Reviewerly/exHarmony huggingface dataset:
Improving the Use of Blockchain Technology in Stroke Care Information Management Systems

Blockchain is a new and popular technology in the digital age. Blockchain technology is referred to as decentralised and distributed digital ledgers, which are called blocks. These blocks are linked together with the cryptographic hashes and are used to record transactions between many computers. No single block can be altered without altering the related blocks. Modification of individual block data is impossible because each block contains information from the previous block. This is the unique strength of blockchain. Timestamps and hashes are some of the important terms when blockchains are considered. Data security is guaranteed with this advanced technology. Blockchain technology finds its application in the healthcare industry with many advantages in a queue. Medical data can be transferred safely and securely for fool-proof management of the medicine supply chain, which helps in healthcare research. Blockchains are used to securely encrypt a patient's information in the event of an outbreak of a pandemic disease. A stroke is referred to as a brain attack, also called cerebral infarction. A cerebral infarction is a sudden stoppage of blood flow in the blood vessels connected to the brain. This study focused on evaluating the application of blockchain technology in Stroke Nursing Information Management Systems. This emerging technology is already in use in the healthcare industry. The patient's data is kept decentralized, transparent, and mainly incorruptible, thus keeping it secured and sharing of data is quick.
Interpreting convolutional neural network decision for earthquake detection with feature map visualization, backward optimization and layer-wise relevance propagation methods

SUMMARY In the recent years, the seismological community has adopted deep learning (DL) models for many diverse tasks such as discrimination and classification of seismic events, identification of P- and S-phase wave arrivals or earthquake early warning systems. Numerous models recently developed are showing high accuracy values, and it has been attested for several tasks that DL models perform better than the classical seismological state-of-art models. However, their performances strongly depend on the DL architecture, the training hyperparameters, and the training data sets. Moreover, due to their complex nature, we are unable to understand how the model is learning and therefore how it is making a prediction. Thus, DL models are usually referred to as a ‘black-box’. In this study, we propose to apply three complementary techniques to address the interpretability of a convolutional neural network (CNN) model for the earthquake detection. The implemented techniques are: feature map visualization, backward optimization and layer-wise relevance propagation. Since our model reaches a good accuracy performance (97%), we can suppose that the CNN detector model extracts relevant characteristics from the data, however a question remains: can we identify these characteristics? The proposed techniques help to answer the following questions: How is an earthquake processed by a CNN model? What is the optimal earthquake signal according to a CNN? Which parts of the earthquake signal are more relevant for the model to correctly classify an earthquake sample? The answer to these questions help understand why the model works and where it might fail, and whether the model is designed well for the predefined task. The CNN used in this study had been trained for single-station detection, where an input sample is a 25 s three-component waveform. The model outputs a binary target: earthquake (positive) or noise (negative) class. The training database contains a balanced number of samples from both classes. Our results shows that the CNN model correctly learned to recognize where is the earthquake within the sample window, even though the position of the earthquake in the window is not explicitly given during the training. Moreover, we give insights on how a neural network builds its decision process: while some aspects can be linked to clear physical characteristics, such as the frequency content and the P and S waves, we also see how different a DL detection is compared to a visual expertise or an STA/LTA detection. On top of improving our model designs, we also think that understanding how such models work, how they perceive an earthquake, can be useful for the comprehension of events that are not fully understood yet such as tremors or low frequency earthquakes.
Comparison of Sentiment Analysis Algorithms Using Twitter and Review Dataset

Abstract: Sentiment Analysis or opinion mining is the computational examination of sentiments, perspectives and emotions conveyed in written language. In recent years, sentiment analysis has become an active study in fields of natural language processing and text mining. The internet is full of textual data in the form of papers, articles and blogs. Sentiment analysis is the best way to extract key information from this data. In this paper we compare classification of tweets using rule based methods, Machine Learning method and Deep Learning method. Keywords: Sentiment Analysis, Opinion Mining, Twitter Dataset, Classification, Natural Language Processing.
Analysis of Online Education Reviews of Universities Using NLP Techniques and Statistical Methods

