Reviewerly/exHarmony huggingface dataset:
Transformation of the Transaction Cost and the Agency Cost in an Organization and the Applicability of Blockchain—A Case Study of Peer-to-Peer Insurance

Blockchain supports a variety of decentralized applications enabled by its immutable, decentralized, and trustless properties. However, there are no unifying criteria for blockchain architecture across the organizations and business models. This variance has created complex and diverse blockchain products. Costs in every economic exchange with partners are associated with two metrics: transaction costs due to market imperfections and agency costs due to conflict of interest and information asymmetry in an organization. To understand the effectiveness of economic activities by blockchain intervention and facilitate strategic alignment, we use transaction cost and agency cost as theoretical lenses to explore the impacts of blockchain, discuss the transformation of those costs, and support our arguments using a case study. Our study proposes that blockchain technology brings two more benefits, trust and transparency, to the existing Internet-based business services, and helps improve corporate governance. Smart contracts improve the execution time of transactions significantly and increase transaction volume rapidly. As the internet shifts hierarchies towards electronic markets, lack of trust between peers inhibits exchanges. Blockchain applications provide a framework for building trust between peers through its consent mechanism, which allows organizations to construct trust and operate in a more decentralized manner. Thus, by including blockchain in the current Internet infrastructure, the decision boundary of organization forms would extend outward. Finally, the transformation of costs in different stages of the blockchain transition, as described in our study, has important managerial implications for the organization structure and the role of third parties. Blockchain does not assume away transaction and agency costs but pushes the transformation of the two, forming a more efficient economic entity. This study contributes to the academia and the industry. We first add to the understanding of blockchain from the perspective of exchange technology. Second, we contribute to the prediction of organization boundaries. Third, the shift in the role of third parties supports the transaction cost theory in terms of controlling opportunism. Lastly, this study facilitates the development of blockchain business models and contributes to the practice.
Improving Disfluency Detection by Self-Training a Self-Attentive Model

Self-attentive neural syntactic parsers using contextualized word embeddings (e.g.ELMo or BERT) currently produce state-of-the-art results in joint parsing and disfluency detection in speech transcripts.Since the contextualized word embeddings are pre-trained on a large amount of unlabeled data, using additional unlabeled data to train a neural model might seem redundant.However, we show that self-training -a semi-supervised technique for incorporating unlabeled data -sets a new state-of-the-art for the self-attentive parser on disfluency detection, demonstrating that self-training provides benefits orthogonal to the pre-trained contextualized word representations.We also show that ensembling selftrained parsers provides further gains for disfluency detection.
Fact-based Content Weighting for Evaluating Abstractive Summarisation

ive summarisation is notoriously hard to evaluate since standard word-overlap-based metrics are insufficient. We introduce a new evaluation metric which is based on fact-level content weighting, i.e. relating the facts of the document to the facts of the summary. We fol- low the assumption that a good summary will reflect all relevant facts, i.e. the ones present in the ground truth (human-generated refer- ence summary). We confirm this hypothe- sis by showing that our weightings are highly correlated to human perception and compare favourably to the recent manual highlight- based metric of Hardy et al. (2019).
Scheduling Periodical Multi-Stage Jobs With Fuzziness to Elastic Cloud Resources

We investigate a workflow scheduling problem with stochastic task arrival times and fuzzy task processing times and due dates.The problem is common in many real-time and workflow-based applications, where tasks with fixed stage number and linearly dependency are executed on scalable cloud resources with multiple price options.The challenges lie in proposing effective, stable and robust algorithms under stochastic and fuzzy tasks.A triangle fuzzy number based model is formulated.Two metrics are explored: the cost and the degree of satisfaction.An iterated heuristic framework is proposed to periodically schedule tasks, which consists of a task collection and a fuzzy task scheduling phases.Two task collection strategies are presented and two task prioritization strategies are employed.In order to achieve a high satisfaction degree, deadline constraints are defined at both job and task levels.By designing delicate experiments and applying sophisticated statistical techniques, experimental results show that the proposed algorithm is more effective and robust than the two existing methods.
Distance Encoding -- Design Provably More Powerful GNNs for Structural Representation Learning

