Reviewerly/exHarmony huggingface dataset:
A Co-Word Analysis of Global Research on Knowledge Organization: 1900-2019

The study’s objective is to analyze the structure of knowledge organization studies conducted worldwide. This applied research has been conducted with a scientometrics approach using the co-word analysis. The research records consisted of all articles published in the journals of Knowledge Organization and Cataloging &amp; Classification Quarterly and keywords related to the field of knowledge organization indexed in Web of Science from 1900 to 2019, in which 17,950 records were analyzed entirely with plain text format. The total number of keywords was 25,480, which was reduced to 12,478 keywords after modifications and removal of duplicates. Then, 115 keywords with a frequency of at least 18 were included in the final analysis, and finally, the co-word network was drawn. BibExcel, UCINET, VOSviewer, and SPSS software were used to draw matrices, analyze co-word networks, and draw dendrograms. Furthermore, strategic diagrams were drawn using Excel software. The keywords “information retrieval,” “classification,” and “ontology” are among the most frequently used keywords in knowledge organization articles. Findings revealed that “Ontology*Semantic Web”, “Digital Library*Information Retrieval” and “Indexing*Information Retrieval” are highly frequent co-word pairs, respectively. The results of hierarchical clustering indicated that the global research on knowledge organization consists of eight main thematic clusters; the largest is specified for the topic of “classification, indexing, and information retrieval.” The smallest clusters deal with the topics of “data processing” and “theoretical concepts of information and knowledge organization” respectively. Cluster 1 (cataloging standards and knowledge organization) has the highest density, while Cluster 5 (classification, indexing, and information retrieval) has the highest centrality. According to the findings of this research, the keyword “information retrieval” has played a significant role in knowledge organization studies, both as a keyword and co-word pair. In the co-word section, there is a type of related or general topic relationship between co-word pairs. Results indicated that information retrieval is one of the main topics in knowledge organization, while the theoretical concepts of knowledge organization have been neglected. In general, the co-word structure of knowledge organization research indicates the multiplicity of global concepts and topics studied in this field globally.
Byzantine-Resilient Counting in Networks

We present two distributed algorithms for the Byzantine counting problem, which is concerned with estimating the size of a network in the presence of a large number of Byzantine nodes.In an n-node network (n is unknown), our first algorithm, which is deterministic, finishes in O(log n) rounds and is time-optimal. This algorithm can tolerate up to O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1−γ</sup> ) arbitrarily (adversarially) placed Byzantine nodes for any arbitrarily small (but fixed) positive constant γ. It outputs a (fixed) constant factor estimate of log n that would be known to all but o(1) fraction of the good nodes. This algorithm works for any bounded degree expander network. However, this algorithms assumes that good nodes can send arbitrarily large-sized messages in a round.Our second algorithm is randomized and most good nodes send only small-sized messages. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> This algorithm works in almost all d-regular graphs. It tolerates up to $B(n) = {n^{\frac{1}{2} - \xi }}$ (note that n and B(n) are unknown to the algorithm) arbitrarily (adversarially) placed Byzantine nodes, where ξ is any arbitrarily small (but fixed) positive constant. This algorithm takes O(B(n) log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> n) rounds and outputs a constant factor estimate of log n with probability at least 1−o(1). The said estimate is known to most nodes, i.e., ≥ (1 − β)n nodes for any arbitrarily small (but fixed) positive constant β.To complement our algorithms, we also present an impossibility result that shows that it is impossible to estimate the network size with any reasonable approximation with any non-trivial probability of success if the network does not have sufficient vertex expansion.Both algorithms are the first such algorithms that solve Byzantine counting in sparse, bounded degree networks under very general assumptions. Both algorithms are fully local and need no global knowledge.Our algorithms can be used for the design of efficient distributed algorithms resilient against Byzantine failures, where the knowledge of the network size — a global parameter — may not be known a priori.
Comparing the efficacy of AR-based training with video-based training

