Reviewerly/exHarmony huggingface dataset:
Report on the 2nd Linked Archives International Workshop (LinkedArchives 2022) at TPDL 2022

The 2nd edition of the International Workshop on Archives and Linked Data ran in conjunction with the 26th International Conference on Theory and Practice of Digital Libraries (TPDL 2022). TPDL 2022 was an in-person event in Padua with an online-only registration for non-speakers. Archives, the guardians of large volumes of historical and current information, are showing a growing interest in linked data and semantic web technologies. These technologies can be used to link archives' information with data from other cultural heritage institutions and more informal sources. In a world of linked data, users can access richer interfaces where the archival records are available in their context, with explicit metadata. The workshop gathered about 20 researchers and specialists engaged in initiatives crossing linked data technologies, archives, and cultural heritage in general, discussing advances and challenges in this area. The workshop was a successful event, with active participation in the discussions that followed the presentations. The participants found the workshop interesting (40%) or extremely interesting (60%), and everyone who answered a post-workshop survey would be interested in another edition of the workshop. Date: 20 September, 2022. Website: https://linkedarchives.inesctec.pt/.
Research on the Application of Paperless Computerized College English Test Based on iTEST Platform

With the rapid development of educational technology and information technology, paperless computer-based test has become an inevitable means of college English teaching and assessment. Based on the paperless test through ¡TEST platform, this study summarizes the preparations and test procedures needed for conducting paperless computer-based English tests through ¡TEST platform. This study also finds that paperless computer-based English test through ¡TEST has a positive impact on students’ English learning interests. It concludes that paperless computer-based English test through ¡TEST platform has high credibility and acceptability, which not only assists classroom English teaching, but also enhances their interests in learning English.
Dynamic allocation of service robot resources to an order picking task considering functional and non-functional properties

Industry 4.0 processes have often varying requirements. A service robot and a team of service robots respectively represent a flexible resource. That means, it possesses variability that can possibly be configured in such a way that it is able to fulfill the requirements of industry 4.0 processes. Determining whether that is the case and how that has to happen is an important part of variability management. Based on a model-driven general method for variability management in a robotics software ecosystem, we present here a concrete use case (model) in which we allocate for an order picking task with specific time requirements either a single fitting service robot or a collaboration of two fitting service robots. Relevant properties of the service robots considered are both functional (are the capabilities to execute the tasks available?) as well as non-functional (the desired velocity parameterization while executing the individual navigation sub tasks limited by the respective maximum speed of a service robot).
Kinship Verification Analysis Based on Color Features

Kinship verification is an attractive research area as it has been used in several applications, such as finding missing relatives and family album analysis. This paper proposes a new technique for verifying kin relationship dependent on a new color feature (color moments). It consists of 4 stages. First, preprocessing stage is responsible for improving the quality of input images by transforming the image from the red, green, and blue (RGB) color space into a lightness, channel A and channel B (LAB) color model. Second, the feature extraction stage is responsible for extracting the most discriminative information. The third is the pair representation stage, which combines the extracted feature vectors into one vector. Finally, the stage responsible for decision whether the two input images are kin or not, namely, kinship verification. The evaluated mean accuracy on KinFaceW-II was 83.9%. Therefore, the proposed method’s mean accuracy outperforms many of the state-of-the-art approaches, as well as convolution neural networks (CNN).
Towards private stylists via personalized compatibility learning

Personalized outfit compatibility learning is an emerging yet challenging task. Most of the existing methods focus on general outfit compatibility learning. Although a few works have been proposed for personalized fashion compatibility, they either considered user preference on fashion items with specific patterns or design elements or recommended outfits based on the overall visual similarity according to the users’ preferred collections. This paper adopts physical and fashion attributes for effective personalized fashion compatibility evaluation and recommendation. The physical attributes are concluded into seven aspects: body shape, skin color, hairstyle, hair color, height, breast size (breasts), and color contrast. The personalized outfit compatibility problem in this paper is a multi-label classification problem and formulated as an optimization function with outfit images, fashion attributes, and physical attributes as input. It is the first attempt to solve the problem by discovering the correlation between visual image features, fashion attributes, and physical attributes. Specifically, the correlation is learned with two transformer encoders by updating attention weights of different embedding pairs during the training process. The model can not only predict the fashion attributes of the outfit’s top, bottom, shoes, and bag items, but also predict the incompatible physical attributes of an individual towards the given outfit. It can be used to recommend outfits that best fit an individual and the predicted fashion attributes can be used for result explanation. The O4U dataset, which contains rich annotations of fashion item attributes and human physical attributes of the outfits, is used to evaluate the performance of the proposed method. The quantitative and qualitative results show that the proposed method outperforms state-of-the-art methods for personalized outfit compatibility evaluation.
Adaptive null broadening beamforming of uniform rectangular array based on interference plus noise covariance matrix reconstruction