Learning structural representations of node sets from graph-structured datais crucial for applications ranging from node-role discovery to link predictionand molecule classification. Graph Neural Networks (GNNs) have achieved greatsuccess in structural representation learning. However, most GNNs are limitedby the 1-Weisfeiler-Lehman (WL) test and thus possible to generate identicalrepresentation for structures and graphs that are actually different. Morepowerful GNNs, proposed recently by mimicking higher-order-WL tests, only focuson entire-graph representations and cannot utilize sparsity of the graphstructure to be computationally efficient. Here we propose a general class ofstructure-related features, termed Distance Encoding (DE), to assist GNNs inrepresenting node sets with arbitrary sizes with strictly more expressive powerthan the 1-WL test. DE essentially captures the distance between the node setwhose representation is to be learnt and each node in the graph, which includesimportant graph-related measures such as shortest-path-distance and generalizedPageRank scores. We propose two general frameworks for GNNs to use DEs (1) asextra node attributes and (2) further as controllers of message aggregation inGNNs. Both frameworks may still utilize the sparse structure to keepscalability to process large graphs. In theory, we prove that these twoframeworks can distinguish node sets embedded in almost all regular graphswhere traditional GNNs always fail. We also rigorously analyze theirlimitations. Empirically, we evaluate these two frameworks on node structuralroles prediction, link prediction and triangle prediction over six realnetworks. The results show that our models outperform GNNs without DEs by up-to15% improvement in average accuracy and AUC. Our models also significantlyoutperform other SOTA baselines particularly designed for those tasks.
The INESC-ID Multi-Modal System for the ADReSS 2020 Challenge

This paper describes a multi-modal approach for the automatic detection of Alzheimer's disease proposed in the context of the INESC-ID Human Language Technology Laboratory participation in the ADReSS 2020 challenge. Our classification framework takes advantage of both acoustic and textual feature embeddings, which are extracted independently and later combined. Speech signals are encoded into acoustic features using DNN speaker embeddings extracted from pre-trained models. For textual input, contextual embedding vectors are first extracted using an English Bert model and then used either to directly compute sentence embeddings or to feed a bidirectional LSTM-RNNs with attention. Finally, an SVM classifier with linear kernel is used for the individual evaluation of the three systems. Our best system, based on the combination of linguistic and acoustic information, attained a classification accuracy of 81.25%. Results have shown the importance of linguistic features in the classification of Alzheimer's Disease, which outperforms the acoustic ones in terms of accuracy. Early stage features fusion did not provide additional improvements, confirming that the discriminant ability conveyed by speech in this case is smooth out by linguistic data.
Contextual Text Style Transfer

We introduce a new task, Contextual Text Style Transfer -translating a sentence into a desired style with its surrounding context taken into account.This brings two key challenges to existing style transfer approaches: (i) how to preserve the semantic meaning of target sentence and its consistency with surrounding context during transfer; (ii) how to train a robust model with limited labeled data accompanied by context.To realize high-quality style transfer with natural context preservation, we propose a Context-Aware Style Transfer (CAST) model, which uses two separate encoders for each input sentence and its surrounding context.A classifier is further trained to ensure contextual consistency of the generated sentence.To compensate for the lack of parallel data, additional selfreconstruction and back-translation losses are introduced to leverage non-parallel data in a semi-supervised fashion.Two new benchmarks, Enron-Context and Reddit-Context, are introduced for formality and offensiveness style transfer.Experimental results on these datasets demonstrate the effectiveness of the proposed CAST model over state-of-the-art methods across style accuracy, content preservation and contextual consistency metrics. 1
Movement Pruning: Adaptive Sparsity by Fine-Tuning

Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning; however, it is less effective in the transfer learning regime that has become standard for state-of-the-art natural language processing applications. We propose the use of movement pruning, a simple, deterministic first-order weight pruning method that is more adaptive to pretrained model fine-tuning. We give mathematical foundations to the method and compare it to existing zeroth- and first-order pruning methods. Experiments show that when pruning large pretrained language models, movement pruning shows significant improvements in high-sparsity regimes. When combined with distillation, the approach achieves minimal accuracy loss with down to only 3% of the model parameters.
Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher

Cross-lingual text classification alleviates the need for manually labeled documents in a target language by leveraging labeled documents from other languages.Existing approaches for transferring supervision across languages require expensive cross-lingual resources, such as parallel corpora, while less expensive cross-lingual representation learning approaches train classifiers without target labeled documents.In this work, we propose a cross-lingual teacher-student method, CLTS, that generates "weak" supervision in the target language using minimal cross-lingual resources, in the form of a small number of word translations.Given a limited translation budget, CLTS extracts and transfers only the most important task-specific seed words across languages and initializes a teacher classifier based on the translated seed words.Then, CLTS iteratively trains a more powerful student that also exploits the context of the seed words in unlabeled target documents and outperforms the teacher.CLTS is simple and surprisingly effective in 18 diverse languages: by transferring just 20 seed words, even a bag-of-words logistic regression student outperforms state-of-theart cross-lingual methods (e.g., based on multilingual BERT).Moreover, CLTS can accommodate any type of student classifier: leveraging a monolingual BERT student leads to further improvements and outperforms even more expensive approaches by up to 12% in accuracy.Finally, CLTS addresses emerging tasks in low-resource languages using just a small number of word translations.
Heterogeneous Graph Neural Networks to Predict What Happen Next

