Reviewerly/exHarmony huggingface dataset:
Robust Stochastic Gradient Descent With Student-t Distribution Based First-Order Momentum

Remarkable achievements by deep neural networks stand on the development of excellent stochastic gradient descent methods. Deep-learning-based machine learning algorithms, however, have to find patterns between observations and supervised signals, even though they may include some noise that hides the true relationship between them, more or less especially in the robotics domain. To perform well even with such noise, we expect them to be able to detect outliers and discard them when needed. We, therefore, propose a new stochastic gradient optimization method, whose robustness is directly built in the algorithm, using the robust student-t distribution as its core idea. We integrate our method to some of the latest stochastic gradient algorithms, and in particular, Adam, the popular optimizer, is modified through our method. The resultant algorithm, called t-Adam, along with the other stochastic gradient methods integrated with our core idea is shown to effectively outperform Adam and their original versions in terms of robustness against noise on diverse tasks, ranging from regression and classification to reinforcement learning problems.
DLGNN: A Double-layer Graph Neural Network Model Incorporating Shopping Sequence Information for Commodity Recommendation

The essence of recommendation methods for online shopping is to predict whether the user will buy specified products.The existing methods are usually modeled on a user-item-score matrix, which does not consider the transformation of users and items.The transformation is hidden in the behavior and reflected in the shopping sequence.In a smart city of the future, products can be expressed by sensors: for a given item, the changes in its audience can be observed from the sequence of users who have bought these products, and for a given user the changes in his or her need submitted as requirement can be obtained according to the sequence of purchased commodities.To better capture the shopping sequence and apply it to recommendations, we proposed an E-commerce recommendation algorithm similar to the recommendation problem in the smart city using a double-layer graph neural network (DLGNN) to model the user sequence and item sequence and predict the purchasing behavior of users.In the DLGNN, the sequence data, including the sequence of users who have bought a particular item and the sequence of purchased commodities from each user, are first modeled as graphstructure data.Then by using a graph neural network (GNN), one can express the state of each node of the graph as cells in the neural network.On this basis, the pretrained representation vectors of users and commodities are used to represent their global information.The state vectors and representation vectors can be used to model the purchasing behavior of users.Experiments based on a real dataset show that the proposed method yields better performance than existing recommendation algorithms.
Document-Level Relation Extraction with Reconstruction

In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years. However, the learned graph representation universally models relation information between all entity pairs regardless of whether there are relationships between these entity pairs. Thus, those entity pairs without relationships disperse the attention of the encoder-classifier DocRE for ones with relationships, which may further hind the improvement of DocRE. To alleviate this issue, we propose a novel encoder-classifier-reconstructor model for DocRE. The reconstructor manages to reconstruct the ground-truth path dependencies from the graph representation, to ensure that the proposed DocRE model pays more attention to encode entity pairs with relationships in the training. Furthermore, the reconstructor is regarded as a relationship indicator to assist relation classification in the inference, which can further improve the performance of DocRE model. Experimental results on a large-scale DocRE dataset show that the proposed model can significantly improve the accuracy of relation extraction on a strong heterogeneous graph-based baseline. The code is publicly available at https://github.com/xwjim/DocRE-Rec.
A Smart Biometric Identity Management Framework for Personalised IoT and Cloud Computing-Based Healthcare Services

This paper proposes a novel identity management framework for Internet of Things (IoT) and cloud computing-based personalized healthcare systems. The proposed framework uses multimodal encrypted biometric traits to perform authentication. It employs a combination of centralized and federated identity access techniques along with biometric based continuous authentication. The framework uses a fusion of electrocardiogram (ECG) and photoplethysmogram (PPG) signals when performing authentication. In addition to relying on the unique identification characteristics of the users’ biometric traits, the security of the framework is empowered by the use of Homomorphic Encryption (HE). The use of HE allows patients’ data to stay encrypted when being processed or analyzed in the cloud. Thus, providing not only a fast and reliable authentication mechanism, but also closing the door to many traditional security attacks. The framework’s performance was evaluated and validated using a machine learning (ML) model that tested the framework using a dataset of 25 users in seating positions. Compared to using just ECG or PPG signals, the results of using the proposed fused-based biometric framework showed that it was successful in identifying and authenticating all 25 users with 100% accuracy. Hence, offering some significant improvements to the overall security and privacy of personalized healthcare systems.
Aggressive complaining on Social Media: The case of #MuckyMerton

