Reviewerly/exHarmony huggingface dataset:
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems

In task-oriented dialogue (ToD), a user holds a conversation with an artificial agent with the aim of completing a concrete task. Although this technology represents one of the central objectives of AI and has been the focus of ever more intense research and development efforts, it is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of languages (e.g., English, Chinese). This work provides an extensive overview of existing methods and resources in multilingual ToD as an entry point to this exciting and emerging field. We find that the most critical factor preventing the creation of truly multilingual ToD systems is the lack of datasets in most languages for both training and evaluation. In fact, acquiring annotations or human feedback for each component of modular systems or for data-hungry end-to-end systems is expensive and tedious. Hence, state-of-the-art approaches to multilingual ToD mostly rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages (almost exclusively English), either by means of (i) machine translation or (ii) multilingual representations. These approaches are currently viable only for typologically similar languages and languages with parallel / monolingual corpora available. On the other hand, their effectiveness beyond these boundaries is doubtful or hard to assess due to the lack of linguistically diverse benchmarks (especially for natural language generation and end-to-end evaluation). To overcome this limitation, we draw parallels between components of the ToD pipeline and other NLP tasks, which can inspire solutions for learning in low-resource scenarios. Finally, we list additional challenges that multilinguality poses for related areas (such as speech, fluency in generated text, and human-centred evaluation), and indicate future directions that hold promise to further expand language coverage and dialogue capabilities of current ToD systems.
Joint Summarization-Entailment Optimization for Consumer Health Question Understanding

Understanding the intent of medical questions asked by patients, or Consumer Health Questions, is an essential skill for medical Conversational AI systems.We propose a novel dataaugmented and simple joint learning approach combining question summarization and Recognizing Question Entailment (RQE) in the medical domain.Our data augmentation approach enables to use just one dataset for joint learning.We show improvements on both tasks across four biomedical datasets in accuracy (+8%), ROUGE-1 (+2.5%) and human evaluation scores.Human evaluation shows joint learning generates faithful and informative summaries.Finally, we release our code, the two question summarization datasets extracted from a large-scale medical dialogue dataset, as well as our augmented datasets 1 .
Improving Evidence Retrieval for Automated Explainable Fact-Checking

Automated fact-checking on a large-scale is a challenging task that has not been studied systematically until recently.Large noisy document collections like the web or news articles make the task more difficult.In this paper, we describe the components of a threestage automated fact-checking system, named Quin+.We demonstrate that using dense passage representations increases the evidence recall in a noisy setting.We experiment with two sentence selection approaches, an embeddingbased selection using a dense retrieval model, and a sequence labeling approach for contextaware selection.Quin+ is able to verify opendomain claims using a large-scale corpus or web search results.
A broad review on non-intrusive active user authentication in biometrics

Authentication is the process of keeping the user's personal information as confidential in digital applications. Moreover, the user authentication process in the digital platform is employed to verify the own users by some authentication methods like biometrics, voice recognition, and so on. Traditionally, a one-time login based credential verification method was utilized for user authentication. Recently, several new approaches were proposed to enhance the user authentication framework but those approaches have been found inconsistent during the authentication execution process. Hence, the main motive of this review article is to analyze the advantage and disadvantages of authentication systems such as voice recognition, keystroke, and mouse dynamics. These authentication models are evaluated in a continuous non-user authentication environment and their results have been presented in way of tabular and graphical representation. Also, the common merits and demerits of the discussed authentication systems are broadly explained discussion section. Henceforth, this study will help the researchers to adopt the best suitable method at each stage to build an authentication framework for non-intrusive active authentication.
Post-digital, Post-internet: Propositions for Art Education in the Context of Digital Cultures

Abstract Concepts such as Post-Digital and Post-Internet act as symptomatic descriptors of digitally permeated cultures. Starting from a broad understanding of digitality in its societal and cultural-historical interdependencies, both terms are first introduced and afterward extended through the discussion of four theses, with a particular emphasis on aesthetic aspects. Elaborating on (1) distributed artworks, (2) hybrid subjects, (3) fluid materiality, and (4) blind spots, each thesis leads to a proposition for art education dealing with digital cultures. The chapter concludes by highlighting art education’s potential in reflecting on digital cultures and in developing new models and methods for practical application.
TILGAN: Transformer-based Implicit Latent GAN for Diverse and Coherent Text Generation