Null broadening is an effective method to suppress the motion interference, but most methods are suitable for the uniform linear array. To solve this problem, an adaptive null broadening method based on uniform rectangular array (URA) is proposed in this paper. The method first estimates the power of interference and adds virtual interference with the same power near the real interference. Then, the method estimates the noise power and reconstructs the interference plus noise covariance matrix (INCM) which contains the null broadening information. Finally, the desired signal steering vector is corrected to obtain the optimal weighting vector. Simulation results show that the method can form wide nulls in the interference direction to suppress the motion interference and is robust to the desired signal steering vector mismatch.
A gigapixel image mosaicking approach based on SURF and color transfer

In order to solve the problem that a single camera cannot take into consideration the broad imaging field and high-resolution details, we propose a novel cross-resolution image mosaic framework that embeds local images into global images to synthesize gigapixel images with wide field-of-view and high-definition details. Firstly, the framework adopts a color transfer algorithm to maintain the color consistency between images; secondly, it adopts multi-level feature point matching and homography transformation for image registration; thirdly, the registered local images are segmented into small patches and fused into the global image to provide detailed information. The experimental results show that the framework proposed in this paper can alleviate color and content inconsistency between images and generate large-field images with excellent visual effects when the resolution gap is huge.
Quantum Computing for the Wess–Zumino Model

Future quantum computers will enable novel sign-problem-free studies of dynamical phenomena in non-perturbative quantum field theories, including real-time evolution and spontaneous supersymmetry breaking.We are investigating applications of quantum computing to low-dimensional supersymmetric lattice systems that can serve as testbeds for existing and near-future quantum devices.Here we present initial results for the N = 1 Wess-Zumino model in 1+1 dimensions, building on our prior analyses of 0+1-dimensional supersymmetric quantum mechanics.In addition to exploring supersymmetry breaking using the variational quantum eigensolver, we consider the prospects for real-time evolution.
Evening Comes to Long Island Sound

Other| August 01 2019 Evening Comes to Long Island Sound Fred Muratori Fred Muratori Search for other works by this author on: This Site Google Italian Americana (2019) XXXVII (2): 161. https://doi.org/10.5406/2327753X.37.2.17 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Fred Muratori; Evening Comes to Long Island Sound. Italian Americana 1 August 2019; XXXVII (2): 161. doi: https://doi.org/10.5406/2327753X.37.2.17 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressItalian Americana Search Advanced Search The text of this article is only available as a PDF. Copyright 2016 Carla Anne Simonini & Carol Bonomo Albright2016 Article PDF first page preview Close Modal Issue Section: POETRY You do not currently have access to this content.
Risk and Security Measurement Based on ISO 27001 Using FMEA Methodology Case Study: National Government Agency

XYZ government agency utilizes information technology in its operations. The utilization of Information Technology (IT) assets cannot be separated from the risk of information security. This research focuses on evaluating security risks and determining appropriate risk mitigation by ISO 27001 and 27002 standards. The risk assessment process uses the Failure Mode and Effect Analysis method. The FMEA method will produce a Risk Priority Number value, which will determine the priority level from the highest to the lowest risk. The risk assessment in the organization resulted in 14 risk classifications that needed to be handled. Of the 14 chances, 3 have a High-risk level, 6 have a medium-risk level and 5 have a Low level. The list of risks and the results of the assessment will be handled or mitigated by the ISO 27002:2013 standard. XYZ Agencies use risk mitigation documents to reduce risk levels and improve information security management aspects.
Japan Association of Mineralogical Sciences

