Reviewerly/exHarmony huggingface dataset:
HARGAN: Heterogeneous Argument Attention Network for Persuasiveness Prediction

Argument structure elaborates the relation among claims and premises. Previous works in persuasiveness prediction do not consider this relation in their architectures. To take argument structure information into account, this paper proposes an approach to persuasiveness prediction with a novel graph-based neural network model, called heterogeneous argument attention network (HARGAN). By jointly training on the persuasiveness and stance of the replies, our model achieves the state-of-the-art performance on the ChangeMyView (CMV) dataset for the persuasiveness prediction task. Experimental results show that the graph setting enables our model to aggregate information across multiple paragraphs effectively. In the meanwhile, our stance prediction auxiliary task enables our model to identify the viewpoint of each party, and helps our model perform better on the persuasiveness prediction.
On Commonsense Cues in BERT for Solving Commonsense Tasks

BERT has been used for solving commonsense tasks such as CommonsenseQA.While prior research has found that BERT does contain commonsense information to some extent, there has been work showing that pre-trained models can rely on spurious associations (e.g., data bias) rather than key cues in solving sentiment classification and other problems.We quantitatively investigate the presence of structural commonsense cues in BERT when solving commonsense tasks, and the importance of such cues for the model prediction.Using two different measures, we find that BERT does use relevant knowledge for solving the task, and the presence of commonsense knowledge is positively correlated to the model accuracy.
A Literature Review of Studies on Production-oriented Approach (POA) in China

The Production-Oriented Approach (POA) is a novel teaching approach with Chinese characteristics that is also one of China's research hotspots in the field of English instruction. The purpose of this study is to investigate the theory's future development and challenges based on the research state of POA in China, in order to promote POA's application in a wider range. The researcher retrieved academic journals, master's and doctoral dissertations from CNKI regarding POA from 2016 to 2020 and discussed its application fields, research methods, and research contents in this study. The findings demonstrate that: 1) Chinese researchers' research on POA has advanced significantly in the last five years, with an overall increasing trend in the number of studies. 2) POA's research methodologies mostly consist of non-empirical and empirical research. 3) POA's research focuses mostly on the development of theoretical systems, teaching effectiveness research, teaching reform practice, and teaching compilation and usage. Simultaneously, this study outlines the present research on POA in China's limitations and evaluates its future development prospects.
Multi-Frequency RF Sensor Data Adaptation for Motion Recognition with Multi-Modal Deep Learning

The widespread availability of low-cost RF sensors has made it easier to construct RF sensor networks for motion recognition, as well as increased the availability of RF data across a variety of frequencies, waveforms, and transmit parameters. However, it is not effective to directly use disparate RF sensor data for the training of deep neural networks, as the phenomenological differences in the data result in significant performance degradation. In this paper, we consider two approaches for the exploitation of multi-frequency RF data: 1) a single sensor case, where adversarial domain adaptation is used to transform the data from one RF sensor to resemble that of another, and 2) a multi-sensor case, where a multi-modal neural network is designed for joint target recognition using measurements from all sensors. Our results show that the developed approaches offer effective techniques for leveraging multi-frequency RF sensor data for target recognition.
A Formalization of Dedekind Domains and Class Groups of Global Fields

Dedekind domains and their class groups are notions in commutative algebra that are essential in algebraic number theory. We formalized these structures and several fundamental properties, including number theoretic finiteness results for class groups, in the Lean prover as part of the mathlib mathematical library. This paper describes the formalization process, noting the idioms we found useful in our development and mathlib's decentralized collaboration processes involved in this project.
Multi-perspective Coherent Reasoning for Helpfulness Prediction of Multimodal Reviews

