Reviewerly/exHarmony huggingface dataset:
P<sup>3</sup> AD: Privacy-Preserved Payload Anomaly Detection for Industrial Internet of Things

Payload-based anomaly detection (PAD) model is commonly built upon a big data of normal payload samples, and hence is able to discover zero-day attacks and unknown faults without the need of any negative samples in training phase. But such detection model encounters new challenges to adapt well to the emerging Industrial Internet of Things (IIoT). That is, the modern industrial processes are usually running in a very high complexity, resulting the payloads much more complex and diverse. Further, the industrial data is likely too sensitive to be shared in public, and thus induces a new privacy concern. To tackle these challenges, we propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ P}}^{3}{\mathrm{ AD}}$ </tex-math></inline-formula> , a novel privacy-preserved payload-based anomaly detection model for IIoT. The basic idea is to train a two-dimensional convolutional neural network (2D-CNN) based auto-encoder using normal payloads over a federated GAN (Generative Adversarial Network) architecture, and then to detect anomalies by an unexpected dissimilarity between the original payloads and the payloads reconstructed by the auto-encoder. By the 2D-CNN, we can model the normal payloads from both the request and response directions simultaneously, and thus have more opportunities to capture the complex and dynamic industrial behaviors that are possibly reflected in the bi-directional network communications. By the GAN, we can train a more generalized auto-encoder that is able to reconstruct more general payload samples without the need to have them in advance for model training. By the federated architecture, we can remove the need of direct sharing of normal payloads, and learn them indirectly by aggregating local models across different industrial data owners, hence ensuring the payload privacy. We have evaluated <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ P}}^{3}{\mathrm{ AD}}$ </tex-math></inline-formula> using four public industrial payload datasets as well as considering four typical IIoT PAD scenarios. The detection results achieve more than 0.966 in terms of F1 score for global condition and at least 0.753 for all kinds of federated settings, proving the effectiveness of our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm{ P}}^{3}{\mathrm{ AD}}$ </tex-math></inline-formula> with privacy preserved.
Classification of Classifications: Species of Library Classifications

Acknowledging the importance of classification not only for library and information science but also for the study and mapping of the world phenomena, in this paper we revisit and systematize the main types of classifications and focus on the species of classification mainly drawing on the work of S. R. Ranganathan. We trace the evolution of library classification systems by their structures and modes of design of various shades of classification systems and make a comparative study of enumerative and faceted species of library classifications. The value of this paper is to have a picture of the whole spectrum of existing classifications, which may serve for the study of future developments and constructions of new systems. This paper updates previous works by Comaromi and Ranganathan and is also theoretically inspired by them.
Using Immersive Virtual Reality in an Online Biology Course

Interest in virtual reality (VR) for teaching and learning in higher education is growing, given its many potential applications. VR offers a socially interactive environment with novel ways to engage students with materials, objects, and activities and provide students with experiences such as "field trips" that would be otherwise very difficult. Preliminary work indicates overall positive gains in student learning across disciplines compared to other technology and traditional techniques, although more studies are needed to better our understanding of this tool. We employed an "immersive" VR (with a head-mounted display) in an online course which provided students with the opportunity to interact with peers and engage in activities. We asked about perceptions of the learning experience with the technology and how using VR impacts students' performance. We also noted the benefits and challenges of VR in an online course. Students perceived VR as a helpful component of the course, although performance on the cardiovascular unit assessment did not differ compared to the previous semester without VR.The online version contains supplementary material available at 10.1007/s41979-023-00095-9.
Interoperability in Blockchain: A Survey

This paper presents a systematic and comprehensive survey on blockchain interoperability, where interoperability is defined as the ability of blockchains to flexibly transfer assets, share data, and invoke smart contracts across a mix of public, private, and consortium blockchains without any changes to the underlying blockchain systems. Analyzing the vast landscape of both research papers and industry projects, we classify the existing works into five categories, namely, (1) sidechains, (2) notary schemes, (3) hashed time lock contracts (HTLC), (4) relays, and (5) blockchain agnostic protocols. We analyze the existing works under a taxonomy that consists of system and safety characteristics, such as decentralization, direction of communication, locking mechanism, verification mechanism, trust, safety, liveness, and atomicity. Different from other surveys, we are the first to evaluate the performance of some representative interoperability approaches between Bitcoin and Ethereum covering sidechains, notary schemes, and HTLCs. Even though the performance of cross-chain transactions is low (typically fewer than 10 transactions per second), the main reason is the underlying blockchain (e.g., Bitcoin and Ethereum) and not the interoperability approach. Finally, we discuss existing challenges and possible research directions in blockchain interoperability. For example, we identify challenges in interoperability across permissioned and permissionless blockchains, in interacting with scripting blockchains, in security and privacy.
Augmented Reality Development Opportunities in Geometry Learning for Elementary Students

