Reviewerly/exHarmony huggingface dataset:
MEASUREMENTS OF INEQUALITY IN POLITICAL METAMODERNISM (HANZI FREINACHT) AND THE AGE OF ACCESS (JEREMY RIFKIN)

The article covers a multidimensional issue of social inequality. Most of sociological and philosophical studies concern informational, economic and gender inequality. However, for holistic understanding of the problem, it is important to take into account emotional, ecological and psychological components. Informational, economic and ecological inequality directly depend on access to truthful and scientifically based information from reliable sources, to opportunities of supporting oneself financially, to quality food products and safe environment, respectively. To overcome these types of inequality, access should be taken into account, as a concept, characteristics of which define the availability and accessibility of certain resources and the ability to satisfy either the need in clean drinking water or one’s social recognition by others. The correlations between the concept of “Age of Access” coined by Jeremy Rifkin and Hanzi Freinacht’s theory of metamodern politics are presented. The relevance of “access” to multidimensional inequality (one of the issues ideologists of political metamodernism work on) was analysed. A cohort of central actors in the age of access was reviewed. It includes metamodern aristocracy (hackers, hippies, hipsters, hermeneutics) and a group formed by cleanweb‑, bio‑, info‑ and 3D‑hackers. It was demonstrated that the abovementioned specialists are deeply involved in the regulatory processes of access control and/or its deprivation compared to other members of post-industrial societies. In post-industrial societies with networked economy the role of access has significantly increased relatively to accumulation of material wealth and property ownership. On the one hand, building a just society requires overcoming the forces that somehow perpetuate inequality, and, on the other hand, providing the highest possible level of access to everything that increases equality between people. Those who are excluded from networks and deprived of access should not be forgotten; they don’t have access not only to resources, but can’t even have a discussion with the privileged ones about their own inferior condition. The reproduction mechanisms of the above-mentioned types of inequalities should be complemented by such a concept of access that would contribute to the eradication of inequality and/or provide us with more equitable solutions in case of insurmountable circumstances. Such solutions also might be applied to emotional and psychological inequality.
Blockchain-Based Electronic Voting: A Secure and Transparent Solution

Since its appearance in 2008, blockchain technology has found multiple uses in fields such as banking, supply chain management, and healthcare. One of the most intriguing uses of blockchain is in voting systems, where the technology can overcome the security and transparency concerns that plague traditional voting systems. This paper provides a thorough examination of the implementation of a blockchain-based voting system. The proposed system employs cryptographic methods to protect voters’ privacy and anonymity while ensuring the verifiability and integrity of election results. Digital signatures, homomorphic encryption (He), zero-knowledge proofs (ZKPs), and the Byzantine fault-tolerant consensus method underpin the system. A review of the literature on the use of blockchain technology for voting systems supports the analysis and the technical and logistical constraints connected with implementing the suggested system. The study suggests solutions to problems such as managing voter identification and authentication, ensuring accessibility for all voters, and dealing with network latency and scalability. The suggested blockchain-based voting system can provide a safe and transparent platform for casting and counting votes, ensuring election results’ privacy, anonymity, and verifiability. The implementation of blockchain technology can overcome traditional voting systems’ security and transparency shortcomings while also delivering a high level of integrity and traceability.
Two-Step Unsupervised Approach for Sand-Dust Image Enhancement

In sand-dust environments, light is scattered and absorbed, and sand-dust images thus suffer from severe image degradation problems, such as color shifts, low contrast, and blurred details. To address these problems, we propose a two-step unsupervised sand-dust image enhancement algorithm. In the first step, a convenient and competent color correction method is put forward to solve the color shift problem. Considering the wavelength attenuation features of sand-dust images, a linear stretching and blue channel compensation method is designed, and an adaptive color shift correction factor is developed to remove the color shift. In the second step, to enhance the clarity and details of the images, an unsupervised generative adversarial network is proposed, which does not require pairs of data for training. To reduce detail loss, the detail enhancement branch is designed, and the generator considers to more details through the constructed coarse-grained and fine-grained discriminators. The introduced multiscale perceptual loss promotes the image fidelity well. Experiments show that the proposed method achieves better color correction, enhances image details and clarity, has a better subjective effect, and outperforms existing sand-dust image enhancement methods both quantitatively and qualitatively. Similarly, our method promotes the application capability of the target detection algorithm and also has a good enhancement effect on underwater images and haze images.
High-Sensitivity Quantum-Enhanced Interferometers

