Reviewerly/exHarmony huggingface dataset:
Real Geographies in Virtual Space: A Practical Workflow for Geovisualization with Immersive VR

Propelled by the video game industries, virtual reality (VR) systems are on the verge of entering the electronic mass media market. VR not just allows generating imaginary worlds, but also visualizing real geographies and possible past and future scenarios of real-world environments. However, VR capabilities of current geographic information systems (GIS) are limited as yet, thus challenging the transport of geodata into VR applications. Given the shortcomings of GIS, the present article proposes a practical workflow for the visualization of geographic information with immersive environments. Principal work steps are 3D-modeling, composition, performance optimization and the final dissemination on VR platforms. We will illustrate all elements of our workflow using the example of an immersive VR application of a coral reef in the Mexican Caribbean, thus providing both theoretical and practical background on the visualization of real-world data in VR space.
Creation of control algorithms thermal vacuum tests in the SimInTech environment

Paper is divote to results of the development of the thermal vacuum tests computer model of allowing to investigate impact of external factors on the devices which are in conditions of own external atmosphere of the spacecraft in the SimInTech environment of the technical systems dynamic modeling. For a solution an objective models of a control object of the thermal vacuum TVAC-1400 camera are created, and the regulators executed on the basis of the programmable industrial controller Siemens S7-300 setup of key parameters is made, control algorithms are implemented by the modes of tests. The developed model allows to exercise control of test programs of the different equipment, prevention of failures at an error of the operator or interruptions in engineering networks, to reveal the most adverse situations which can arise process of functioning of the thermal vacuum camera. Besides, the presented model can be used for a research of behavior of controlled parameters of thermal vacuum tests and working off of control algorithms. In article development process and settings of model of thermal vacuum tests, its submodels and a control bar, implementation of the main algorithms are step by step described. Also results of adequacy check to the developed model are presented.
Probability and Statistics for Data Sciences: Math + R + Data

The text introduces junior–senior undergraduates or first-year graduate students in data science to the basic probability, statistical concepts, and theory well. The text is designed to tell studen...
Abnormal Electricity Consumption Detection from Incomplete Records in Power System

Due to the limited channel bandwidth or interference signals in the advance metering infrastructures, there are usually some missing or human-revised data among the electricity consumption records of civilian customers. In order to make full use of this kind of records, machine learning techniques are introduced in this paper for electricity consumption sensitivity analysis regarding to the weather features. With the missing and revised records filtered out, each customer would have an individual regression model between weather conditions and the power demand. The importance of variables in the regression model is regarded as the sensitivity to various weather features. Then the abnormal consumption patterns are detected with a typical outlier identification algorithm based on different weather sensitivities among all the customers. The methods used in this paper show good results to identify the abnormal consumption patterns effectively regardless the quality of the original data.
A Quantum Feature Selection Algorithm for Multi-Classification Problem

ReliefF is a feature selection algorithm for the multi-classification problem, and its complexity of the algorithm grows rapidly as the number of samples and features increases. In order to reduce the complexity, a quantum-based feature selection algorithm for the multi-classification problem, also called QReliefF algorithm, is proposed. Firstly, all features of each sample are encoded into the quantum state by CMP and rotation operation for similarity calculation. After that, the similarity is encoded into a quantum state using the amplitude estimation, the nearest k neighbor samples in each class are found by Grover method, and are used to update the weight vector. Finally, the features are selected according to the final weight vector and threshold. Compared with the classical ReliefF algorithm, our algorithm changes from O(MN) to O(M) in terms of the complexity of similarity calculation and the complexity of finding the nearest neighbor is changed from O(M) to O(√ M). Our algorithm consumes O(MlogN) qubits in terms of resource consumption, while the ReliefF algorithm consumes O(MN) bits. Obviously, our algorithm is better than the ReliefF algorithm in efficiency and resource consumption.
A Visual Semantic Relations Detecting Method Based on WordNet

In order to implement automatic inference, this paper proposes a visual semantic-relations detecting method (VSRDM) based on WordNet. WordNet is an excellent relational dictionary, but it lacks deep semantic topology function because of its index-based text storage structure. As a graphical database, Neo4J provides visualization of its internal data. Since the abstract data structure in WordNet matches Neo4J’s ternary storage structure, it is very suitable to map WordNet completely with Neo4J graph instance. This paper studies how to fully describe WordNet in Neo4J through a ternary structure. Neo4J stores the data as graphs (nodes and edges) and provides certain native graph algorithms to search the data. The speed of matching query between nodes is varying linearly with the number of nodes, so the efficiency of basic operation is guaranteed. With the help of Neo4J, VSRDM works as a semantic dictionary providing relationships matching, reasoning auxiliary and other functions.
The Legitimacy of Conductive Arguments: What Are the Logical Roles of Negative Considerations?