He has studied mantle-derived and related rocks and contributed to studies of the mantle evolution of oceanic plates from mid-ocean ridge to subduction and the subarc mantle.He studied at the Horoman Peridotite Complex in Samani, Japan, from his undergraduate to PhD under the supervision of Prof. Shoji Arai.The discovery of corundum-bearing mafic rocks by him and his collaborators suggested the possibility of the recycling of subducted lower oceanic crustal materials in the mantle.Since then, he has been investigating ultramafic rocks around the world from ophiolites, orogenic belts, xenoliths, and the ocean floor to better understand partial melting, melt-rock interactions, and metasomatism in the mantle.Dr. Morishita had the opportunity to learn laser ablation ICM-MS analysis for trace element analysis of minerals during his postdoctoral research, and was involved in setting up the laser ablation ICP-MS system installed at Kanazawa University.Dr. Morishita is also actively involved in the Integrated Ocean Drilling Project (IODP) and International Continental Scientific Drilling Program (ICDP).He played an important role as a member of the international scientific evaluation committee on drilling proposals and proposed scientific drilling projects to investigate areas such as oceanic plates subjected to plate bending prior to subduction, as well as intact mantle drilling of the oceanic plate.He currently serves on the editorial board of Scientifc Drilling, which is a interdisciplinary journal that reports the latest scientific results, scientific drilling proposals, and related programs to the geoscience community.
Boosting deep image prior by integrating external and internal image priors

Recently, an unsupervised method [denoted as deep image prior (DIP)] that interprets images as the output of a convolutional network with randomly initialized inputs has received much attention, as the DIP can adapt its network parameters to any given noisy image without requiring training pairs, providing a great flexibility for handling those noisy images with complex structures and low degree of self-similarity. However, the DIP method still suffers from a crucial limitation that only the noisy image is used as target image, which implies that only internal prior information is used for noise removal and leads to poor denoising performance, compared with supervised denoising models. In this work, we aim to boost the DIP method by integrating external and internal image priors. Specifically, with a given noisy image, we first exploited a state-of-the-art supervised method to denoise it and obtain its corresponding denoised image called initial denoised image. Then the initial denoised image containing rich external prior information was used as extra target image together with the given noisy image within the standard DIP framework, resulting in a highly effective unsupervised recovery process. Next, to take full advantage of the uncertainty of the DIP network, the above DIP denoising routine with the different random inputs was executed multiple times to generate enough complementary denoised images (samples). Finally, with an unsupervised weight map generative network, the generated samples were fused in a pixel-wise manner. The fused image with better image quality was treated as the final denoised image. We had verified the denoising performance of our method on a large of images from several benchmark databases as well as real-world noisy images. The comparative results show that our method outperforms the original DIP method and other unsupervised methods by a large margin, and surpasses state-of-the-art supervised counterparts with comparable peak signal-to-noise rate. Source code is publicly available at https://github.com/chenxiaojun0101/BDIP.
Introduction: Analytics in Accounting and Auditing

Big data and analytics offer new opportunities and challenges for academics and practitioners in all business disciplines including accounting and auditing. In the backdrop of increasing growth of emerging technologies, the organizations in public, private and not-for-profit sectors are embracing digital economy and the fourth industrial revolution journey. This requires knowledge of better practice examples, lessons learned and future directions in addressing the new challenges and seizing new opportunities. In this chapter, we discuss the implications of data analytics, artificial intelligence and machine learning on the accounting and auditing practices. We focus on the technological, social, political, economic, institutional, and behavioral aspects of these technologies in the public, private, non-governmental and hybrid contexts. We present state-of-the-art research directions on philosophical, theoretical, methodological, and practical issues, new developments and innovations of big data, analytics, artificial intelligence, machine learning, blockchain, cryptocurrencies and other emerging technologies related to accounting and auditing.
ESPC-BCS-Net: A network-based CS method for underwater image compression and reconstruction

The Internet of Underwater Things (IoUT) is a typical energy-limited and bandwidth-limited system where the technical bottleneck is the asymmetry between the massive demand for information access and the limited communication bandwidth. Therefore, storing and transmitting high-quality underwater images is a challenging task. The data measured by cameras need to be effectively compressed before transmission to reduce storage and reconstruc-ted with minor errors, which is the best solution. Compressed sensing (CS) theory breaks through the Nyquist sampling theorem and has been widely used to reconstruct sparse signals accurately. For adaptive sampling underwater images and improving the reconstruction performance, we propose the ESPC-BCS-Net by combining the advantages of CS and Deep Learning. The ESPC-BCS-Net consists of three parts: Sampling-Net, ESPC-Net, and BCS-Net. The parameters (e.g. sampling matrix, sparse transforms, shrinkage thresholds, etc.) in ESPC-BCS-Net are learned end-to-end rather than hand-crafted. The Sampling-Net achieves adaptive sampling by replacing the sampling matrix with a convolutional layer. The ESPC-Net implements image upsampling, while the BCS-Net is used to image reconstruction. The efficient sub-pixel layer of ESPC-Net effectively avoids blocking artifacts. The visual and quantitative evaluation of the experimental results shows that the underwater image reconstruction still performs well when the CS ratio is 0.1 and the PSNR of the reconstructed underwater images is above 29.
Uncertainty-Aware Interactive LiDAR Sampling for Deep Depth Completion

