Reviewerly/exHarmony huggingface dataset:
Adolescents’ Digital Literacies in Flux: Intersections of Voice, Empowerment, and Practices

This article features a collaborative autoethnographic examination of three adolescent-researchers' digital literacies.The participatory design punctuates the role of the adolescent-researchers as they explored their meaning-making practices.Such collaborative research, which included three adolescents and their parents, not only resurfaces parent-inquiry, but also brings the adolescentresearcher voice to the forefront of literacy research.Two research questions guided the investigation: (a) What do adolescent-researchers tell us about their digital and nondigital literacy practices?and (b) In what ways do adolescent-researchers' retrospective examinations of their own practices reveal their perspectives of these practices and the power (and power struggles) that underlie them?The research team engaged in two rounds of coding, embracing first dramaturgical coding and then versus coding.Results suggested that Perspective/Attitude was the most prevalent attribute in the adolescent-researchers' discourse.Moreover, versus coding revealed strong relationships between "then versus now."Overall, the voices of the adolescent-researchers offer ongoing authenticity to discussions of their practices, creating continued opportunities to rethink the implications and applications of digital and nondigital practices in adolescents' lives.
Modeling the Relationship between User Comments and Edits in Document Revision

Management of collaborative documents can be difficult, given the profusion of edits and comments that multiple authors make during a document's evolution.Reliably modeling the relationship between edits and comments is a crucial step towards helping the user keep track of a document in flux.A number of authoring tasks, such as categorizing and summarizing edits, detecting completed to-dos, and visually rearranging comments could benefit from such a contribution.Thus, in this paper we explore the relationship between comments and edits by defining two novel, related tasks: Comment Ranking and Edit Anchoring.We begin by collecting a dataset with more than half a million comment-edit pairs based on Wikipedia revision histories.We then propose a hierarchical multi-layer deep neuralnetwork to model the relationship between edits and comments.Our architecture tackles both Comment Ranking and Edit Anchoring tasks by encoding specific edit actions such as additions and deletions, while also accounting for document context.In a number of evaluation settings, our experimental results show that our approach outperforms several strong baselines significantly.We are able to achieve a precision@1 of 71.0% and a precision@3 of 94.4% for Comment Ranking, while we achieve 74.4% accuracy on Edit Anchoring.
Rates of Convergence for Large-scale Nearest Neighbor Classification

Nearest neighbor is a popular class of classification methods with many desirable properties. For a large data set which cannot be loaded into the memory of a single machine due to computation, communication, privacy, or ownership limitations, we consider the divide and conquer scheme: the entire data set is divided into small subsamples, on which nearest neighbor predictions are made, and then a final decision is reached by aggregating the predictions on subsamples by majority voting. We name this method the big Nearest Neighbor (bigNN) classifier, and provide its rates of convergence under minimal assumptions, in terms of both the excess risk and the classification instability, which are proven to be the same rates as the oracle nearest neighbor classifier and cannot be improved. To significantly reduce the prediction time that is required for achieving the optimal rate, we also consider the pre-training acceleration technique applied to the bigNN method, with proven convergence rate. We find that in the distributed setting, the optimal choice of the neighbor k should scale with both the total sample size and the number of partitions, and there is a theoretical upper limit for the latter. Numerical studies have verified the theoretical findings.
Graded Monads and Graded Logics for the Linear Time - Branching Time Spectrum.

State-based models of concurrent systems are traditionally considered under a variety of notions of process equivalence. In the particular case of labelled transition systems, these equivalences range from trace equivalence to (strong) bisimilarity, and are organized in what is known as the linear time -- branching time spectrum. A combination of universal coalgebra and graded monads provides a generic framework in which the semantics of concurrency can be parametrized both over the branching type of the underlying transition systems and over the granularity of process equivalence. We show in the present paper that this framework of graded semantics does subsume the most important equivalences from the linear time -- branching time spectrum. An important feature of graded semantics is that it allows for the principled extraction of characteristic modal logics. We have established invariance of these graded logics under the given graded semantics in earlier work; in the present paper, we extend the logical framework with an explicit propositional layer and provide a generic expressiveness criterion that generalizes the classical Hennessy-Milner theorem to coarser notions of process equivalence. We extract graded logics for a range of graded semantics on labelled transition systems and probabilistic systems, and give exemplaric proofs of their expressiveness based on our generic criterion.
Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes

Neural network models of memory and error correction famously include the Hopfield network, which can directly store---and error-correct through its dynamics---arbitrary N-bit patterns, but only for ~N such patterns. On the other end of the spectrum, Shannon's coding theory established that it is possible to represent exponentially many states (~e^N) using N symbols in such a way that an optimal decoder could correct all noise upto a threshold. We prove that it is possible to construct an associative content-addressable network that combines the properties of strong error correcting codes and Hopfield networks: it simultaneously possesses exponentially many stable states, these states are robust enough, with large enough basins of attraction that they can be correctly recovered despite errors in a finite fraction of all nodes, and the errors are intrinsically corrected by the network’s own dynamics. The network is a two-layer Boltzmann machine with simple neural dynamics, low dynamic-range (binary) pairwise synaptic connections, and sparse expander graph connectivity. Thus, quasi-random sparse structures---characteristic of important error-correcting codes---may provide for high-performance computation in artificial neural networks and the brain.
Scalable inference of topic evolution via models for latent geometric structures

We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference. Our model is nonparametric Bayesian and the corresponding inference algorithm is able to discover new topics as the time progresses. By exploiting the connection between the modeling of topic polytope evolution, Beta-Bernoulli process and the Hungarian matching algorithm, our method is shown to be several orders of magnitude faster than existing topic modeling approaches, as demonstrated by experiments working with several million documents in under two dozens of minutes.
Impact of Personality Traits (BFI-2-XS) on Use of Cryptocurrencies

Cryptocurrencies are around for less than a decade but their usage steadily increases.There is no doubt that functional, social (cool-factor) and financial (high risk -high reward investment) motives drive its adoption and usage.But it is also worth to investigate whether personality and demographics can play some role in the adoption.The research have focused on impact of personality traits (according to the Big Five Inventory framework) on the usage of cryptocurrencies.The research was conducted in the Czech Republic.Respondents were university students, 478 in total (272 male, 206 female; 20.5 years old on average), of whom 62 respondents indicated that they use cryptocurrencies.Gender, age, and type of student job were used as control variables.With regards to the results, openness to experience, gender, and type of student job influence the adoption.Subjects more open to experience, male, and working full time in the field they study use cryptocurrencies more.
WeLink: A Named Entity Disambiguation Approach for a QAS over Knowledge Bases

Question Answering Systems (QASs) are usually built behind queries described by short texts. The explosion of knowledge graphs and Linked Open Data motivates researchers for constructing QASs over these rich data resources. The shortness nature of user questions contributes to complicate the problem of Entity Linking, widely studied for long texts. In this paper, we propose an approach, called WeLink, based on the context and types of entities of a given query. The context of an entity is described by synonyms of the words used in the question and the definition of the named entity, whereas the type describes the category of the entity. During the named entity recognition step, we first identify different entities, their types, and contexts (by the means of the Wordnet). The expanded query is then executed on the target knowledge base, where several candidates are obtained with their contexts and types. Similarity distances among these different contexts and types are computed in order to select the appropriate candidate. Finally, our system is evaluated on a dataset with 5000 questions and compared with some well-known Entity Linking systems.
Next Generation of Spatial Data Infrastructure: Lessons from Linked Data implementations across Europe

The need for integration of geospatial data across national borders poses questions on how to overcome technical and organizational barriers between national mapping agencies. Existing National Spatial Data Infrastructures (NSDIs) inherited heterogeneous technology stacks and user cultures. Example integration solutions are based on cascading data services on the Web using open standards. However, this approach is often cumbersome since it requires substantial efforts aimed at harmonisation of data structures and semantics of the content between NSDIs. In contrast, the Linked Data technology as an innovative approach for publishing heterogeneous data sources on the Web is able to transcend the traditional confines of separate databases, as well as, the confines of separate institutions - keeping existing infrastructures intact. Moreover, exposing national data as Linked Data on the Web makes it a part of the Semantic Web. This allows shifting focus from collection and dissemination of data to meaningful data consumption. Here, we present and discuss the results of the Open European Location Services project, a collaboration between the national mapping agencies of Finland, the Netherlands, Norway, and Spain which is aimed at demonstrating the capabilities of Linked Data technology in the context of Pan-European geospatial data provision.
Efficient Evaluation of Activation Functions over Encrypted Data

We describe a method for approximating any bounded activation function given encrypted input data. The utility of our method is exemplified by simulating it within two typical machine learning tasks: namely, a Variational Autoencoder that learns a latent representation of MNIST data, and an MNIST image classifier.
Reinforcement Fuzzy Tree: A Method extracting Rules from Reinforcement Learning Models