As more and more product reviews are posted in both text and images, Multimodal Review Analysis (MRA) becomes an attractive research topic.Among the existing review analysis tasks, helpfulness prediction on review text has become predominant due to its importance for e-commerce platforms and online shops, i.e. helping customers quickly acquire useful product information.This paper proposes a new task Multimodal Review Helpfulness Prediction (MRHP) aiming to analyze the review helpfulness from text and visual modalities.Meanwhile, a novel Multi-perspective Coherent Reasoning method (MCR) is proposed to solve the MRHP task, which conducts joint reasoning over texts and images from both the product and the review, and aggregates the signals to predict the review helpfulness.Concretely, we first propose a productreview coherent reasoning module to measure the intra-and inter-modal coherence between the target product and the review.In addition, we also devise an intra-review coherent reasoning module to identify the coherence between the text content and images of the review, which is a piece of strong evidence for review helpfulness prediction.To evaluate the effectiveness of MCR, we present two newly collected multimodal review datasets as benchmark evaluation resources for the MRHP task.Experimental results show that our MCR method can lead to a performance increase of up to 8.5% as compared to the best performing text-only model.
An Efficient and Low-Power MLP Accelerator Architecture Supporting Structured Pruning, Sparse Activations and Asymmetric Quantization for Edge Computing

Multilayer perceptron (MLP) is one of the most popular neural network architectures used for classification, regression, and recommendation systems today. In this paper, we propose an efficient and low-power MLP accelerator for edge computing. The accelerator has three key features. First, it aligns with a novel structured weight pruning algorithm that merely needs minimal hardware support. Second, it takes advantage of activation sparsity for power minimization. Third, it supports asymmetric quantization on both weights and activations to boost the model accuracy especially when those values are in low-precision formats. Furthermore, the number of PEs is determined based on the available external memory bandwidth to ensure the high PE utilization, which avoids area and energy wastes. Experiment results show that the proposed MLP accelerator with only 8 MACs operates at 1.6GHz using the TSMC 40nm technology, delivers 899GOPS equivalent computing power after structured weight pruning on a well-known image classification model, and achieves an equivalent energy efficiency of 9.7TOPS/W, while the model accuracy loss is less than 0.3% with the help of asymmetric quantization.
Modified Hilbert Curve for Rectangles and Cuboids and Its Application in Entropy Coding for Image and Video Compression

In our previous work, by combining the Hilbert scan with the symbol grouping method, efficient run-length-based entropy coding was developed, and high-efficiency image compression algorithms based on the entropy coding were obtained. However, the 2-D Hilbert curves, which are a critical part of the above-mentioned entropy coding, are defined on squares with the side length being the powers of 2, i.e., 2n, while a subband is normally a rectangle of arbitrary sizes. It is not straightforward to modify the Hilbert curve from squares of side lengths of 2n to an arbitrary rectangle. In this short article, we provide the details of constructing the modified 2-D Hilbert curve of arbitrary rectangle sizes. Furthermore, we extend the method from a 2-D rectangle to a 3-D cuboid. The 3-D modified Hilbert curves are used in a novel 3-D transform video compression algorithm that employs the run-length-based entropy coding. Additionally, the modified 2-D and 3-D Hilbert curves introduced in this short article could be useful for some unknown applications in the future.
Evolutionary Approach for AutoAugment Using the Thermodynamical Genetic Algorithm

Data augmentation is one of the most effective ways to stabilize learning by improving the generalization of machine-learning models. In recent years, automatic data augmentation methods, such as AutoAugment or Fast AutoAugment have been attracting attention; and these methods improved the results of image classification and object detection tasks. However, several problems remain. Most notably, a larger training dataset requires higher computational costs. When searching with a small dataset in an attempt to determine the data augmentation approach, the true data space and sampling data space do not fully correspond with each other, thereby causing the generalization performance to deteriorate. Moreover, in the existing automatic augmentation methods, the search phase is often dominated by an exceptional sub-policy, which results in a loss of diversity of operations. In this study, we solved these problems by introducing evolutionary computation to previous methods. As mentioned earlier, maintaining diversity is essential. Therefore, we adopted the thermodynamical genetic algorithm (TDGA), which can control the population diversity with a specific genetic operator, known as the thermodynamical selection rule. To confirm the effectiveness of the proposed method, computational experiments were conducted using two benchmark datasets, CIFAR-10 and SVHN, as examples. The experimental results show that the proposed method can obtain various useful augmentation sub-policies for the problems while reducing the computational cost.
Learning Rewards From Linguistic Feedback

