Reviewerly/exHarmony huggingface dataset:
An Atmospheric Visibility Grading Method Based on Ensemble Learning and Stochastic Weight Average

In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, and complicated steps, this paper proposed an ensemble learning method for atmospheric visibility grading based on deep neural network and stochastic weight averaging. An experiment was conducted using the scene of an expressway, and three visibility levels were set, i.e., Level 1, Level 2, and Level 3. Firstly, the EfficientNet was transferred to extract the abstract features of the images. Then, training and grading were performed on the feature sets through the SoftMax regression model. Subsequently, the feature sets were ensembled using the method of stochastic weight averaging to obtain the atmospheric visibility grading model. The obtained datasets were input into the grading model and tested. The grading model classified the results into three categories, with the grading accuracy being 95.00%, 89.45%, and 90.91%, respectively, and the average accuracy of 91.79%. The results obtained by the proposed method were compared with those obtained by the existing methods, and the proposed method showed better performance than those of other methods. This method can be used to classify the atmospheric visibility of traffic and reduce the incidence of traffic accidents caused by atmospheric visibility.
PhD Forum: Encrypted Traffic Analysis &amp; Content Awareness of 360-Degree Video Streaming Optimization

360°/VR videos are getting popular. However, these videos demand high bandwidth and processing power at the end devices. Though, viewport (VP) aware streaming can reduce the amount of data transmitted by streaming a limited portion of the frame covering the current user viewport, popular content providers still transfer the entire panoramic frame which demands more bandwidth. Also, these mechanisms, which partition the frames into a fixed number of tiles can not provide a finer boundary to cover the user VP, causing high pixel redundancy. To address these issues, first, we propose an offline and near-realtime 360° vs normal video classification tool which is further extended to analyse 360° video streaming distribution in the wild. Secondly, we propose a content aware 360° video partitioning tool leveraging a computational geometric approach. Our initial results show the feasibility of both proposals.
The Effect of YouTube Video on Students’ Listening Comprehension Performance

The difficulties to catch any intended information conveyed by speakers on the audio recording by the students still become problematic during learning listening comprehension in the classroom. This study aimed to find out the effect of applying YouTube videos on the students’ listening comprehension performance. This study was experimental research (quasi-experimental with non-equivalent control group design). The researchers used all the first-semester students of English language education as a sample through the total sampling technique. To get the data, the researchers used a listening test as the instrument. There were two groups that were tested in the study; experimental and control groups. The experimental group was treated by applying YouTube videos while the control group was treated by Audio recording. Based on the result of the study, it was found that the t-test was 1.834 and the t-table was 0.073 (t-test was higher than t-table). In the other words, the alternative hypothesis was accepted and the null hypothesis was rejected. The result of the study also showed that the students felt more interested and motivated to learn listening comprehension by using YouTube videos as the learning media during the learning process takes place in the classroom. Thus, it could be concluded that the application of YouTube videos has given a positive and significant effect on students’ listening comprehension performance.
Towards Developing Community Guidelines for Sharing and Reusing Quality Information of Earth Science Datasets

&amp;lt;p&amp;gt;The knowledge of data quality and the quality of the associated information, including metadata, is critical for data use and reuse. Assessment of data and metadata quality is key for ensuring credible available information, establishing a foundation of trust between the data provider and various downstream users, and demonstrating compliance with requirements established by funders and federal policies.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Data quality information should be consistently curated, traceable, and adequately documented to provide sufficient evidence to guide users to address their specific needs. The quality information is especially important for data used to support decisions and policies, and for enabling data to be truly findable, accessible, interoperable, and reusable (FAIR).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Clear documentation of the quality assessment protocols used can promote the reuse of quality assurance practices and thus support the generation of more easily-comparable datasets and quality metrics. To enable interoperability across systems and tools, the data quality information should be machine-actionable. Guidance on the curation of dataset quality information can help to improve the practices of various stakeholders who contribute to the collection, curation, and dissemination of data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This presentation outlines a global community effort to develop international guidelines to curate data quality information that is consistent with the FAIR principles throughout the entire data life cycle and inheritable by any derivative product.&amp;lt;/p&amp;gt;
UAV Path Planning Based on Variable Neighborhood Search Genetic Algorithm