Reinforcement learning methods are more and more popular in machine learning fields, and have achieved outstanding breakthroughs in various applications combined with deep convolution neural networks. Deep convolution neural networks have excellent fitting abilities and promote the prosperity of machine learning. However, we want to look inside the model and get some rules sometime, but it is a hard task for deep convolution neural networks and reinforcement learning methods now. Our reinforcement fuzzy tree model unites reinforcement learning methods, deep convolution neural networks and decision tree, could extract explicit rules and keep excellent performance at the same time. Our paper show that models based on reinforcement learning and deep convolution neural networks could be redundancy sometime, but the extracted rules could be simpler than the model we constructing and keep the same performances.
Dense Peelable Random Uniform Hypergraphs

We describe a new family of $k$-uniform hypergraphs with independent random edges. The hypergraphs have a high probability of being peelable, i.e. to admit no sub-hypergraph of minimum degree $2$, even when the edge density (number of edges over vertices) is close to $1$. In our construction, the vertex set is partitioned into linearly arranged segments and each edge is incident to random vertices of $k$ consecutive segments. Quite surprisingly, the linear geometry allows our graphs to be peeled the outside in. The density thresholds $f_k$ for peelability of our hypergraphs ($f_3 \approx 0.918$, $f_4 \approx 0.977$, $f_5 \approx 0.992$, ...) are well beyond the corresponding thresholds ($c_3 \approx 0.818$, $c_4 \approx 0.772$, $c_5 \approx 0.702$, ...) of standard $k$-uniform random hypergraphs. To get a grip on $f_k$, we analyse an idealised peeling process on the random weak limit of our hypergraph family. The process can be described in terms of an operator on functions and $f_k$ can be linked to thresholds relating to the operator. These thresholds are then tractable with numerical methods. Random hypergraphs underlie the construction of various data structures based on hashing. These data structures frequently rely on peelability of the hypergraph or peelability allows for simple linear time algorithms. To demonstrate the usefulness of our construction, we used our $3$-uniform hypergraphs as a drop-in replacement for the standard $3$-uniform hypergraphs in a retrieval data structure by Botelho et al. This reduces memory usage from $1.23m$ bits to $1.12m$ bits ($m$ being the input size) with almost no change in running time.
Heuristics-based Responsiveness Evaluation of a Telemedicine Computational Web System

Computational technologies are increasingly included in our lives, presenting themselves as indispensable tools in most human activities and benefiting different areas of knowledge, such as the medical one. In this way, it is essential that the computational systems are prepared to function well in various devices, such as notebooks, tablets, and smartphones. Such an example of a tool is the Integrated Telemedicine and Data Management System, developed by our research group, which allows medical procedures to be transmitted and watched remotely in real time. Its initial development was focused on personal computers, but due to the need of providing better experiences with different devices, important changes were made in the main features. The idea was to implement responsive design concepts in the system and allow users to access it in mobile devices, such as tablets and smartphones. This article aims to evaluate the system responsiveness by applying a usability script for three devices: notebook, tablet, and smartphone. In particular, ten collaborators completed a questionnaire composed of twelve affirmative heuristics related to the system usability to evaluate their level of satisfaction for each valued aspect. The statistical evaluation revealed no significant differences among the devices considering the heuristics individually. However, when the heuristics were taken all together, the tests showed that the smartphone performed better, despite the fact that the notebook evaluation showed fewer usability problems. The results show that the changes performed in the system are functional.
Efficient reconstruction of light fields for consistently augmented reality using dense multi-view systems

In this paper, we study dense multi-view systems transmitting light fields beyond a visual obstruction as if it were transparent. Multi-view 3d displays appropriately combined with camera arrays or lens arrays provide consistently augmented reality for many users at the same time as a simple solution of such occlusion in the real world. We first describe a design of our proposed multi-view system, appearances of which are evaluated by assuming state-of-the-art visual devices to be used. Then, in order to reduce image sensors acquiring light fields for inexpensive systems, efficient interpolation is introduced to reconstruct the entire 4d light field in real time for consistency with the real world. We show that our practical implementation on a GPGPU actually achieves real-time interpolation obtaining enormous data of light fields without severe degradation for various depths.
Classification of Elastic and Collagen Fibers in H&amp;E Stained Hyperspectral Images

This study investigates the classification performance of elastic and collagen fibers using H&E stained hyperspectral images. As many as 1200 sample pixels were trained by using Linear discriminant analysis (LDA) and support vector machine (SVM) methods for reduction and classification. The classification result both using LDA and SVM show that H&E stained hyperspectral images performed better classification than H&E stained RGB image by comparing the classification of EVG stained images visually and quantitatively.
Product Recommendation System Design Using Cosine Similarity and Content-based Filtering Methods