We explore unconstrained natural language feedback as a learning signal for artificial agents. Humans use rich and varied language to teach, yet most prior work on interactive learning from language assumes a particular form of input (e.g., commands). We propose a general framework which does not make this assumption, instead using aspect-based sentiment analysis to decompose feedback into sentiment over the features of a Markov decision process. We then infer the teacher's reward function by regressing the sentiment on the features, an analogue of inverse reinforcement learning. To evaluate our approach, we first collect a corpus of teaching behavior in a cooperative task where both teacher and learner are human. We implement three artificial learners: sentiment-based "literal" and "pragmatic" models, and an inference network trained end-to-end to predict rewards. We then re-run our initial experiment, pairing human teachers with these artificial learners. All three models successfully learn from interactive human feedback. The inference network approaches the performance of the "literal" sentiment model, while the "pragmatic" model nears human performance. Our work provides insight into the information structure of naturalistic linguistic feedback as well as methods to leverage it for reinforcement learning.
Sharing secured data on peer-to-peer applications using attribute-based encryption

The strong growth of communication and storage gives rise to the significantly increasing demand for collecting, storing, and sharing a large amount of data on networks. This is further enhanced by the data-driven market, where everyone wants to access other parties' data. Data is the bedrock of today's technologies and researchers, especially in machine learning and deep learning. The business value of organizations is also massively data-dependent. Recent studies and industry applications can apply analytic techniques for exploiting data, or Internet users can exchange data on social networks or peer-to-peer (P2P) networks. However, sharing secured data is a challenging problem that attracts much attention from researchers. Sharing secure data with a group of users using P2P applications faces the unavailability problem of peer nodes. Thus users cannot certify and download the protected data. This affects a P2P-based application class of sharing and storing online services or customer-to-customer e-commerce applications. This article proposes a solution for sharing secured data on P2P-based applications using blockchain and attribute-based encryption. The attribute-based encryption guarantees sharing keys among a group of users, while blockchain guarantees keys distribution. We have simulated the proposed solution on the mobile peer-to-peer network that provides services for sharing and storing data securely.
PyFed: extending PySyft with N-IID Federated Learning Benchmark

Federated Learning (FL) is an emerging learning paradigm that enables collaborative model training, across multiple devices using decentralized data, allowing each device to keep the privacy of its local data.Heterogeneity of data distributions is an inherent characteristic of FL.Generally, data samples across user devices are Not-Independent and Identically Distributed (N-IID), making learning in federated settings a challenging task.In this paper, we aim to contribute to FL benchmarking by introducing PyFed, an open source and scalable simulation framework of federated settings, supporting N-IID data.PyFed is fully compatible with PySyft, the secure and private framework for deep learning.It includes a set of benchmark datasets and implements different types of N-IID data distributions.PyFed also provides a set of implementations that can be used as reference for FL development.
An Improved Model for Voicing Silent Speech

In this paper, we present an improved model for voicing silent speech, where audio is synthesized from facial electromyography (EMG) signals.To give our model greater flexibility to learn its own input features, we directly use EMG signals as input in the place of handdesigned features used by prior work.Our model uses convolutional layers to extract features from the signals and Transformer layers to propagate information across longer distances.To provide better signal for learning, we also introduce an auxiliary task of predicting phoneme labels in addition to predicting speech audio features.On an open vocabulary intelligibility evaluation, our model improves the state of the art for this task by an absolute 25.8%.
A Neural Group-wise Sentiment Analysis Model with Data Sparsity Awareness

Sentiment analysis on user-generated content has achieved notable progress by introducing user information to consider each individual’s preference and language usage. However, most existing approaches ignore the data sparsity problem, where the content of some users is limited and the model fails to capture discriminative features of users. To address this issue, we hypothesize that users could be grouped together based on their rating biases as well as degree of rating consistency and the knowledge learned from groups could be employed to analyze the users with limited data. Therefore, in this paper, a neural group-wise sentiment analysis model with data sparsity awareness is proposed. The user-centred document representations are generated by incorporating a group-based user encoder. Furthermore, a multi-task learning framework is employed to jointly modelusers’ rating biases and their degree of rating consistency. One task is vanilla populationlevel sentiment analysis and the other is groupwise sentiment analysis. Experimental results on three real-world datasets show that the proposed approach outperforms some state-of the-art methods. Moreover, model analysis and case study demonstrate its effectiveness of modeling user rating biases and variances.
Exploration via State influence Modeling

