Reviewerly/exHarmony huggingface dataset:
Two part envelopes for rejection sampling of some completely random measures

This paper shows that rejection sampling with a two-piece Lévy intensity envelope can outperform both the Ferguson–Klass algorithm and previously proposed envelopes for simulating realizations of completely random measures typically used in Bayesian nonparametric statistics.
Propositional attitudes, harm and public hate speech situations: towards a maieutic approach

In this article, I provide an argument against the idea that public hate-speech events are harmful because they cause a discrete, traceable and harmful change in one’s propositional attitudes. To do so, I identify the essential conceptual architecture of public hate-speech situations, I assess existing arguments for the direct and indirect harm of public hate speech and I propose a novel way to approach public hate-speech situations: a maieutic approach. On this perspective, public hate-speech events do not cause changes in propositional attitudes, but rather, if successful, either such events bring a person’s latent propositional attitudes into clear consciousness, or they play with propositional attitudes speakers and their audience had prior to the public hate-speech situation.
Image semantic segmentation with finer edges and complete parts from bounding box annotations

Image semantic segmentation understands and parses the image scene from the pixel-level. We work on the weakly supervised image semantic segmentation method on bounding box annotations. Inspired by Roberts edge detection operator, we develop an edge gradient module, which includes the max-pooling layer and average-pooling layer to extract edge gradient features. We combine the pyramid pooling (PP) module extracting global features with the atrous spatial PP module building the relationships between nodes to form long-range context dependency module. It can strengthen the ties between various parts of the object, just like a simplified fully connected conditional random field. To obtain more accurate feedback from bounding box annotations, we design a random label transformation algorithm. Finally, we demonstrate the validity of our module on PASCAL VOC 2012 and MS-COCO datasets, and our whole model has achieved a better performance than other mainstream methods.
From Anderson to ORDAC: A History of Bibliographic Policy Discussion in Australia

This article presents a history of national bibliographic policy discussion in Australia, from the turn of the twentieth century to the present day, assessing the contribution this discussion has made to the development of international bibliographic standards. In earlier times, various factors worked against progress towards standardisation and policy innovation, including the extant Anglo-American codes and inherited bibliographic conventions, the difficulties of establishing a national cataloguing agency, and the tyranny of distance experienced by national cataloguing committees. In the mid-twentieth century, Australian cataloguers became more involved in international bibliographic developments, but it was the advent of Machine Readable Cataloguing (MARC) cataloguing in Australia, in the 1970s and 1980s, that gave them a much stronger voice both within and outside the country, as records became much more sharable. Tensions between institutional and individual cataloguing views were eased through the establishment of the Australian Committee on Cataloguing, which represented both, while a broader level of engagement was successfully fostered by the Cataloguers Sections of the Library Association of Australia. This engagement has weakened in recent times, but this is symptomatic of a more general trend towards the globalisation of cataloguing and metadata, whereby national policy discussions are less relevant than are discussions in particular domains.
Improved target tracking algorithm based on kernelized correlation filter

We propose an improved object tracking algorithm based on kernelized correlation filter (KCF), which can overcome the drawback of traditional KCF algorithms in that they cannot effectively adapt to target-scale variations and target occlusion in tracking. First, the target-scale pyramid is built, whose histogram of oriented gradients feature extracted of every layer multiplied by the correlation filters; the maximum response of current scale of the filters is the best target scale. In addition, the improved algorithm is combined with the improved correlation filter framework, and the background information around the target is appropriately increased. When the target is occluded, the background information can be effectively used to track the target. The proposed algorithm is validated on the benchmark evaluation and compared with the traditional algorithms, such as KCF and circulation structure of tracking-by-detection with kernel. The results indicate that our tracking accuracy can reach 66.9% and the success rate can be 58.2. When the target is scale variation, the accuracy and success rate increase by 1.1% and 10.3%, respectively, compared with KCF. If the tracked target is occluded, a second improved algorithm is compared with the detection algorithm which only adds the scale detection, the tracking accuracy increases by 8%, and the success rate increases by 4.9%.
Unpaired Image-to-Image Translation with Domain Supervision.

