Reviewerly/exHarmony huggingface dataset:
The Effect of Interactive Cues on the Perception of Angiographic Volumes in Virtual Reality

In this paper, we evaluate the effect of depth cues on the perception of three-dimensional cerebral angiographic data in a virtual reality (VR) environment. Specifically, a user study was conducted to evaluate the effectiveness of shading, pseudo-chromadepth and aerial perspective, both with and without a dynamic component, where the volume rendering parameters are modified based on the motion of the VR controllers. The results of the study showed that the type of cue that is used has little impact on decision time or relative depth perception, contrary to what has been previously observed in related works using 2D displays. However, shading resulted in better spatial understanding of local vascular structures. In terms of the effect of the dynamic cues, although they resulted in worse depth perception, they also resulted in less head movement which may provide a more ergonomic and intuitive solution for data exploration. This work is a first step towards gaining a better understanding of the interplay between interactive perceptual rendering and the impact is on spatial understanding of volume rendered medical data within VR contexts.
Interacting with academic readings — A comparison of paper and laptop

Academic reading, a form of active reading, often involves interaction with the text. Highlighting and annotating are some of the most common strategies of interacting with academic texts, yet we have limited understanding of exactly how such interactions affect reading comprehension in digital versus analog reading environments. In this paper, we present an exploratory study, comparing how university students (n = 50) interact with a digital and a physical text, focusing on highlights and annotations. We compare reading time, interaction with, and subsequent memory of the texts. We make nine observations about reading time, highlighting and annotation frequency and memory scores between paper and laptop. We find that students annotate significantly less on laptop than on paper, but that neither highlighting nor annotations influence subsequent memory of the text in either condition. Our broader contribution is to augment understanding of how different interaction features aid academic reading in a natural environment.
Tackling Zero Pronoun Resolution and Non-Zero Coreference Resolution Jointly

Zero pronoun resolution aims at recognizing dropped pronouns and pointing out their anaphoric mentions, while non-zero coreference resolution targets at clustering mentions referring to the same entity.Existing efforts often deal with the two problems separately regardless of their close essential correlations.In this paper, we investigate the possibility of jointly solving zero pronoun resolution and coreference resolution via a novel end-to-end neural model.Specifically, we design a gapmasked self-attention model that encodes gaps and tokens in the same space, where gaps could capture valuable contextual information according to their surrounding tokens while tokens could maintain original sequential information without disturbance.Additionally, we also propose a two-stage interaction mechanism to make full use of the exclusive relationship between zero pronouns and mentions.Our empirical study conducted on the OntoNotes 5.0 Chinese dataset shows that our model could outperform corresponding stateof-the-art approaches on both tasks.
The Interesting and Complete Artificial Intelligence (ICAI) – Version 1

A new field titled “The Interesting and Complete Artificial Intelligence (ICAI)” is invented in this work. In this article, we define this new ICAI field. Four new ICAI algorithms are designed in this work. This paper titled “The Interesting and Complete Artificial Intelligence (ICAI) – Version 1” is just the starting point of this new field. We request Research Scientists across the globe to work in this new direction of Artificial Intelligence and publish their work with titles such as “The Interesting and Complete Artificial Intelligence (ICAI) – Version 1.1”, “The Interesting and Complete Artificial Intelligence (ICAI) – Version 2” or “The Interesting and Complete Artificial Intelligence (ICAI) – Final Version”.
Encouraging Lexical Translation Consistency for Document-Level Neural Machine Translation

Recently a number of approaches have been proposed to improve translation performance for document-level neural machine translation (NMT).However, few are focusing on the subject of lexical translation consistency.In this paper we apply "one translation per discourse" in NMT, and aim to encourage lexical translation consistency for document-level NMT.This is done by first obtaining a word link for each source word in a document, which tells the positions where the source word appears at.Then we encourage the translations of those words within a link to be consistent in two ways.On the one hand, when encoding sentences within a document we properly exchange context information of those words.On the other hand, we propose an auxiliary loss function to better constrain that their translations should be consistent.Experimental results on Chinese↔English and English→French translation tasks show that our approach not only achieves state-of-the-art performance in BLEU scores, but also greatly improves lexical translation consistency.
MIRTT: Learning Multimodal Interaction Representations from Trilinear Transformers for Visual Question Answering