This paper examines the ways in which the speech act of complaint is realised in social media contexts. To date, little attention has been devoted to the realisation of this speech act in online settings, despite the fact that such settings provide different affordances for complaining than many of their spoken and written counterparts. In the current study, we aim to make a step towards filling this knowledge gap by demonstrating that in online settings – in particular, social media – complaining can become very intensive and aggressive. This is because social media operates with features such as complex participation and multimodality, which allow users to reflect on each other's complaints in an increasingly aggressive escalatory manner, especially when these users are united by a joint cause. We deploy the concepts of 'addressivity' and 'diachronicity' to conceptualise those features of social media that boost complaint to become aggressive. As a case study, we investigate an online protest against the waste management policy of the Borough of Merton in London.
A Hybrid Model for Central Bank Digital Currency Based on Blockchain

With the development of blockchain technology, the research on digital currency is attracting more and more attention, especially Central Bank Digital Currency (CBDC), which plays an important role in national economic construction. However, compared with existing cryptocurrencies, CBDC needs a more controllable decentralization and more emphasized supervision. Therefore, the critical part of CBDC is the network architecture that saves computing resources, the technical scheme that is in line with economic ecology, and efficient consensus algorithms. In this paper, we propose a hybrid blockchain system with a modularity network for CBDC. The account scheme is used to record frequently circulated digital currencies, especially for massive small payment transactions when the digital assets and smart contracts with large value fluctuations and weak liquidity are recorded using the Unspent Transaction Output (UTXO) scheme. In terms of network architecture, a modular blockchain architecture is proposed, and a sliced data storage solution is designed to enhance the concurrency of this structured network. We also proposed a CBDC supervision mode for blockchain, and on this basis, the DPOS-BFT algorithm is optimized, which reduces the two rounds of consensus of the original algorithm to one round. Finally, three simulation experiments on scheme, network, and consensus are carried out, which show this system can comprehensively improve the transaction processing and the consensus speed.
OBFP: Optimized Blockchain-Based Fair Payment for Outsourcing Computations in Cloud Computing

Outsourcing computations have been widely used to meet the growing computing demands, although achieving trust in an untrusted (or a zero-trust) environment can be challenging in practice. Fair payment, a candidate solution, can potentially facilitate fair trading among outsourcing computation participants such as users and workers. However, most existing solutions including traditional e-cash-based or blockchain-based, may potentially compromise the worker’s fairness (i.e., does not achieve robust fairness, since trusted third parties are required during the trading process), or involve heavy zero-knowledge proofs (ZKPs, with significant computation costs). To mitigate these limitations, we propose a system model of an optimized blockchain-based fair payment (OBFP) for outsourcing computations. Then, we construct a ZKP-free solution based on blockchain by combining any secure commitment, accumulator, and symmetric encryption schemes, as well as a hash function. To demonstrate the utility of our proposed OBFP system, we provide security analysis, performance evaluation and a comparison with existing popular solutions. Specifically, the cryptographic tools are instantiated as commitment (Perdesen commitment), accumulator (RSA-based accumulator), and symmetric encryption (a concrete scheme with the indistinguishability under chosen-plaintext attack (IND-CPA) security), and a hash function (Keccak-256). The prototype is implemented in COSBench and Remix to analyze cloud scalability and concurrency, as well as gas cost.
Teachers’ Digital Skills Readiness During COVID-19 Pandemic

The COVID-19 crisis revealed the necessity for teachers to have digital skills in order to effectively teach online. Teachers should be able to exploit, use, and apply digital technologies in all educational activities. This paper investigates teachers’ perceptions regarding their digital skills for performing their teaching and professional responsibilities during the pandemic. More than eight hundred teachers participated in a survey regarding the use of digital technologies in their teaching and their professional responsibilities. Indicative digital tools that can be used by digital competent teachers are also presented to cover all areas of the teachers’ professional activities. Their answers revealed that they mostly used digital tools for finding, evaluating, and developing educational resources as well for teaching. They also used digital tools for self-study, students’ assessment, as well as interacting and communicating with students. However, they hardly used digital tools for other teaching activities such as feedback and final evaluation of the students, or revising the educational resources. Finally, they could not deal with long-term planning, management, and development of either their school or education in general. Although it is important for teachers to effectively respond to their daily emergent teaching responsibilities, consideration should also be given to the long-term planning and development of the digital school and digital education in general.
Benchmarking Advantage and D-Wave 2000Q quantum annealers with exact cover problems