In the long-running battle against COVID-19, it is worth further discussing how well online education is accepted by users as a new type of education method which is popularized suddenly. The launch of a large-scale online teaching during the COVID-19 pandemic gave us the opportunity to investigate the opinions and emotions of online teaching users. This paper employs NLP techniques and statistical methods to process 93754 comments collected from the three online teaching platforms designated by the Ministry of Education for universities. This study aims to gain a better understanding of the changing characteristics of students’ opinions and emotions regarding online teaching, as well as to learn what students required and how the platforms functioned during the epidemic period. The findings revealed that (1) users of online teaching platforms were mostly concerned about whether the teaching resources provided by the platforms were rich and of high quality all along. The main influencing factors affecting online teaching experiences during the implementation of large-scale online teaching are hardware device deficiencies such as server congestion and network lag. (2) The majority of users expressed positive attitude towards online education which played a positive role during the outbreak. (3) The epidemic had a significant impact on the emotions of online teaching users. In short, the research themes showed that online teaching users are always concerned with the richness and quality of the platform’s resources. Furthermore, the most important issues for users during the period of large-scale online teaching are whether the network infrastructure is efficient and smooth, as well as the platform’s function perfection. The popularity of online teaching helped to solidify the online teaching space and improve its social image. This study provides a point of reference for the development and improvement of online education and compensates for a lack of relevant studies.
Research on Vessel Speed Heading and Collision Detection Method Based on AIS Data

In order to better predict the sailing information data of fishing boats, make accurate prediction and spacing budget for the sailing status of ships, achieve more accurate coordination and early warning in advance, and ensure the safety of fishing boats’ laneway, the essay combined the kinematics equation and artificial neural network model to adapt to the traffic situation of fishing boats in the far sea. A course and collision test technique based on ship AIS data is proposed, and the course collision detection method of fishing boats is studied by means of actual ship beacon collision accident data. Through the practical test, taking the navigation mark 4560.117 as an example, under the detection track of the navigation mark field corresponding to R = 70, the two ships have the same track, thus verifying the practicality and feasibility of the ship navigation mark collision detection method.
Privacy protection based distributed clustering with deep learning algorithm for distributed data mining

Distributed Data Mining (DDM) is vital in various applications for processing large volumes of data. The datasets are saved in the local databases and operated by local communities, but it provides the solution locally and globally. However, the datasets are stored in a distributed manner which affects the scalability and reliability issues. In addition, locally stored data is influenced by security and privacy challenges. In addition, the third party may access the DDM, which causes authorization issues. Therefore, the DDM process fuses sensor data from different sources to improve knowledge discovery. During this process, the DDM faces several issues such as security concerns, privacy restrictions, technical barriers, and trust issues. To address these issues, distributed data mining (DDM) should be improved to handle homogeneous and heterogeneous data. This work uses the privacy protection-based distributed clustering (PPDC) algorithm to handle the privacy and security challenges while analyzing the distributed data. The clustering algorithm generates the semi-trusted third parties to form the cluster, which protects the data from unauthorized users. The semi-trusted party protect the locally analyzed solution by creating the random vector-based trusted process. Further, the process uses the optimized deep learning approach and clustering to improve the heterogeneous data analysis. Then the effectiveness of the introduced PPDC method is compared with existing methods, and the PPDC algorithm ensures the 0.202 error rate, 0.95 % of accuracy and manages the data security.
Dense Coding with Locality Restriction on Decoders: Quantum Encoders versus Superquantum Encoders

We investigate practical dense coding by imposing locality restrictions on decoders and symmetry restrictions on encoders within the resource theory of asymmetry framework. In this task, the sender Alice and the helper Fred preshare an entangled state. Alice encodes a classical message into the quantum state using a symmetric preserving encoder and sends the encoded state to Bob through a noiseless quantum channel. The receiver Bob and helper Fred are limited to performing quantum measurements satisfying certain locality restrictions to decode the classical message. We are interested in the ultimate dense coding capacity under these constraints. Our contributions are summarized as follows. First, we derive both one-shot and asymptotic optimal achievable transmission rates of the dense coding task under different encoder and decoder combinations. Surprisingly, our results reveal that the transmission rate cannot be improved even when the decoder is relaxed from one-way local operations and classical communication (LOCC) to two-way LOCC, separable measurements, and partial transpose positive measurements of the bipartite system. Second, depending on the class of allowed decoders with certain locality restrictions, we relax the class of encoding operations to superquantum encoders in the general probability theory framework and derive dense coding transmission rates under this setting. For example, when the decoder is fixed to a separable measurement, theoretically, a positive operation is allowed as an encoding operation. Remarkably, even under this superquantum relaxation, the transmission rate still cannot be lifted. This fact highlights the universal validity of our analysis on practical dense coding beyond quantum theory.Received 10 October 2021Revised 6 May 2022Accepted 29 August 2022DOI:https://doi.org/10.1103/PRXQuantum.3.030346Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum channelsQuantum communicationQuantum entanglementResource theoriesQuantum Information
Challenges Smart Grid in Blockchain Applications