Recent disputes on the negative considerations of conduction (conductive arguments) give rise to the legitimacy crisis of conduction. Adler (Argumentation 27:245–257, 2013) argues that the definition of conduction implies two incompatible claims which make conduction impossible. The crux of the impossibility of conduction argued by Adler can be summarized as a question: is it possible that the conclusions of conductive arguments are unqualified while the negative considerations remain viable? For this question, Xie & Xiong (Commentary on: J. Anthony Blair’s “are conductive arguments really not possible?”. Windsor, ON: OSSA, pp. 1–6, 2013) and Xie (Informal Logic 37:2–22, 2017) provide the rhetorical solution, which claims that the negative considerations only have the rhetorical roles (functions) so they are not viable at all in the arguments. On the contrary, Blair (Argumentation 30:109–128, 2016) presents the logical solution, which contends that the negative considerations have logical roles so they can weaken the strength of argument. This chapter first reconstructs Adler’s argument as well as the variant of it and then presents the criticism of the rhetorical and the logical solutions. Although Blair’s logical solution can resolve Adler’s challenge by transferring the qualification of conclusions to the qualification of argument strength, it does not completely defend the legitimacy of conduction. In order to better defend the legitimacy of conduction, the logical roles of the negative considerations are needed to be fully justified. In this connection, I provide a new proposal which focuses on the criteria of argument strength. From non-conductive defeasible arguments to conductive arguments, the criteria of argument strength will be changed from one item (i.e. “to what extent the positive considerations provide support for the conclusions”) to two items (i.e. “to what extent the positive considerations provide support for the conclusions” and “to what extent the positive considerations outweigh the negative considerations”). This change is caused by the existence of negative considerations, which shows the logical roles of negative considerations. Therefore, the legitimacy of conduction can be fully justified.
ALGORITHMS FOR COMBINING INFORMATION ABOUT WEB-PAGES WITH BACKGROUND ONTOLOGICAL KNOWLEDGE

Currently, web information technologies are widely developed on the basis of the development of approaches related to the addition of semantics to web data, and the development of the apparatus of semantic networks. This article proposes the development of methods of semantic information retrieval based on algorithms of its association. This approach allows to use the analysis of web-pages with background ontological knowledge and increases its efficiency in comparison with standard approaches.
Portuguese POS Tagging Using BLSTM Without Handcrafted Features

Training state-of-the-art Part-of-speech (POS) taggers traditionally requires many handcraft features and external data. In this paper, we propose a neural network architecture for POS tagging task for both contemporary and historical Portuguese texts. The proposed architecture does not use the two traditional requirements cited above. It uses word embeddings and character embeddings representations combined with a BLSTM layer. We apply the architecture on three Portuguese corpora and obtaining state-of-the-art accuracy of 97.87% on the Mac-Morpho corpus, 97.62% accuracy on the revised Mac-Morpho and 97.36% on Tycho Brahe. We also improve the tagging accuracy for Out of Vocabulary (OOV) words in the Mac-Morpho corpus and in the revised Mac-Morpho.
Pattern-Based Causal Feature Extraction

This cause-effect pairs challenge was motivated by the contrast between the costs of performing controlled experiments in order to determine causality and the abundance of observational data. Our goal was to provide a value representing our confidence of causality determined by the observation data which would help identify the most promising variables for experimental verification of their causal relationship. By identifying patterns in functions that generate relevant features, a feature extraction pipeline was architected to allow for the creation of large amounts of complex features with minimal human intervention. Using this pipeline, we were able to finish second in the public leaderboard and first in the private leaderboard. Furthermore, this process by default generates over 20,000 features. In this paper, we analyze which aspects are most important, and create a new pipeline that gets comparable performance with only 324 features.
Augmented Reality for Accessible Digital Mental Healthcare

It is estimated that one person dies every 40 seconds due to suicide and there are indications that for each fatal suicide attempt conducted by an adult, there may be more than 20 other suicide attempts. Public healthcare resources to support individuals who are at risk of mental ill health are increasingly thinly stretched. This calls for new ways of disseminating mental health knowledge and support to those who need, but lack it. In this demonstration, we present an interactive Augmented Reality (AR) system for mental health information dissemination and self-assessment. The advantage of the system is that it provides direct pathway to relevant mental health resources and offers a positive incentive and interventions for at-risk users.
ECONoMy: Ensemble Collaborative Learning Using Masking

