Reviewerly/exHarmony huggingface dataset:
IMPLICATIONS OF BLOCKCHAIN AND TRANSPARENCY FOR BUSINESS SUSTAINABILITY: AN INTEGRATIVE REVIEW

ABSTRACT Purpose: Blockchain protocol comprises several attributes, among which transparency stands out. In this vein, the purpose of this article is to advance research on the implications of transparency (as derived from the use of blockchain) for business sustainability (BS). Originality/value: There is continuing interest in applying blockchain for creating and improving transparency, with this latter representing a strategic asset in BS. However, despite advancing this subject, the literature still lacks studies that explain the organizational circumstances in which blockchain can enable progress on transparency in the scope of BS. Design/method/approach: The methodology involved an integrative literature review on blockchain, transparency, and BS, and it was conducted with the support of Web of Science and Scopus to attend the research objective. Findings: The results favored the proposal of two analytical frameworks: one refers to the concepts of blockchain and transparency presented by the academic literature, and the other to the benefits of blockchain and transparency for the economic, social, and environmental BS dimensions. Furthermore, discussions involved deducing propositions about the use of blockchain to improve transparency in the BS context. The propositions open future research directions to studies that aim to test them qualitatively or quantitatively.
emergence of the discourse marker sonojitsu ‘in fact’ in Japanese

This study investigates the development of the hybrid noun phrase sonojitsu (‘that fact, its fact’) to a discourse marker (DM) ‘in fact, in reality, as a matter of fact’ in the history of Japanese. We examined the history of sonojitsu, drawing on various corpora, databases, and dictionaries. The findings are summarised as follows: (1) sonojitsu was sporadically used as a noun phrase mainly attached to an accusative marker or a topic marker until the early 18th century; (2) the zero-marked sonojitsu began to be used as a DM of adversativity from the early 18th century; (3) throughout its history, the DM sonojitsu typically occurs in sentence-medial and clause-initial position, serving an adversative relationship between two clauses; (4) throughout its history, the DM sonojitsu is often used redundantly with a preceding expression of adversativity.
In-Situ Spatial Inference on Climate Simulations with Sparse Gaussian Processes

As extreme-scale physics simulation becomes increasingly memory and storage expensive, the ability to access full simulation data for statistical analysis is becoming increasingly limited. The capability to perform in-situ statistical inference of state variables is becoming increasingly important for the comprehensive utilization of the huge amounts of information generated by these simulations. In this work, we report the first results fitting scalable Gaussian process regression to the state information of an expensive simulation in-situ. For this, spatial regression of upper atmosphere temperature data was performed using Julia coupled to the E3SM climate model. The resulting sparse Gaussian process model shows strong predictive performance using a small number of representative observations. These results provide the backbone for more general in-situ spatial inference with Gaussian process models in complex physics simulations.
The Tap Strap 2: Evaluating Performance of One-Handed Wearable Keyboard and Mouse

This paper presents an evaluation of Tap Strap 2, a one-handed wearable keyboard that uses tap combinations to type characters. The purpose of the Tap Strap 2 is to give the user a portable keyboard that is easier to use for mobile devices than the on-screen QWERTY keyboards included on those devices. The user taps different combinations of fingers on a surface to enter different characters and perform a variety of commands. By using the embedded sensors in each ring, the Tap Strap 2 can recognize which fingers are tapping at any given time. Tap Strap 2 also has a mouse mode that can be switched to from keyboard mode, allowing the user to browse websites on a mobile device as they would on a computer. The third mode that Tap Strap has is a gesture mode that can be used for mobile devices as well as in virtual reality settings. This article details what Tap Strap 2 is capable of and describes the hardware. It also provides an evaluation of the device’s usability and tests an auto-correct algorithm meant to reduce the probability of spelling errors when using the Tap Strap 2.
An Ontology for Human-Centered Analysis and Design of Virtual Reality Conferencing

Remote conferencing for collaboration is a fast-growing field.Virtual Reality applications are experiencing a surge in popularity as they afford a feeling of co-presence and 'togetherness' that video-conferencing cannot.But VR environments for conferencing are not fully mature yet in terms of space, and even less so in terms of the human activities they are supposed to facilitate.Moreover, for researchers, designers and practitioners from various backgrounds working on and in these spaces, a rigorous, shared language for conferencing activities in virtual environments to facilitate the exchange between different stakeholders does not exist as yet.In this paper, we therefore propose a simple, clear method for the development of a human-centered ontology for VR-conferencing, combining existing methodological protocols with Activity Theory and Installation Theory.We then present the first iteration of the ontology focusing specifically on human activity which we developed based on a recent 5-week conference with experts from academia and industry that took place in a 3D VR-environment.
Using Extended Reality to Promote Team Learning

