Reviewerly/exHarmony huggingface dataset:
Conceptualizing Data Curation Activities Within Two Academic Libraries

INTRODUCTION As funders and journals increasingly create policies that require effective data management and data sharing, many institutions have developed research data management (RDM) programs to help researchers meet these mandates. While there is not a standard set of services for these RDM programs, some institutions, particularly those with repositories that accept data deposits, provide data curation services as a way to add value to research data and help make data more accessible and reusable. Stakeholder communities within the field, such as the Data Curation Network (DCN), are also developing guidelines, procedures, and best practices to support and expand data curation practices. DESCRIPTION OF PROJECT This paper examines the data curation activities defined by the DCN, and describes an activity undertaken by library staff at Duke University and the University of North Carolina at Chapel Hill to create a structured model of these tasks to more easily conceptualize and communicate data curation within these two institutional settings. The purpose of this paper is to describe how this model provided a basis for the implementation and expansion of data curation services at each institution and concludes with overall lessons learned. NEXT STEPS As we develop our services, libraries have an opportunity to make the often-invisible work of curation more transparent. This paper aims to provide a point of reference for other libraries as they consider how to scale up their data curation programs as well as contribute to discussions around prioritization of services, program assessment, and communication with stakeholders.
Decision Trees as Sociotechnical Objects in Chatbot Design

Designers of dialogue-driven systems and 'conversational' agents like chatbots face huge complexities, both in the rich meanings of language and its sophisticated sequential organisation. To this end designers are starting to work out what it means to treat 'conversation' as a design material. But the elephant in the room is that for the most part, the key way of managing the complexities of chatbot design is the decision tree, or some variant of this. Yet decision trees have received little scrutiny as sociotechnical objects which both provide purchase for---but also simultaneously significantly restrict---design practice. CUI research needs to ramp up its concern for various assumptions built into chatbot design processes, and the various stakeholders which may be at play.
Spherical Gaussian Light‐field Textures for Fast Precomputed Global Illumination

Abstract We describe a method to use Spherical Gaussians with free directions and arbitrary sharpness and amplitude to approximate the precomputed local light field for any point on a surface in a scene. This allows for a high‐quality reconstruction of these light fields in a manner that can be used to render the surfaces with precomputed global illumination in real‐time with very low cost both in memory and performance. We also extend this concept to represent the illumination‐weighted environment visibility , allowing for high‐quality reflections of the distant environment with both surface‐material properties and visibility taken into account. We treat obtaining the Spherical Gaussians as an optimization problem for which we train a Convolutional Neural Network to produce appropriate values for each of the Spherical Gaussians' parameters. We define this CNN in such a way that the produced parameters can be interpolated between adjacent local light fields while keeping the illumination in the intermediate points coherent.
Insider Threat Detection: A Solution in Search of a Problem

Insider threats (IT) reflect a growing concern in security communities. Despite a rapid increase in the number of papers examining IT, definitions, research methods, models, and critical evaluations are rare. The present paper provides a critical review of these issues. Definitions of insider threat have varied from general: focusing on all forms of organizational deviant behavior, to specific: focusing on individual difference and social context variables. Research methods are based on deductive principles and intuitions of subject matter experts, computational models based on social media activity, and empirical observations based on synthetic or inaccessible data sets, i.e., black data. Following a review of these considerations, we demonstrate that many existing approaches within the behavioral and social sciences can provide more solid foundations to the IT literature. Using insight from research on organizational deviant behaviour and workplace incivility, we conclude by proposing a multidimensional classification system for insider threat SIEVE: severity (S), intentionality (I), type of employee norm violation (EV), and ethicality (E).
Adversarial transfer learning for cross-domain visual recognition

In many practical visual recognition scenarios, feature distributions between source domain and the target domain are quite different, which results in the emergence of general cross-domain visual recognition problems. To address the problems of visual domain mismatch, we propose a novel shallow semi-supervised adversarial transfer learning network, which is called Coupled adversarial transfer Domain Adaptation (CatDA), for distribution alignment between two domains. The proposed CatDA approach is inspired by cycleGAN, but leveraging multiple shallow multilayer perceptrons (MLPs) instead of deep networks. Specifically, our CatDA comprises of two symmetric and slim sub-networks, such that the coupled adversarial learning framework is formulated. With such symmetry of two generators, the input data from source/target domain can be fed into the MLP network for target/source domain generation, supervised by two confrontation oriented coupled discriminators. Notably, in order to avoid the critical flaw of high-capacity of the feature extraction function during domain adversarial training, domain specific loss and domain knowledge fidelity loss are proposed in each generator, such that the effectiveness of the proposed transfer network is guaranteed. Additionally, the essential difference from cycleGAN is that our method aims to generate domain-agnostic and aligned features for domain adaptation and transfer learning rather than synthesize realistic images. We show experimentally on a number of benchmark datasets and the proposed approach achieves competitive performance over state-of-the-art domain adaptation and transfer learning approaches.
Intelligence, Cyberspace, and National Security