Given an incomplete event chain, script learning aims to predict the missing event, which can support a series of NLP applications.Existing work cannot well represent the heterogeneous relations and capture the discontinuous event segments that are common in the event chain.To address these issues, we introduce a heterogeneous-event (HeterEvent) graph network.In particular, we employ each unique word and individual event as nodes in the graph, and explore three kinds of edges based on realistic relations (e.g., the relations of word-and-word, word-and-event, event-and-event).We also design a message passing process to realize information interactions among homo or heterogeneous nodes.And the discontinuous event segments could be explicitly modeled by finding the specific path between corresponding nodes in the graph.The experimental results on one-step and multi-step inference tasks demonstrate that our ensemble model HeterEvent [W +E] can outperform existing baselines.
Suraksha: Low Cost Device to Maintain Social Distancing during CoVID-19

On the 11th of March 2020, the World Health Organization (WHO) affirmed COVID-19, a pandemic, in response to more than 1,00,000 confirmed cases globally in more than 100 nations and the persistent danger of spreading further.Presently, there's no remedy or vaccine for COVID-19.The only way to curb its menace is taking precautionary measures, as advised by the health experts.Social distancing, that is maintaining a minimum distance of 1 to 1.5 meter between two individuals, is one of the foremost proactive measures advised by WHO.This paper presents a smart wearable device, Suraksha, that can be worn while travelling outside and will help maintain social distancing.The user will be able to rely on this device's alerting mechanism without having to constantly worry about their surroundings.It is a simple device which is easy to use and is built using basic electronic components.The device is also capable of integrating with the health applications over Bluetooth and support contact tracing.
Calibration of Neural Networks using Splines

Calibrating neural networks is of utmost importance when employing them in safety-critical applications where the downstream decision making depends on the predicted probabilities. Measuring calibration error amounts to comparing two empirical distributions. In this work, we introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test in which the main idea is to compare the respective cumulative probability distributions. From this, by approximating the empirical cumulative distribution using a differentiable function via splines, we obtain a recalibration function, which maps the network outputs to actual (calibrated) class assignment probabilities. The spline-fitting is performed using a held-out calibration set and the obtained recalibration function is evaluated on an unseen test set. We tested our method against existing calibration approaches on various image classification datasets and our spline-based recalibration approach consistently outperforms existing methods on KS error as well as other commonly used calibration measures.
Drugs Rating Generation and Recommendation from Sentiment Analysis of Drug Reviews using Machine Learning

A recommendation system can assist the user to compose an understanding of requirements and propose informed decisions from a lot of complicated knowledge. Recommendation from an analysis of sentiments seems to be a great challenge as user-generated content is represented using human language in several complicated ways. Many studies have focused on common fields such as reviews of electrical items, films, and restaurants, but not enough on health and medical issues. Sentiment analysis of healthcare in general and that of the drug experiences of individuals, in particular, may shed considerable light on how to focus on improving public health and reach the correct decision. In this paper, we design and implement a drug recommender system framework that applies sentiment analysis technologies on drug reviews. The objective of this research is to build a decision-making support platform to help patients to achieve more significant choices in drug selection. Firstly, we propose a sentimental measurement approach to drug reviews and generate ratings on drugs. Secondly, we take how much the drug reviews are useful to users, patient's conditions, and dictionary sentiment polarity of drug reviews into consideration. Then, we fuse those factors into the recommendation system to list appropriate medications. Experiments have been carried out using Decision Tree, K-Nearest Neighbors, and Linear Support Vector Classifier algorithm in rating generation and Hybrid model in recommendation based on the given open dataset. The analysis is carried out to tune the parameters for each algorithm in order to achieve greater performance. Finally, Linear Support Vector Classifier is selected for rating generation to obtain a good trade-off among model accuracy, model efficiency, and model scalability.
Civil Rephrases Of Toxic Texts With Self-Supervised Transformers

Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts.But this process does not typically provide feedback to the author that would help them contribute according to the community guidelines.This is prohibitively time-consuming for human moderators to do, and computational approaches are still nascent.This work focuses on models that can help suggest rephrasings of toxic comments in a more civil manner.Inspired by recent progress in unpaired sequence-tosequence tasks, a self-supervised learning model is introduced, called CAE-T5 1 .CAE-T5 employs a pre-trained text-to-text transformer, which is fine tuned with a denoising and cyclic auto-encoder loss.Experimenting with the largest toxicity detection dataset to date (Civil Comments) our model generates sentences that are more fluent and better at preserving the initial content compared to earlier text style transfer systems which we compare with using several scoring systems and human evaluation.1 The code can be found at https://github.com/LeoLaugier/conditional-auto-encoder-textto-text-transfer-transformer.
Bright Quantum Dot Single-Photon Emitters at Telecom Bands Heterogeneously Integrated on Si