Programmable scan LiDAR is able to measure arbitrary areas and is expected to be used in various applications. In this paper, we study a LiDAR sampling strategy for deep depth completion of a programmable scan LiDAR with an RGB camera. General data sampling strategies include adaptive approaches such as active learning, in which candidate data are assessed through a task model for data selection and then the selected data pool is updated sequentially. Although it is an effective approach, the adaptive approach requires many iterations involving the inference process to assess the candidate data, which is not suitable for LiDAR systems. Therefore, we propose a novel interactive LiDAR sampling method without each inference process. Our key insights are that we assess sampling candidates by depth estimation uncertainty and virtually update the uncertainty by an approximation of the candidate assessment. This enables us to add interactivity to the model state without requiring each inference process. We demonstrate the effectiveness of our method on the KITTI dataset and the generalization performance on the NYU-Depth-v2 dataset in comparison with a conventional adaptive LiDAR sampling method, and we find superior results in the depth completion task. We also show ablation studies to analyze our approach.
Hierarchy-Aware Temporal Knowledge Graph Embedding

Knowledge graph embedding has attracted widespread attention in recent years, and since knowledge graphs are dynamically updated in nature, the temporal information embedded is essential. Most of the knowledge graph embedding focuses on static KGs, while temporal knowledge graphs have been poorly studied. In the real-world, much structured knowledge is valid only within a specific temporality, i.e., the development of facts follows a temporal order. Therefore, more and more research works start to incorporate temporal information into knowledge graph representation learning, and the embedding of temporal knowledge graphs focuses on how to embed temporal information into the vector space. Most of the existing temporal knowledge graph embedding models do not model the semantic hierarchy, not fully exploiting the semantic information in the temporal knowledge graph. In this paper, we propose a hierarchy-aware temporal knowledge graph embedding (HA-TKGE), which maps temporal information into a polar coordinate system. The HA-TKGE is mainly inspired by the HAKE model. Specifically, the purpose of radial coordinates is to model temporal information at different levels, where entities with smaller radius are indicated at higher levels, and angular coordinates are intended to represent temporal information at the same level, which has approximately the same radial coordinates and different angles. The HA-TKGE model uses the nature of the polar coordinate system to represent the semantic hierarchy of temporal knowledge graphs and proves its effectiveness in the temporal node prediction task. Experiments show that the HA-TKGE model can effectively model the semantic hierarchy of temporal information and outperforms existing methods overall on the benchmark dataset for the temporal node prediction task.
Deep Semantic-Enhanced Event Detection via Symmetric Graph Convolutional Network

Event detection, an important research topic of information extraction, aims to automatically identify and classify event instances from the text. Previous studies have introduced methods combining syntactic information and graph convolutional networks into the field of event detection and verified their effectiveness. However, such methods often ignore the high-order information on the syntactic tree with noisy words, which limits their classification quality. In this paper, we propose a deep symmetric graph convolutional network to organically integrate high-order and low-order syntactic information to strengthen the semantic features of sentences. Specifically, we design a skip connection with attention gating mechanism, which selects valuable low-order syntactic information under the supervision of high-order syntactic information to strengthen the aggregation of high-order and low-order syntactic information. Then, a graph perturbation mechanism is proposed to discard noisy nodes on the syntactic graph to reduce the noisy information in the high-order syntactic information. We conducted extensive experiments on the widely used ACE 2005 benchmark, and the experimental results demonstrate that our method significantly outperforms state-of-the-art methods. Then, a graph perturbation mechanism is proposed to discard noisy nodes on the syntactic graph to reduce the noisy information in the high-order syntactic information. We conducted extensive experiments on the widely used ACE 2005 benchmark, and the experimental results demonstrate that our method significantly outperforms state-of-the-art methods. We conducted extensive experiments on the widely used ACE 2005 benchmark, and the experimental results demonstrate that our method significantly outperforms state-of-the-art methods. Then, a graph perturbation mechanism is proposed to discard noisy nodes on the syntactic graph to reduce the noisy information in the high-order syntactic information.