is time-consuming and heavily reliant on the experience of a pathologist. In this report, we proposed a deep learning model that required minimal noise reduction or manual annotation by an experienced pathologist for HCC diagnosis and classification. Methods We collected a whole-slide image of hematoxylin and eosin-stained pathological slides from 592 HCC patients at the First Affiliated Hospital, College of Medicine, Zhejiang University between 2015 and 2020. We propose a noise-specific deep learning model. The model was trained initially with 137 cases cropped into multiple-scaled datasets. Patch screening and dynamic label smoothing strategies are adopted to handle the histopathological liver image with noise annotation from the perspective of input and output. The model was then tested in an independent cohort of 455 cases with comparable tumor types and differentiations. Results Exhaustive experiments demonstrated that our two-step method achieved 87.81% pixel-level accuracy and 98.77% slide-level accuracy in the test dataset. Furthermore, the generalization performance of our model was also verified using The Cancer Genome Atlas dataset, which contains 157 HCC pathological slides, and achieved an accuracy of 87.90%. Conclusions The noise-specific histopathological classification model of HCC based on deep learning is effective for the dataset with noisy annotation, and it significantly improved the pixel-level accuracy of the regular convolutional neural network (CNN) model. Moreover, the model also has an advantage in detecting well-differentiated HCC and microvascular invasion.
Improved method of optimization-based ECG signal watermarking

We introduce a robust watermarking method to efficiently embed multiple marks into ECG signals using redundant discrete wavelet transform and generalized singular value decomposition. Multiple marks are embedded within the signal to eliminate any ownership conflict and provide copyright protection. The hybrid optimization technique is also used to balance the invisibility and robustness. Further, wavelet-based compression is adopted to compress the marked signal before transmission reduces the bandwidth demand. To evaluate the proposed algorithm, the standard ECG dataset is used. The proposed algorithm yields very good invisibility and robustness against several attacks, and it is also suitable for applications in the cloud.
Human motion modeling with deep learning: A survey

The aim of human motion modeling is to understand human behaviors and create reasonable human motion like real people given different priors. With the development of deep learning, researchers tend to leverage data-driven methods to improve the performance of traditional motion modeling methods. In this paper, we present a comprehensive survey of recent human motion modeling researches. We discuss three categories of human motion modeling researches: human motion prediction, humanoid motion control and cross-modal motion synthesis and provide a detailed review over existing methods. Finally, we further discuss the remaining challenges in human motion modeling.
Blockchain Technology for Management of Intangible Cultural Heritage

With the development of information technology and network technology, digital archive management systems have been widely used in archive management. Different from the inherent uniqueness and strong tamper-proof modification of traditional paper archives, electronic archives are stored in centralized databases which face more risks of network attacks, data loss, or stealing through malicious software and are more likely to be forged and tampered by internal managers or external attackers. The management of intangible cultural heritage archives is an important part of intangible cultural heritage protection. Because intangible heritage archives are different from traditional official archives, traditional archive management methods cannot be fully applied to intangible heritage archives’ management. This study combines the characteristics of blockchain technology with distributed ledgers, consensus mechanisms, encryption algorithms, etc., and proposes intangible cultural heritage file management based on blockchain technology for the complex, highly dispersed, large quantity, and low quality of intangible cultural heritage files. Optimizing methods, applying blockchain technology to the authenticity protection of electronic archives and designing and developing an archive management system based on blockchain technology, help to solve a series of problems in the process of intangible cultural heritage archives management.
Risk Prediction by Using Artificial Neural Network in Global Software Development

The demand for global software development is growing. The nonavailability of software experts at one place or a country is the reason for the increase in the scope of global software development. Software developers who are located in different parts of the world with diversified skills necessary for a successful completion of a project play a critical role in the field of software development. Using the skills and expertise of software developers around the world, one could get any component developed or any IT-related issue resolved. The best software skills and tools are dispersed across the globe, but to integrate these skills and tools together and make them work for solving real world problems is a challenging task. The discipline of risk management gives the alternative strategies to manage risks that the software experts are facing in today’s world of competitiveness. This research is an effort to predict risks related to time, cost, and resources those are faced by distributed teams in global software development environment. To examine the relative effect of these factors, in this research, neural network approaches like Levenberg–Marquardt, Bayesian Regularization, and Scaled Conjugate Gradient have been implemented to predict the responses of risks related to project time, cost, and resources involved in global software development. Comparative analysis of these three algorithms is also performed to determine the highest accuracy algorithms. The findings of this study proved that Bayesian Regularization performed very well in terms of the MSE (validation) criterion as compared with the Levenberg–Marquardt and Scaled Conjugate Gradient approaches.
BeeSwarm: Enabling Parallel Scaling Performance Measurement in Continuous Integration for HPC Applications

Testing is one of the most important steps in software development–it ensures the quality of software. Continuous Integration (CI) is a widely used testing standard that can report software quality to the developer in a timely manner during development progress. Performance, especially scalability, is another key factor for High Performance Computing (HPC) applications. There are many existing profiling and performance tools for HPC applications, but none of these are integrated into CI tools. In this work, we propose BeeSwarm, an HPC container based parallel scaling performance system that can be easily applied to the current CI test environments. BeeSwarm is mainly designed for HPC application developers who need to monitor how their applications can scale on different compute resources. We demonstrate BeeSwarm using a multi-physics HPC application with Travis CI, GitLab CI and GitHub Actions while using ChameleonCloud and Google Compute Engine as the compute backends. Our results show that BeeSwarm can be used for scalability and performance testing of HPC applications.