Conventional autoregressive models have achieved great success in text generation but suffer from the exposure bias problem in that token sequences in the training and in the generation stages are mismatched.While generative adversarial networks (GANs) can remedy this problem, existing implementations of GANs directly on discrete outputs tend to be unstable and lack diversity.In this work, we propose TILGAN, a Transformerbased Implicit Latent GAN, which combines a Transformer autoencoder and GAN in the latent space with a novel design and distribution matching based on the Kullback-Leibler (KL) divergence.Specifically, to improve local and global coherence, we explicitly introduce a multi-scale discriminator to capture the semantic information at varying scales among the sequence of hidden representations encoded by Transformer.Moreover, the decoder is enhanced by an additional KL loss to be consistent with the latent-generator.Experimental results on three benchmark datasets demonstrate the validity and effectiveness of our model, by obtaining significant improvements and a better quality-diversity trade-off in automatic and human evaluation for both unconditional and conditional generation tasks.
A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation

Deep learning models have achieved state-of-the-art performance in semantic image segmentation, but the results provided by fully automatic algorithms are not always guaranteed satisfactory to users. Interactive segmentation offers a solution by accepting user annotations on selective areas of the images to refine the segmentation results. However, most existing models only focus on correcting the current image's misclassified pixels, with no knowledge carried over to other images. In this work, we formulate interactive image segmentation as a continual learning problem and propose a framework to effectively learn from user annotations, aiming to improve the segmentation on both the current image and unseen images in future tasks while avoiding deteriorated performance on previously-seen images. It employs a probabilistic mask to control the neural network's kernel activation and extract the most suitable features for segmenting images in each task. We also apply a task-aware embedding to automatically infer the optimal kernel activation for initial segmentation and subsequent refinement. Interactions with users are guided through multi-source uncertainty estimation so that users can focus on the most important areas to minimize the overall manual annotation effort. Experiments are performed on both medical and natural image datasets to illustrate the proposed framework's effectiveness on basic segmentation performance, forward knowledge transfer, and backward knowledge transfer.
Adaptive Autonomy in Human-on-the-Loop Vision-Based Robotics Systems

Computer vision approaches are widely used by autonomous robotic systems to sense the world around them and to guide their decision making as they perform diverse tasks such as collision avoidance, search and rescue, and object manipulation. High accuracy is critical, particularly for Human-on-the-loop (HoTL) systems where decisions are made autonomously by the system, and humans play only a supervisory role. Failures of the vision model can lead to erroneous decisions with potentially life or death consequences. In this paper, we propose a solution based upon adaptive autonomy levels, whereby the system detects loss of reliability of these models and responds by temporarily lowering its own autonomy levels and increasing engagement of the human in the decision-making process. Our solution is applicable for vision-based tasks in which humans have time to react and provide guidance. When implemented, our approach would estimate the reliability of the vision task by considering uncertainty in its model, and by performing covariate analysis to determine when the current operating environment is illmatched to the model’s training data. We provide examples from DroneResponse, in which small Unmanned Aerial Systems are deployed for Emergency Response missions, and show how the vision model’s reliability would be used in addition to confidence scores to drive and specify the behavior and adaptation of the system’s autonomy. This workshop paper outlines our proposed approach and describes open challenges at the intersection of Computer Vision and Software Engineering for the safe and reliable deployment of vision models in the decision making of autonomous systems.
Augmented binary multi-labeled CNN for practical facial attribute classification

Both human face recognition and generation by machines are currently an active area of computer vision, drawing curiosity of researchers, capable of performing amazing image analysis, and producing applications in multiple domains. In this paper, we propose a new approach for face attributes classification (FAC) taking advantage from both binary classification and data augmentation. With binary classification we can reach high prediction scores, while augmented data prevent overfitting and overcome the lack of data for sketched photos. Our approach, named Augmented binary multilabel CNN (ABM-CNN), consists of three steps: i) splitting data; ii) transformed-it to sketch (simplification process); iii) train separately each attribute with two convolutional neural networks; the whole process includes two networks: the first (resp. the second) one is to predict attributes on real images (resp. sketches) as inputs. Through experimentation, we figure out that some attributes give high prediction rates with sketches rather than with real images. On the other hand, we build a new face dataset, more consistent and complete, by generating images using Style-GAN model, to which we apply our method for extracting face attributes. As results, our proposal demonstrates more performances compared to those of related works.
An Uncertainty-Aware Performance Measure for Multi-Object Tracking