This study proposed a new genetic algorithm with variable neighbourhood search (GAVNS) for UAV path planning in three-dimensional space. First, an 0–1 integer programming mathematical model is established by inspired from the vehicle routing planning model with time window (VRPTW), and then a heuristic rule based on space vector projection is designed to quickly initialize high-quality solutions that meet constraints of upper error limit and minimum turning radius. Second, it improves mutation operator with a reselected mutation strategy, and incorporates Variable Neighborhood Search strategy based on adding and deleting route during the search process; Finally, GAVNS is compared with general Genetic Algorithm on a set of experiments. It is demonstrated that GAVNS algorithm is both effective and efficient. Moreover, the introduction of variable neighborhood search strategy enhances the local search ability of Genetic Algorithm.
What database do you choose for heterogeneous security log events analysis

The heterogeneous massive logs incoming from multiple sources pose major challenges to professionals responsible for IT security and system administrator. One of the challenges is to develop a scalable heterogeneous logs database for storage and further analysis. In fact, it is difficult to decide which database is suitable for the needs, the best of a use case, execution time and storage performances. In this paper, we explore, study, and compare the performance of SQL and NoSQL databases on large heterogeneous event logs. We implement the relational database using MySQL, the column-oriented database using Impala on the top of Hadoop, and the graph database using Neo4j. We experiment the databases on a large heterogeneous logs and provide advice, the pros and cons of each SQL and NoSQL database. Our findings that Impala outperforms MySQL and Neo4j databases in terms of loading logs, execution time of simple queries, and storage of logs. However, Neo4j outperforms Impala and MySQL in the execution time of complex queries.
Exploring How Rhetorical Organization Contributes to the Readability of Essays

The facilitative benefits of genre-specific reading have often been cited as a truism in the field of writing education. In line with this, writing center self-access libraries typically provide a selection of composition texts, including rhetorics (anthologies of model paragraphs and essays). Readability formulae (e.g., the Lexile Readability Formula) are often used to determine whether these texts will be a good fit for potential readers, and although the Lexile Formula reliably and validly assesses two features (i.e., semantic and syntactic), it does not consider other contributing features during the text selection process (e.g., rhetorical organization). To address this, this sequential, mixed-methods study explored the effects of rhetorical organization on undergraduate English language learners’ perceptions of difficulty when reading exemplars (i.e., essays) excerpted from rhetorics. The results indicated that rhetorical organization influences readability both as (a) a primary (i.e., an isolated feature) and (b) a conjoined feature (i.e., comprising two or more associated entities where the second impacts the first). The article also provides a suggestion for writing education professionals and the publishing industry: Readability formulae should be administered in a hybrid fashion, where additional features such as rhetorical organization are subjectively considered when assessing the difficulty of exemplars.
Historia Augusta authorship: an approach based on Measurements of Complex Networks

Abstract In this work, we analyze in detail the topology of the written language network using co-occurrence of words to recognize authorship. The Latin texts object of this study are excerpts from Historia Augusta , a collection of biographies of Roman emperors extending from Hadrian, who started to reign in 117 CE, to Carus and his sons Numerian and Carinus, that is, to the years up 284–285 CE. According to the manuscript tradition, the biographies are attributed to six different authors. Scholarship since the late 19th century has been arguing for a single authorship instead. The aim of this paper is to verify this hypothesis.
Maximum order complexity of the sum of digits function in Zeckendorf base and polynomial subsequences

Abstract Automatic sequences are not suitable sequences for cryptographic applications since both their subword complexity and their expansion complexity are small, and their correlation measure of order 2 is large. These sequences are highly predictable despite having a large maximum order complexity. However, recent results show that polynomial subsequences of automatic sequences, such as the Thue–Morse sequence, are better candidates for pseudorandom sequences. A natural generalization of automatic sequences are morphic sequences, given by a fixed point of a prolongeable morphism that is not necessarily uniform. In this paper we prove a lower bound for the maximum order complexity of the sum of digits function in Zeckendorf base which is an example of a morphic sequence. We also prove that the polynomial subsequences of this sequence keep large maximum order complexity, such as the Thue–Morse sequence.
Dynamic Management of Security Policies in PrivOrBAC

This article is a continuation of our previous work on identifying and developing tools and concepts to provide automatic management and derivation of security and privacy policies.In this document we are interested in the extension of the PrivOrBAC model in order to ensure a dynamic management of privacy-aware security policies.Our approach, based on smart contracts (SC) and the WS-Agreement Specification, allows automatic agents representing data providers and access requesters to enter into an access agreement that takes into consideration not only service level clauses but also security rules to protect the privacy of individuals.Our solution can be deployed in such a way that no human intervention is required to reach this type of agreement.This work shows how to use the WS-Agreement Specification to set up a process for negotiating, creating and monitoring Service Level Agreements (SLAs) in accordance with a predefined access control policy.This article concludes with a case study accompanied by a representative implementation of our solution.
Domain Adaptation for Imitation Learning Using Generative Adversarial Network