In recent years, US Emergency Medical Services (EMS) have faced a massive shortage of EMS workers. The sudden outbreak of the pandemic has further exacerbated this issue by limiting in-person training. Additionally, current training modalities for first responders are costly and time-consuming, further limiting training opportunities. To overcome these challenges, this paper compares the efficacy of augmented reality (AR), an emerging training modality, and video-based training to address many of these issues without compromising the quality of the training with reduced instructor interaction. We examined performance, subjective, and physiological data to better understand workload, user engagement, and cognitive load distribution of 51 participants during training. The statistical analysis of physiological data and subjective responses indicate that performance during AR and video-based training and retention phases depended on gender perception of workload and cognitive load (intrinsic, germane, extraneous). However, user engagement was higher in AR-based training for both genders during training.
Potential and Limitation of PlanetScope Images for 2-D and 3-D Earth Surface Monitoring With Example of Applications to Glaciers and Earthquakes

The Planet PlanetScope cubesat constellation acquires high-resolution optical images which cover the entire surface of the Earth daily, enabling an unprecedented capability to monitor the Earth surface changes. However, our analysis reveals artifacts of the geometry of PlanetScope images related to the imaging system and processing issues, limiting the usability of these data for various earth science applications, including the monitoring of glaciers, dune motion, or the measurement of ground deformation due to earthquakes and landslides. Here we analyze these artifacts and propose ways to remediate them. We use two examples to evaluate the data and assess the performance of our proposed approaches. The first is the ground deformation caused by the 2019 Ridgecrest earthquake sequence, California, and the second is the 2018-2019 surge of the Shisper glacier in the Karakorum. Using an image correlation technique, we show that PlanetScope images exhibit several geometric artifacts, such as scene-to-scene misregistration, inconsistence geolocation accuracy between spectral bands, and topographic artifacts. Altogether these artifacts make a quantitative analysis of ground displacement difficult and inaccurate. We present a method that remediates most of these geometric artifacts. In addition, we propose a framework for selecting the most appropriate images and a procedure for refining the Rational Function Model (RFM) of unrectified images to monitor surface displacements and topography changes in 3D. These tools should enhance the use of PlanetScope images for Earth Science applications.
An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines

Adversarial perturbations have drawn great attentions in various machine learning models. In this paper, we investigate the sample adversarial perturbations for nonlinear support vector machines (SVMs). Due to the implicit form of the nonlinear functions mapping data to the feature space, it is difficult to obtain the explicit form of the adversarial perturbations. By exploring the special property of nonlinear SVMs, we transform the optimization problem of attacking nonlinear SVMs into a nonlinear KKT system. Such a system can be solved by various numerical methods. Numerical results show that our method is efficient in computing adversarial perturbations.
A Fine-grained Classification Method of Thangka Image Based on SENet

"Thangka", is a word in the Tibetan language that refers to a kind of scroll painting mounted on silk. Thangka art, which is a very cherished and intangible cultural heritage, has a long history and distinctive characteristics. As an important prerequisite for digital protection, the research on how to classify the Thangka images quickly and accurately has become a problem of concern to scholars from in various fields. Due to the high similarity and complex structure of the Thangka image, manual classification needs to consume a large number of human resources with sufficient knowledge reserves. To improve the efficiency and accuracy of Thangka classification, researchers began to focus on computer-based machine learning and deep learning technology to complete the Thangka image classification task. Due to the complexity of the Thangka image and the lack of training samples, the existing classification methods cannot well complete the Thangka classification task. To solve the above problems, this paper proposes a fine-grained classification method Tk-SENet for Thangka images based on SENet. This method introduces the dual mechanism of spatial attention and channel attention and uses different sizes of convolution kernels to perform convolution operations in images and gives different weights according to the importance of channels and regions, which improves the classification efficiency. The pooling method in the squeeze operation and the activation function in the excitation operation are optimized to improve the classification accuracy. In the training process, the unique training method of the Thangka image is used to prepare for the excellent completion of the fine-grained classification task of the Thangka image. The experimental results show that the improved network model improves the classification accuracy by 3.6658% compared with SENet. Compared with AlexNet, ZFNet, VGG16, VGG19, ResNet-152 , DenseNet and SKNet, the accuracy increases by 6.7747%, 6.2804%, 6.6528%, 5.2586%, 6.0404% , 5.6962% and 2.9241% respectively. At the same time, compared with the existing classification methods of Thangka images, the proposed method can not only accurately identify the categories of Thangka statues, but also identify the production process types of Thangka images. Therefore, this method is more suitable for the fine-grained classification of Thangka images and provides strong technical support for the digital protection of intangible cultural heritage.
Semantic Navigation of PowerPoint-Based Lecture Video for AutoNote Generation

