Reviewerly/exHarmony huggingface dataset:
Satisfaction Measurement in the Blended Learning System of the University: The Literacy Mediated-Discourses (LM-D) Framework

Effective incorporation of new technologies into the education system has become a basic need in today’s globalized world, especially in adapting life to the new normal, which is seen by an increase in online learning. Thus, a blended learning system (BLS) becomes the alternative solution to enhance the effectiveness and efficiency of the online learning system by adding certain characteristics from the traditional approach to the online learning system. Therefore, the educational institution still has reluctance to adopt this kind of system as a means to deliver knowledge to the student due to several reasons, such as remote effects, learning progress assessment, alignment of the course roadmap to learning strategy, self-paced environment support, retaining lecturer authenticity, quantifying informal learning, and engagement of the student community. Thus, this study wants to investigate factors that influence the satisfaction of students in terms of using mediated communication consisting of the modules, the channels, and the lecturers as the component to evaluate the understanding of literacy within offline and online classes. In fact, satisfaction is a threshold target for the BLS contribution in bringing the discourses aligned with the demands as well as delivering necessary value for the related users. Interestingly, the results showed Computer Self-Efficacy and Expectation of Quality have valid and reliable value as well significancy within the proposed model.
Learnware: small models do big

There are complaints about current machine learning techniques such as the requirement of a huge amount of training data and proficient training skills, the difficulty of continual learning, the risk of catastrophic forgetting, the leaking of data privacy/proprietary, etc. Most research efforts have been focusing on one of those concerned issues separately, paying less attention to the fact that most issues are entangled in practice. The prevailing big model paradigm, which has achieved impressive results in natural language processing and computer vision applications, has not yet addressed those issues, whereas becoming a serious source of carbon emissions. This article offers an overview of the learnware paradigm, which attempts to enable users not need to build machine learning models from scratch, with the hope of reusing small models to do things even beyond their original purposes, where the key ingredient is the specification which enables a trained model to be adequately identified to reuse according to the requirement of future users who know nothing about the model in advance.
Quasi Lossless Satellite Image Compression

We describe an end-to-end trainable neural network for satel-lite image compression. The proposed approach builds upon an image compression scheme based on variational auto-encoders with a learned hyperprior that captures depen-dencies in the latent space for entropy coding. We explore this architecture in light of specificities of satellite imaging: processing constraints onboard the satellite (complexity and memory constraints) and quality needed in terms of reconstruction for the processing task on the ground. We explore data augmentation to improve the reconstruction of challenging image patterns. The proposed model outperforms the current standard of lossy image compression onboard satel-lite based on JPEG 2000, as well as the initial hyper-prior architecture designed for natural images.
Revisiting the Design Patterns of Composite Visualizations

Composite visualization is a popular design strategy that represents complex datasets by integrating multiple visualizations in a meaningful and aesthetic layout, such as juxtaposition, overlay, and nesting. With this strategy, numerous novel designs have been proposed in visualization publications to accomplish various visual analytic tasks. However, there is a lack of understanding of design patterns of composite visualization, thus failing to provide holistic design space and concrete examples for practical use. In this article, we opted to revisit the composite visualizations in IEEE VIS publications and answered what and how visualizations of different types are composed together. To achieve this, we first constructed a corpus of composite visualizations from the publications and analyzed common practices, such as the pattern distributions and co-occurrence of visualization types. From the analysis, we obtained insights into different design patterns on the utilities and their potential pros and cons. Furthermore, we discussed usage scenarios of our taxonomy and corpus and how future research on visualization composition can be conducted on the basis of this study.
A New Investment Opportunity: Bitcoin &amp; Ethereum Cryptocurrency

Cryptocurrency is fastly gaining interest among Investors in India. In the past few years, there was a tremendous change in the number of investors and analysts suggest that cryptocurrency is not only for city slickers but also for small towns. Many of the investors of Cryptocurrency are from small towns and villages in India. This may prove that the cryptocurrency may serve as another medium of exchange and also another way of storing valuable metals etc as a property. This paper estimates the different types of Crypto Currency of BitCoin and Ethereum through different type of Analytical tools like Time Series Analysis ,ANOVA ,Correlation and Decision tree analysis and suggest for better investment strategies.
Hypernudging in the changing European regulatory landscape for digital markets