Educational institutions will basically get many benefits from using technology, such as augmented reality technology which is designed to support the learning process in the classroom, plus augmented reality technology can be operated on smartphones. This study aims to analyze the opportunities for the use of smartphones in the context of innovation in the development of learning media with augmented reality formats. A qualitative approach was used in this study with a survey method. The population used is Madrasah Ibtidaiyah students with a sample was 127 students. The data collection technique in this study was a non-test technique, with the data collection instrument being a questionnaire. The results of this study indicate that smartphones are a technology that is quite familiar to students, considering that most students have them, students also like learning activities that utilize smartphones because they contain several components such as text, graphics, narration, video and even animation. The results of this study can certainly be the basis for developing an augmented reality-based learning media product considering the components that make students motivated to learn are contained in it. Through this research, it is hoped that new learning resource innovations will emerge with the hope that students can be facilitated in learning the material and mastering the various competencies they need.
Analysis of Users’ Most Discussed Topics and Trends on Blockchain Technologies and Smart Contracts

Nowadays, blockchain technology and smart contracts (SCs) spread as hot computer science and finance topics, catching the interest of a heterogeneous range of users. Thinking about blockchain technologies, users can choose between a broad set of alternatives, like Bitcoin, Ethereum, Hyperledger Fabric, Cardano, and many others. The choice of a blockchain platform over another relies on the user’s purpose. For what concerns smart contracts programming, Ethereum is still the most popular platform despite other popular blockchain technologies recently increasing their popularity for SCs development, such as Algorand, Stellar, Hyperledger Fabric, and many others. Such a tremendous number of alternatives makes it difficult to keep track of blockchain trends and use cases of the different technologies. This work explores users’ most discussed topics and opinions about popular blockchain technologies, focusing on smart contracts development, taking advantage of topic modeling techniques, and in particular, taking advantage of the BERT model. The primary data source is Reddit, which was proven to provide different and exciting blockchain topics. The chosen blockchain platform are four of the most popular platforms for SCs development. These technologies include Ethereum, Stellar, Algorand, and Hyperledger Fabric. The analysis of 14.156 sub-reddits highlighted several exciting topics, such as smart contracts auditing, currency inflation, comparisons between crypto, and general blockchain trends. The most present arguments concern Stellar trends, comparisons between Stellar and Ripple, which Wallet to use to store tokens and crypto, SCs auditing, and Ethereum trends.
Evaluation and Verification of NLP Datasets for the Albanian Language

Computational Linguistics has seen tremendous growth and provided users with high end applications in the form of automatic translation tools, speech recognition, speech synthesis etc. However, such advancements are lacking for low resource languages. Our research aims to tackle one of these challenges, specifically advancing Computational Linguistics and Natural Language Processing for the Albanian Language. To develop accurate NLP tools, one must have a consistent and clean dataset for that language. In this paper we evaluate two well-known text corpora: OSCAR and CCAligned. The results are compared with a dataset that we have collected and curated, which we will refer in this paper as alb_dataset. Various statistical means have been used to compare and evaluate the datasets. Conclusions of this paper can be used by NLP researchers of the Albanian language before they use one of the text corpora mentioned above.
VLSP 2022 Abmusu Task Dataset: A Resource for Vietnamese Abstractive Multi-Document Summarization

The performance of automatic summarization systems has improved significantly with the development of supervised approaches. However, in the Vietnamese abstractive multi-document summarization task, the available datasets are insufficient for training the model. With this motivation, we contribute a new gold standard Vietnamese abstractive multi-document summarization dataset, named Abmusu. Following the collecting and clustering of articles, we have built a hierarchical annotation process to generate summaries, with three roles: annotator, supervisor, and curator. As a result, the dataset contains 600 news clusters formed from 1839 articles and the corresponding human-generated summaries. To the best of our knowledge, Abmusu dataset is the biggest dataset for Vietnamese abstractive multi-document summarization that is freely available for research. Moreover, summaries are more concise, making it challenging to train the summarization models. We also used various summarization baselines to benchmark the Abmusu dataset.
Secure Unrepeated Fiber Transmission with Quantum Deliberate Signal Randomization on Y-00 Protocol