Image-to-image translation tasks have been widely investigated with Generative Adversarial Networks (GANs). However, existing approaches are mostly designed in an unsupervised manner while little attention has been paid to domain information within unpaired data. In this paper, we treat domain information as explicit supervision and design an unpaired image-to-image translation framework, Domain-supervised GAN (DosGAN), which takes the first step towards the exploration of explicit domain supervision. In contrast to representing domain characteristics using different generators or domain codes, we pre-train a classification network to explicitly classify the domain of an image. After pre-training, this network is used to extract the domain-specific features of each image. Such features, together with the domain-independent features extracted by another encoder (shared across different domains), are used to generate image in target domain. Extensive experiments on multiple facial attribute translation, multiple identity translation, multiple season translation and conditional edges-to-shoes/handbags demonstrate the effectiveness of our method. In addition, we can transfer the domain-specific feature extractor obtained on the Facescrub dataset with domain supervision information to unseen domains, such as faces in the CelebA dataset. We also succeed in achieving conditional translation with any two images in CelebA, while previous models like StarGAN cannot handle this task.
Entanglement-Based Quantum Networking

We discuss some of the challenges for the generation, distribution, control, measurement, and certification of quantum resources like entanglement in quantum communication networks. This spans integrated photonic devices to certifying multi-partite entanglement.
Feasibility of satellite quantum key distribution with continuous variable

We analyze the feasibility of continuous variable quantum key distribution from satellite. By modeling the signal transmission, we quantify the achievable key rate including finite size effects. Results will help in determining mission requirements.
Extensive Use of Morpheme Features in Korean Dependency Parsing

Dependency parsing is considered to be one of the most important tasks in natural language understanding. With the advance of deep learning based models, dependency parsing performances were improved. However, little attention was given to features specific to Korean. Morphemes play especially important role in Korean linguistics, and incorporating morpheme level information into the model can result in performance improvement. In this paper, we propose a way to use multiple morpheme features with their orders preserved in Korean dependency parsing. In our experiment, our proposed method gave a performance improvement in deep biaffine network and stack pointer network. We also report the state-of - the-art result of UAS 92.17 and LAS 90.07 on Sejong dependency parsing data with stack pointer network augmented with our proposed method.
Predicting Server Power Consumption from Standard Rating Results

Data center providers and server operators try to reduce the power consumption of their servers. Finding an energy efficient server for a specific target application is a first step in this regard. Estimating the power consumption of an application on an unavailable server is difficult, as nameplate power values are generally overestimations. Offline power models are able to predict the consumption accurately, but are usually intended for system design, requiring very specific and detailed knowledge about the system under consideration.In this paper, we introduce an offline power prediction method that uses the results of standard power rating tools. The method predicts the power consumption of a specific application for multiple load levels on a target server that is otherwise unavailable for testing. We evaluate our approach by predicting the power consumption of three applications on different physical servers. Our method is able to achieve an average prediction error of 9.49% for three workloads running on real-world, physical servers.
Employing Dependency Tree in Machine Learning Based Indonesian Factoid Question Answering

We proposed the usage of dependency tree information to increase the accuracy of Indonesian factoid question answering. We employed MSTParser and Universal Dependency corpus to build the Indonesian dependency parser. The dependency tree information as the result of the Indonesian dependency parse is used in the answer finder component of Indonesian factoid question answering system. Here, we used dependency tree information in two ways: 1) as one of the features in machine learning based answer finder (classifying each term in the retrieved passage as part of a correct answer or not); 2) as an additional heuristic rule after conducting the machine learning technique. For the machine learning technique, we combined word based calculation, phrase based calculation and similarity dependency relation based calculation as the complete features. Using 203 data, we were able to enhance the accuracy for the Indonesian factoid QA system compared to related work by only using the phrase information. The best accuracy was 84.34% for the correct answer classification and the best MRR was 0.954.
On the Hyperdense Coding and Proposal of Hyperdense Coding Quantum Secure Communication Protocol

The work proposes hyperdense coding - a combination of superdense coding and optimal quantum channel compression (a type of data encoding). Based on the hyperdense coding, an efficient quantum secure direct communication protocol is introduced.
OAuth 2.0 Demonstration of Proof-of-Possession at the Application Layer

This document describes a mechanism for sender-constraining OAuth 2.0tokens via a proof-of-possession mechanism on the application level.This mechanism allows to detect replay attacks with access and refreshtokens.
A study on application container resource efficiency

