Reviewerly/exHarmony huggingface dataset:
Protecting Data Mobility in Cloud Networks Using Metadata Security

At present, health care applications, government services, and banking applications use big data with cloud storage to process and implement data. Data mobility in cloud environments uses protection protocols and algorithms to secure sensitive user data. Sometimes, data may have highly sensitive information, leading users to consider using big data and cloud processing regardless of whether they are secured are not. Threats to sensitive data in cloud systems produce high risks, and existing security methods do not provide enough security to sensitive user data in cloud and big data environments. At present, several security solutions support cloud systems. Some of them include Hadoop Distributed File System (HDFS) baseline Kerberos security, socket layer-based HDFS security, and hybrid security systems, which have time complexity in providing security interactions. Thus, mobile data security algorithms are necessary in cloud environments to avoid time risks in providing security. In our study, we propose a data mobility and security (DMoS) algorithm to provide security of data mobility in cloud environments. By analyzing metadata, data are classified as secured and open data based on their importance. Secured data are sensitive user data, whereas open data are open to the public. On the basis of data classification, secured data are applied to the DMoS algorithm to achieve high security in HDFS. The proposed approach is compared with the time complexity of three existing algorithms, and results are evaluated.
Peeking Into the Black Box: Towards Understanding User Understanding of E2EE

End-to-end encryption (E2EE) has become available to end users, but they need to understand the nature and limitations of the protection it offers to benefit in terms of protection. Attempts to explain cryptography in general, and E2EE in particular, to non-specialists have had limited success – in part because they tried to convey detailed expert knowledge. Metaphors are a way to communicate the benefits and limitations more compactly, and support the construction of functional mental models. Previous research that attempted to do this for E2EE reported mixed results, but offered no detailed insight into how participants constructed their understanding and which aspects of particular metaphors helped or hindered their functional understanding. We repeated the previous experiment in form of a qualitative interview study with 12 participants (all users of messaging apps) and used detailed questions to better understand why the participants rated the security properties of E2EE correctly or incorrectly, and how the metaphors had been interpreted and applied. Therefore, we are able to describe to what extent, and how, the metaphors for E2EE changed participants' understanding of the security properties. We found that participants inferred the security properties of E2EE partly from the metaphors, but also from existing beliefs, for instance about the trustworthiness of providers. While the metaphors improved the assessment about confidentiality, they did not correct misconceptions about authenticity. Based on our findings we recommend the development and testing of interventions aimed at the process of changing mental models and correcting persistent misconceptions.
Extractive and Abstractive Summarization for Hindi Text using Hierarchical Clustering

Text Summarization is a widely researched and successful area of Natural Language Processing application. However, it remains limited to established languages such as English, French, etc. In this paper, we propose and compare extractive and abstractive summarization techniques for Hindi text documents. For either summarization, we first propose ward hierarchical agglomerative clustering. This is followed by the PageRank algorithm for extractive summarization while in abstractive summarization, we present an approach based on multi-sentence compression which only requires a POS tagger to generate Hindi text summaries.
gShare: A centralized GPU memory management framework to enable GPU memory sharing for containers

Owing to low overhead and rapid deployment, containers are increasingly becoming an attractive system software platform for deep learning and high performance computing (HPC) applications that leverage GPUs. Unfortunately, existing container software does not concern how each container allocates GPU memory. Therefore, if a certain container consumes the majority of GPU memory, other containers may not run their workloads because of insufficient memory. This paper presents gShare, a centralized GPU memory management framework to enable GPU memory sharing for containers. As with a modern operating system, gShare allocates the entire GPU memory inside the framework and manages the memory with sophisticated memory allocators. gShare is then able to enforce the GPU memory limit of each container by mediating the memory allocation calls. To achieve its objective, gShare introduces the API remoting components, the mediator, and the three-level memory allocator, which enable lightweight and efficient GPU memory management. Our prototype implementation achieves near-native performance with secure isolation and little memory waste in popular deep learning and HPC workloads.
Color Zero-Watermarking Algorithm for Medical Images Based on BEMD-Schur Decomposition and Color Visual Cryptography