Extended reality (XR) has evolved since the invention of the first XR device in 1839, resulting in the interactive head-mounted displays (HMDs) that are available today. XR is mostly used for gaming, socializing, enterprise, and training, although it has many benefits for education. XR experiences are immersive, may appeal to various learning preferences, and can make impossible learning activities a reality. XR may be particularly beneficial for team learning through shared immersive experiences and by providing a sense of presence for remote group work. As with many new technologies, there are challenges to implementing XR for team learning, including technological and software limitations, problems with comfort for long-term use, cost and accessibility issues, and space limitations. This chapter describes XR and its uses for team learning and explains some of the challenges of implementing this technology for learning.
An Image is Worth More Than a Thousand Words: Towards Disentanglement in The Wild

Unsupervised disentanglement has been shown to be theoretically impossible without inductive biases on the models and the data. As an alternative approach, recent methods rely on limited supervision to disentangle the factors of variation and allow their identifiability. While annotating the true generative factors is only required for a limited number of observations, we argue that it is infeasible to enumerate all the factors of variation that describe a real-world image distribution. To this end, we propose a method for disentangling a set of factors which are only partially labeled, as well as separating the complementary set of residual factors that are never explicitly specified. Our success in this challenging setting, demonstrated on synthetic benchmarks, gives rise to leveraging off-the-shelf image descriptors to partially annotate a subset of attributes in real image domains (e.g. of human faces) with minimal manual effort. Specifically, we use a recent language-image embedding model (CLIP) to annotate a set of attributes of interest in a zero-shot manner and demonstrate state-of-the-art disentangled image manipulation results.
Influence of Cumulative Risk Priming on Security Update Decision Making

Installing security updates is one of the important security actions individuals can take to prevent potential cybersecurity threats. The cumulative risk of delaying the installation of security updates over an extended period can be substantial, and yet, people often choose to delay such actions. Past research suggests that people neglect to update because the majority overestimate the cost (e.g., time) of an update and underestimate an attack risk. Utilizing the repeated protective decision paradigm, we conducted a laboratory experiment to examine whether priming people about the cumulative risk of not updating could influence their update speed. Results from our experiment show that communicating cumulative risk would only have a momentary eﬀect on peoples’ update decisions and that people would quickly learn from experience to delay or neglect to update. Our ﬁndings highlight the importance of augmenting user habits to improve update decision-making.
Bayesian Argumentation-Scheme Networks: A Probabilistic Model of Argument Validity Facilitated by Argumentation Schemes

We propose a methodology for representing the reasoning structure of arguments using Bayesian networks and predicate logic facilitated by argumentation schemes.We express the meaning of text segments using predicate logic and map the boolean values of predicate logic expressions to nodes in a Bayesian network.The reasoning structure among text segments is described with a directed acyclic graph.While our formalism is highly expressive and capable of describing the informal logic of human arguments, it is too open-ended to actually build a network for an argument.It is not at all obvious which segment of argumentative text should be considered as a node in a Bayesian network, and how to decide the dependencies among nodes.To alleviate the difficulty, we provide abstract network fragments, called idioms, which represent typical argument justification patterns derived from argumentation schemes.The network construction process is decomposed into idiom selection, idiom instantiation, and idiom combination.We define 17 idioms in total by referring to argumentation schemes as well as analyzing actual arguments and fitting idioms to them.We also create a dataset consisting of pairs of an argumentative text and a corresponding Bayesian network.Our dataset contains about 2,400 pairs, which is large in the research area of argumentation schemes.2008).For example, one of the argumentation schemes, practical reasoning, is defined as follows.
Patient Representation Learning From Heterogeneous Data Sources and Knowledge Graphs Using Deep Collective Matrix Factorization: Evaluation Study