Imitation learning is an effective approach for an autonomous agent to learn control policies when an explicit reward function is unavailable, using demonstrations provided from an expert. However, standard imitation learning methods assume that the agents and the demonstrations provided by the expert are in the same domain configuration. Such an assumption has made the learned policies difficult to apply in another distinct domain. The problem is formalized as domain adaptive imitation learning, which is the process of learning how to perform a task optimally in a learner domain, given demonstrations of the task in a distinct expert domain. We address the problem by proposing a model based on Generative Adversarial Network. The model aims to learn both domain-shared and domain-specific features and utilizes it to find an optimal policy across domains. The experimental results show the effectiveness of our model in a number of tasks ranging from low to complex high-dimensional.
Time Series Forecasting Using Support Vector Machines

This chapter explains the important concepts and basics requirements of support vector machine (SVM) and apply it to real-life models. It focuses on forecasting the outcome values of time series using an SVM. The objectives of statistical learning are understanding and forecasting. SVM is used as a popular tool for classification of problems in machine learning. SVMs simply separate the given data into different classes by finding the line or hyper-plane and separate the kernel supports to find the hyper-plane in the higher dimension. In regression problems, the support vector regression works on the same principle as SVM. To reduce computational complexity in SVM, J. A. K. Suykensand and J. Vandewalle introduced the LS-SVM classifier in 1999. Though LS-SVM is suitable for time series forecasting, to make it more convenient, it has been modified to DLS-SVM using a slightly improved computation technique.
Augmented Reality Based on Object Recognition for Piping System Maintenance

Augmented Reality (AR) applications can be used to improve tasks and mitigate errors during facilities operation and maintenance. This article presents an AR system for facility management using a three-dimensional (3D) object tracking method. Through spatial mapping, the object of interest, a pipe trap underneath a sink, is tracked and mixed onto the AR visualization. From that, the maintenance steps are transformed into visible and animated instructions. Although some tracking issues related to the component parts were observed, the designed AR application results demonstrated the potential to improve facility management tasks.
SeqDialN: Sequential Visual Dialog Network in Joint Visual-Linguistic Representation Space

The key challenge of the visual dialog task is how to fuse features from multimodal sources and extract relevant information from dialog history to answer the current query.In this work, we formulate a visual dialog as an information flow in which each piece of information is encoded with the joint visuallinguistic representation of a single dialog round.Based on this formulation, we consider the visual dialog task as a sequence problem consisting of ordered visual-linguistic vectors.For featurization, we use a Dense Symmetric Co-Attention network (Nguyen and Okatani, 2018) as a lightweight vison-language joint representation generator to fuse multimodal features (i.e., image and text), yielding better computation and data efficiencies.For inference, we propose two Sequential Dialog Networks (SeqDialN): the first uses LSTM (Hochreiter and Schmidhuber, 1997) for information propagation (IP) and the second uses a modified Transformer (Vaswani et al., 2017) for multi-step reasoning (MR).Our architecture separates the complexity of multimodal feature fusion from that of inference, which allows simpler design of the inference engine.On VisDial v1.0 test-std dataset, our best single generative SeqDialN achieves 62.54% NDCG 1 and 48.63% MRR 2 ; our ensemble generative SeqDialN achieves 63.78% NDCG and 49.98% MRR, which set a new state-of-the-art generative visual dialog model.We fine-tune discriminative Se-qDialN with dense annotations 3 and boost the performance up to 72.41% NDCG and 55.11% MRR.In this work, we discuss the extensive experiments we have conducted to demonstrate the effectiveness of our model 1 Normalized Discounted Cumulative Gain 2 Mean Reciprocal Rank 3 Relevance scores for 100 answer options corresponding to each question on a subset of the training set, publicly available on visualdialog.org/datacomponents.We also provide visualization for the reasoning process from the relevant conversation rounds and discuss our finetuning methods.
Dealing with training and test segmentation mismatch: FBK@IWSLT2021