In Visual Question Answering (VQA), existing bilinear methods focus on the interaction between images and questions.As a result, the answers are either spliced into the questions or utilized as labels only for classification.On the other hand, trilinear models such as the CTI model of Do et al. ( 2019) efficiently utilize the inter-modality information between answers, questions, and images, while ignoring intramodality information.Inspired by these observations, we propose a new trilinear interaction framework called MIRTT (Learning Multimodal Interaction Representations from Trilinear Transformers), incorporating the attention mechanisms for capturing inter-modality and intra-modality relationships.Moreover, we design a two-stage workflow where a bilinear model reduces the free-form, open-ended VQA problem into a multiple-choice VQA problem.Furthermore, to obtain accurate and generic multimodal representations, we pretrain MIRTT with masked language prediction.Our method achieves state-of-the-art performance on the Visual7W Telling task and VQA-1.0Multiple Choice task and outperforms bilinear baselines on the VQA-2.0,TDIUC and GQA datasets.
ROFF - A Romanian Twitter Dataset for Offensive Language

This paper describes the annotation process of an offensive language data set for Romanian on social media.To facilitate comparable multi-lingual research on offensive language, the annotation guidelines follow some of the recent annotation efforts for other languages.The final corpus contains 5000 micro-blogging posts annotated by a large number of volunteer annotators.The inter-annotator agreement and the initial automatic discrimination results we present are in line with earlier annotation efforts.
Tourism Resource Evaluation and Countermeasures Based on Network Communication and TOPSIS Algorithm

Tourism resources are the basis of tourism activities. Affected by traditional values and the reusable characteristics of tourism resources, tourism resources have been regarded as worthless for a long time. In recent years, people have begun to establish correct values of tourism resources. However, due to the particularity of tourism resources, the issue of their value evaluation has been controversial. Value evaluation is value recognition or evaluative recognition. In order to develop a cost-effective method to obtain tourism resource evaluation and countermeasures, this paper has done a research on tourism resource evaluation and countermeasures based on network communication and the TOPSIS algorithm. For this reason, we selected a tourist attraction to conduct survey experiments. During the experiment, network communication was used to realize the exchange and sharing of their information between different people through specific media. At the same time, the TOPSIS method is used in the selection of the plan, based on the calculation results of the combined weighted TOPSIS, and the SPSS is used for the principal component factor. The suggestion and the exact conditions of the local tourism resources are combined to establish a model of the tourism resource evaluation system in this place. The experimental results show that the relative importance of viewing value is prominent in the evaluation of tourism resource value. Its weight reaches 0.509, and its popularity weight is 0.257. The evaluation and countermeasures of tourism resources based on network communication and TOPSIS algorithm are effective.
Does It Happen? Multi-hop Path Structures for Event Factuality Prediction with Graph Transformer Networks

The goal of Event Factuality Prediction (EFP) is to determine the factual degree of an event mention, representing how likely the event mention has happened in text.Current deep learning models has demonstrated the importance of syntactic and semantic structures of the sentences to identify important context words for EFP.However, the major problem with these EFP models is that they only encode the one-hop paths between the words (i.e., the direct connections) to form the sentence structures.In this work, we show that the multihop paths between the words are also necessary to compute the sentence structures for EFP.To this end, we introduce a novel deep learning model for EFP that explicitly considers multi-hop paths with both syntax-based and semantic-based edges between the words to obtain sentence structures for representation learning in EFP.We demonstrate the effectiveness of the proposed model via the extensive experiments in this work.
A Machine Learning Based Framework for Preprocessing and Classification of Medical Images

Disease diagnosis and therapy rely heavily on medical imaging. For example, image processing has been used to depict different cell types using textural aspects in the medical and biological fields, as well as to distinguish between living and dead cells using pictures. There are major difficulties in reproducibility and predictability since traditional techniques of microscopic image analysis rely on a labor-intensive approach with a restricted number of bone samples. To construct a novel technique, image processing in a digital system was used to include several steps, including preprocessing, object representation, feature extraction, classifying, and picture interpretation. It was found that the Wavelet denoising tool was a potent picture enhancing technique. Second-stage preprocessing of pictures is validated by the wavelet, which divides the image into subbands like near, horizontal, and vertical estimations. To cure at the beginning stage, any bone fracture or abnormality (cancer) must be anticipated. Human osteosarcoma, a malignant primitive bone tumor, is the most common form of bone cancer in the human body. Metastasis is a term for the spread of stage condition to other sections of the body or to organs. Our proposed work was associated with preprocessing to eliminate noise and get smooth images. This procedure would aid in improving image quality and removing false segments. KNN, CNN, and ANN classification algorithms are used to classify image datasets.
The Benefits of Implicit Regularization from SGD in Least Squares Problems