High-sensitivity interferometers are one of the basic tools for precision measurement, and their sensitivity is limited by their shot noise limit (SNL), which is determined by vacuum fluctuations of the probe field. The quantum interferometer with novel structures can break the SNL and measure the weak signals, such as the direct observation of gravity wave signal. Combining classical interferometers and the optical parametric amplifier (OPA) can enhance the signal; meanwhile, the quantum noise is kept at the vacuum level, so that the sensitivity of the nonlinear interferometer beyond the SNL can be achieved. By analyzing in detail the influence of system parameters on the precision of quantum metrology, including the intensity of optical fields for phase sensing, the gain factor of OPA, and the losses inside and outside the interferometers, the application conditions of high-sensitivity nonlinear quantum interferometers are obtained. Quantum interferometer-based OPAs provide the direct references for the practical development of quantum precise measurement.
Hybrid Value-Aware Transformer Architecture for Joint Learning from Longitudinal and Non-Longitudinal Clinical Data

Transformer is the latest deep neural network (DNN) architecture for sequence data learning, which has revolutionized the field of natural language processing. This success has motivated researchers to explore its application in the healthcare domain. Despite the similarities between longitudinal clinical data and natural language data, clinical data presents unique complexities that make adapting Transformer to this domain challenging. To address this issue, we have designed a new Transformer-based DNN architecture, referred to as Hybrid Value-Aware Transformer (HVAT), which can jointly learn from longitudinal and non-longitudinal clinical data. HVAT is unique in the ability to learn from the numerical values associated with clinical codes/concepts such as labs, and in the use of a flexible longitudinal data representation called clinical tokens. We have also trained a prototype HVAT model on a case-control dataset, achieving high performance in predicting Alzheimer's disease and related dementias as the patient outcome. The results demonstrate the potential of HVAT for broader clinical data-learning tasks.
Sentimental Analysis on Webio Comments using Machine Learning

Sentiment analysis is a powerful technique that allows us to automatically classify the sentiment of a text into positive, negative or neutral categories. In this study, we explore the application of sentiment analysis on Webio comments using machine learning algorithms. Webio is a popular online platform that enables users to interact with chatbots and customer service representatives. By analysing the sentiment of Webio comments, businesses can gain value insights into customer satisfaction and identify areas of improvement in their products or services. We used a dataset of Webio comments and employed various machine learning algorithms, including Support Vector Machines(SVM), Naive Bayes(NB), and Random Forest(RF), to classify the sentiment of the comments. We evaluated the performance of the algorithms using metrics such as accuracy, precision, recall, and F1-score. Our results show that the SVM algorithm outperformed the algorithms, achieving an accuracy of 85% in classifying the sentiment of Webio comments. We also conducted feature selection experiments to identify the most important features in the classification task, and found that the presence of certain keywords and emoticons had a significant impact on sentiment classification. Overall, our study demonstrates the effectiveness of machine learning algorithms in analysing sentiment on Webio comments and provides insights into the features that are most indicative of sentiment in this context
Project-Based Cyber Security Course Teaching Based on Digital Training Platform

As cyber security issues are becoming obvious around the world, the training of cyber security professionals is particularly important.Educators are trying to find effective teaching ways for cyber security education.In order to help students develop both theoretical and practical abilities, project-based teaching mode is used in cyber security course.During the teaching process, teachers match chapters with the components of the real project, use project to introduce theories, and make project design tasks for students.During the semester, students should complete six module design task and a whole project design scheme at the final.There are two digital platforms, one for basic experiments and the other for design tasks.This teaching mode can help to achieve the integration of theoretical knowledge and engineering practice.It also helps students make a good habit of study.
Trip planning mobile application: a perspective case study of user experience

Abstract Mobile trip planning applications may contribute to popularising public transport, provided they work efficiently and gain high user acceptance. This article aims to take a closer look at the functioning of the JakDojade application, which has been the most popular platform in Poland for several years, supporting travel planning by public transport. In the presented case study, the authors tried to diagnose problems and indicate the directions of application development. At the same time, through this analysis, the authors aimed to demonstrate the usefulness of researching user comments from the viewpoint of managing the development of mobile applications and related services. A case study methodology was used to perform a descriptive study. Data on user feedback on JakDojade mobile application in Poland comes from Google Play Store. Semantic categorisation of user comments and sentiment analysis allowed for identifying user problems and diagnosing emotions related to its use. The presented methodology allowed for diagnosing typical user problems for the JakDojade application, which may help indicate further development directions. The authors attempted to demonstrate the usefulness of researching user comments from the point of view of managing the development of mobile applications and related services. The semi-automatic approach to text analysis presented in the article highlights the problems related to the study of user reviews. The limitations of the proposed methodology and the possibilities for its improvement were indicated.
Review of Advances in Speech Processing with Focus on Artificial Neural Networks

