Reviewerly/exHarmony huggingface dataset:
Too Much Information: Assessing Privacy Risks of Contact Trace Data Disclosure on People With COVID-19 in South Korea

Introduction: With the COVID-19 outbreak, South Korea has been making contact trace data public to help people self-check if they have been in contact with a person infected with the coronavirus. Despite its benefits in suppressing the spread of the virus, publicizing contact trace data raises concerns about individuals' privacy. In view of this tug-of-war between one's privacy and public safety, this work aims to deepen the understanding of privacy risks of contact trace data disclosure practices in South Korea. Method: In this study, publicly available contact trace data of 970 confirmed patients were collected from seven metropolitan cities in South Korea (20th Jan-20th Apr 2020). Then, an ordinal scale of relative privacy risk levels was introduced for evaluation, and the assessment was performed on the personal information included in the contact trace data such as demographics, significant places, sensitive information, social relationships, and routine behaviors. In addition, variance of privacy risk levels was examined across regions and over time to check for differences in policy implementation. Results: It was found that most of the contact trace data showed the gender and age of the patients. In addition, it disclosed significant places (home/work) ranging across different levels of privacy risks in over 70% of the cases. Inference on sensitive information (hobby, religion) was made possible, and 48.7% of the cases exposed the patient's social relationships. In terms of regional differences, a considerable discrepancy was found in the privacy risk for each category. Despite the recent release of government guidelines on data disclosure, its effects were still limited to a few factors (e.g., workplaces, routine behaviors). Discussion: Privacy risk assessment showed evidence of superfluous information disclosure in the current practice. This study discusses the role of "identifiability" in contact tracing to provide new directions for minimizing disclosure of privacy infringing information. Analysis of real-world data can offer potential stakeholders, such as researchers, service developers, and government officials with practical protocols/guidelines in publicizing information of patients and design implications for future systems (e.g., automatic privacy sensitivity checking) to strike a balance between one's privacy and the public benefits with data disclosure.
Named Entity Recognition without Labelled Data: A Weak Supervision Approach

Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training.When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain.But what should one do when there is no hand-labelled data for the target domain?This paper presents a simple but powerful approach to learn NER models in the absence of labelled data through weak supervision.The approach relies on a broad spectrum of labelling functions to automatically annotate texts from the target domain.These annotations are then merged together using a hidden Markov model which captures the varying accuracies and confusions of the labelling functions.A sequence labelling model can finally be trained on the basis of this unified annotation.We evaluate the approach on two English datasets (CoNLL 2003 and news articles from Reuters and Bloomberg) and demonstrate an improvement of about 7 percentage points in entity-level F 1 scores compared to an out-of-domain neural NER model.
Impact of employees engagement and knowledge sharing on organizational performance: Study of HR challenges in COVID-19 pandemic

PURPOSE: When the economy’s in terrible shape, any of us is lucky to have a job, human resources managers worrying about whether or not employees are contributing to the organizational goals and objectives, and how to pursue the recruitment and selection process, employee’s engagement, and, training and development activities, these are the current human resource management challenges which are created due to COVID-19 pandemic. Organizations around the world are facing a tough situation, first time in history, the worth of one-barrel oil fell to less than nothing on 20th April 2020, the only reason is behind this is the current lockdown around the world. The physical workplace is converted into a virtual workplace, now the HR managers are planning how to engage the employees efficiently. This study pursued to examine the effect of employee’s engagement on organizational performance through the mediating effect of knowledge sharing for employees of higher educational institutions. METHOD: A quantitative research method is used in this study, non-probability sampling design with a focus on convenient sampling frame is deployed in this study. The questionnaire is adapted from the past studies, Initially Google forms were used to collect the data, due to lower response rate questionnaire were distributed in hard copies and sent to the targeted respondents. Structured Equation Modeling (SEM) is used to test the conceptual framework. RESULTS: The results showed that employee engagement has a significant and positive impact on organizational performance. Further, it was found that knowledge sharing has a significant and positive impact on organizational performance. Moreover, knowledge sharing only partially mediated the association between employee engagement and organizational performance. Covid-19 pandemic is destroying global economies, but at the same time its bringing different opportunities for organizations also. Now organizations have to think about how to avail this opportunity, organizations are going virtually, and in this situation, it is the biggest challenge for the Human Resource (HR) managers to manage the employee effectively.
Learning Graph Embeddings for Compositional Zero-shot Learning