With the widespread use of medical images in telemedicine, personal information may be leaked. The traditional zero-watermarking technology has poor robustness under large-scale attacks. At the same time, most of the zero-watermarking information generated is a binary sequence with a single information structure. In order to effectively solve the poor robustness problem of traditional zero-watermarking under large-scale attacks, a color zero-watermarking algorithm for medical images based on bidimensional empirical mode decomposition (BEMD)-Schur decomposition and color visual cryptography is proposed. Firstly, the color carrier image and the color copyright logo are decomposed into R, G, and B three color components, respectively, and the feature value of each sub-block are extracted by wavelet transform, BEMD decomposition, block operation, and Schur decomposition. Then, the R, G, and B components of the copyright logo are scrambled by Arnold scramble and converted into binary watermark information. Finally, a color visual cryptography scheme is proposed to generate two color shared images based on the carrier characteristics and copyright information. One shared image is used to generate a color zero-watermark, and the other is used for copyright authentication phase. Experimental results show that this algorithm has strong robustness and stability in resisting large-scale noise attacks, filtering attacks, JPEG compression, cropping attacks, and translation attacks at different positions. Compared with similar zero-watermarking algorithms, the robust performance is improved by about 10%, and it can adapt to more complex network environments.
Classification of Batik Authenticity Using Convolutional Neural Network Algorithm with Transfer Learning Method

Batik is one of Indonesia's cultural heritages that UNESCO has recognized as an Intangible Cultural Heritage, so we should be proud and preserve it. However, there are problems in the batik industry related to the labelling of traditional and modern batik products. The prevalence of fraud in printed batik, which is given a price equivalent to written batik, which is much more expensive, and public ignorance of the aesthetic value and authenticity of written batik, can disrupt the traditional batik industry in Indonesia. Based on these problems, the authors innovate to develop a machine learning model that aims to classify the authenticity of batik using the Convolutional Neural Network Algorithm with Transfer Learning Method. The classification process consists of several stages: collecting datasets, preprocessing data, developing CNN models with transfer learning, and compiling and training models. The development of the machine learning model that has been trained produces an accuracy of 96.91%. The author hopes that this research can make it easier for people to distinguish between written and printed batik, minimize the existence of batik price fraud, and increase consumer confidence in batik transactions by ensuring the originality of batik products.
Research on energy-saving design strategies of libraries in severe cold regions: Taking a university in Xinjiang as an example

Energy conservation, emission reduction, green and low-carbon, and environmental protection have become contemporary hot topics. Taking a green and sustainable development path is an important measure to realize a resource-saving and environment-friendly society. As a large-scale public building, the library wastes a lot of energy and resources due to the unreasonable planning, design, operation and management of the library. Therefore, the green energy-saving design of the library is of great significance to the times. This article adopts a combination of theory and practice analysis method, through systematic combining and summarization of related documents, thinking about library buildings, and combining local special climatic conditions, explores the energy-saving design strategy of libraries in severe cold areas, with a view to more The researchers and practitioners of the company provide references for the future energy-saving design and practical innovation of library buildings.
C4I System Security Architecture: A Perspective on Big Data Lifecycle in a Military Environment

Although the defense field is also one of the key areas that use big data for security reasons, there is a lack of study that designs system frameworks and presents security requirements to implement big data in defense. However, we overcome the security matters by examining the battlefield environment and the system through the flow of data in the battlefield. As such, this research was conducted to apply big data in the defense domain, which is a unique field. In particular, a three-layered system framework was designed to apply big data in the C4I system, which collects, manages, and analyzes data generated from the battlefield, and the security measures required for each layer were developed. First, to enhance the general understanding of big data and the military environment, an overview of the C4I system, the characteristics of the 6V’s, and the five-phase big data lifecycle were described. While presenting a framework that divides the C4I system into three layers, the roles and components of each layer are described in detail, considering the big data lifecycle and system framework. A security architecture is finally proposed by specifying security requirements for each field in the three-layered C4I system. The proposed system framework and security architecture more accurately explain the unique nature of the military domain than those studied in healthcare, smart grids, and smart cities; development directions requiring further research are described.
Vessel Classification Using A Regression Neural Network Approach

Marine vessels are subject to high wear and tear due to the conditions they operate in. To reduce risk of failure during operation, vessels are inspected periodically every five years. These inspections are prone to high subjectiveness that makes them hard to reproduce for the shipping owners. The purpose of this paper is to present a regressor to a Faster R-CNN network that can help alleviate some of the subjective assessment currently performed by human surveyors by estimating the severity of a corroded area, autonomously using drones. A feature pyramid backbone is shared between the Faster R-CNN and the added regression head. The goal of the regressor is to introduce a more objective assessment of the vessel that gives a consistent output for a consistent input. The system is evaluated on a real dataset, acquired in ballast tanks and the experimental results indicate that our deep learning approach can be used to detect and quantify corroded areas during the inspection process of marine vessels.
Conceptual model of project digital footprint