Nowadays, the IT service environment develops in a dynamic, rapid, and unpredictable way. Microservices and application containers in this process have a significant impact on new generation IT service models. The fact that they have important capabilities such as modelability, presentability as service, and restructurability, are reasons for preferring them in many areas. Moreover, microservices can meet various needs of IT personnel. As it is known, all server system components, such as CPU, network, hard-drive I/O, affect energy consumption. At this point, microservices also play an important mediator role in resource management. Energy consumption of microservice-based applications is lower than that of the traditional approaches. However, there are still cases of recoverable and unnecessary consumption at some points. Microservices can be monitored and controlled using many methods. Thus, this provides us with opportunities to recover the wasted energy resources considerably. In this article, the effects of container-based microservice architectures on the energy consumption of the system and how to reduce these effects are presented. For this purpose, a methodology, which has 3 approaches (disconnect, pause, stop), and a tracing mechanism are proposed. The results show that this methodology has a considerable effect on energy efficiency.
The Main Trends in the Development of Engineering Education: The Role of the University Teacher in Systemic Changes

The article is devoted to the development of competences of the teacher of an engineering university in accordance with the trends of modern education. Therefore, it is necessary to improve the qualifications of teachers in the direction of solving emerging new educational problems. He should understand new types of relationships with students, use new teaching technologies. Teaching teachers facilitates understanding of systemic changes and, as a result, the implementation of a new department of the educational process.
MAKE IT WORK: A HOLISTIC APPROACH FOR DIGITAL LEARNING IN VOCATIONAL TRAINING

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 5178-5181ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1290Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Large-scale Text-based Video Classification using Contextual Features

The production of video has increased and expanded dramatically. There is a need to reach accurate video classification. In our work, we use deep learning as a mean to accelerate the video retrieval task by classifying them into categories. We classify a video depending on the text extracted from it. We trained our model using fastText, a library for efficient text classification and representation learning, and tested our model on 15000 videos. Experimental results show that our approach is efficient and has good performance. Our technique can be used on huge datasets. It produces a model that can be used to classify any video into a specific category very quickly.
Learning to Fuse Latent Representations for Multimodal Data

Multimodal learning leverages data from different modalities to improve the performance of a trained model. Typically, latent representations extracted from multimodal data are provided via direct feature fusion for end-to-end training of a deep neural network towards a specific task. However, the informativeness of the different data modalities can easily vary across a collected dataset. As such, naively or directly fusing the latent representations obtained for one modality and the other, as is commonly done in state-of-the-art works, may burden the model in finding concise representations that are indeed useful for learning. In this paper, we propose to instead learn the fusion of latent representations for multimodal data by using a modality gating mechanism that allows the dynamic weighting of extracted latent representations based on their informativness. Extensive experiments using the BU-3DFE dataset for facial expression recognition and the Washington object classification multimodal RGB-D dataset show that learning the fusion of the latent representations for different data modalities leads to improved model generalization than the conventional naive fusion method.
Relation-Aware Global Attention.

Attention mechanism aims to increase the representation power by focusing on important features and suppressing unnecessary ones. For convolutional neural networks (CNNs), attention is typically learned with local convolutions, which ignores the global information and the hidden relation. How to efficiently exploit the long-range context to globally learn attention is underexplored. In this paper, we propose an effective Relation-Aware Global Attention (RGA) module for CNNs to fully exploit the global correlations to infer the attention. Specifically, when computing the attention at a feature position, in order to grasp information of global scope, we propose to stack the relations, i.e., its pairwise correlations/affinities with all the feature positions, and the feature itself together for learning the attention with convolutional operations. Given an intermediate feature map, we have validated the effectiveness of this design across both the spatial and channel dimensions. When applied to the task of person re-identification, our model achieves the state-of-the-art performance. Extensive ablation studies demonstrate that our RGA can significantly enhance the feature representation power. We further demonstrate the general applicability of RGA to vision tasks by applying it to the scene segmentation and image classification tasks resulting in consistent performance improvement.
Portable Tutor: Whatsapp In Nursing Students’ Internship And Final Degree Project Processes

Introduction: Many university students use mobile applications such as WhatsApp, and nursing students are no exception. But there is little research related to the use of this application and its possibilities in various academic fields, including university tutoring. Objective: The objective of this study was to try to fill this research void and learn the opinions of nursing students regarding the use of this application during their studies, in two crucial stages where tutoring plays an important role: their final project and their internship. Methodology: The sample population of the study was 38 nursing students of the Nursing College of Melilla. To gather data, a written questionnaire was used, which could be filled out through Google Docs or WhatsApp, and which was qualitatively analysed by means of content analysis. Results: The results show that users have several concerns when using this type of tool, especially regarding their privacy, but that on the other hand, they highlight the user-friendliness and usefulness of this platform in the educational field. Conclusion: The use of this tool in the process of virtual tutoring is essential for students, but the importance of face-to-face tutoring should not be overlooked.
Memetic Evolution of Classification Ensembles