Evaluating the performance of multi-object tracking (MOT) methods is not straightforward, and existing performance measures fail to consider all the available uncertainty information in the MOT context. This can lead practitioners to select models which produce uncertainty estimates of lower quality, negatively impacting any downstream systems that rely on them. Additionally, most MOT performance measures have hyperparameters, which makes comparisons of different trackers less straightforward. We propose the use of the negative log-likelihood (NLL) of the multi-object posterior given the set of ground-truth objects as a performance measure. This measure takes into account all available uncertainty information in a sound mathematical manner without hyperparameters. We provide efficient algorithms for approximating the computation of the NLL for several common MOT algorithms, show that in some cases it decomposes and approximates the widely-used GOSPA metric, and provide several illustrative examples highlighting the advantages of the NLL in comparison to other MOT performance measures.
The Exploitation of Distance Distributions for Clustering

Although distance measures are used in many machine learning algorithms, the literature on the context-independent selection and evaluation of distance measures is limited in the sense that prior knowledge is used. In cluster analysis, current studies evaluate the choice of distance measure after applying unsupervised methods based on error probabilities, implicitly setting the goal of reproducing predefined partitions in data. Such studies use clusters of data that are often based on the context of the data as well as the custom goal of the specific study. Depending on the data context, different properties for distance distributions are judged to be relevant for appropriate distance selection. However, if cluster analysis is based on the task of finding similar partitions of data, then the intrapartition distances should be smaller than the interpartition distances. By systematically investigating this specification using distribution analysis through a mirrored-density plot, it is shown that multimodal distance distributions are preferable in cluster analysis. As a consequence, it is advantageous to model distance distributions with Gaussian mixtures prior to the evaluation phase of unsupervised methods. Experiments are performed on several artificial datasets and natural datasets for the task of clustering.
Predicting Semantic Categories in Text Based on Knowledge Graph Combined with Machine Learning Techniques

The Quran and the Sunnah are the two principal elements of the Islamic religion, and the hadith is an interpreter of the Quran. Hadith is everything that the Messenger Muhammad said, whether it was a word, an action, or a good adjective of the Prophet. Given the status of the hadith of Muslims everywhere in the world, digging into it is the m ain perspective to evoke the guiding principles and institutions that Muslims must follow. The mining of the hadith has received much attention in recent times, but so far, the work has not been fully implemented. This study focuses on predicting the semantic categories of the unclassified hadith text based on its text. The model can distinguish between several categories to predict the optimal one such as ablution, fasting, Hajj, and Zakat. To achieve this goal, a Knowledge-Graphic (KG) prediction model was developed to improve machine learning classifiers from the standpoint of two unique traits. 1) Define pivotal terms that have high values. II) Taking into account all the paths of those pivotal terms through their convergence with the categories in the Knowledge-Graphic of all the paths that link them. We rely on six books with more than 30,000 hadith and 120 classifications. Empirically, we found optimistic results in combining the KG model and machine learning classifiers.
Acquisitions and the Accelerated Shift to Digital in Academic Libraries in the UK: Reflecting on the Covid Experience at De Montfort University and Imperial College London

Summer 2020 witnessed a large-scale temporary release of content from the publishing community to support UK universities as they moved to online-only delivery, followed by a scramble by those institutions to maintain access to resources. This paper reflects on the experiences of De Montfort University and Imperial College libraries during this period as they supported the move to remote teaching. It focuses on the complexities experienced during this transitional period, and considers how the speed of these changes increased staff workloads, stretched budgets, and compelled acquisitions teams to act without always fully gathering evidence or strategically planning how new practices might work in the longer term. The authors, who sit on various national contract management and acquisitions strategy groups, examine the repercussions of navigating from an unplanned, accelerated digital shift to a more managed, sustainable paradigm, and contemplate how the advent of multimode teaching may impact on the way libraries are resourced.
Beliefs and Practices About Remote Teaching Processes During the Pandemic: A Study with Chilean Mathematics Teachers

This study determines the factors associated with teachers’ beliefs and practices during the pandemic. An online instrument designed and implemented for Chilean mathematics teachers, with a total of 423 math teachers participating. The instrument is found psychometrically valid to study teachers’ pedagogical-technological beliefs and their remote practices. The results show teachers’ high self-efficacy levels regarding the personal use of technology, but moderate self-efficacy in integrating technology in teaching. Moreover, teachers see a much more active role for themselves than for students in online activities. Gender differences were only significant for teaching practices, where the results reported women as more active than men. Teacher age is not recognized as a factor affecting teachers’ pedagogical-technological beliefs and their remote practices. Finally, the study confirms a significant influence of socioeconomic context in teachers’ beliefs and practices during the pandemic. It recommends that policymakers need to provide adequate resources and knowledge to support teachers in integrating technologies in distance education, especially those working in the public sector.
Composably secure data processing for Gaussian-modulated continuous-variable quantum key distribution