Whereas the Si photonic platform is highly attractive for scalable optical quantum information processing, it lacks practical solutions for efficient photon generation. Self-assembled semiconductor quantum dots (QDs) efficiently emit photons in the telecom bands (1460-1625 nm) and allow for heterogeneous integration with Si. In this work, we report on a novel, robust, and industry-compatible approach for achieving single-photon emission from InAs/InP QDs heterogeneously integrated with a Si substrate. As a proof of concept, we demonstrate a simple vertical emitting device, employing a metallic mirror beneath the QD emitter, and experimentally obtained photon extraction efficiencies of ∼10%. Nevertheless, the figures of merit of our structures are comparable with values previously only achieved for QDs emitting at shorter wavelength or by applying technically demanding fabrication processes. Our architecture and the simple fabrication procedure allows for the demonstration of high-purity single-photon generation with a second-order correlation function at zero time delay, g(2)(τ = 0) < 0.02, without any corrections at continuous wave excitation at the liquid helium temperature and preserved up to 50 K. For pulsed excitation, we achieve the as-measured g(2)(0) down to 0.205 ± 0.020 (0.114 ± 0.020 with background coincidences subtracted).
V$^3$H: View Variation and View Heredity for Incomplete Multiview Clustering

Real data often appear in the form of multiple incomplete views. Incomplete multiview clustering is an effective method to integrate these incomplete views. Previous methods only learn the consistent information between different views and ignore the unique information of each view, which limits their clustering performance and generalizations. To overcome this limitation, we propose a novel View Variation and View Heredity approach (V[Formula: see text]H). Inspired by the variation and the heredity in genetics, V[Formula: see text]H first decomposes each subspace into a variation matrix for the corresponding view and a heredity matrix for all the views to represent the unique information and the consistent information respectively. Then, by aligning different views based on their cluster indicator matrices, V[Formula: see text]H integrates the unique information from different views to improve the clustering performance. Finally, with the help of the adjustable low-rank representation based on the heredity matrix, V[Formula: see text]H recovers the underlying true data structure to reduce the influence of the large incompleteness. More importantly, V[Formula: see text]H presents possibly the first work to introduce genetics to clustering algorithms for learning simultaneously the consistent information and the unique information from incomplete multiview data. Extensive experimental results on fifteen benchmark datasets validate its superiority over other state-of-the-arts. Impact Statement-Incomplete multiview clustering is a popular technology to cluster incomplete datasets from multiple sources. The technology is becoming more significant due to the absence of the expensive requirement of labeling these datasets. However, previous algorithms cannot fully learn the information of each view. Inspired by variation and heredity in genetics, our proposed algorithm V[Formula: see text]H fully learns the information of each view. Compared with the state-of-the-art algorithms, V[Formula: see text]H improves clustering performance by more than 20% in representative cases. With the large improvement on multiple datasets, V[Formula: see text]H has wide potential applications including the analysis of pandemic, financial and election datasets. The DOI of our codes is 10.24 433/CO.2 119 636.v1.
Tri-regularized nonnegative matrix tri-factorization for co-clustering

The objective of co-clustering is to simultaneously identify blocks of similarity between the sample set and feature set. Co-clustering has become a widely used technique in data mining, machine learning, and other research areas. The nonnegative matrix tri-factorization (NMTF) algorithm, which aims to decompose an objective matrix into three low-dimensional matrices, is an important tool to achieve co-clustering. However, noise is usually introduced during objective matrix factorization, and the method of square loss is very sensitive to noise, which significantly reduces the performance of the model. To solve this issue, this paper proposes a tri-regularized NMTF (TRNMTF) model for co-clustering, which combines graph regularization, Frobenius norm, and l1 norm to simultaneously optimize the objective function. TRNMTF can execute feature selection well, enhance the sparseness of the model, adjust the eigenvalues in the low-dimensional matrix, eliminate noise in the model, and obtain cleaner data matrices to approximate the objective matrix, which significantly improves the performance of the model and its generalization ability. Furthermore, to solve the iterative optimization schemes of TRNMTF, this study converts the objective function into elemental form to infer and provide detailed iterative update rules. Experimental results on 8 data sets show that the proposed model displays superior performance.