This study evaluates military intelligence as the process of data collection and knowledge development and assessment for decision-making by the military and other governmental agencies. We argue that dominance in modern warfare is enabled by human and technological intelligence that uncovers the rivals’ capabilities and intentions, increases the effectiveness of the country’s own weapon systems, and facilitates the development of high-quality defense systems. Hence, gathering and evaluating intelligence is essential for countries involved in conflict or exposed to terror threats. We focus here on the strategic and tactical implications of intelligence in the context of an arms and intelligence race between two rivals. We present and assess models that show how security agencies in countries in a state of conflict (with other countries and/or non-country entities) should invest in developing their own intelligence capabilities to ensure adequate military (security) capabilities, national security, and welfare. Since advanced cyber attackers can infiltrate almost all complex computer networks to gather intelligence (and/or cause other harms), we show how countries can establish procedures and determine the budgets to optimally allocate cyber-defense resources to prevent harmful cyber-attacks on the complex computer networks that manage their infrastructure, business, security, and government operations.
Can Machine Learning Facilitate Remote Pair Programming? Challenges, Insights &amp; Implications

Remote pair programming encapsulates the benefits of well-researched (co-located) pair programming. However, its effectiveness is hindered by challenges including pair incompatibility, imbalanced roles, and inclinations to work alone. Recent research has explored pedagogical methods to alleviate these challenges, but none have considered the integration of machine learning agents to facilitate remote pair programming. Therefore, we investigated the capabilities of popular text classification algorithms on identifying three facets of pair programming: dialogue acts, creativity stages, and pair programming roles. We collected a dataset of 3,436 utterances from a lab study of 18 pair programmers in a simulated remote environment. We found that pair programming dialogue poses a challenge as it is often unpremeditated and inadequately structured. Despite this, the accuracy of our machine learning classifier was improved by the choice of contextual dialogue features. Our results have implications for facilitating pair programming in global software development and online computer science education.
Generation of Synthetic Continuous Numerical Data Using Generative Adversarial Networks

Abstract Continuous numerical is a type of data which often used for unsupervised learning such as clustering. However, this valuable data often provided in a small amount because it is hard to obtain, expensive, required an expert to collect them, or not available because it contains confidential information that cannot be published. These limited data situations can be an obstacle for processing and analyzing data or restrain clustering related research in general. Therefore, there is a need to be an alternative that can replace or increase the amount of data. The proposed method is generating synthetic continuous numerical data using Generative Adversarial Networks (GANs). This study used two GAN architectures (GAN and CGAN) and focused on unlabeled continuous numerical data to provide replacement or additional data for the clustering task. The Quality of synthetic data was measured using the accuracy of the xgboost algorithm in classifying real and synthetic data. When the xgboost accuracy of perfectly realistic data is 50%, synthetic data based on CGAN achieving 63%. The result of this study shows that GAN can generate data similar enough and not significantly different from the real data.
Multi-Task WaveRNN With an Integrated Architecture for Cross-Lingual Voice Conversion

Spoken languages are similar phonetically because humans have a common vocal production system. However, each language has a unique phonetic repertoire and phonotactic rule. In cross-lingual voice conversion, source speaker and target speaker speak different languages. The challenge is how to project the speaker identity of the source speaker to that of the target across two different phonetic systems. A typical voice conversion system employs a generator-vocoder pipeline, where the generator is responsible for conversion, and the vocoder is for waveform reconstruction. We propose a novel Multi-Task WaveRNN with an integrated architecture for cross-lingual voice conversion. The WaveRNN is trained on two sets of monolingual data via a two-task learning. The integrated architecture takes linguistic features as input and outputs speech waveform directly. Voice conversion experiments are conducted between English and Mandarin, which confirm the effectiveness of the proposed method in terms of speech quality and speaker similarity.
Motion Retargetting based on Dilated Convolutions and Skeleton‐specific Loss Functions