Stochastic gradient descent (SGD) exhibits strong algorithmic regularization effects in practice, which has been hypothesized to play an important role in the generalization of modern machine learning approaches. In this work, we seek to understand these issues in the simpler setting of linear regression (including both underparameterized and overparameterized regimes), where our goal is to make sharp instance-based comparisons of the implicit regularization afforded by (unregularized) average SGD with the explicit regularization of ridge regression. For a broad class of least squares problem instances (that are natural in high-dimensional settings), we show: (1) for every problem instance and for every ridge parameter, (unregularized) SGD, when provided with logarithmically more samples than that provided to the ridge algorithm, generalizes no worse than the ridge solution (provided SGD uses a tuned constant stepsize); (2) conversely, there exist instances (in this wide problem class) where optimally-tuned ridge regression requires quadratically more samples than SGD in order to have the same generalization performance. Taken together, our results show that, up to the logarithmic factors, the generalization performance of SGD is always no worse than that of ridge regression in a wide range of overparameterized problems, and, in fact, could be much better for some problem instances. More generally, our results show how algorithmic regularization has important consequences even in simpler (overparameterized) convex settings.
Influence of the Listening Environment on Recognition of Immersive Reproduction of Orchestral Music Sound Scenes

This study investigates how a listening environment (the combination of a room's acoustics and reproduction loudspeaker) influences a listener's perception of reproduced sound fields.Three distinct listening environments with different reverberation times and clarity indices were compared for their perceptual characteristics.Binaural recordings were made of orchestral music, mixed for 22.2 and 2-channel audio reproduction, within each of the three listening rooms.In a subjective listening test, 48 listeners evaluate these binaural recordings in terms of overall preference and five auditory attributes: perceived width, perceived depth, spatial clarity, impression of being enveloped, and spectral fidelity.Factor analyses of these five attribute ratings show that listener perception of the reproduced sound fields focused on two salient factors, spatial and spectral fidelity, yet the attributes' weightings in those two factors differs depending on a listener's previous experience with audio production and 3D immersive audio listening.For the experienced group, the impression of being enveloped was the most salient attribute, with spectral fidelity being the most important for the non-experienced group.
When Two are Better Than One: Synthesizing Heavily Unbalanced Data

Nowadays, data is king and if treated and used properly it promises to give organizations a competitive edge over rivals by enabling them to develop and design Intelligent Systems to improve their services. However, they need to fully comply with not only ethical but also regulatory obligations, where, e.g., privacy (strictly) needs to be respected when using or sharing data, thus protecting both the interests of users and organizations. Fraud Detection systems are examples of such systems where Machine Learning algorithms leverage information to classify financial transactions as legitimate or illicit. The data used to create these solutions is usually highly structured and contains categorical and continuous features characterised by complex distributions. One of the main challenges of fraud detection is concerned with the scarcity of fraudulent instances which results in highly unbalanced datasets. Additionally, privacy is crucial, and it is usually forbidden, or not possible, to share the data of organizations and individuals for creating or improving models. In this paper we propose a framework for private data sharing based on synthetic data generation using Generative Adversarial Networks (GAN) that learns the specificities of financial transactions data and generates fictitious data that keeps the utility of the original datasets. Our proposal, called Duo-GAN, uses two GAN generators to handle the data imbalance problem, one generator for fraudulent instances and the other for legitimate instances. With this approach, we observed, at most, a 5% disparity in F1 scores between classifiers trained and tested with actual data and the ones trained with synthetic data and tested with actual data.
Simple and effective neural coreference resolution for Korean language

We propose an end-to-end neural coreference resolution for the Korean language that uses an attention mechanism to point to the same entity. Because Korean is a head-final language, we focused on a method that uses a pointer network based on the head. The key idea is to consider all nouns in the document as candidates based on the head-final characteristics of the Korean language and learn distributions over the referenced entity positions for each noun. Given the recent success of applications using bidirectional encoder representation from transformer (BERT) in natural language-processing tasks, we employed BERT in the proposed model to create word representations based on contextual information. The experimental results indicated that the proposed model achieved state-of-the-art performance in Korean language coreference resolution.
Enhancing the pattern recognition capacity of machine learning techniques: The importance of feature positioning