Abstract The European regulatory landscape for digital markets is undergoing a transformative change. There is an observed shift toward the protection of public values and fundamental rights, as the market mechanism and market values that traditionally led regulatory processes in digital markets seem to have fallen short. In the context of the user‐centric digital economy, a clear commitment to safeguarding citizens' interests is ever‐more salient. This article provides a comprehensive account of hypernudging—dynamically personalized user steering, which represents the next generation user influencing techniques online, with the potential to lead to multifaceted individual and collective harms. However, problematizing the phenomenon for digital policy purposes is not a straightforward task. Due to the complexity and opaqueness of its underlying mechanisms and effects, policymakers are operating under conditions of uncertainty, necessitating a shared understanding of what impact hypernudging has on users as well as crafting a shared vision of values that ought to be embedded and safeguarded in digital choice architectures. To highlight the developing European approach in relation to hypernudging, the assessment of the recent legislative initiatives—the Artificial Intelligence Act, the Digital Markets Act, and the Digital Services Act—showcases underlying learning opportunities for addressing emergent challenges.
Continual Contrastive Learning for Cross-Dataset Scene Classification

With the development of remote sensing technology, the continuing accumulation of remote sensing data has brought great challenges to the remote sensing field. Although multiple deep-learning-based classification methods have made great progress in scene classification tasks, they are still unable to address the problem of model learning continuously. Facing the constantly updated remote sensing data stream, there is an inevitable problem of forgetting historical information in the model training, which leads to catastrophic forgetting. Therefore, we propose a continual contrastive learning method based on knowledge distillation and contrastive learning in this paper, which is named the Continual Contrastive Learning Network (CCLNet). To overcome the problem of knowledge forgetting, we first designed a knowledge distillation module based on a spatial feature which contains sufficient historical knowledge. The spatial and category-level knowledge distillation enables the model to effectively preserve the already learned knowledge in the current scene classification model. Then, we introduced contrastive learning by leveraging the comparison of augmented samples and minimizing the distance in the feature space to further enhance the extracted feature during the continual learning process. To evaluate the performance of our designed model on streaming remote sensing scene data, we performed three steps of continuous learning experiments on three datasets, the AID, RSI, and NWPU datasets, and simulated the streaming of remote sensing scene data with the aggregate of the three datasets. We also compared other benchmark continual learning models. The experimental results demonstrate that our method achieved superior performance in the continuous scene classification task.
Systematic Literature Review of Innovative Schools: A Map and a Characterization from Which We Learn

The traditional school model is subject to growing social pressure. The collective awareness of the need to find new pedagogical and organizational approaches has fueled the dynamics of school innovation. Over the last two decades, several schools have emerged worldwide bearing innovative models. The present study pursued two objectives: first—map the innovative schools worldwide, with students between 10/11 and 17/18 years old, referenced by academic publications; second—identify the dimensions of school innovation that those same academic publications indicate in the referenced schools. A systematic literature review was carried out in English, Portuguese, and Spanish between 2000 and 2021 in the search engines SCOPUS, Web of Science (WoS), EBSCO, Google Scholar, and RCAAP (Open Access Scientific Repositories of Portugal). There may be an increasingly broad consensus on the need for a change in the current school model, but are those so-called innovative schools more effective in promoting learning? The results obtained may raise further research to answer this question.
3D GAMING FOR YOUNG GENERATIONS IN HERITAGE PROTECTION: A REVIEW

Abstract. Heritage buildings, landscape, and structure are threatened because of natural disasters, development pressure, and might be deserted due to high maintenance costs and lack of people awareness. To preserve community values and pass them from generation to generation, protecting heritage is essential. In some countries, the awareness to preserve heritage arises strongly among young generation through different types of organisations. In Indonesia, many young people, such as university students, architects, journalists, historians, and academics, have strong willingness to learn and protect cultural heritage. However, they have limited support to do that. 3D games, Virtual Reality (VR), and Augmented Reality (AR) technologies are considered effective means in teaching about heritage. Moreover, in urban planning, these technologies especially 3D game is used to involve young people in taking part to design the city. Therefore, similar technologies could be utilized to facilitate young people in heritage protection. Certain functionality should be ensured to allow engaging activities such as taking decision, sharing opinions, and expressing creativity. This paper presents a literature review on the use of 3D games and similar technologies for heritage in the last five years. The study shows that the literature related to this topic is still very limited, i.e., only 14 articles focus on the topics of 3D gaming technology and heritage.
SKDStream: a dynamic clustering algorithm on time-decaying data stream