Continuous-variable (CV) quantum key distribution (QKD) employs the quadratures of a bosonic mode to establish a secret key between two remote parties, and this is usually achieved via a Gaussian modulation of coherent states. The resulting secret key rate depends not only on the loss and noise in the communication channel, but also on a series of data processing steps that are needed for transforming shared correlations into a final string of secret bits. Here we consider a Gaussian-modulated coherent-state protocol with homodyne detection in the general setting of composable finite-size security. After simulating the process of quantum communication, the output classical data is post-processed via procedures of parameter estimation, error correction, and privacy amplification. In particular, we analyze the high signal-to-noise regime which requires the use of high-rate (non-binary) low-density parity check codes. We implement all these steps in a Python-based library that allows one to investigate and optimize the protocol parameters to be used in practical experimental implementations of short-range CV-QKD.
Formalizing and Estimating Distribution Inference Risks

Distribution inference, sometimes called property inference, infers statistical properties about a training set from access to a model trained on that data. Distribution inference attacks can pose serious risks when models are trained on private data, but are difficult to distinguish from the intrinsic purpose of statistical machine learning—namely, to produce models that capture statistical properties about a distribution. Motivated by Yeom et al.’s membership inference framework, we propose a formal definition of distribution inference attacks general enough to describe a broad class of attacks distinguishing between possible training distributions. We show how our definition captures previous ratio-based inference attacks as well as new kinds of attack including revealing the average node degree or clustering coefficient of training graphs. To understand distribution inference risks, we introduce a metric that quantifies observed leakage by relating it to the leakage that would occur if samples from the training distribution were provided directly to the adversary. We report on a series of experiments across a range of different distributions using both novel black-box attacks and improved versions of the state-of-the-art white-box attacks. Our results show that inexpensive attacks are often as effective as expensive meta-classifier attacks, and that there are surprising asymmetries in the effectiveness of attacks.
Boomerang uniformity of a class of power maps

We consider the boomerang uniformity of an infinite class of (locally-APN) power maps and show that their boomerang uniformity over the finite field $$\mathbb {F}_{2^n}$$ is 2 and 4, when $$n \equiv 0 \pmod 4$$ and $$n \equiv 2 \pmod 4$$ , respectively. As a consequence, we show that for this class of power maps, the differential uniformity is strictly greater than their boomerang uniformity.
infer: An R package for tidyverse-friendly statistical inference

infer implements an expressive grammar to perform statistical inference that adheres to the tidyverse design framework (Wickham et al., 2019).Rather than providing methods for specific statistical tests, this package consolidates the principles that are shared among common hypothesis tests and confidence intervals into a set of four main verbs (functions), supplemented with many utilities to visualize and extract value from their outputs.
Ecosystem approach and innovative educational ecosystems

The article discusses innovative educational ecosystems. Special attention is paid to the analysis of the concept of innovation ecosystem (eco-innovation). This study analyses environmental innovations as a part of the innovation ecosystem. As information resources, we used periodical, analytical, and translated literature, which investigated the issues raised in this article.
bert2BERT: Towards Reusable Pretrained Language Models

In recent years, researchers tend to pre-train ever-larger language models to explore the upper limit of deep models.However, large language model pre-training costs intensive computational resources, and most of the models are trained from scratch without reusing the existing pre-trained models, which is wasteful.In this paper, we propose bert2BERT 1 , which can effectively transfer the knowledge of an existing smaller pre-trained model to a large model through parameter initialization and significantly improve the pre-training efficiency of the large model.Specifically, we extend the previous function-preserving (Chen et al., 2016) method proposed in computer vision on the Transformer-based language model, and further improve it by proposing a novel method, advanced knowledge for the large model's initialization.In addition, a two-stage learning method is proposed to further accelerate the pre-training.We conduct extensive experiments on representative PLMs (e.g., BERT and GPT) and demonstrate that (1) our method can save a significant amount of training cost compared with baselines including learning from scratch, StackBERT (Gong et al., 2019) and MSLT (Yang et al., 2020); (2) our method is generic and applicable to different types of pretrained models.In particular, bert2BERT saves about 45% and 47% computational cost of pretraining BERT BASE and GPT BASE by reusing the models of almost their half sizes.