We design several algorithms representing evaluation processes of different complexity, ranging from basic environments based on a predetermined number of features to complex structures involving alternatives defined through decision trees whose number of nodes is determined by the cardinality of the respective power sets. The sequential structure of these evaluation processes builds on the information retrieval behavior of users in online search environments. The algorithms generate two strings of data, namely, numerical evaluations determining the retrieval behavior of users and the subsequent choices made by the latter. The way the output obtained from the algorithms is inputted within the vectors summarizing the complexity of the evaluation processes conditions the capacity of machine learning techniques to categorize them correctly. The main purpose of the research is to illustrate numerically two main results. First, machine learning techniques categorize processes correctly even if their characteristic features are presented in a way that prevents their identification using standard statistical techniques. Second, the accuracy of the categorization capacities of these techniques can be substantially enhanced by describing the retrieval processes in the way required to implement standard statistical analyses. We perform a battery of tests using machine learning techniques to demonstrate and analyze these results. Their applicability to classification and prediction problems in medical environments, particularly those constrained by the quality of the data available, is emphasized.
Improved Harris Hawks Optimization for Configuration of PV Intelligent Edge Terminals

Photovoltaics intelligent edge terminals (PV IETs) are new devices for data acquisition and management of PV power station. However, due to the massive, scattered, and disordered nature of PV stations and the expensive price of PV IET, installing a PV IET at each PV station has prohibited for large-scale adoption. Therefore, to reduce the life cycle cost of PV IET in a region, this paper proposes an optimal configuration method for PV IET. We propose a mathematical model for the optimal configuration of PV IETs. For a given regional grid, the model aims to obtain the optimal number and location of PV IETs and the connection mode between PV IETs and PV power stations. In addition, an improved Harris hawk optimization (IHHO) is proposed to solve the nonlinear mathematical model. A case study is carried out under different problem sizes, boundary and devices parameters. The simulation results show that under different case, the PV IET configuration method in this paper can obtain lower life cycle cost than the conventional method, and IHHO has higher accuracy than other algorithms. The optimal configuration method in this article can effectively solve the problem of the optimal configuration of PV IET.
Building Adaptive Acceptability Classifiers for Neural NLG

We propose a novel framework to train models to classify acceptability of responses generated by natural language generation (NLG) models, improving upon existing sentence transformation and model-based approaches.An NLG response is considered acceptable if it is both semantically correct and grammatical.We don't make use of any human references making the classifiers suitable for runtime deployment.Training data for the classifiers is obtained using a 2-stage approach of first generating synthetic data using a combination of existing and new model-based approaches followed by a novel validation framework to filter and sort the synthetic data into acceptable and unacceptable classes.Our 2stage approach adapts to a wide range of data representations and does not require additional data beyond what the NLG models are trained on.It is also independent of the underlying NLG model architecture, and is able to generate more realistic samples close to the distribution of the NLG model-generated responses.We present results on 5 datasets (WebNLG, Cleaned E2E, ViGGO, Alarm, and Weather) with varying data representations.We compare our framework with existing techniques that involve synthetic data generation using simple sentence transformations and/or modelbased techniques, and show that building acceptability classifiers using data that resembles the generation model outputs followed by a validation framework outperforms the existing techniques, achieving state-of-the-art results.We also show that our techniques can be used in few-shot settings using self-training.
Cross-lingual Fine-tuning for Abstractive Arabic Text Summarization

While abstractive summarization in certain languages, like English, has already reached fairly good results due to the availability of trend-setting resources, like the CNN/Daily Mail dataset, and considerable progress in generative neural models, progress in abstractive summarization for Arabic, the fifth mostspoken language globally, is still in baby shoes.While some resources for extractive summarization have been available for some time, in this paper, we present the first corpus of human-written abstractive news summaries in Arabic, hoping to lay the foundation of this line of research for this important language.The dataset consists of more than 21 thousand items.We used this dataset to train a set of neural abstractive summarization systems for Arabic by fine-tuning pre-trained language models such as multilingual BERT, AraBERT, and multilingual BART-50.As the Arabic dataset is much smaller than e.g. the CNN/Daily Mail dataset, we also applied cross-lingual knowledge transfer to significantly improve the performance of our baseline systems.The setups included two M-BERT-based summarization models originally trained for Hungarian/English and a similar system based on M-BART-50 originally trained for Russian that were further fine-tuned for Arabic.Evaluation of the models was performed in terms of ROUGE, and a manual evaluation of fluency and adequacy of the models was also performed.
Control Variables in Interactive Models

Accounting studies often examine whether the relation between X and Y varies with a moderating variable, M, by including an interactive term, X×M, in a regression. We provide plain-English guidance on why, how, and when to use control variables (Z) in interaction tests. Simple descriptions and simulations demonstrate the roles of control variables and how interacted controls affect coefficient estimates and interpretations. In particular, we demonstrate how including Z without an accompanying interaction of X×Z and/or M×Z generally does not eliminate the confounding effect of Z on X×M. We conclude with guidance for future research.