Abstract Data stream is a type of data that continue to grow over time. For example, network security data stream will constantly be generated in the field of data security, and encrypted data stream will be generated in the privacy protection scenario. Clustering is a basic task in the analysis of data stream. In addition to the large amount of data and limited computer memory, there are the following challenges in time-decaying data stream clustering: (1) How to quickly process time-varying data stream and how to quickly save vaild data. (2) How to maintain and update clusters and track their evolution in real time. Based on the fact that the existing data stream algorithms do not provide a good strategy to the above problems, this paper proposes a dynamic clustering algorithm named SKDStream. The algorithm divides the entire data space into distinct minimal bound hypercubes, which are maintained and indexed by a newly defined structure, SKDTree, that aggregates and updates clusters in real time without requiring large primary storage. Clusters are composed of dense hypercubes. Experiments on synthetic datasets and real datasets show that the response time of the algorithm is similar to that of existing dataflow algorithms, but the quality of the generated clusters is relatively stable over time. Furthermore, the SKDStream algorithm is able to track the evolution of the number of clusters, centers, and density in real time, and compared to D-stream, SKDStream is efficient and effective in clustering.
NILK: Entity Linking Dataset Targeting NIL-linking Cases

The NIL-linking task in Entity Linking deals with cases where the text mentions do not have a corresponding entity in the associated knowledge base. NIL-linking has two sub-tasks: NIL-detection and NIL-disambiguation. NIL-detection identifies NIL-mentions in the text. Then, NIL-disambiguation determines if some NIL-mentions refer to the same out-of-knowledge base entity. Although multiple existing datasets can be adapted for NIL-detection, none of them address the problem of NIL-disambiguation. This paper presents NILK, a new dataset for NIL-linking processing, constructed from WikiData and Wikipedia dumps from two different timestamps. The NILK dataset has two main features: 1) It marks NIL-mentions for NIL-detection by extracting mentions which belong to newly added entities in Wikipedia text. 2) It provides an entity label for NIL-disambiguation by marking NIL-mentions with WikiData IDs from the newer dump. We make available the annotated dataset along with the code1. The NILK dataset is available at: https://zenodo.org/record/6607514
Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems

What explains the dramatic progress from 20th-century to 21st-century AI, and how can the remaining limitations of current AI be overcome? The widely accepted narrative attributes this progress to massive increases in the quantity of computational and data resources available to support statistical learning in deep artificial neural networks. We show that an additional crucial factor is the development of a new type of computation. Neurocompositional computing adopts two principles that must be simultaneously respected to enable human-level cognition: the principles of Compositionality and Continuity. These have seemed irreconcilable until the recent mathematical discovery that compositionality can be realized not only through discrete methods of symbolic computing, but also through novel forms of continuous neural computing. The revolutionary recent progress in AI has resulted from the use of limited forms of neurocompositional computing. New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
3D Geophysical Predictive Modeling by Spectral Feature Subset Selection in Mineral Exploration

Several technical challenges are related to data collection, inverse modeling, model fusion, and integrated interpretations in the exploration of geophysics. A fundamental problem in integrated geophysical interpretation is the proper geological understanding of multiple inverted physical property images. Tackling this problem requires high-dimensional techniques for extracting geological information from modeled physical property images. In this study, we developed a 3D statistical tool to extract geological features from inverted physical property models based on a synergy between independent component analysis and continuous wavelet transform. An automated interpretation of multiple 3D geophysical images is also presented through a hybrid spectral feature subset selection (SFSS) algorithm based on a generalized supervised neural network algorithm to rebuild limited geological targets from 3D geophysical images. Our self-proposed algorithm is tested on an Au/Ag epithermal system in British Columbia (Canada), where layered volcano-sedimentary sequences, particularly felsic volcanic rocks, are associated with mineralization. Geophysical images of the epithermal system were obtained from 3D cooperative inversion of aeromagnetic, direct current resistivity, and induced polarization data sets. The recovered cooperative susceptibilities allowed locating a magnetite destructive zone associated with porphyritic intrusions and felsic volcanoes (Au host rocks). The practical implementation of the SFSS algorithm in the study area shows that the proposed spectral learning scheme can efficiently learn the lithotypes and Au grade patterns and makes predictions based on 3D physical property inputs. The SFSS also minimizes the number of extracted spectral features and tries to pick the best representative features for each target learning case. This approach allows interpreters to understand the relevant and irrelevant spectral features in addition to the 3D predictive models. Compared to conventional 3D interpolation methods, the 3D lithology and Au grade models recovered with SFSS add predictive value to the geological understanding of the deposit in places without access to prior geological and borehole information.
Plug-and-Play-Based Algorithm for Mixed Noise Removal with the Logarithm Norm Approximation Model