Abstract Motion retargetting refers to the process of adapting the motion of a source character to a target. This paper presents a motion retargetting model based on temporal dilated convolutions. In an unsupervised manner, the model generates realistic motions for various humanoid characters. The retargetted motions not only preserve the high‐frequency detail of the input motions but also produce natural and stable trajectories despite the skeleton size differences between the source and target. Extensive experiments are made using a 3D character motion dataset and a motion capture dataset. Both qualitative and quantitative comparisons against prior methods demonstrate the effectiveness and robustness of our method.
Multi-level detector for pornographic content using CNN models

This paper focuses on detecting and classifying pornographic content (images and videos) by using a multi-level CNN model with some supportive models. The main approaching method is to determine the images (keyframes extracted from videos) containing sensitives content or not by applying object detection model Mask R-CNN, which is the completely new approaching method in pornographic recognition. Moreover, the proposed model also adapts some other methods such as feature extraction and classifying based on CNN to increase the accuracy of the adaptive methods and ignore non-pornographic images and videos. Experimental results using the Pornography-800 and Pornography-2K datasets, performance of our method is reaching the accuracy of 92.13% and 90.40% respectively, show the effectiveness of the proposed method.
Smart Contracts for Government Processes: Case Study and Prototype Implementation (Short Paper)

We study blockchain-based integrity-protected smart contracts as an implementation mechanism for municipal government processes. To this end, we attempted a prototype implementation of such a process in collaboration with a Danish Municipality. We find that such an implementation is possible, despite the obvious confidentiality requirements, and that it does provide benefits: integrity guarantees, verifiability, direct collaboration and payments between the parties. These benefits come at the cost of latency, pr. transactions charges, immutability of errors, and a very concerning single point of failure the municipal government: losing blockchain private keys means losing control over municipal government casework, with no recourse. Our municipal government partner felt that altogether no immediately pressing problem was solved by the implementation, and that the latter risk clearly outweighed any benefits. We note that smart contract implementations of government processes needs to be immutable and outside of the government’s control when running; however, they also need to be updatable when laws change, and provide an “out” for the rare case when errors in the contract implementation result in unlawful behaviour. We propose these conflicting requirements as a foundational research challenge for blockchain to be applicable to governmental processes.
Introduction

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Reading Traces: Scalable Exploration in Elastic Visualizations of Cultural Heritage Data

Abstract Through a design study, we develop an approach to data exploration that utilizes elastic visualizations designed to support varying degrees of detail and abstraction. Examining the notions of scalability and elasticity in interactive visualizations, we introduce a visualization of personal reading traces such as marginalia or markings inside the reference library of German realist author Theodor Fontane. To explore such a rich and extensive collection, meaningful visual forms of abstraction and detail are as important as the transitions between those states. Following a growing research interest in the role of fluid interactivity and animations between views, we are particularly interested in the potential of carefully designed transitions and consistent representations across scales. The resulting prototype addresses humanistic research questions about the interplay of distant and close reading with visualization research on continuous navigation along several granularity levels, using scrolling as one of the main interaction mechanisms. In addition to presenting the design process and resulting prototype, we present findings from a qualitative evaluation of the tool, which suggest that bridging between distant and close views can enhance exploration, but that transitions between views need to be crafted very carefully to facilitate comprehension.
Graph classification algorithm based on graph structure embedding

With the application of data mining in many fields such as information science, bioinformatics, and network intrusion detection, more and more data are showing new features such as strong structuration and complex relationships between data. As a complex data structure, a graph can be used to describe the relationship between things. Traditional graph classification methods based on graph feature vector construction need to select a feature vector construction criterion in advance, such as graph-based theoretical indicators or graph-based topology occurrences, and then extract features from each graph in the graph set according to the designated criterion. However, the construction method of the graph feature vector is easy to lose the graph structural information and requires strong professional knowledge. Inspired by the Word2Vec and Doc2Vec models in the Natural Language Processing (NLP), this paper first constructs a "word list" of graph data consisting of subgraphs. Then a neural network for training graph embedding is designed with the graph itself as its input, and the "word" in the graph and the attribute features of the graph are used as its output, so that the neural network automatically learns the graph embedding corresponding to each graph. The graph embedding not only reflects the features of the graph itself but also includes the relative relationship among graphs. Finally, on the basis of the well-trained graph embedding, the common classifier can be used to classify graphs. Based on real-world bioinformatics and social data sets, the experiments demonstrate that the proposed graph classification algorithm has advantages over the existing graph classification algorithms based on feature vector construction.
Multi-Operator Video Retargeting Method Based on Improved Seam Carving