Speech is the primary way via which most humans communicate. Computers facilitate this transfer of information, especially when people interact with databases. While some methods to manipulate and interpret speech date back many decades (e.g., Fourier analysis), other processing techniques were developed late last century (e.g., linear predictive coding and hidden Markov models). Nonetheless, the last 25 years have seen major advances leading to the wide acceptance of computer-based speech processing, e.g., cellular telephones and real-time online conversations. This paper reviews older techniques and recent methods that focus largely on artificial neural networks. The major highlights in speech research are examined, without delving into mathematical detail, while giving insight into the research choices that have been made. The focus of this work is to understand how and why the discussed methods function well.
WeExt: A Framework of Extending Deterministic Knowledge Graph Embedding Models for Embedding Weighted Knowledge Graphs

With the further development of knowledge graphs, many weighted knowledge graphs (WKGs) have been published and greatly promote various applications. However, current deterministic knowledge graph embedding algorithms cannot encode weighted knowledge graphs well. This paper gives a promising framework WeExt that can extend deterministic knowledge graph embedding models to enable them to learn weighted knowledge graph embeddings. In addtion, we introduce weighted link prediction to evaluate the weighted knowledge graph embedding models’ performance on completing WKGs. Finally, we give concrete implementation of WeExt based on two translational distance models and two semantic matching models. Our experimental results show the proposed framework achieves promising performance in link prediction, weight prediction, and weighted link prediction.
Polynomial‐time universality and limitations of deep learning

Abstract The goal of this paper is to characterize function distributions that general neural networks trained by descent algorithms (GD/SGD), can or cannot learn in polytime. The results are: (1) The paradigm of general neural networks trained by SGD is poly‐time universal: any function distribution that can be learned from samples in polytime can also be learned by a poly‐size neural net trained by SGD with polynomial parameters. In particular, this can be achieved despite polynomial noise on the gradients, implying a separation result between SGD‐based deep learning and statistical query algorithms, as the latter are not comparably universal due to cases like parities. This also shows that deep learning does not suffer from the limitations of shallow networks. (2) The paper further gives a lower‐bound on the generalization error of descent algorithms, which relies on two quantities: the cross‐predictability, an average‐case quantity related to the statistical dimension, and the null‐flow, a quantity specific to descent algorithms. The lower‐bound implies in particular that for functions of low enough cross‐predictability, the above robust universality breaks down once the gradients are averaged over too many samples (as in perfect GD) rather than fewer (as in SGD). (3) Finally, it is shown that if larger amounts of noise are added on the initialization and on the gradients, then SGD is no longer comparably universal due again to distributions having low enough cross‐predictability.
Fortifying Health Care Intellectual Property Transactions With Blockchain

Intellectual property (IP) is a substantial competitive advantage in the health care industry. However, the COVID-19 pandemic highlighted the need for open innovation and collaboration for the greater good. Despite this, the industry faces challenges with innovation owing to organizational and departmental barriers. A secure platform is necessary to facilitate IP sharing without compromising the rights of IP owners.This study proposes a blockchain-based framework to secure IP transactions in health care and bring social impact.This study reviews existing researches, publications, practical cases, firm and organization websites, and conferences related to blockchain technology, blockchain in health care, blockchain in IP management, IP pledge research, and practice of IP management blockchain. The platform architecture has 7 components: pledgers, advanced research technology (ART), IP pledge platforms, IP databases, health care research, seeking ART, and transaction condition setting. These components work together seamlessly to support the sharing and pledging of ART and knowledge, while ensuring the platform's transparency, security, and trust.The open IP pledge framework can promote technology dissemination and use, reduce research and development costs, foster collaboration, and serve the public interest. Medical organizations' leadership and support and active participation from stakeholders are necessary for success. By leveraging blockchain technology, the platform ensures tamper-proof and transparent transactions and protects the rights of IP owners. In addition, the platform offers incentive mechanisms through pledge tokens that encourage stakeholders to share their ART and contribute to the platform.Overall, the proposed framework can facilitate technological innovation, tackle various challenges, and secure IP transactions. It provides a secure platform for stakeholders to share their IP without compromising their rights, promoting collaboration and progress in the health care industry. The implementation of the framework has the potential to revolutionize the industry's approach to innovation, allowing a more open and collaborative environment driven by the greater good.
Implementation of School (GLS) Literation Action SMK Negeri Bali mandala and SMK Negeri 1 Sawan