Globally, interest in renewable energy has grown recently. The microgrid integrates traditional dispersed energy resources utilizing technological tools and information technology, making it more effective and flexible to enhance the economic and environmental situations (IT). One of these technologies that has drawn interest in microgrid applications to create a sustainable society is blockchain. The blockchain idea can offer immutability for microgrid transactions such as recording power generation levels and confirming transactions between generators and end consumers. By eliminating the need to connect with third parties, blockchain-based smart contracts can be utilized for auditing or resolving a transaction dispute between the producers and the users. In this paper, we highlight many recent research projects on blockchain applications in microgrids and the use of smart contracts to enhance the transactive resilience in microgrids.
Formal Modeling and Verification of Smart Contracts with Spin

Smart contracts are the key software components to realize blockchain applications, from single encrypted digital currency to various fields. Due to the immutable nature of blockchain, any bugs or errors will become permanent once published and could lead to huge economic losses. Recently, a great number of security problems have been exposed in smart contracts. It is important to verify the correctness of smart contracts before they are deployed on the blockchain. This paper aims to verify the correctness of smart contracts in Ethereum transactions, and the model checker Spin is adopted for the formal verification of smart contracts in order to ensure their execution with respect to parties’ willingness, as well as their reliable interaction with clients. In this direction, we propose a formal method to construct the models for smart contracts. Then, the method is applied to a study case in the Ethereum commodity market. Finally, a case model is implemented in Spin, which can simulate the process’s execution and verify the properties that are abstracted from the requirements. Compared with existing techniques, formal analysis can verify whether smart contracts comply with the specifications for given behaviors and strengthen the credibility of smart contracts in the transaction.
English Teaching in Artificial Intelligence-based Higher Vocational Education Using Machine Learning Techniques for Students&amp;rsquo; Feedback Analysis and Course Selection Recommendation

Higher vocational education is a self-contained method of higher education that is aligned with global productivity and economic development. Its goal is to develop talented workers who contribute significantly to the economy and industry. Teaching analysis, teaching strategy, teaching practice, and assessment are all part of the course design process in high vocational education. Teaching assessment is one of the most effective methods for improving the quality of course teaching among teaching processes. This research proposes novel techniques in English teaching based on artificial intelligence for course selection based on students&amp;#39; feedback. Here, the dataset has been collected based on the students&amp;rsquo; feedback on courses for Higher Vocational Education in English teaching. This dataset has been processed to remove invalid data, missing values, and noise. The processed data features have been dimensionality reduction integrated with K-means neural network. And the extracted features have been classified with higher accuracy using recursive elimination-based convolutional neural network. Based on this feedback data classification, recommendation for courses in Higher Vocational Education in English teaching has been suggested. The experimental analysis shows various students&amp;#39; feedback dataset validation and training in terms of accuracy of 96%, precision of 92%, recall of 93%, RMSE of 68%, and computational time of 65%.
Re-QGAN: an optimized adversarial quantum circuit learning framework

Adversarial learning represents a powerful technique for generating data statistics. Its successful implementation in quantum computational platforms is not straightforward due to limitations in connectivity, quantum operation fidelity, and limited access to the quantum processor for statistically relevant results. Constraining the number of quantum operations and providing a design with a low compilation cost, we propose a quantum generative adversarial network design that uses real Hilbert spaces as the framework for the generative model and a novel strategy to encode classical information into the quantum framework. We consider quantum generator and discriminator architectures based on a variational quantum circuit. We encode classical information by the stereographic projection, which allows us to use the entire classical domain without normalization procedures. For low-depth ans\"atze designs, we consider the real Hilbert space as the working space for the quantum adversarial game. This architecture improves state-of-the-art quantum generative adversarial performance while maintaining a shallow-depth quantum circuit and a reduced parameter set. We tested our design in a low resource regime, generating handwritten digits with the MNIST as the reference dataset. We could generate undetected data (digits) with just 15 epochs working in the real Hilbert space of 2, 3, and 4 qubits. Our design uses native quantum operations established in superconducting-based quantum processors and is compatible with ion-trapped-based architectures.
A Tutorial on Sparse Gaussian Processes and Variational Inference

