Reviewerly/exHarmony huggingface dataset:
Use of blockchain in designing smart city

Purpose A blockchain is a shared distributed ledger technology that stores the information of every transaction in the network. The blockchain has emerged with a huge diversity of applications not only in the economic but in the non-economical domain as well. Blockchain technology promises to provide a wide range of solutions to the problems faced during implementation of smart cities. It has the potential to build smart contracts more secure, thus eliminating the need for centralized authority. Design/methodology/approach This paper presents a proof-of-concept for a use case that uses an Ethereum platform to build a blockchain network to buy, sell or rent a property. Findings The findings of this study provide an opportunity to create novel decentralized scalable solutions to develop smart cities by enabling paperless transactions. There are enormous opportunities in this distributed ledger technology which will bring a revolutionary change in upcoming years. Originality/value The concept of blockchain along with smart contracts can be used as a promising technology for sharing services which is a common requirement in smart cities. All the blockchain transactions are stored in decentralized shared database. The transaction recorded in decentralized system is immutable, it cannot be altered and hence chance of forgery is negligible.
Learning with Fenchel-Young losses

Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally structured prediction. Understanding the core principles and theoretical properties underpinning these losses is key to choose the right loss for the right problem, as well as to create new losses which combine their strengths. In this paper, we introduce Fenchel-Young losses, a generic way to construct a convex loss function for a regularized prediction function. We provide an in-depth study of their properties in a very broad setting, covering all the aforementioned supervised learning tasks, and revealing new connections between sparsity, generalized entropies, and separation margins. We show that Fenchel-Young losses unify many well-known loss functions and allow to create useful new ones easily. Finally, we derive efficient predictive and training algorithms, making Fenchel-Young losses appealing both in theory and practice.
An Adversarial Discriminative Convolutional Neural Network for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a promising approach to help to allocate testing efforts efficiently and guarantee software reliability in the early software lifecycle. A CPDP method usually trains a software defect classifier based on labeled data sets. Then the trained classifier can predict new projects without labeled data. Most previous CPDP techniques focused on manually designing handcrafted features. However, these handcrafted features ignore the programs' semantic information. Moreover, some other existing defect prediction approaches learned semantic features from source code to build classifiers directly. However, they did not consider the distribution divergence between source and target projects. To address these limitations, we put forward a new method called Adversarial Discriminative Convolutional Neural Network (ADCNN). It can extract the transferable semantic features from source code for CPDP tasks. Specifically, we first parse source files into token vectors and then map them to integer vectors via word embedding. Second, we combine adversarial learning with discriminative feature learning to train the ADCNN model. The key of the ADCNN model is to learn the discriminative mapping of the target project to the source feature space by deceiving a domain discriminator. A domain discriminator tries to distinguish the target project files from the source project files. Finally, we use the extracted transferable semantic features to build a classifier for CPDP tasks. We evaluate our method on ten benchmark projects in terms of F-measure, AUC, and PofB20 (an effort-aware evaluation metric). The experimental results demonstrate that our ADCNN method performs better compared with other related CPDP methods.
Ultracompact optical switch using a single semisymmetric Fano nanobeam cavity

We propose and theoretically analyze a generalized four-port waveguide-cavity system with a novel, to the best of our knowledge, semisymmetric Fano structure. By applying the system to a silicon-based photonic crystal nanobeam cavity (PCNC), we experimentally demonstrate an ultracompact crossbar optical switch with high drop port transmission. A low insertion loss of 1.5 dB in the drop port at the cross state is achieved comparing to the 8.5 dB insertion loss in a traditional non-Fano structure. The device is also benefited by the high quality factor and small mode volume for efficient switching and the ultracompact footprint of $ 14\;\unicode{x00B5} {{\rm m}^2} $14µm2 in the core structure. The proposed PCNC switch shows great potential in various optical systems on chip to increase the integration and reduce energy consumption.
Did Bitcoin Act as An Antidote to the Chinese Equity Market and Booster to Altcoins during the Novel Coronavirus Outbreak?