Abstract We benchmark the quantum processing units of the largest quantum annealers to date, the $$5000+$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>5000</mml:mn> <mml:mo>+</mml:mo> </mml:mrow> </mml:math> qubit quantum annealer Advantage and its 2000+ qubit predecessor D-Wave 2000Q, using tail assignment and exact cover problems from aircraft scheduling scenarios. The benchmark set contains small, intermediate, and large problems with both sparsely connected and almost fully connected instances. We find that Advantage outperforms D-Wave 2000Q for almost all problems, with a notable increase in success rate and problem size. In particular, Advantage is also able to solve the largest problems with 120 logical qubits that D-Wave 2000Q cannot solve anymore. Furthermore, problems that can still be solved by D-Wave 2000Q are solved faster by Advantage. We find, however, that D-Wave 2000Q can achieve better success rates for sparsely connected problems that do not require the many new couplers present on Advantage, so improving the connectivity of a quantum annealer does not per se improve its performance.
Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

Neural image-to-text radiology report generation systems offer the potential to improve radiology reporting by reducing the repetitive process of report drafting and identifying possible medical errors.However, existing report generation systems, despite achieving high performances on natural language generation metrics such as CIDEr or BLEU, still suffer from incomplete and inconsistent generations.Here we introduce two new simple rewards to encourage the generation of factually complete and consistent radiology reports: one that encourages the system to generate radiology domain entities consistent with the reference, and one that uses natural language inference to encourage these entities to be described in inferentially consistent ways.We combine these with the novel use of an existing semantic equivalence metric (BERTScore).We further propose a report generation system that optimizes these rewards via reinforcement learning.On two open radiology report datasets, our system substantially improved the F 1 score of a clinical information extraction performance by +22.1 (∆ + 63.9%).We further show via a human evaluation and a qualitative analysis that our system leads to generations that are more factually complete and consistent compared to the baselines.
Multiple Kernel Clustering with Kernel k-Means Coupled Graph Tensor Learning

Kernel k-means (KKM) and spectral clustering (SC) are two basic methods used for multiple kernel clustering (MKC), which have both been widely used to identify clusters that are non-linearly separable. However, both of them have their own shortcomings: 1) the KKM-based methods usually focus on learning a discrete clustering indicator matrix via a combined consensus kernel, but cannot exploit the high-order affinities of all pre-defined base kernels; and 2) the SC-based methods require a robust and meaningful affinity graph in kernel space as input in order to form clusters with desired clustering structure. In this paper, a novel method, kernel k-means coupled graph tensor (KCGT), is proposed to graciously couple KKM and SC for seizing their merits and evading their demerits simultaneously. In specific, we innovatively develop a new graph learning paradigm by leveraging an explicit theoretical connection between clustering indicator matrix and affinity graph, such that the affinity graph propagated from KKM enjoys the valuable block diagonal and sparse property. Then, by using this graph learning paradigm, base kernels can produce multiple candidate affinity graphs, which are stacked into a low-rank graph tensor for capturing the high-order affinity of all these graphs. After that, by averaging all the frontal slices of the tensor, a high-quality affinity graph is obtained. Extensive experiments have shown the superiority of KCGT compared with the state-of-the-art MKC methods.
STEP: Stochastic Traversability Evaluation and Planning for Risk-Aware Off-road Navigation

Although ground robotic autonomy has gained widespread usage in structured and controlled environments, autonomy in unknown and off-road terrain remains a difficult problem.Extreme, off-road, and unstructured environments such as undeveloped wilderness, caves, and rubble pose unique and challenging problems for autonomous navigation.To tackle these problems we propose an approach for assessing traversability and planning a safe, feasible, and fast trajectory in real-time.Our approach, which we name STEP (Stochastic Traversability Evaluation and Planning), relies on: 1) rapid uncertaintyaware mapping and traversability evaluation, 2) tail risk assessment using the Conditional Value-at-Risk (CVaR), and 3) efficient risk and constraint-aware kinodynamic motion planning using sequential quadratic programming-based (SQP) model predictive control (MPC).We analyze our method in simulation and validate its efficacy on wheeled and legged robotic platforms exploring extreme terrains including an abandoned subway and an underground lava tube.(See video: https://youtu.
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