The aims of this study are to describe (1) The implementation of school literacy action (TISL), and (2) supporting elements of TISL at SMK Negeri Bali Mandara and SMK Negeri 1 Singaraja. The type of this study is qualitative qualitative. The subject of this research was the teachers and the students of SMK Negeri Bali Mandara and SMK Negeri 1 Singaraja. The object of this study was the implementation of school literacy action and supporting elements of the implementation school literacy action. The data was analyzed through several steps: reduction, classification, and description, interpretation, and conclusion. The result showed that SMK Negeri Bali Mandara were able to implemented 11 literacy-related activities meanwhile SMK Negeri 1 Singaraja only achieved 10. The supporting elements of school literacy action are (1) School Literacy Committee (SLC), (2) TISL Program, (3) The availability of infrastructure such as library and reading corner, (4) Student, teacher, and parent involvement, (5) Budget quota to reinforce infrastructure. Based on the result of this study, the school authority should be maintain maintenance of the facilities, the teachers should be collaborate with the students in implementing this literacy program. The students should be interested in reading and writing through literacy program.
Modified bee colony optimization with opposition learning algorithm on use of medical data clustering

Clustering has gained popularity in the data mining field as one of the primary approaches for obtaining data distribution and data analysis. The medical data analysis for different diseases is a great challenge in current research. The benefits of opposition based learning such as faster convergence rate and better approximate result in finding global optimum can be helpful in this area. To achieve faster convergence and better clustering results for medical data, in this work, the authors have proposed an approach utilising modified bee colony optimization with opposition based learning and k-medoids technique. The initial centroid plays an important role in the bee colony optimization based clustering. The proposed approach uses k-medoids algorithm for this task. In order to facilitate faster convergence, it adds the opposite bees which are located at exactly the opposite location of the initial bees. The exploration task is performed by both of these kinds of bees to find potential solutions. This increases the algorithm’s capacity for exploration and, consequently, the rate of convergence. Five distinct medical datasets collected from the UCI library are investigated to demonstrate the algorithm’s efficacy. The implementation results demonstrate that the algorithm gives better convergence rate and clustering quality compared to some the existing algorithms.
Distributed zonotopic estimation for interconnected systems: A fusing overlapping states strategy

This paper is concerned with the distributed estimation problem for a class of interconnected systems with overlapping states. Under local communication, a distributed zonotopic estimator is designed by using the information from each subsystem and its neighbors. However, the obtained state estimates are also overlapping and contain redundant information that can be shared to further improve estimation performance for subsystems. To better utilize such overlapping estimates from neighbors, zonotopic fusion criteria are proposed to combine local estimates into a better result. Notice that the proposed distributed zonotopic estimator does not require knowing the correlations of estimation errors, such as the complex calculation of cross-covariances, so it can be implemented online. Moreover, robust stability conditions are provided to ensure the boundedness of estimation errors. Finally, illustrative examples are employed to show the effectiveness of the proposed methods.
Spherical video synopsis generation and visualization framework

With the advances in video technology, the advent of spherical video (360° video) recorded using an omnidirectional camera offers a limitless field-of-view (FoV) to the viewers. However, they suffer from the fear of missing out (FOMO) because they can only see a particular FoV at a time. Reviewing a long recorded surveillance video i.e., 24 hours a day is a time-consuming process due to temporal and spatial redundancy. A solution to this problem is to compactly represent the video synopsis by shifting the objects along the time domain. Using a multi-camera setup for surveillance creates blind spots. This problem is solved by using a spherical camera. Therefore, in this paper, we focus on creating and visualizing the video synopsis recorded by the spherical camera. The optimization algorithm plays a key role in condensing the recorded video. Hence, a novel spherical video synopsis optimization framework has been introduced to generate compact videos that eliminate FOMO. The synopsis is generated by shifting objects on the temporal axis and displays them simultaneously by optimizing multiple constraints. It minimizes activity loss, virtual collisions, temporal inconsistencies, and synopsis video length by preserving interactions between objects. The proposed multiobjective optimization includes a new constraint to restrict the number of objects displayed per frame due to the limitation of the human visual system. Direction-based visualization methods have been proposed to improve the viewer’s experience without FOMO. Comparative performance of the proposed framework using the latest metaheuristic optimization algorithms with existing video synopsis optimization algorithms is performed. It is found that chronological disorder ratio and overall virtual collision are minimized effectively through the recent metaheuristics optimization algorithms compared to the related works on video synopsis.