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Camera calibration method with focus-related intrinsic parameters based on the thin-lens model

In this paper, a calibration method for a camera with focus-related intrinsic parameters based on the thin-lens model is proposed to realize highly accurate measurement for small objects with extended depth of field (DOF). It mainly solves inaccurate calibration and small DOF problems at high magnification of vision systems. The mathematical camera model, initial camera calibration based on the iterative radial alignment constraint (IRAC) and optimization strategy are presented. The effectiveness, accuracy and practicality of the proposed calibration method are verified by both simulations and experiments. The root mean square errors of measured points in the 3D world coordinates with the proposed calibration method decrease from 22.02 μm to 1.66 μm when the magnification of the vision system increases from 0.12× to 0.66×. With the proposed calibration method based on the thin-lens model, accurate measurement, extended DOF and low calibration workload can be achieved.
Lightweight Authentication for Quantum Key Distribution

Quantum key distribution (QKD) enables unconditionally secure communication between distinct parties using a quantum channel and an authentic public channel. Reducing the portion of quantum-generated secret keys, that is consumed during the authentication procedure, is of significant importance for improving the performance of QKD systems. In the present work, we develop a lightweight authentication protocol for QKD based on a `ping-pong' scheme of authenticity check for QKD. An important feature of this scheme is that the only one authentication tag is generated and transmitted during each of the QKD post-processing rounds. For the tag generation purpose, we design an unconditionally secure procedure based on the concept of key recycling. The procedure is based on the combination of almost universal$_2$ polynomial hashing, XOR universal$_2$ Toeplitz hashing, and one-time pad (OTP) encryption. We demonstrate how to minimize both the length of the recycled key and the size of the authentication key, that is required for OTP encryption. As a result, in real case scenarios, the portion of quantum-generated secret keys that is consumed for the authentication purposes is below 1\%. Finally, we provide a security analysis of the full quantum key growing process in the framework of universally composable security.
Exploring and Understanding Participants’ Perceptions of Facial Emoji Likert Scales in Online Surveys

The present study aims to explore participants’ experiences in interacting with a facial emoji Likert scale in online surveys to understand their perceptions, interpretations, and opinions of emojis in online surveys. A qualitative research approach has been adopted. This article contributes to the existing body of knowledge on emojis and online survey questionnaires by presenting a detailed exploratory qualitative study of 42 participants’ insights, perceptions, and interpretations of the facial emoji Likert scale in a specific context (i.e., a course evaluation survey). The data analysis reveals seven categories that emerged from the transcribed interviews and the interviewers’ written notes from the cognitive interviews regarding participants’ perceptions of interacting with the facial emoji–based Likert scale.
Field demonstration of time-bin reference-frame-independent quantum key distribution via an intracity free-space link

Free-space quantum key distribution (QKD) based on mobile platforms, such as satellites, drones, and vehicles, is considered a promising way to overcome the rate-distance limit without a quantum repeater. Real-time reference frame calibration is required in most recent implemented polarization encoded QKD systems due to the relative motion between sender and receiver. Although active compensations can be used to calibrate the reference frame, doing so increases the complexity of the system and reduces the key rate. To overcome this problem, the reference-frame-independent (RFI) QKD was proposed in which fixed deviations of the reference frame between the two parties are tolerated automatically. In this Letter, we report the experimental implementation of a time-bin encoded RFI QKD in an urban environment through free space. The quantum bit error rate for key-distill is as low as 1% over a 2 km free-space link with a total equivalent loss of 31.5 dB. Our demonstration shows that a stable RFI QKD can be implemented in the free-space channel.
Parameter-free fuzzy histogram equalisation with illumination preserving characteristics dedicated for contrast enhancement of magnetic resonance images

Low-field MRI scanners do not offer sufficient image contrast. Hence, offline algorithms for improving image contrast are often needed. Even though modified versions of Histogram Equalisation (HE) are extensively used on panoramic images, they have serious limitations. Most of such modified algorithms have multiple operational parameters which need to be tuned manually. Parameter-free modifications lag in terms of illumination-preserving features. To address these issues, a novel formulation of Parameter-free Fuzzy Histogram Equalisation (PFHE) algorithm with good illumination-preserving characteristics, dedicated for contrast enhancement of MRI is introduced in this paper. In PFHE, a Homogeneity Fuzzy Sub-set (HFS) and its fuzzy complement, termed as Texture Fuzzy Sub-set (TFS) are computed based on the fuzzy similarity of the pixels in the input image with their eight-connected neighbours. Following this, an approximate output is estimated by applying a transformation similar to the histogram equalisation on the Fuzzy Textural Histogram (FTH) derived from TFS. The final output is computed as a nonlinear combination of the approximate output and the input image. The fuzzy weighting vectors used in the nonlinear combination are derived from the HFS. Both Qualitative and quantitative evaluations reveal that the PFHE is superior to Bi-Histogram Equalisation (BHE), Weighted Threshold Histogram Equalisation (WTHE), Contrast Limited Adaptive Histogram Equalisation (CLAHE), Non-parametric Modified Histogram Equalisation (NMHE), Exposure-based Sub-Image Histogram Equalisation (ESIHE), Median–Mean Based Sub-Image-Clipped Histogram Equalisation (MMSICHE) and Dominant Orientation-based Texture Histogram Equalisation (DOTHE), in terms of ability to preserve diagnostically significant features in the MR image.
Schooling Beyond COVID-19: An Unevenly Distributed Future