The problem of the creation of effective models, methods and means of formation of digital traces of projects and development programs in the conditions of digitalization is considered. The life cycle of projects, knowledge and management technologies is significantly shortened and digitalization processes create requirements for the creation of their digital footprints and shadows. The technical and technological complexity of development projects increases due to innovation. These trends create significant challenges in the development of project and program management systems in the context of the digitalization of society. It is proposed to use genomic representations as a basic model and methodology for the formation of digital traces of projects. The proposed conceptual model of forming a digital trace of projects allows taking into account the external and internal environment of the project within a certain activity.
H.264 and H.265 video traffic modeling using neural networks

As video has become the dominant type of traffic over wired and wireless networks, the efficient transmission of video streams is of paramount importance. Hence, especially for wireless networks, the optimum utilization of the available bandwidth while preserving the users’ Quality of Service and Quality of Experience requirements is crucial. Towards this goal, the accurate prediction of upcoming video frame sizes can play a significant role. This work focuses on achieving such an accurate prediction for videos encoded with H.264 and H.265, which are the major state-of-the-art standards based on their current market share. Unlike previous studies, we use single-step and multi-step approaches to capture the long-range dependence and short-range dependence properties of variable bit rate video traces through neural networks-based modeling. We evaluate the accuracy of Long Short Term Memory, Convolutional Neural Networks and Sequence-to-Sequence models and compare them with existing approaches. Our models show significantly higher accuracy for a variety of videos. We also provide a case study on how our model can be used for traffic policing purposes.
Harass, mislead, &amp; polarize: An analysis of Twitter political bots’ tactics in targeting the immigration debate before the 2018 U.S. midterm election

(2021). Harass, mislead, & polarize: An analysis of Twitter political bots’ tactics in targeting the immigration debate before the 2018 U.S. midterm election. Journal of Information Technology & Politics. Ahead of Print.
Deep Learning for Object Detection in Materials-Science Images: A tutorial

Deep neural networks and deep learning have achieved great success in many signal and image processing applications, especially those with large-scale annotated training data for supervised learning. Although in principle deep-learning methods can be applied to boost the performance of processing materials-science images, i.e., microscopic images that capture important microstructures of various material samples, many priors and requirements in materials science must be considered to maximize performance gains. In this article, we focus on the important problem of detecting objects of interest from microscopic materials-science images and introduce different approaches to incorporate several such priors, including object shape, symmetry, and 3D consistency, into deep learning to tackle this problem. In particular, we explore the use of these three priors to enable network training with fewer data annotations, which is highly desired in materials science. This tutorial-style article will summarize contributions in the literature as well as our current research achievements, and we hope it can provide an initial insight to new researchers who are interested in using deep learning for materials-science image processing.
Digital Talent Management Model for Smart Village in Indonesia

Mainstreaming digital transformation in developing Smart Village in Indonesia become crucial to allow sooner realization of Self-sustained Village in the midst of current pandemic, one of it is by managing talented village human resources. This research studies how village human resources digital talent management model to build Smart Village in Indonesia. This research uses qualitative approach with documentation study/literature study, interactive data analysis and data source triangulation. Research finding shows that digital talent management model in order to develop smart village in Indonesia covers five stages, namely: (1) talent need analysis; (2) talent identification; (3) talent development; (4) talent retention; and (5) talent evaluation with collaborative governance as its implementation strategy in Indonesia.
Medieval Manuscripts and Their Migrations: Using SPARQL to Investigate the Research Potential of an Aggregated Knowledge Graph

Although the RDF query language SPARQL has a reputation for being opaque and difficult for traditional humanists to learn, it holds great potential for opening up vast amounts of Linked Open Data to researchers willing to take on its challenges. This is especially true in the field of premodern manuscripts studies as more and more datasets relating to the study of manuscript culture are made available online. This paper explores the results of a two-year long process of collaborative learning and knowledge transfer between the computer scientists and humanities researchers from the Mapping Manuscript Migrations (MMM) project to learn and apply SPARQL to the MMM dataset. The process developed into a wider investigation of the use of SPARQL to analyse the data, refine research questions, and assess the research potential of the MMM aggregated dataset and its Knowledge Graph. Through an examination of a series of six SPARQL query case studies, this paper will demonstrate how the process of learning and applying SPARQL to query the MMM dataset returned three important and unexpected results: 1) a better understanding of a complex and imperfect dataset in a Linked Open Data environment, 2) a better understanding of how manuscript description and associated data involving the people and institutions involved in the production, reception, and trade of premodern manuscripts needs to be presented to better facilitate computational research, and 3) an awareness of need to further develop data literacy skills among researchers in order to take full advantage of the wealth of unexplored data now available to them in the Semantic Web.
Contrast enhancement based on reflectance-oriented probabilistic equalization