We demonstrate security-enhanced 10-Gbit/s PSK Y-00 cipher transmission with deliberate signal randomization driven by a quantum random number generator in a 362 km ultra-low-loss fiber link without optical amplifiers. High security is achieved at high optical powers.
The Impact of Online Education on Higher Education in the Context of Education Equity

With the large-scale application of information technology in higher education, online education becomes more important and provides possibility to promote the equity of higher education. This article focus on the impact of online education on higher education in the context of educational equity, analyzes the current situation of higher education equity and online education, the basis that online education can promote higher education and offer the path options to promote higher education.
Machine Learning for Medical Data Integration

Making health data available for secondary use enables innovative data-driven medical research. Since modern machine learning (ML) methods and precision medicine require extensive amounts of data covering most of the standard and edge cases, it is essential to initially acquire large datasets. This can typically only be achieved by integrating different datasets from various sources and sharing data across sites. To obtain a unified dataset from heterogeneous sources, standard representations and Common Data Models (CDM) are needed. The process of mapping data into these standardized representations is usually very tedious and requires many manual configuration and refinement steps. A potential way to reduce these efforts is to use ML methods not only for data analysis, but also for the integration of health data on the syntactic, structural, and semantic level. However, research on ML-based medical data integration is still in its infancy. In this article, we describe the current state of the literature and present selected methods that appear to have a particularly high potential to improve medical data integration. Moreover, we discuss open issues and possible future research directions.
IDSov and the silent data revolution: Indigenous Peoples and the decentralized building blocks of web3

This article explores the technology underpinning the decentralized data revolution and encourages Indigenous Peoples (IPs) to secure their Indigenous Data Sovereignty (IDSov) over the Metaverse and Web3. More specifically, this article will survey blockchain technologies, exploring some disturbing colonial uses and providing an international legal framework that IPs can use to advance their IDSov internationally and domestically. This article will consider the role that cryptocurrencies, smart contracts, decentralized oracles, non-fungible tokens (NFTs), decentralized finance (DeFi), and decentralized autonomous organizations (DAOs) might play in advancing IDSov as it relates to western conceptualizations of Web3 and the Metaverse. The worldwide web's global data structure is undergoing a seismic shift that will significantly impact IPs. As inherent sovereigns, IPs are uniquely positioned to use and regulate these technologies in manners consistent with their cultural values and international indigenous human rights instruments. However, the march toward Web3 also looms menacingly over IPs. As such, we intend to examine IPs' novel risks and opportunities with Web3 and the Metaverse. We conclude by encouraging IPs to become fluent in the minutia of these technologies and to exert their inherent sovereignty over these nascent technologies in international and domestic arenas by building culturally informed systems to address their particularized needs. Future research should look toward the specific hurdles, and successes IPs are experiencing as they apply the technologies and principles discussed here.
The Influence of Virtual Character Design on Emotional Engagement in Immersive Virtual Reality: The Case of Feelings of Being

Immersive virtual reality applications based on head-mounted displays are gaining momentum among students and educational institutes, but there is a lack of information about the preferences of virtual characters and emotional engagement in these applications. The objectives of this study were to: (i) evaluate participants’ preferences on virtual characters in virtual reality; (ii) measure emotional engagement among the users in terms of Feelings of Being; and (iii) identify relationships between virtual characters and emotional engagement. We conducted a mixed-method user experience evaluation on the HHVR virtual reality application that introduces the premises of a Finnish university and has three virtual characters: a human virtual character based on a real person, a fictional human virtual character, and a cat virtual character. We set up an eSports event where presenters (N = 12, mean age: 31.09) experienced HHVR using a head-mounted display and spectators (N = 38, mean age: 25.95) observed the experiment through large screens. We administered a questionnaire and conducted semi-structured interviews to gain insights into the participants’ preferences on virtual characters and emotional engagement. The results indicated that the virtual character preferences varied between the presenters and spectators; the cat was a highly liked virtual character in both groups, and the realistic human virtual character garnered mixed reactions from the spectators, although she was generally liked by the presenters. Both groups experienced several Feelings of Being, such as engagement, effectiveness, security, trust, enjoyment, and excitement, during the HHVR experience. Moderate and significant correlations were identified between the virtual characters and some of the Feelings of Being, thus indicating that the type of virtual character could impact emotional engagement; however, this requires further exploration.
AgriMFLN: Mixing Features LSTM Networks for Sentiment Analysis of Agricultural Product Reviews