The COVID-19 pandemic crisis has had a major impact on how schooling is done. With schools closed, teaching and learning continue dependent on information and communication technologies (ICT). To the degree that this has been a success, there is the possibility that post-pandemic societies might choose to de-school, switching to online teaching and learning only. In this perspective piece, I describe two major risks if that future were to be embraced; that is, lack of equitable access and dehumanization. My argument is that these futures already exist in pockets around the globe and we can use those experiences to evaluate those options. I suggest instead that the post-pandemic period gives us an opportunity to re-imagine what schools and schooling are for and advocate for a re-schooled society in which our investment in schools builds and develops society.
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport

Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text matching, where the goal is to jointly select and align text pieces, such as tokens or sentences, as a justification for the downstream prediction. Our approach employs optimal transport (OT) to find a minimal cost alignment between the inputs. However, directly applying OT often produces dense and therefore uninterpretable alignments. To overcome this limitation, we introduce novel constrained variants of the OT problem that result in highly sparse alignments with controllable sparsity. Our model is end-to-end differentiable using the Sinkhorn algorithm for OT and can be trained without any alignment annotations. We evaluate our model on the StackExchange, MultiNews, e-SNLI, and MultiRC datasets. Our model achieves very sparse rationale selections with high fidelity while preserving prediction accuracy compared to strong attention baseline models.
Comparison of rule-based and neural network models for negation detection in radiology reports

Abstract Using natural language processing, it is possible to extract structured information from raw text in the electronic health record (EHR) at reasonably high accuracy. However, the accurate distinction between negated and non-negated mentions of clinical terms remains a challenge. EHR text includes cases where diseases are stated not to be present or only hypothesised, meaning a disease can be mentioned in a report when it is not being reported as present. This makes tasks such as document classification and summarisation more difficult. We have developed the rule-based EdIE-R-Neg, part of an existing text mining pipeline called EdIE-R (Edinburgh Information Extraction for Radiology reports), developed to process brain imaging reports, ( https://www.ltg.ed.ac.uk/software/edie-r/ ) and two machine learning approaches; one using a bidirectional long short-term memory network and another using a feedforward neural network. These were developed on data from the Edinburgh Stroke Study (ESS) and tested on data from routine reports from NHS Tayside (Tayside). Both datasets consist of written reports from medical scans. These models are compared with two existing rule-based models: pyConText (Harkema et al . 2009. Journal of Biomedical Informatics 42 (5), 839–851), a python implementation of a generalisation of NegEx, and NegBio (Peng et al . 2017. NegBio: A high-performance tool for negation and uncertainty detection in radiology reports. arXiv e-prints, p. arXiv:1712.05898 ), which identifies negation scopes through patterns applied to a syntactic representation of the sentence. On both the test set of the dataset from which our models were developed, as well as the largely similar Tayside test set, the neural network models and our custom-built rule-based system outperformed the existing methods. EdIE-R-Neg scored highest on F1 score, particularly on the test set of the Tayside dataset, from which no development data were used in these experiments, showing the power of custom-built rule-based systems for negation detection on datasets of this size. The performance gap of the machine learning models to EdIE-R-Neg on the Tayside test set was reduced through adding development Tayside data into the ESS training set, demonstrating the adaptability of the neural network models.
C-Net: Contextual Network for Sarcasm Detection

Automatic Sarcasm Detection in conversations is a difficult and tricky task. Classifying an utterance as sarcastic or not in isolation can be futile since most of the time the sarcastic nature of a sentence heavily relies on its context. This paper presents our proposed model, C-Net, which takes contextual information of a sentence in a sequential manner to classify it as sarcastic or non-sarcastic. Our model showcases competitive performance in the Sarcasm Detection shared task organised on CodaLab and achieved 75.0% F1-score on the Twitter dataset and 66.3% F1-score on Reddit dataset.
A multi-feature fusion model for Chinese relation extraction with entity sense