Patient representation learning aims to learn features, also called representations, from input sources automatically, often in an unsupervised manner, for use in predictive models. This obviates the need for cumbersome, time- and resource-intensive manual feature engineering, especially from unstructured data such as text, images, or graphs. Most previous techniques have used neural network-based autoencoders to learn patient representations, primarily from clinical notes in electronic medical records (EMRs). Knowledge graphs (KGs), with clinical entities as nodes and their relations as edges, can be extracted automatically from biomedical literature and provide complementary information to EMR data that have been found to provide valuable predictive signals.This study aims to evaluate the efficacy of collective matrix factorization (CMF), both the classical variant and a recent neural architecture called deep CMF (DCMF), in integrating heterogeneous data sources from EMR and KG to obtain patient representations for clinical decision support tasks.Using a recent formulation for obtaining graph representations through matrix factorization within the context of CMF, we infused auxiliary information during patient representation learning. We also extended the DCMF architecture to create a task-specific end-to-end model that learns to simultaneously find effective patient representations and predictions. We compared the efficacy of such a model to that of first learning unsupervised representations and then independently learning a predictive model. We evaluated patient representation learning using CMF-based methods and autoencoders for 2 clinical decision support tasks on a large EMR data set.Our experiments show that DCMF provides a seamless way for integrating multiple sources of data to obtain patient representations, both in unsupervised and supervised settings. Its performance in single-source settings is comparable with that of previous autoencoder-based representation learning methods. When DCMF is used to obtain representations from a combination of EMR and KG, where most previous autoencoder-based methods cannot be used directly, its performance is superior to that of previous nonneural methods for CMF. Infusing information from KGs into patient representations using DCMF was found to improve downstream predictive performance.Our experiments indicate that DCMF is a versatile model that can be used to obtain representations from single and multiple data sources and combine information from EMR data and KGs. Furthermore, DCMF can be used to learn representations in both supervised and unsupervised settings. Thus, DCMF offers an effective way of integrating heterogeneous data sources and infusing auxiliary knowledge into patient representations.
ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers

Adversarial patch attacks that craft the pixels in a confined region of the input images show their powerful attack effectiveness in physical environments even with noises or deformations. Existing certified defenses towards adversarial patch attacks work well on small images like MNIST and CIFAR-10 datasets, but achieve very poor certified accuracy on higher-resolution images like ImageNet. It is urgent to design both robust and effective defenses against such a practical and harmful attack in industry-level larger images. In this work, we propose the certified defense methodology that achieves high provable robustness for high-resolution images and largely improves the practicality for real adoption of the certified defense. The basic insight of our work is that the adversarial patch intends to leverage localized superficial important neurons (SIN) to manipulate the prediction results. Hence, we leverage the SIN-based DNN compression techniques to significantly improve the certified accuracy, by reducing the adversarial region searching overhead and filtering the prediction noises. Our experimental results show that the certified accuracy is increased from 36.3% (the state-of-the-art certified detection) to 60.4% on the ImageNet dataset, largely pushing the certified defenses for practical use.
Blockchain Smart Contract Meta-modeling

One of the key benefits of blockchain technology is its ability to keep a permanent, unalterable record of transactions. In business environments, where companies interact with each other without a centralized authority to ensure trust between them, this has led to blockchain platforms and smart contracts being proposed as a means of implementing trustworthy collaborative processes. Software engineers must deal with them to ensure the quality of smart contracts in all phases of the smart contract lifecycle, from requirements specifications to design and deployment. This broad scope and criticality of smart contracts in business environments means that they have to be expressed in a language that is intuitive, easy-to-use, independent of the blockchain platform employed, and oriented towards software quality assurance. In this paper we present a key component: a first outline of a UML-based smart contract meta-model that would allow us to achieve these objectives. This meta-model will be enriched in future work to represent blockchain environments and automated testing.
Interactive Segmentation Using U-Net with Weight Map and Dynamic User Interactions

Interactive segmentation has recently attracted attention for specialized tasks where expert input is required to further enhance the segmentation performance. In this work, we propose a novel interactive segmentation framework, where user clicks are dynamically adapted in size based on the current segmentation mask. The clicked regions form a weight map and are fed to a deep neural network as a novel weighted loss function. To evaluate our loss function, an interactive U-Net (IU-Net) model which applies both foreground and background user clicks as the main method of interaction is employed. We train and validate on the BCV dataset, while testing on spleen and colon cancer CT images from the MSD dataset to improve the overall segmentation accuracy in comparison to the standard U-Net using our weighted loss function. Applying dynamic user click sizes increases the overall accuracy by 5.60% and 10.39% respectively by utilizing only a single user interaction.
Graph Convolutional Networks for Exercise Motion Classification