The wide variety of products offered by a company, combined with the consistent demands of specific products from customers, create a certain problem for the organization when they want to market a new product. Organization need information that could help them promote the most suitable product based on their customer’s characteristics. The organization also need to suggest alternative products for customer if the requested product is unavailable. In this research, we design a Recommender System that could suggest either new or alternatif products to customer based on their characteristic and transaction history. This proposed system adopts Cosine Similarity method to calculate product similarity score and Content-based Filtering to calculate customer recommendation score and used as a model for the proposed system. Subsequently, these models are used to classify customers as well as products according to their transaction behavior and consequently recommends new products more likely to be purchased by them. Based on the testing results of the proposed system, it can be concluded that the chosen methods can be utilized to recommend products and costumer of products. It is shown that Precision and Recall of product similarity scores and customer recommendation for product scores are 100% and 93.47%.
Distributed Hybrid Two-Stage Multi-Sensor Fusion for Cooperative Modulation Classification in Large-Scale Wireless Sensor Networks

Recent studies showed that the performance of the modulation classification (MC) is considerably improved by using multiple sensors deployed in a cooperative manner. Such cooperative MC solutions are based on the centralized fusion of independent features or decisions made at sensors. Essentially, the cooperative MC employs multiple uncorrelated observations of the unknown signal to gather more complete information, compared to the single sensor reception, which is used in the fusion process to refine the MC decision. However, the non-cooperative nature of MC inherently induces large loss in cooperative MC performance due to the unreliable measure of quality for the MC results obtained at individual sensors (which causes the partial information loss while performing centralized fusion). In this paper, the distributed two-stage fusion concept for the cooperative MC using multiple sensors is proposed. It is shown that the proposed distributed fusion, which combines feature (cumulant) fusion and decision fusion, facilitate preservation of information during the fusion process and thus considerably improve the MC performance. The clustered architecture is employed, with the influence of mismatched references restricted to the intra-cluster data fusion in the first stage. The adopted distributed concept represents a flexible and scalable solution that is suitable for implementation of large-scale networks.
Symmetric-Approximation Energy-Based Estimation of Distribution (SEED): A Continuous Optimization Algorithm

Estimation of Distribution Algorithms (EDAs) maintain and iteratively update a probabilistic model to tackle optimization problems. The Boltzmann Probability Distribution Function (Boltzmann-PDF) provides advantages when used in energy based EDAs. However, direct sampling from the Boltzmann-PDF to update the probabilistic model is unpractical, and several EDAs employ an approximation to the Boltzmann-PDF by means of a Gaussian distribution that is usually derived by the minimization of the Kullback-Leibler divergence (KL-divergence) computed between the Gaussian and the Boltzmann-PDFs. The KL-divergence measure is not symmetric, and this causes the Gaussian approximation to fail at correctly modeling the target function for the EDAs, because the parameters of the Gaussian are not optimally estimated. In this paper, we derive an approximation to the Boltzmann-PDF using Jeffreys' divergence (a symmetric measure) in lieu of the KL-divergence and thus improve the performance of the optimization algorithm. Our approach is termed Symmetric-approximation Energy-based Estimation of Distribution (SEED) algorithm. The SEED algorithm is experimentally compared under a univariate approach against two other EDAs (UMDAc and BUMDA) on several benchmark optimization problems. The results show that the SEED algorithm is more effective and more efficient than the other algorithms.
Quantum gravity as a promising new information processing resource

Quantum EngineeringVolume 1, Issue 4 e24 RESEARCH HIGHLIGHT Quantum gravity as a promising new information processing resource Pan Gao, Corresponding Author Pan Gao gaop15@mails.tsinghua.edu.cn orcid.org/0000-0003-3851-3023 State Key Laboratory of Low-dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing, China Beijing Academy of Quantum Information Science, Beijing, China Pan Gao, State Key Laboratory of Low-dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China; or Beijing Academy of Quantum Information Science, Beijing 100193, China. Email: gaop15@mails.tsinghua.edu.cnSearch for more papers by this author Pan Gao, Corresponding Author Pan Gao gaop15@mails.tsinghua.edu.cn orcid.org/0000-0003-3851-3023 State Key Laboratory of Low-dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing, China Beijing Academy of Quantum Information Science, Beijing, China Pan Gao, State Key Laboratory of Low-dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China; or Beijing Academy of Quantum Information Science, Beijing 100193, China. Email: gaop15@mails.tsinghua.edu.cnSearch for more papers by this author First published: 16 October 2019 https://doi.org/10.1002/que2.24Citations: 5Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume1, Issue4December 2019e24 RelatedInformation