Supervised learning under label noise has seen numerous advances recently, while existing theoretical findings and empirical results broadly build up on the class-conditional noise (CCN) assumption that the noise is independent of input features given the true label. In this work, we present a theoretical hypothesis testing and prove that noise in real-world dataset is unlikely to be CCN, which confirms that label noise should depend on the instance and justifies the urgent need to go beyond the CCN assumption.The theoretical results motivate us to study the more general and practical-relevant instance-dependent noise (IDN). To stimulate the development of theory and methodology on IDN, we formalize an algorithm to generate controllable IDN and present both theoretical and empirical evidence to show that IDN is semantically meaningful and challenging. As a primary attempt to combat IDN, we present a tiny algorithm termed self-evolution average label (SEAL), which not only stands out under IDN with various noise fractions, but also improves the generalization on real-world noise benchmark Clothing1M. Our code is released. Notably, our theoretical analysis in Section 2 provides rigorous motivations for studying IDN, which is an important topic that deserves more research attention in future.
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the Web

We show that margin-based bitext mining in a multilingual sentence space can be successfully scaled to operate on monolingual corpora of billions of sentences.We use 32 Common Crawl snapshots (Wenzek et al., 2019), totalling 71 billion unique sentences.Using one unified approach for 90 languages, we were able to mine 10.8 billion parallel sentences, out of which only 2.9 billions are aligned with English.We illustrate the capability of our scalable mining system to create high quality training sets from one language to any other by training hundreds of different machine translation models and evaluating them on the many-to-many TED benchmark.Further, we evaluate on competitive translation benchmarks such as WMT and WAT.Using only mined bitext, we set a new state of the art for a single system on the WMT'19 test set for English-German/Russian/Chinese.In particular, our English/German and English/Russian systems outperform the best single ones by over 4 BLEU points and are on par with best WMT'19 systems, which train on the WMT training data and augment it with backtranslation.We also achieve excellent results for distant languages pairs like Russian/Japanese, outperforming the best submission at the 2020 WAT workshop.All of the mined bitext will be freely available.
UnNatural Language Inference

Recent investigations into the inner-workings of state-of-the-art large-scale pre-trained Transformer-based Natural Language Understanding (NLU) models indicate that they appear to know humanlike syntax, at least to some extent.We provide novel evidence that complicates this claim: we find that state-of-the-art Natural Language Inference (NLI) models assign the same labels to permuted examples as they do to the original, i.e. they are largely invariant to random wordorder permutations.This behavior notably differs from that of humans; we struggle with ungrammatical sentences.To measure the severity of this issue, we propose a suite of metrics and investigate which properties of particular permutations lead models to be word-order invariant.In the MNLI dataset, for example, we find almost all (98.7%) examples contain at least one permutation which elicits the gold label.Models are sometimes even able to assign gold labels to permutations that they originally failed to predict correctly.We provide a comprehensive empirical evaluation of this phenomenon, and further show that this issue exists for both Transformers and pre-Transformer RNN / ConvNet based encoders, as well as across multiple languages (English and Mandarin Chinese).
Generalized Zero-Shot Learning via Disentangled Representation

Zero-Shot Learning (ZSL) aims to recognize images belonging to unseen classes that are unavailable in the training process, while Generalized Zero-Shot Learning (GZSL) is a more realistic variant that both seen and unseen classes appear during testing. Most GZSL approaches achieve knowledge transfer based on the features of samples that inevitably contain information irrelevant to recognition, bringing negative influence for the performance. In this work, we propose a novel method, dubbed Disentangled-VAE, which aims to disentangle category-distilling factors and category-dispersing factors from visual as well as semantic features, respectively. In addition, a batch re-combining strategy on latent features is introduced to guide the disentanglement, encouraging the distilling latent features to be more discriminative for recognition. Extensive experiments demonstrate that our method outperforms the state-of-the-art approaches on four challenging benchmark datasets
E2E-VLP: End-to-End Vision-Language Pre-training Enhanced by Visual Learning

Vision-language pre-training (VLP) on largescale image-text pairs has achieved huge success for the cross-modal downstream tasks.The most existing pre-training methods mainly adopt a two-step training procedure, which firstly employs a pre-trained object detector to extract region-based visual features, then concatenates the image representation and text embedding as the input of Transformer to train.However, these methods face problems of using task-specific visual representation of the specific object detector for generic crossmodal understanding, and the computation inefficiency of two-stage pipeline.In this paper, we propose the first end-to-end vision-language pre-trained model for both V+L understanding and generation, namely E2E-VLP, where we build a unified Transformer framework to jointly learn visual representation, and semantic alignments between image and text.We incorporate the tasks of object detection and image captioning into pretraining with a unified Transformer encoderdecoder architecture for enhancing visual learning.An extensive set of experiments have been conducted on well-established visionlanguage downstream tasks to demonstrate the effectiveness of this novel VLP paradigm.