Despite advances in contrast enhancement, it remains difficult to adaptively improve both the global contrast (base structures) and the local contrast (detailed textures) of an image. To solve this problem, we first propose a novel 2D histogram equalization approach called ROPE for global contrast enhancement. ROPE assumes intensity occurrence and co-occurrence to be dependent on each other and newly estimates the distribution of intensity occurrence (1D histogram) by marginalizing over the distribution of intensity co-occurrence (2D histogram). The 2D histogram is generated by incorporating local reflectance differences into the density estimation, so that ROPE can provide sufficient brightness enhancement not only for normal-light images but also for dark images. For local contrast enhancement, ROPE is extended to compute locally adaptive 2D histograms, giving birth to a novel approach called AdaROPE. Extensive experiments on four public datasets consisting of 8400 images demonstrate the superiority of our approaches over state-of-the-art image enhancement.
COINet: Adaptive Segmentation with Co-Interactive Network for Autonomous Driving

Semantic segmentation serves as a cornerstone for safety autonomous driving and has been achieved remarkable progress at the price of dense annotations. Unsupervised domain adaptation was widely utilized to addresses this labor-intensive problem, which transfers the knowledge learned from labeled synthetic datset to real-world without any annotations. However, most existing adaptation works predict the segmentation results and domain identification results separately only with the last-layer feature, and ignore the intrinsic relationship among these two tasks. To address this issue, we present a CO-Interactive Network (COINet) for unsupervised adaptive segmentation. In particular, we propose a scale-aware distilled decoder to integrate multi-scale features dynamically through the designed inter-distilled module (IDM) and obtain fine-grained feature representations. A dual-task classifier is advanced with this decoder, to jointly predict the segmentation results and pixel-wise domain prediction results, which extracts shared complementary information for accurate segmentation. We further devise a co-interactive loss to explicitly model the intrinsic relationship among the segmentation and domain prediction, enabling the feature distribution alignment in pixel-level and an optimal segmentation decision boundary. We demonstrate the effectiveness of the proposed COINet on benchmark adaptation settings with extensive experimental and ablation results, and our model shows favorable performance against existing algorithms.
Slim-RFFNet: Slim deep convolution random Fourier feature network for image classification

The standard kernel method is computationally expensive because it needs to store and compute the inverse of the Gram matrix. Furthermore, the classification accuracy of the single kernel method is not effective or efficient as the method’s ability to extract features. The random Fourier feature method establishes a connection between the kernel method and deep learning to solve the above problems. In this paper, we propose a novel slim deep random Fourier feature network named Slim-RFFNet, which introduces convolution into kernel learning. We use the hierarchical strategy and skip connection to construct a deep network structure and lighten the model by using quantization. Experiments conducted on classification benchmarks MNIST and CIFAR10 demonstrate that the proposed Slim-RFFNet significantly outperforms current state-of-the-art deep kernel learning methods. Our algorithm also achieves a trade-off between accuracy and latency. The proposed network can be applied to resource-constrained embedded AI devices. The experimental results on the edge computing system show that our algorithm has a small memory footprint and fast inference speed on small edge devices, and thus meets the requirements for practical applications.
<i>AIgean</i> : An Open Framework for Deploying Machine Learning on Heterogeneous Clusters

AIgean , pronounced like the sea, is an open framework to build and deploy machine learning (ML) algorithms on a heterogeneous cluster of devices (CPUs and FPGAs). We leverage two open source projects: Galapagos , for multi-FPGA deployment, and hls4ml , for generating ML kernels synthesizable using Vivado HLS. AIgean provides a full end-to-end multi-FPGA/CPU implementation of a neural network. The user supplies a high-level neural network description, and our tool flow is responsible for the synthesizing of the individual layers, partitioning layers across different nodes, as well as the bridging and routing required for these layers to communicate. If the user is an expert in a particular domain and would like to tinker with the implementation details of the neural network, we define a flexible implementation stack for ML that includes the layers of Algorithms, Cluster Deployment &amp; Communication, and Hardware. This allows the user to modify specific layers of abstraction without having to worry about components outside of their area of expertise, highlighting the modularity of AIgean . We demonstrate the effectiveness of AIgean with two use cases: an autoencoder, and ResNet-50 running across 10 and 12 FPGAs. AIgean leverages the FPGA’s strength in low-latency computing, as our implementations target batch-1 implementations.