In view of the existing video retargeting algorithm based on seam carving, when the moving object changes greatly in the position for adjacent frames, the seam cannot move along with the object, making the seam pass through important objects, which can result in the deformation finally. Herein, we proposed a novel multi-operator video retargeting algorithm based on improved seam carving and uniform scaling. This algorithm can make the video sequence be processed in groups, so that the seam can move along with the object. In addition, in combination with uniform scaling, it can be avoided for deformation of important objects caused by excessive deletion of seams. The results showed that when the position of the moving object changes greatly, the current algorithm can better avoid passing through the moving object for the seam than the previous algorithms, and can maintain the spatio-temporal coherence, providing better visual experiences.
Automatic Tuning of Rule-Based Evolutionary Machine Learning via Problem Structure Identification

The success of any machine learning technique depends on the correct setting of its parameters and, when it comes to large-scale datasets, hand-tuning these parameters becomes impractical.However, very large-datasets can be pre-processed in order to distil information that could help in appropriately setting various systems parameters.In turn, this makes sophisticated machine learning methods easier to use to end-users.Thus, by modelling the performance of machine learning algorithms as a function of the structure inherent in very large datasets one could, in principle, detect "hotspots" in the parameters' space and thus, auto-tune machine learning algorithms for better dataset-specific performance.In this work we present a parameter setting mechanism for a rule-based evolutionary machine learning system that is capable of finding the adequate parameter value for a wide variety of synthetic classification problems with binary attributes and with/without added noise.Moreover, in the final validation stage our automated mechanism is able to reduce the computational time of preliminary experiments up to 71% for a challenging real-world bioinformatics dataset.
NottReal

We present NottReal, an application designed for simulating Voice User Interfaces (VUIs) in Wizard of Oz studies. We briefly discuss the premise and advantages of the Wizard of Oz method before moving onto introducing the design of the application, which we have iteratively developed and refined through a number of studies.
Visual Analysis of the Finite‐Time Lyapunov Exponent

Abstract In this paper, we present an integrated visual analytics approach to support the parametrization and exploration of flow visualization based on the finite‐time Lyapunov exponent. Such visualization of time‐dependent flow faces various challenges, including the choice of appropriate advection times, temporal regions of interest, and spatial resolution. Our approach eases these challenges by providing the user with context by means of parametric aggregations, with support and guidance for a more directed exploration, and with a set of derived measures for better qualitative assessment. We demonstrate the utility of our approach with examples from computation fluid dynamics and time‐dependent dynamical systems.
Understanding Differences between Heavy Users and Light Users in Difficulties with Voice User Interfaces

Voice user interfaces (VUIs) are growing in popularity. At this stage of VUIs adoption, distinctions between heavy users and light users are becoming emerging challenge. Some studies have focused on investigating how general users interact with VUIs; however few studies have focused solely on the differences in VUIs use between heavy and light users. In this paper, we conduct user study using our new restaurant reservation VUI, AiCall, to explore what kind of difficulties those two groups are facing and what are the differences between them. We found out that 1) heavy users could identify more diverse difficulty types than light users; 2) the types of difficulties that affect each group of users are different, and 3) in particular, the repetition of agent utterance was considered the most inconvenient by heavy users. Based on these findings, we discuss the VUI design and development considerations to satisfy both groups of users.
An Airdrop that Preserves Recipient Privacy

A common approach to bootstrapping a new cryptocurrency is an airdrop, an arrangement in which existing users give away currency to entice new users to join. But current airdrops offer no recipient privacy: they leak which recipients have claimed the funds, and this information is easily linked to off-chain identities. In this work, we address this issue by defining a private airdrop and describing concrete schemes for widely-used user credentials, such as those based on ECDSA and RSA. Our private airdrop for RSA builds upon a new zero-knowledge argument of knowledge of the factorization of a committed secret integer, which may be of independent interest. We also design a private genesis airdrop that efficiently sends private airdrops to millions of users at once. Finally, we implement and evaluate. Our fastest implementation takes 40–180 ms to generate and 3.7–10 ms to verify an RSA private airdrop signature. Signatures are 1.8–3.3 kiB depending on the security parameter.