The growth in self-fitness mobile applications has encouraged people to turn to personal fitness, which entails integrating self-tracking applications with exercise motion data to reduce fatigue and mitigate the risk of injury. The advancements in computer vision and motion capture technologies hold great promise to improve exercise classification performance. This study investigates a supervised deep learning model performance, Graph Convolutional Network (GCN) to classify three workouts using the Azure Kinect device’s motion data. The model defines the skeleton as a graph and combines GCN layers, a readout layer, and multi-layer perceptrons to build an end-to-end framework for graph classification. The model achieves an accuracy of 95.86% in classifying 19,442 frames. The current model exchanges feature information between each joint and its 1-nearest neighbor, which impact fades in graph-level classification. Therefore, a future study on improved feature utilization can enhance the model performance in classifying inter-user exercise variation.
Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction

Distantly supervised relation extraction (RE) automatically aligns unstructured text with relation instances in a knowledge base (KB). Due to the incompleteness of current KBs, sentences implying certain relations may be annotated as N/A instances, which causes the so-called false negative (FN) problem. Current RE methods usually overlook this problem, inducing improper biases in both training and testing procedures. To address this issue, we propose a two-stage approach. First, it finds out possible FN samples by heuristically leveraging the memory mechanism of deep neural networks. Then, it aligns those unlabeled data with the training data into a unified feature space by adversarial training to assign pseudo labels and further utilize the information contained in them. Experiments on two wildly-used benchmark datasets demonstrate the effectiveness of our approach.
A New Tool for Efficiently Generating Quality Estimation Datasets

Building of data for quality estimation (QE) training is expensive and requires significant human labor. In this study, we focus on a data-centric approach while performing QE, and subsequently propose a fully automatic pseudo-QE dataset generation tool that generates QE datasets by receiving only monolingual or parallel corpus as the input. Consequently, the QE performance is enhanced either by data augmentation or by encouraging multiple language pairs to exploit the applicability of QE. Further, we intend to publicly release this user friendly QE dataset generation tool as we believe this tool provides a new, inexpensive method to the community for developing QE datasets.
ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian

As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort.ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter.Using Cohen's Kappa coefficient, the consistency of annotators is 0.95, a high score.In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture.Experimental results show that the model works well in informal Persian as well as in formal Persian.
Near-Optimal Offline Reinforcement Learning via Double Variance Reduction

We consider the problem of offline reinforcement learning (RL) -- a well-motivated setting of RL that aims at policy optimization using only historical data. Despite its wide applicability, theoretical understandings of offline RL, such as its optimal sample complexity, remain largely open even in basic settings such as \emph{tabular} Markov Decision Processes (MDPs). In this paper, we propose Off-Policy Double Variance Reduction (OPDVR), a new variance reduction based algorithm for offline RL. Our main result shows that OPDVR provably identifies an $\epsilon$-optimal policy with $\widetilde{O}(H^2/d_m\epsilon^2)$ episodes of offline data in the finite-horizon stationary transition setting, where $H$ is the horizon length and $d_m$ is the minimal marginal state-action distribution induced by the behavior policy. This improves over the best known upper bound by a factor of $H$. Moreover, we establish an information-theoretic lower bound of $\Omega(H^2/d_m\epsilon^2)$ which certifies that OPDVR is optimal up to logarithmic factors. Lastly, we show that OPDVR also achieves rate-optimal sample complexity under alternative settings such as the finite-horizon MDPs with non-stationary transitions and the infinite horizon MDPs with discounted rewards.
Post-Editing Extractive Summaries by Definiteness Prediction

Extractive summarization has been the mainstay of automatic summarization for decades.Despite all the progress, extractive summarizers still suffer from shortcomings including coreference issues arising from extracting sentences away from their original context in the source document.This affects the coherence and readability of extractive summaries.In this work, we propose a lightweight postediting step for extractive summaries that centers around a single linguistic decision: the definiteness of noun phrases.We conduct human evaluation studies that show that human expert judges substantially prefer the output of our proposed system over the original summaries.Moreover, based on an automatic evaluation study, we provide evidence for our system's ability to generate linguistic decisions that lead to improved extractive summaries.We also draw insights about how the automatic system is exploiting some local cues related to the writing style of the main article texts or summary texts to make the decisions, rather than reasoning about the contexts pragmatically.