Relation extraction is an important task of information extraction. Most existing methods of Chinese language relation extraction are based on word input. They are highly dependent on the quality of word segmentation and suffer from the ambiguity of polysemic words. Therefore, a multi-feature fusion model is presented on the basis of character input, which integrates character-level features, word-level features and entity sense features into deep neural network models. Specifically, to alleviate the ambiguity of polysemy, the entity sense is introduced as external language knowledge to provide supplementary information for understanding the semantics of an entity in a given sentence. The Attention-Based Bidirectional Long Short-Term Memory Networks (Att-BLSTM) are proposed to capture features at the character level. To obtain more structural information, the convolutional layer (C-Att-BLSTM) is built upon the Att-BLSTM to capture features at the word level. Experiments are conducted on a public dataset of SanWen, and show that the proposed model achieves state-of-the-art results.
AdHealth: a feasibility study to measure digital food marketing to adolescents through Facebook

Abstract Objective: To test the feasibility of a browser extension to estimate the exposure of adolescents to (un)healthy food and beverage advertisements on Facebook and the persuasive techniques used to market these foods and beverages. Design: A Chrome browser extension (AdHealth) was developed to automatically collect advertisements seen by participants on their personal Facebook accounts. Information was extracted and sent to a web server by parsing the Document Object Model tree representation of Facebook web pages. Key information retrieved included the advertisement type seen and duration of each ad sighting. The WHO-Europe Nutrient Profile Model was used to classify the healthiness of products advertised as permitted (healthy) or not permitted (unhealthy) to be advertised to children. Setting: Auckland, New Zealand. Participants: Thirty-four Facebook users aged 16–18 years. Results: The browser extension retrieved 4973 advertisements from thirty-four participants, of which 204 (4 %) were food-related, accounting for 1·1 % of the exposure duration. Of those food advertisements, 98 % were classified as not permitted, and 33·7 and 31·9 %, respectively, of those featured promotional characters or premium offers. The mean rate of exposure to not permitted food was 4·8 ( sd = 2·5) advertisements per hour spent on Facebook. Conclusions: Using a Chrome extension to monitor exposure to unhealthy food and beverage advertisements showed that the vast majority of advertisements were for unhealthy products, despite numerous challenges to implementation. Further efforts are needed to develop tools for use across other social media platforms and mobile devices, and policies to protect young people from digital food advertising.
Can You Trust Predictive Uncertainty Under Real Dataset Shifts in Digital Pathology?

Deep learning-based algorithms have shown great promise for assisting pathologists in detecting lymph node metastases when evaluated based on their predictive accuracy. However, for clinical adoption, we need to know what happens when the test set dramatically changes from the training distribution. In such settings, we should estimate the uncertainty of the predictions, so we know when to trust the model (and when not to). Here, we i) investigate current popular methods for improving the calibration of predictive uncertainty, and ii) compare the performance and calibration of the methods under clinically relevant in-distribution dataset shifts. Furthermore, we iii) evaluate their performance on the task of out-of-distribution detection of a different histological cancer type not seen during training. Of the investigated methods, we show that deep ensembles are more robust in respect of both performance and calibration for in-distribution dataset shifts and allows us to better detect incorrect predictions. Our results also demonstrate that current methods for uncertainty quantification are not necessarily able to detect all dataset shifts, and we emphasize the importance of monitoring and controlling the input distribution when deploying deep learning for digital pathology.
A Dataset for Tracking Entities in Open Domain Procedural Text

Niket Tandon, Keisuke Sakaguchi, Bhavana Dalvi, Dheeraj Rajagopal, Peter Clark, Michal Guerquin, Kyle Richardson, Eduard Hovy. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.
AutoQA: From Databases To QA Semantic Parsers With Only Synthetic Training Data

We propose AutoQA, a methodology and toolkit to generate semantic parsers that answer questions on databases, with no manual effort.Given a database schema and its data, AutoQA automatically generates a large set of high-quality questions for training that covers different database operations.It uses automatic paraphrasing combined with templatebased parsing to find alternative expressions of an attribute in different parts of speech.It also uses a novel filtered auto-paraphraser to generate correct paraphrases of entire sentences.We apply AutoQA to the Schema2QA dataset and obtain an average logical form accuracy of 62.9% when tested on natural questions, which is only 6.4% lower than a model trained with expert natural language annotations and paraphrase data collected from crowdworkers.To demonstrate the generality of AutoQA, we also apply it to the Overnight dataset.AutoQA achieves 69.8% answer accuracy, 16.4% higher than the state-of-the-art zero-shot models and only 5.2% lower than the same model trained with human data.