With the increasing popularity of open educational resources in the past few decades, more and more users watch online videos to gain knowledge. However, most educational videos only provide monotonous navigation tools and lack elaborating annotations. This makes the task of locating interesting contents time consuming. To address this limitation, in this article, we propose a slide-based video navigation tool that is able to extract the hierarchical structure and semantic relationship of visual entities in videos, by integrating multichannel information. Features of visual entities are first extracted from the presentation slides by a novel deep learning framework. Then, we propose a clustering approach to extract hierarchical relationships between visual entities (e.g., formulas, texts, or graphs appearing in educational slides). We use this information to associate visual entities with their corresponding audio speech text, by evaluating their semantic relationship. We present two cases where we use the structured data produced by this tool to generate a multilevel table of contents and notes to provide additional navigation materials for learning. The evaluation experiments demonstrate the effectiveness of our proposed solutions for visual entity extraction, hierarchical relationship extraction, as well as corresponding speech text matching. The user study also shows promising improvement in the autogenerated table of contents and notes for facilitating learning.
Equivariance and Invariance Inductive Bias for Learning from Insufficient Data

We are interested in learning robust models from insufficient data, without the need for any externally pre-trained checkpoints. First, compared to sufficient data, we show why insufficient data renders the model more easily biased to the limited training environments that are usually different from testing. For example, if all the training swan samples are "white", the model may wrongly use the "white" environment to represent the intrinsic class swan. Then, we justify that equivariance inductive bias can retain the class feature while invariance inductive bias can remove the environmental feature, leaving the class feature that generalizes to any environmental changes in testing. To impose them on learning, for equivariance, we demonstrate that any off-the-shelf contrastive-based self-supervised feature learning method can be deployed; for invariance, we propose a class-wise invariant risk minimization (IRM) that efficiently tackles the challenge of missing environmental annotation in conventional IRM. State-of-the-art experimental results on real-world benchmarks (VIPriors, ImageNet100 and NICO) validate the great potential of equivariance and invariance in data-efficient learning. The code is available at https://github.com/Wangt-CN/EqInv .
Decentralized Application (DAPP) to enable E-voting system using Blockchain Technology

This paper presents a detailed analysis of the Voting System via Decentralized App using an upcoming well-received technology: Blockchain. In recent years, Blockchain has become the finest way to store data and transmit data safely without any involvement from a third party because of its key features, including anonymity, security, privacy, and reliability. This paper commences by analyzing Blockchain technology and identifying its advantages and disadvantages. The report explains the proposed steps we took to draw out a prototype of the Voting System on DAPP. It further analyzes and explains the problems with the current voting system and how this(Blockchain) technology could immensely contribute to bringing change in the election system of a country by enveloping all the disadvantages of the current system. It describes the advantages of using Blockchain for the voting system and highlights what could still play out as a major disadvantage and an obstruction in encouraging change in methodologies for the voting system. Probing further, it can be deduced how this technology could provide safe, secure, transparent, and decentralization data in the voting or election system. As security is the main highlight in choosing or not choosing a new platform, we also reveal the potential problems with this e-voting system and further propose some ideas that could help resolve the issue, making the system more safe and secure and favorable to rebuild the current system.
MSFC: Multi-scale Significant Feature Correction Method for Distorted Underwater Target Recognition

The features of small-scale targets are less in underwater, and the complex underwater environment and light refraction cause the images to be distorted. These problems caused the AUVs difficulty to recognize distorted and multiscale targets in underwater. In view of the above problems, a multi-scale significant feature correction method was proposed for distorted underwater target recognition. Firstly, building a feature pyramid network through DenseNet backbone network to extract the multi-scale significant feature. Secondly, the method in this paper performs contrast correction of distorted image feature locations and recovers a clear grayscale image of the target. Finally, the method uses the extracted corrected multi-scale significant features for accurate recognition of multiscale underwater targets in images. Experimental results show that the method in this paper has better results in both image distortion correction and multi-scale targets recognition, with an accuracy improvement of 2.27% over existing methods.
Document Clustering Using Keyword Extraction

Increase in the number of research documents on a daily basis, we find difficulty in identifying proper documents as per our requirements. This paper discusses an effective method in document clustering using automatic keyword extraction. Keyword is the smallest unit that can convey the meaning of an entire page, it helps a user in deciding whether or not to read or skip an article. In this work, we compare different methods of keyword extraction and choose the best method of keyword extraction based on accuracy and precision. The proposed approach takes extracted keywords as input and constructs a variety of different clusters using Euclidean distance measure to group the document together. As a result, a user can conduct a keyword search and obtain the results within seconds. The use of keyword clusters reduces noise in data and consequently enhances cluster quality.
Secure Updatable Storage Access Control System for EHRs in the Cloud