In a society where digital data has become ubiquitous and has been projected to continue in this trajectory for the foreseeable future, machine learning has become a dependable tool to aid in analyzing these big datasets. However, where the data or machine learning algorithms are considered to be privacy-sensitive, one is then faced with the challenge of preserving the utility of machine learning in a privacy-preserving setting. In this paper, we focus on a use case where decentralized parties have privately owned machine learning algorithms, and would want to jointly generate a public model while not violating the privacy of their individual models, and data. We present ECONoMy: a privacy-preserving protocol that supports collaborative learning using an ensemble technique. Set in an honest-but-curious security model, ECONoMy is lightweight and provides efficiency and privacy in settings with large participant such as with IoT devices.
Split &amp; Dual Screen Comparison of Classic vs Object-based Video

Over-the-top (OTT) streaming services like YouTube and Netflix induce massive amounts of video data, hereby putting substantial pressure on network infrastructure. This paper describes a demonstration of the object-based video (OBV) methodology that allows for the quality-variant MPEG-DASH streaming of respectively the background and foreground object(s) of a video scene. The OBV methodology is inspired by research into human visual attention and foveated compression, in that it allows to adaptively and dynamically assign bitrate to those portions of the visual scene that have the highest utility in terms of perceptual quality. Using a content corpus of interview-like video footage, the described demonstration proves the OBV methodology's potential to downsize video bitrate requirements while incurring at most marginal perceptual impact (i.e., in terms of subjective video quality). Thanks to its standards-compliant Web implementation, the OBV methodology is directly and broadly deployable without requiring capital expenditure.
Research of Virtual Simulation Experiment Platform for Marine Auxiliary Machinery Based on VR/AR

Aiming at the shortcomings in the current experimental teaching of marine auxiliary machinery, this paper develops a virtual experimental teaching platform for marine auxiliary machinery based on the emerging VR/AR (Virtual Reality/Augmented Reality) technology. This platform adopts 3Ds Max as modeling tool and Unity3D as virtual reality engine, realizes the multi-experiment-mode of marine auxiliary machinery cognition and virtual disassembly experiment under cloud service framework, this platform can be used on terminals such as PC, intelligent mobile devices, HTC Vive and HoloLens. In this paper, the framework design, developing environment construction, key technologies and operational effects of the marine auxiliary machinery virtual simulation experiment platform are elaborated. The practical application results show that the platform is easy to use and can motivate the enthusiasm of students. To a certain extent, it improves the effect of the marine auxiliary machinery experimental teaching and enhances practical ability of students.
A Novel Approach to Overcome Dictionary and Plaintext Attack in SMS Encryption and Decryption Using Vignere Cipher

Our modern technologies are increasingly moving towards cryptography to secure the data transmission via a wireless medium. This is because transmission via wireless media is convenient to the modern world, but there still exists a major drawback cryptography in the name of Security. The Vigenere cipher belongs to the family of ciphers in which the cipher text is the ‘sum’ of the plaintext and some keystream. The Vigenere cipher is the encryption technique used in most of the communications, particularly in SMS encryption and decryption. The Vigenere key stream is not random, and therefore the cipher is vulnerable to attack. The plaintext attack and dictionary attack are the most commonly occurring attack leading to man in the middle attack. To overcome these two common attacks in vigenere cipher, we propose a noble approach that uses numbers instead of English alphabets to generate the key.
A Generic Maturity Model for Verification and Validation of Modeling and Simulation Applications

As a consequence of the increasing complexity and variety of modeling and simulation (M&S) applications, the demand for demonstrating the credibility and acceptability of M&S and its results by introducing appropriate quality measures, techniques, and tools is obvious. Verification and validation (V&V) is used as an instrument to obtain sufficient confidence in credibility of M&S results. The sufficient confidence is dictated not only by the amount of the invested V&V efforts, but rather by the maturity of the V&V efforts and quality of the collected V&V evidences, such as their completeness and strength. This paper presents a generic V&V maturity model, which (1) can be used (or with slight modifications) for any simulation study with structured M&S and V&V processes; (2) takes different influence factors into consideration: levels of independent V&V, intensity of V&V efforts, and significance of V&V techniques; (3) defines and describes the V&V maturity and its levels in a comprehensible and self-explanatory manner, so that no specialized skills and expertise are required for application of this approach.
High-Throughput BitPacking Compression