Gaussian processes (GPs) provide a framework for Bayesian inference that can offer principled uncertainty estimates for a large range of problems. For example, if we consider regression problems with Gaussian likelihoods, a GP model enjoys a posterior in closed form. However, identifying the posterior GP scales cubically with the number of training examples and requires to store all examples in memory. In order to overcome these obstacles, sparse GPs have been proposed that approximate the true posterior GP with pseudo-training examples. Importantly, the number of pseudo-training examples is user-defined and enables control over computational and memory complexity. In the general case, sparse GPs do not enjoy closed-form solutions and one has to resort to approximate inference. In this context, a convenient choice for approximate inference is variational inference (VI), where the problem of Bayesian inference is cast as an optimization problem -- namely, to maximize a lower bound of the log marginal likelihood. This paves the way for a powerful and versatile framework, where pseudo-training examples are treated as optimization arguments of the approximate posterior that are jointly identified together with hyperparameters of the generative model (i.e. prior and likelihood). The framework can naturally handle a wide scope of supervised learning problems, ranging from regression with heteroscedastic and non-Gaussian likelihoods to classification problems with discrete labels, but also problems with multidimensional labels. The purpose of this tutorial is to provide access to the basic matter for readers without prior knowledge in both GPs and VI. A proper exposition to the subject enables also access to more recent advances (like importance-weighted VI as well as interdomain, multioutput and deep GPs) that can serve as an inspiration for new research ideas.
Generating Data to Mitigate Spurious Correlations in Natural Language Inference Datasets

Natural language processing models often exploit spurious correlations between task-independent features and labels in datasets to perform well only within the distributions they are trained on, while not generalising to different task distributions. We propose to tackle this problem by generating a debiased version of a dataset, which can then be used to train a debiased, off-the-shelf model, by simply replacing its training data. Our approach consists of 1) a method for training data generators to generate high-quality, label-consistent data samples; and 2) a filtering mechanism for removing data points that contribute to spurious correlations, measured in terms of z-statistics. We generate debiased versions of the SNLI and MNLI datasets, and we evaluate on a large suite of debiased, out-of-distribution, and adversarial test sets. Results show that models trained on our debiased datasets generalise better than those trained on the original datasets in all settings. On the majority of the datasets, our method outperforms or performs comparably to previous state-of-the-art debiasing strategies, and when combined with an orthogonal technique, product-of-experts, it improves further and outperforms previous best results of SNLI-hard and MNLI-hard.
Attuning smart home scripts to household and energy care

Past techno-feminist studies have shown that smart home technologies (SHTs) have inadvertently increased household demand for energy and reinforced gender disparities. This confirms the need to gauge any changes in smart home visions, given that the domestication of SHTs depends on accurate and reliable technology and messages to householders. To determine whether smart home visions are now supporting more gender-equitable household practices and energy efficiency, a content analysis of marketing materials (n = 36) is presented. In the context of domestication and the concept of agency scripts, the results show that smart home visions continue to disregard diverse household types and fail to address either energy efficiency or the housework burdens placed on women. This triggers an enquiry into the negative implications for both women’s wellbeing and energy-saving goals. Feminist care ethics indicate how smart home visions can be attuned to promote gender-equitable household practices and energy reduction. By accenting the mutual interdependence between humans and technologies and developing a household care practice approach, a realignment of smart home visons towards a care ethic is proposed. Strategies for repurposing SHTs as technologies of care that care for householders and the environment are offered in conclusion. Practice relevanceThis research reveals that current smart home principles continue to circumvent energy-efficiency goals and reinforce the gender disparities implicated in the household adoption, integration and management of SHTs. The smart home industry and government policy are advised to develop and implement ethical smart home principles designed to shape the technology in ways that foster gender-equitable household practices and energy reduction. A realignment of smart home visions is advocated towards principles of caregiving, tailored specifically to the objectives of serving the care needs of householders and the environment. A series of strategies is proposed for practitioners and policymakers to facilitate the repurposing of SHTs as care technologies.