Creating classification ensembles may be perceived as a regularization technique which aims at improving the generalization capabilities of a classifier. In this paper, we introduce a multi-level memetic algorithm for evolving classification ensembles (they can be either homo- or heterogeneous). First, we evolve the content of such ensembles, and then we optimize the weights (both for the classifiers and for different classes) exploited while voting. The experimental study showed that our memetic algorithm retrieves high-quality heterogeneous ensembles, and can effectively deal with small training sets in multi-class classification.
Relevance-Based Entity Embedding

Entity embedding plays an indispensable role in many entity-related problems. Currently, mainstream entity embedding methods build on the notion that entities with similar contexts or close proximity should be placed adjacently in the embedding space. Nonetheless, this goal fails to meet the objectives of many downstream tasks, where the relevance among entities is more significant. To fill this gap, in this paper, a novel relevance-based entity embedding approach, Lead, is proposed, where the relevance is captured via query-document information. The experimental results verify the superiority of our proposal.
Iteratively reweighted least square for asymmetric <i>L</i><sub>2</sub>-Loss support vector regression

In support vector regression (SVR) model, using the squared ϵ-insensitive loss function makes the objective function of the optimization problem strictly convex and yields a more concise solution. However, the formulation leads to a quadratic programing which is expensive to solve. This paper reformulates the optimization problem by absorbing the constraints in the objective function, and the new formulation shares similarity with weighted least square regression problem. Based on this formulation, we propose an iteratively reweighted least square approach to train the L2-loss SVR, for both linear and nonlinear models. The proposed approach is easy to implement, without requiring any additional computing package other than basic linear algebra operations. Numerical studies on real-world datasets show that, compared to the alternatives, the proposed approach can achieve similar prediction accuracy with substantially higher time efficiency.
A Fuzzy Approach for Sentences Relevance Assessment in Multi-document Summarization

Text summarization is becoming an indispensable solution for dealing with the exponential growth of textual and unstructured information in digital format. In this paper, an unsupervised method for extractive multi-document summarization is presented. This method combines the use of a semantic graph for representing textual contents and identify the most relevant topics with the processing of several sentences features applying a fuzzy logic perspective. A fuzzy aggregation operator is applied in the sentences relevance assessment process as a contribution to the multi-document summarization process. The method was evaluated with the Spanish and English texts collection of MultiLing 2015. The obtained results were measured through ROUGE metrics and compared with those obtained by other solutions reported from MultiLing2015.
Construction and Validation of Micromanagement Questionnaire

Micromanagement, in ordinary parlance, is managing things closely and under strict scrutiny. The management style, which involves excessive control and close supervision, could lead to annoying situations and create an atmosphere of mistrust within the organisational settings. Micromanagers seldom give autonomy to their subordinates, dislike feedback, and get involved even in the most intricate of project details. A fair review of literature found there is no scientifically validated questionnaire to measure micromanagement. The present work is a modest effort to fill this gap in literature. By following the rules of the thumps and the stipulations provided by experts in the fields, the authors have scientifically constructed and validated a four factor, 18-item micromanagement questionnaire. Though there is further scope for refining and generalisation across cultures, the questionnaire is an effective scale for the measurement of the construct of micromanagement.
Improving Open Science Using Linked Open Data: CONICET Digital Use Case

Scientific publication services are changing drastically, researchers demand intelligent search services to discover and relate scientific publications. Publishersneed to incorporate semantic information to better organize their digital assets and make publications more discoverable. In this paper, we present the on-going work to publish a subset of scientific publications of CONICET Digital as Linked Open Data. The objective of this work is to improve the recovery andreuse of data through Semantic Web technologies and Linked Data in the domain of scientific publications.To achieve these goals, Semantic Web standards and reference RDF schema’s have been taken into account (Dublin Core, FOAF, VoID, etc.). The conversion and publication process is guided by the methodological guidelines for publishing government linked data. We also outline how these data can be linked to other datasets DBLP, WIKIDATA and DBPEDIA on the web of data. Finally, we show some examples of queries that answer questions that initially CONICET Digital does not allow