To efficiently support analytical applications from a data management perspective, in-memory column store database systems are state-of-the art. In this kind of database system, lossless lightweight integer compression schemes are crucial to keep the memory storage as low as possible and to speedup query processing. In this specific compression domain, BitPacking is one of the most frequently applied compression scheme. However, (de) compression should not come with any additional cost during run time, but should be provided transparently without compromising the overall system performance. To achieve that, we focus on acceleration of BitPacking using Field Programmable Gate Arrays (FPGAs). Therefore, we outline several FPGA designs for BitPacking in this paper. As we are going to show in our evaluation, our specific designs provide the BitPacking compression scheme with high-throughput.
Knowledge-Based Dataless Text Categorization

Text categorization is an important task due to the rapid growth of online available text data in various domains such as web search snippets, news documents, etc. Traditional supervised methods require a significant amount of training data and manually labeling such data can be very time-consuming and costly. Moreover, in case the text to be labeled is of a specific domain, then only the expensive domain experts are able to fulfill the manual labeling task. This thesis focuses on the problem of missing labeled data and aims to develop a novel and generic model which does not require any labeled training data to categorize text. Instead, it utilizes the semantic similarity between documents and the predefined categories by leveraging graph embedding techniques.
Dealing with Imperfect Elicitation Results

The trial-and-roulette method is a popular method to extract experts’ beliefs about a statistical parameter. However, most studies examining the validity of this method only use ‘perfect’ elicitation results. In practice, it is sometimes hard to obtain such neat elicitation results. In our project about predicting fraud and questionable research practices among Ph.D. candidates, we ran into issues with imperfect elicitation results. The goal of the current chapter is to provide an overview of the solutions we used for dealing with these imperfect results, so that others can benefit from our experience. We present information about the nature of our project, the reasons for the imperfect results and how we resolved these supported by annotated R-syntax.
MOC

In online education systems, the courseware plays a pivotal role in helping educators present and impart knowledge to students. The originality of courseware heavily impacts the choice of educators, because the teaching content evolves and so does courseware. However, how to measure the originality of a courseware is a challenging task, due to the lack of labels and the difficulty of quantification. To this end, we contribute a similarity ranking-based unsupervised approach to measure the originality of a courseware. In particular, we first exploit a pre-trained deep visual-text embedding to obtain the representations of images and texts in a local manner. Next, inspired by the design of capsule neural network, a vector-based pooling network is proposed to learn multimodal representations of images and texts. Finally, we propose a Discriminator to optimize the model by maximizing the mutual information between local features and global features in an unsupervised manner. To evaluate the performance of our proposed model, we further subtly collect a dataset for evaluating the originality of courseware by treating sequential versions of each courseware as ranking lists. Therefore, the learning-to-rank scheme can be utilized to evaluate the similarity-based ranking performance. Extensive experimental results have demonstrated the superiority of our proposed framework as compared to other state-of-the-art competitors.
Dealing with Missing Depth: Recent Advances in Depth Image Completion and Estimation

Even though obtaining 3D information has received significant attention in scene capture systems in recent years, there are currently numerous challenges within scene depth estimation which is one of the fundamental parts of any 3D vision system focusing on RGB-D images. This has lead to the creation of an area of research where the goal is to complete the missing 3D information post capture. In many downstream applications, incomplete scene depth is of limited value, and thus, techniques are required to fill the holes that exist in terms of both missing depth and colour scene information. An analogous problem exists within the scope of scene filling post object removal in the same context. Although considerable research has resulted in notable progress in the synthetic expansion or reconstruction of missing colour scene information in both statistical and structural forms, work on the plausible completion of missing scene depth is contrastingly limited. Furthermore, recent advances in machine learning using deep neural networks have enabled complete depth estimation in a monocular or stereo framework circumnavigating the need for any completion post-processing, hence increasing both efficiency and functionality. In this chapter, a brief overview of the advances in the state-of-the-art approaches within RGB-D completion is presented while noting related solutions in the space of traditional texture synthesis and colour image completion for hole filling. Recent advances in employing learning-based techniques for this and related depth estimation tasks are also explored and presented.
Is it Consensus or is it Collusion? Does it Matter for Cryptocurrencies?

Cryptocurrencies are based on cryptography-based asset disposals broadcasted peer-to-peer to be validated decentrally according to consensus mechanisms in compliance with consented protocols. The consensus mechanisms are crucial for reaching a consistent state of a cryptocurrency ledger in a decentralized manner. However, there are several ways to attack consensus. The technical aspects of such attacks have been well explored in the literature, but the legal consequences of such attacks have been less explored. This paper discusses possible legal bases for liability for attacks on consensus and the limits of liability in deterring attacks and providing victim compensation.