This paper describes FBK's system submission to the IWSLT 2021 Offline Speech Translation task.We participated with a direct model, which is a Transformer-based architecture trained to translate English speech audio data into German texts.The training pipeline is characterized by knowledge distillation and a two-step fine-tuning procedure.Both knowledge distillation and the first fine-tuning step are carried out on manually segmented real and synthetic data, the latter being generated with an MT system trained on the available corpora.Differently, the second fine-tuning step is carried out on a random segmentation of the MuST-C v2 En-De dataset.Its main goal is to reduce the performance drops occurring when a speech translation model trained on manually segmented data (i.e. an ideal, sentencelike segmentation) is evaluated on automatically segmented audio (i.e.actual, more realistic testing conditions).For the same purpose, a custom hybrid segmentation procedure that accounts for both audio content (pauses) and for the length of the produced segments is applied to the test data before passing them to the system.At inference time, we compared this procedure with a baseline segmentation method based on Voice Activity Detection (VAD).Our results indicate the effectiveness of the proposed hybrid approach, shown by a reduction of the gap with manual segmentation from 8.3 to 1.4 BLEU points.
Fuzzy-Regulated Assistant based on Mental Engagement: A Multi-Agent Approach to Elderly Care

We present a device designed to provide assistance to an older adult in their home. Conceptually, this assistant is conceived as a multi-agent system designed to monitor and regulate itself according to the mental engagement of the user and contribute to help them maintain adequate levels. Physically, the assistant is realized as a desk, equipped with an automated pill dispenser and interconnected with other devices intended to provide security and comfort to the user. Through simulations it was possible to confirm that the system performs as desired for diverse scenarios. This conclusion encourages us to proceed with the next stage of development, where real data will be collected employing the prototype of the system described herein.
Multilingual Neural Semantic Parsing for Low-Resourced Languages

Multilingual semantic parsing is a costeffective method that allows a single model to understand different languages.However, researchers face a great imbalance of availability of training data, with English being resource rich, and other languages having much less data.To tackle the data limitation problem, we propose using machine translation to bootstrap multilingual training data from the more abundant English data.To compensate for the data quality of machine translated training data, we utilize transfer learning from pretrained multilingual encoders to further improve the model.To evaluate our multilingual models on humanwritten sentences as opposed to machine translated ones, we introduce a new multilingual semantic parsing dataset in English, Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset.We show that joint multilingual training with pretrained encoders substantially outperforms our baselines on the TOP dataset and outperforms the state-of-theart model on the public NLMaps dataset.We also establish a new baseline for zero-shot learning on the TOP dataset.We find that a semantic parser trained only on English data achieves a zero-shot performance of 44.9% exact-match accuracy on Italian sentences.
An Overview of a Blockchain Application in Education Using Hyperledger Project

During the last years, blockchain has been getting interest by both academic and professional researchers. This is primarily due to the innovative techniques in which records are effectively stored in a distributed manner. Started as a value transfer mechanism, blockchain now has several applications in a vast array of fields including healthcare, banking, and Internet of Things (IoTs). Despite the considerable amount of interest given to these fields, little is known about the possible applications of blockchain in education. Because of COVID-19 outbreak and the rise in the need of online and automated solutions in academia, this paper proposes an overview of a blockchain solution in education. This technology relies on the unique features it offers, such as decentralization, data integrity, and security. The presented solution aims to reduce the paperwork needed to perform tasks, secure the stored data, increase transparency, as well as to offer new recruitment possibilities and statistics capabilities to the current educational system.
A Modest Pareto Optimisation Analysis of Dependency Parsers in 2021

We evaluate three leading dependency parser systems from different paradigms on a small yet diverse subset of languages in terms of their accuracy-efficiency Pareto front.As we are interested in efficiency, we evaluate core parsers without pretrained language models (as these are typically huge networks and would constitute most of the compute time) or other augmentations that can be transversally applied to any of them.Biaffine parsing emerges as a well-balanced default choice, with sequence-labelling parsing being preferable if inference speed (but not training energy cost) is the priority.
Powering the PID Graph: announcing the DataCite GraphQL API

Today DataCite launches a new API that powers the PID Graph, the graph formed by scholarly resources described by persistent identifiers (PIDs) and the connections between them. The API is powered by GraphQL, a widely adopted Open Source technology that enables queries of this graph, addressing use cases of our community in ways that were not possible before.
NEW APPROACH IN CYBERSECURITY EDUCATION – INTRODUCING NEW PRACTICES AND INNOVATIONS

Appears in: EDULEARN21 Proceedings Publication year: 2021Pages: 6619-6626ISBN: 978-84-09-31267-2ISSN: 2340-1117doi: 10.21125/edulearn.2021.1340Conference name: 13th International Conference on Education and New Learning TechnologiesDates: 5-6 July, 2021Location: Online Conference