In compositional zero-shot learning, the goal is to recognize unseen compositions (e.g. old dog) of observed visual primitives states (e.g. old, cute) and objects (e.g. car, dog) in the training set. This is challenging because the same state can for example alter the visual appearance of a dog drastically differently from a car. As a solution, we propose a novel graph formulation called Compositional Graph Embedding (CGE) that learns image features, compositional classifiers and latent representations of visual primitives in an end-to-end manner. The key to our approach is exploiting the dependency between states, objects and their compositions within a graph structure to enforce the relevant knowledge transfer from seen to unseen compositions. By learning a joint compatibility that encodes semantics between concepts, our model allows for generalization to unseen compositions without relying on an external knowledge base like WordNet. We show that in the challenging generalized compositional zero-shot setting our CGE significantly outperforms the state of the art on MIT-States and UT-Zappos. We also propose a new benchmark for this task based on the recent GQA dataset. Code is available at: https://github.com/ExplainableML/czsl
Causal Attention for Vision-Language Tasks

We present a novel attention mechanism: Causal Attention (CATT), to remove the ever-elusive confounding effect in existing attention-based vision-language models. This effect causes harmful bias that misleads the attention module to focus on the spurious correlations in training data, damaging the model generalization. As the confounder is unobserved in general, we use the front-door adjustment to realize the causal intervention, which does not require any knowledge on the confounder. Specifically, CATT is implemented as a combination of 1) In-Sample Attention (IS-ATT) and 2) Cross-Sample Attention (CS-ATT), where the latter forcibly brings other samples into every IS-ATT, mimicking the causal intervention. CATT abides by the QK-V convention and hence can replace any attention module such as top-down attention and self-attention in Transformers. CATT improves various popular attention-based vision-language models by considerable margins. In particular, we show that CATT has great potential in large-scale pre-training, e.g., it can promote the lighter LXMERT [57], which uses fewer data and less computational power, comparable to the heavier UNITER [14]. Code is published in https://github.com/yangxuntu/lxmertcatt.
Deep learning based human activity recognition (HAR) using wearable sensor data

Motion or inertial sensors such as gyroscope and accelerometer commonly found in smartwatches and smartphones can measure characteristics such as acceleration and angular velocity of movements in the human body and use them to learn models capable of identifying human activities, that has applicability in various fields such as biometrics, remote patient health monitoring, etc. Recently deep learning-based methods have become popular for human activity recognition because they use representation learning techniques that can automatically generate optimal features from raw input data generated from sensors without any human intervention and can identify hidden patterns in data. This work proposes a novel hybrid deep neural network model, CNN-GRU that combines convolutional and gated recurrent units for human activity recognition. This model was successfully validated on WISDM dataset and produced accuracy that is suggestively better than other state-of-the-art deep neural network models such as Inception Time and DeepConvLSTM created using AutoML.
IA-Net$:$ An Inception–Attention-Module-Based Network for Classifying Underwater Images From Others

To distinguish underwater images from natural images is one of the challenge of collecting and generation of underwater image data. Common image classification and recognition models classify the objects in an image depending on the saliency while suppressing the background. In this article, an inception–attention network (IA-Net), a convolutional neural network (CNN)-based model to classify the underwater images from natural images is reported, in which an inception–attention (I-A) module is constructed to simulate the visual correlation mechanism of classifying images taken from special environments such as fog, nighttime and under water. It is illustrated that the context background is as important as the salient object when understanding the underwater images. We executed experiments on a data set, which consists of 4000 underwater images and 5000 nonunderwater images, and demonstrate that the proposed IA-Net achieves an accuracy of 99.3 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> on underwater image classification, which is significantly better than classical image classification networks, such as AlexNet, InceptionV3, and ResNet. In addition, the comparative experiments prove that the IA-Net is superior to other networks when distinguishing underwater images from foggy, nighttime images and fish images taken in nonunderwater environments, although these images have indistinguishable characteristics with underwater images. Moreover, we demonstrate the I-A structure we proposed can be used to boost the performance of the existing object recognition networks. By substituting the inception module with the I-A module, the Inception-ResNetV2 network achieves a 10.7 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> top-1 error rate on the subset of ILSVRC-2012, which further illustrates the effectiveness of the correlation between the image background and subjective perception in improving the performance of the visual analysis tasks.
BING: Binarized normed gradients for objectness estimation at 300fps

Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected by looking at the norm of gradients, with a suitable resizing of their corresponding image windows to a small fixed size. Based on this observation and computational reasons, we propose to resize the window to 8 × 8 and use the norm of the gradients as a simple 64D feature to describe it, for explicitly training a generic objectness measure. We further show how the binarized version of this feature, namely binarized normed gradients (BING), can be used for efficient objectness estimation, which requires only a few atomic operations (e.g., add, bitwise shift, etc.). To improve localization quality of the proposals while maintaining efficiency, we propose a novel fast segmentation method and demonstrate its effectiveness for improving BING's localization performance, when used in multi-thresholding straddling expansion (MTSE) post-processing. On the challenging PASCAL VOC2007 dataset, using 1000 proposals per image and intersection-over-union threshold of 0.5, our proposal method achieves a 95.6% object detection rate and 78.6% mean average best overlap in less than 0.005 second per image.
Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

Abstract We propose a novel geometric approach for learning bilingual mappings given monolingual embeddings and a bilingual dictionary. Our approach decouples the source-to-target language transformation into (a) language-specific rotations on the original embeddings to align them in a common, latent space, and (b) a language-independent similarity metric in this common space to better model the similarity between the embeddings. Overall, we pose the bilingual mapping problem as a classification problem on smooth Riemannian manifolds. Empirically, our approach outperforms previous approaches on the bilingual lexicon induction and cross-lingual word similarity tasks. We next generalize our framework to represent multiple languages in a common latent space. Language-specific rotations for all the languages and a common similarity metric in the latent space are learned jointly from bilingual dictionaries for multiple language pairs. We illustrate the effectiveness of joint learning for multiple languages in an indirect word translation setting.
Hierarchical Attention Networks for Cyberbullying Detection on the Instagram Social Network

Cyberbullying has become one of the most pressing online risks for young people and has raised serious concerns in society.The emerging literature identifies cyberbullying as repetitive acts that occur over time rather than one-off incidents.Yet, there has been relatively little work to model the hierarchical structure of social media sessions and the temporal dynamics of cyberbullying in online social network sessions.We propose a hierarchical attention network for cyberbullying detection that takes these aspects of cyberbullying into account.The primary distinctive characteristics of our approach include: (i) a hierarchical structure that mirrors the structure of a social media session; (ii) levels of attention mechanisms applied at the word and comment level, thereby enabling the model to pay different amounts of attention to words and comments, depending on the context; and (iii) a cyberbullying detection task that also predicts the interval of time between two adjacent comments.These characteristics allow the model to exploit the commonalities and differences across these two tasks to improve the performance of cyberbullying detection.Experiments on a real-world dataset from Instagram, the social media platform on which the highest percentage of users have reported experiencing cyberbullying, reveal that the proposed architecture outperforms the state-of-the-art method.
Better Modeling of Incomplete Annotations for Named Entity Recognition

Zhanming Jie, Pengjun Xie, Wei Lu, Ruixue Ding, Linlin Li. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Latent Variable Model for Multi-modal Translation

In this work, we propose to model the interaction between visual and textual features for multi-modal neural machine translation (MMT) through a latent variable model.This latent variable can be seen as a multi-modal stochastic embedding of an image and its description in a foreign language.It is used in a target-language decoder and also to predict image features.Importantly, our model formulation utilises visual and textual inputs during training but does not require that images be available at test time.We show that our latent variable MMT formulation improves considerably over strong baselines, including a multi-task learning approach (Elliott and Kádár, 2017) and a conditional variational auto-encoder approach (Toyama et al., 2016).Finally, we show improvements due to (i) predicting image features in addition to only conditioning on them, (ii) imposing a constraint on the KL term to promote models with nonnegligible mutual information between inputs and latent variable, and (iii) by training on additional target-language image descriptions (i.e.synthetic data).
A Niching Memetic Algorithm for Multi-Solution Traveling Salesman Problem