With the rapid development of the Internet, agricultural products have entered e-commerce platforms, and agricultural product reviews have become an important reference for consumers when purchasing agricultural products. However, due to the characteristics of different lengths, rich context-sensitive information, and multi-level information in the sentences of agricultural product reviews, the existing sentiment analysis methods cannot perform well enough to identify the sentiment tendency. To address this issue, we abstract the problem as a binary classification task to extract consumers’ sentiment orientation by proposing a new method. This method utilizes an attention mechanism to assign different weights to different key information in the sentence, thereby extracting abundant semantic information from the sentence. The design of the long short-term memory (LSTM) gate can effectively solve the problem of extracting long sequences and context-related information. The proposed model achieves superior results on two agricultural product datasets compared to other baseline models, providing guidance for merchants to improve agricultural product quality and enhance customer satisfaction.
Methods for Processing and Analyzing the Speckle Patterns of Materials Surface

Study of the stress–strain stateStress-strain state of structural materials using an optical-digital speckle correlationOptical-digital speckle correlation (ODSC) (ODSC) method and digital image correlation (DICDigital image correlation (DIC)) technique are discussed in this chapter. Here, we also consider a method for detecting subsurface defectsSubsurface defect in laminated composites by analyzing dynamic speckle patternsDynamic speckle pattern. The developed ODSCOptical-digital speckle correlation (ODSC) method makes it possible to narrow the peak width and increase the signal-to-noise ratio, thereby providing a more reliable restoration of displacement and deformation fields of the studied specimens near the edges and crackCrack tips. This method uses various architectures of the joint transform correlator (JTCJoint transform correlator (JTC)) to implement a cross-correlation operation between respective subsets of two speckle patternsSpeckle pattern to generate in-plane displacement and deformation vector fields. Computer simulations and experimental studies of the displacement fieldsDisplacement field of real specimens using six JTCJoint transform correlator (JTC) architectures have shown that the optical-digital implementations of the JTCJoint transform correlator (JTC) with adaptive median thresholding and the JTCJoint transform correlator (JTC) with ring median thresholdingRing median thresholding have the smallest surface displacementsSurface displacements errors and demonstrate the highest robustness to output noise. On the other hand, to improve the accuracy of evaluating the stress–strain stateStress-strain state of quasi-brittle materials near the crackCrack tip, a method for determining the components of the stress fieldStress field based on the DICDigital image correlation (DIC) technique was developed. A new method for subsurface defectsSubsurface defect detection based on generation of dynamic speckle patternsDynamic speckle pattern series and the use of speckle decorrelation and speckle blurringSpeckle blurring phenomenon is described. This method allows generating light responses from subsurface defectsSubsurface defect containing in laminate composite panelsLaminate composite panel. The hybrid optical-digital systemHybrid optical-digital system (HODS) with a frequency swept ultrasonic excitationUltrasonic excitation (US excitation) implementing this method makes it possible to detect real subsurface defectsSubsurface defect in composite and metal–composite elements of aircraft structures.
Hazards&amp;Robots: A dataset for visual anomaly detection in robotics

We propose Hazards&Robots, a dataset for Visual Anomaly Detection in Robotics. The dataset is composed of 324,408 RGB frames, and corresponding feature vectors; it contains 145,470 normal frames and 178,938 anomalous ones categorized in 20 different anomaly classes. The dataset can be used to train and test current and novel visual anomaly detection methods such as those based on deep learning vision models. The data is recorded with a DJI Robomaster S1 front facing camera. The ground robot, controlled by a human operator, traverses university corridors. Considered anomalies include presence of humans, unexpected objects on the floor, defects to the robot. Preliminary versions of the dataset are used in [1,3]. This version is available at [12]
Kidney Cancer Detection using Deep Learning Models

This study presents two types of Kidney cancer detection one is with the help of images and another one is with the help of blood test samples value. Kidney disease is condition caused either by renal disease of the kidneys. In the present study, Kidney cancer is one of the critical diseases for patient's diagnosis and classification. Early detection and good treatment can avoid or decrease the growth of cancer disease into the final stage where dialysis or renal transplantation is the only way of saving the life of the patient. And another way is with machine learning models with this model the disease at an early stage can be detected, is one of the important tasks in today's world. This research proposed kidney images detection through deep learning models like Convolutional Neural Networks (CNNs), and blood samples dataset values through Artificial Neural Network (ANN) models that can be helpful for the early diagnosis of cancer. The existing studies have mainly used only simple CNN models and have done another classification of kidney images. This research consists of CNN with more convolution layers for classifying images of cancer kidneys and normal kidneys and ANN is used for kidney cancer prediction using dataset values. This research will be helpful for early and accurate diagnosis of kidney cancer to save the lives of many patients. Lastly, there is an application page that contains a code in the backend that predicts whether a person is suffering from a kidney cancer or not.