During imaging and transmission, images are easily affected by several factors, including sensors, camera motion, and transmission channels. In practice, images are commonly corrupted by a mixture of Gaussian and impulse noises, further complicating the denoising problem. Therefore, in this work, we propose a novel mixed noise removal model that combines a deterministic low-rankness prior and an implicit regularization scheme. In the optimization model, we apply the matrix logarithm norm approximation model to characterize the global low-rankness of the original image. We further adopt the plug-and-play (PnP) scheme to formulate an implicit regularizer by plugging an image denoiser, which is used to preserve image details. The above two building blocks are complementary to each other. The mixed noise removal algorithm is thus established. Within the framework of the PnP scheme, we address the proposed optimization model via the alternating directional method of multipliers (ADMM). Finally, we perform extensive experiments to demonstrate the effectiveness of the proposed algorithm. Correspondingly, the simulation results show that our algorithm can recover the global structure and detailed information of images well and achieves superior performance over competing methods in terms of quantitative evaluation and visual inspection.
Improving Situation Awareness via a Situation Model-Based Intersection of IoT Sensor and Social Media Information Spaces

Existing techniques for distilling situation awareness currently focus on information harvested from either IoT sensors or social media. While the benefits of fusing information from these two distinct information spaces for achieving enhanced situation awareness are well understood, existing techniques and related systems for fusing the IoT sensors and social media information spaces are currently embryonic. Key challenges in intersecting, combining, and fusing these information spaces to distil high-value situation awareness include devising situation models and related techniques for filtering, integrating, and fusing sparse and heterogeneous IoT sensor data and social media postings to provide a richer and more accurate situation awareness. This paper proposes novel, semantically based techniques fusing social media and IoT sensor information spaces and a comprehensive, fully implemented system that utilizes these to provide enhanced situation awareness. More specifically, this paper proposes the design of semantic-based situation models for fusing sensor and social media information spaces and presents techniques for finding similarities across these information spaces and fusing social media posting and IoT sensor data to generate an enhanced situation awareness. Furthermore, the paper presents the design and implementation of a complete system that uses the proposed models and techniques and uses that in an experimental evaluation that illustrates improvements in situation awareness from fusing the IoT sensor and social media information spaces.
Bandwidth-efficient threshold EC-DSA revisited: Online/offline extensions, identifiable aborts proactive and adaptive security

Due to their use in crypto-currencies, threshold ECDSA signatures have received much attention in recent years. Though efficient solutions now exist both for the two party, and the full threshold scenario, there is still much room for improvement, be it in terms of protocol functionality, strengthening security or further optimizing efficiency. In the past few months, a range of protocols have been published, allowing for a non interactive – and hence extremely efficient – signing protocol; providing new features, such as identifiable aborts (parties can be held accountable if they cause the protocol to fail), fairness in the honest majority setting (all parties receive output or nobody does) and other properties. In some cases, security is proven in the strong simulation based model. We combine ideas from the aforementioned articles with the suggestion of Castagnos et al. (2020) [5] to use the class group based CL framework so as to drastically reduce bandwidth consumption. Building upon this latter protocol we present a new, maliciously secure, full threshold ECDSA protocol that achieves additional features without sacrificing efficiency. Our most basic protocol boasts a non interactive signature algorithm and identifiable aborts. We also propose a more advanced variant that achieves adaptive security (for the n-out-of-n case) and proactive security. Our resulting constructions improve upon state of the art Paillier's based realizations achieving similar goals by up to a 10 factor in bandwidth consumption.
Cross-sectional data on stablecoin characteristics

<ns3:p>The article presents a dataset on the characteristics of stablecoins. Stablecoins represent a relatively young but increasingly important branch of the cryptocurrency market. Although they all share the same goal of maintaining a stable value in the digital market, they form a highly heterogeneous group. They differ in terms of collateral and stabilization mechanism, peg, availability of the technical documentation, presence on crypto exchanges or age. The dataset is cross-sectional and was created based on internet research. Individual information was collected from websites of the stablecoin projects and a crypto-data aggregator, and to a lesser extent from other auxiliary sources (websites related to finance and cryptocurrencies). The dataset is unique as there are no publicly available databases encompassing the features of stablecoins. It can be used in all stablecoin-related analyses to characterise the examined coins and to investigate the relationship between cryptocurrency market developments and stablecoin features.</ns3:p>