Multi-solution problems extensively exist in practice. Particularly, the traveling salesman problem (TSP) may possess multiple shortest tours, from which travelers can choose one according to their specific requirements. However, very few efforts have been devoted to the multi-solution problems in the discrete domain. In order to fill this research gap and to effectively tackle the multi-solution TSP, we propose a niching memetic algorithm in this article. The proposed algorithm is characterized by a niche preservation technique to enable the parallel search of multiple optimal solutions; an adaptive neighborhood strategy to balance the exploration and exploitation; a critical edge-aware method to provide effective guidance to the reproduction; and a selective local search strategy to improve the search efficiency. To evaluate the performance of the proposed algorithm, we conduct comprehensive experiments on a recently published multi-solution optimization test suite. The experimental results show that our algorithm outperforms other compared algorithms. Furthermore, the proposed algorithm is adopted to tackle problems from the well-known TSPLIB library to obtain a set of distinct but good solutions.
Do We Really Need Fully Unsupervised Cross-Lingual Embeddings?

Recent efforts in cross-lingual word embedding (CLWE) learning have predominantly focused on fully unsupervised approaches that project monolingual embeddings into a shared cross-lingual space without any cross-lingual signal.The lack of any supervision makes such approaches conceptually attractive.Yet, their only core difference from (weakly) supervised projection-based CLWE methods is in the way they obtain a seed dictionary used to initialize an iterative self-learning procedure.The fully unsupervised methods have arguably become more robust, and their primary use case is CLWE induction for pairs of resourcepoor and distant languages.In this paper, we question the ability of even the most robust unsupervised CLWE approaches to induce meaningful CLWEs in these more challenging settings.A series of bilingual lexicon induction (BLI) experiments with 15 diverse languages (210 language pairs) show that fully unsupervised CLWE methods still fail for a large number of language pairs (e.g., they yield zero BLI performance for 87/210 pairs).Even when they succeed, they never surpass the performance of weakly supervised methods (seeded with 500-1,000 translation pairs) using the same self-learning procedure in any BLI setup, and the gaps are often substantial.These findings call for revisiting the main motivations behind fully unsupervised CLWE methods.
Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension

Reading comprehension models have been successfully applied to extractive text answers, but it is unclear how best to generalize these models to abstractive numerical answers.We enable a BERT-based reading comprehension model to perform lightweight numerical reasoning.We augment the model with a predefined set of executable 'programs' which encompass simple arithmetic as well as extraction.Rather than having to learn to manipulate numbers directly, the model can pick a program and execute it.On the recent Discrete Reasoning Over Passages (DROP) dataset, designed to challenge reading comprehension models, we show a 33% absolute improvement by adding shallow programs.The model can learn to predict new operations when appropriate in a math word problem setting (Roy and Roth, 2015) with very few training examples.1 https://rajpurkar.github.io/SQuAD-explorer/How many more Chinese nationals are there than European nationals?The city of Bangkok has a population of 8,280,925 ...the census showed that it is home to 81,570 Japanese and 55,893 Chinese nationals, as well as 117,071 expatriates from other Asian countries, 48,341 from Europe, 23,418 from the Americas,...
Towards a Stakeholder-Oriented Blockchain-Based Architecture for Electronic Health Records: Design Science Research Study

Background Data security issues still constitute the main reason for the sluggish dissemination of electronic health records (EHRs). Given that blockchain technology offers the possibility to verify transactions through a decentralized network, it may serve as a solution to secure health-related data. Therefore, we have identified stakeholder-specific requirements and propose a blockchain-based architecture for EHRs, while referring to the already existing scientific discussions on the potential of blockchain for use in EHRs. Objective This study aimed to introduce blockchain technology for EHRs, based on identifying stakeholders and systematically eliciting their requirements, and to discuss the key benefits (KBs) and key challenges (KCs) of blockchain technology in the context of EHRs. Methods The blockchain-based architecture was developed in the framework of the design science research paradigm. The requirements were identified using a structured literature review and interviews with nine health care experts. Subsequently, the proposed architecture was evaluated using 4 workshops with 15 participants. Results We identified three major EHR stakeholder groups and 34 respective requirements. On this basis, we developed a five-layer architecture. The subsequent evaluation of the artifact was followed by the discussion of 12 KBs and 12 KCs of a blockchain-based architecture for EHRs. To address the KCs, we derived five recommendations for action for science and practice. Conclusions Our findings indicate that blockchain technology offers considerable potential to advance EHRs. Improvements to currently available EHR solutions are expected, for instance, in the areas of data security, traceability, and automation by smart contracts. Future research could examine the patient’s acceptance of blockchain-based EHRs and cost-benefit analyses.