With an increasing number of IoT devices being deployed in healthcare, massive amounts of electronic health records (EHRs) are generated and shared in the cloud. To preserve data privacy, one promising data-sharing tool named attribute-based encryption (ABE) has been widely employed. However, it is a challenge to achieve flexible data sharing without loss of confidentiality when authorized users are dynamic. Another challenge is how to guarantee fleet data access time when resource-limited devices are used. In this article, a dynamic access policy ABE (DAP-ABE) system for EHRs in the cloud is proposed. The cloud server can update the access policy without sensitive information, while decryption keys of authorized users do not need to be updated. Authorized users enjoy approximately 0.07 ms data access by outsourcing the majority of the decryption overhead to the cloud server. Furthermore, a verification procedure is embedded in DAP-ABE to check the identities of patients in the data sharing stage, which ensures that no malicious user can upload invalid EHRs. Extensive experiments demonstrate the feasibility and efficiency of the DAP-ABE system.
Abstractive Text Summarization Using Attention-based Stacked LSTM

Every day, the amount of textual data created increases exponentially, both in terms of complexity and volume. Massive amounts of information are generated by social media, news articles, emails, text messages and other resources, making it difficult to read lengthy language materials. Our main objective in the paper is to obtain a short understandable and fluent abstractive summary of any given text. The Abstractive Text Summarizer automatically gives the summary of the text by generating new phrase, rephrasing or using the new words which are not present in the original text. In this paper, a machine learning architecture i.e. Stacked LSTM based on attention mechanism using Sequence-to-Sequence model is proposed, to generate the summary using abstractive approach for Amazon reviews of fine foods dataset. Our approach allows the model to accept content and provide a concise summary that may clearly describe the gist of the original text. The experiments on Amazon reviews of fine foods dataset show that our model obtained BLEU Score as 0.91 for a test set.
Secure De-Duplication Over Wireless Sensing Data Using Convergent Encryption

The fast development of cloud computing technology has greatly accelerated the gathering and sharing of numerous types of data. Utilizing data deduplication technologies is crucial to decrease storage overhead and transmission costs. A significant quantity of processing and storage resources are made available for a variety of high-quality service applications using cloud computing, which is an online data center. In any case, cloud clients never again approach their information in a nearby information stockpiling foundation, making it challenging to really look at the respectability of information that has been re-appropriated. It has become crucial to assist the user in fully verifying the accuracy of the data that has been outsourced. This issue is settled utilizing safe encryption and de-duplication systems, and clients might use an outsider examiner to confirm the precision of information that has been re-appropriated and the encryption technique. Existing data deduplication systems still have efficiency and security issues. Information deduplication is achieved by the proposed procedures preceding the information being shipped off the distributed storage server, which brings down the expense of calculation and transmission. in particular, block-level deduplication with variable size. De-duplication strategies have been utilized to forestall information duplication by contrasting records in the client data set utilizing PHP; this further develops stock piling's memory proficiency and rates up handling. Clients encode information utilizing concurrent encryption techniques prior to re-appropriating it to the cloud to keep unapproved clients from getting to the information.
Adaptive Unified Contrastive Learning for Imbalanced Classification

Medical image classifiers often suffer from the imbalanced class distribution of datasets. For example, among the 7 classes in the ISIC2018 skin lesion detection dataset, over 67% of the instances belong to melanocytic nevus while only 1% belong to dermatofibroma. Contrastive feature learning has been shown to achieve promising results in enhancing the performance for imbalanced classification tasks. However, the contrastive learning methods are either not end-to-end or require extra memory, which may lead to less compatible and sub-optimal features and classifiers. In this paper, we propose a novel unified feature and classifier learning framework for imbalanced medical image datasets. We equip our model with an adaptive unified contrastive (AduC) loss which progressively adapts model learning between feature learning and classifier learning. Furthermore, we explore the impact of different sampling methods on model training under data sparsity. The experimental results on two long-tailed medical datasets demonstrate that our methods can substantially improve the classification accuracy and F1-score over all classes without using extra memory storage. Our code is available at https://github.com/thomascong121/AdUni .