This paper studies the challenging problem of reinforcement learning (RL) in hard exploration tasks with sparse rewards. It focuses on the exploration stage before the agent gets the first positive reward, in which case, traditional RL algorithms with simple exploration strategies often work poorly. Unlike previous methods using some attribute of a single state as the intrinsic reward to encourage exploration, this work leverages the social influence between different states to permit more efficient exploration. It introduces a general intrinsic reward construction method to evaluate the social influence of states dynamically. Three kinds of social influence are introduced for a state: conformity, power, and authority. By measuring the state’s social influence, agents quickly find the focus state during the exploration process. The proposed RL framework with state social influence evaluation works well in hard exploration task. Extensive experimental analyses and comparisons in Grid Maze and many hard exploration Atari 2600 games demonstrate its high exploration efficiency.
Iot Network Identity Management Using Smart Contract and Blockchain Technology

The proliferation of Internet of Things (IoT) devices is on the rise, with the number of connected devices exceeding seven billion devices by 2017, and forecasting connects 20-50 billion connections by 2020. Many of those devices have been used in an unusual way and complex networks pose many challenges to the functionality of device management. Among those challenges is patent management related to how devices are verified and authenticated in addition to how devices create mechanisms to authorize and control access to data and services. Blockchain as a computer-distributed computer technology sets itself up as the right person to tackle this challenge. That is mainly due to the use of Blockchain cryptographic indicators, static recording, and background. These features, in combination, provide a platform to use IoT device ID management functions that can ensure global and unique device ownership, and provide a way to maintain it throughout the life cycle of the device. This paper introduces a low-level blockchain-based proprietary management framework that provides features for patenting and transfer of ownership, as well as the ability to manage patents between machine-visited networks. To confirm, we define a set of smart contracts that provide registrar functions and administrative contracts.
User Driven Model Adjustment via Boolean Rule Explanations

AI solutions are heavily dependant on the quality and accuracy of the input training data, however the training data may not always fully reflect the most up-to-date policy landscape or may be missing business logic. The advances in explainability have opened the possibility of allowing users to interact with interpretable explanations of ML predictions in order to inject modifications or constraints that more accurately reflect current realities of the system. In this paper, we present a solution which leverages the predictive power of ML models while allowing the user to specify modifications to decision boundaries. Our interactive overlay approach achieves this goal without requiring model retraining, making it appropriate for systems that need to apply instant changes to their decision making. We demonstrate that user feedback rules can be layered with the ML predictions to provide immediate changes which in turn supports learning with less data.
Modeling Language Usage and Listener Engagement in Podcasts

Sravana Reddy, Mariya Lazarova, Yongze Yu, Rosie Jones. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
Invariant Teacher and Equivariant Student for Unsupervised 3D Human Pose Estimation

We propose a novel method based on teacher-student learning framework for 3D human pose estimation without any 3D annotation or side information. To solve this unsupervised-learning problem, the teacher network adopts pose-dictionary-based modeling for regularization to estimate a physically plausible 3D pose. To handle the decomposition ambiguity in the teacher network, we propose a cycle-consistent architecture promoting a 3D rotation-invariant property to train the teacher network. To further improve the estimation accuracy, the student network adopts a novel graph convolution network for flexibility to directly estimate the 3D coordinates. Another cycle-consistent architecture promoting 3D rotation-equivariant property is adopted to exploit geometry consistency, together with knowledge distillation from the teacher network to improve the pose estimation performance. We conduct extensive experiments on Human3.6M and MPI-INF-3DHP. Our method reduces the 3D joint prediction error by 11.4% compared to state-of-the-art unsupervised methods and also outperforms many weakly-supervised methods that use side information on Human3.6M. Code will be available at https://github.com/sjtuxcx/ITES.
Skill-based Shared Control

Performing a number of motion patterns -referred to as skills -(e.g., wave, spiral, sweeping motions) during teleoperation is an integral part of many industrial processes such as spraying, welding, and wiping (cleaning, polishing).Maintaining these motions whilst simultaneously avoiding obstacles and traversing complex terrain requires expert operators.In this work, we propose a novel skill-based shared control framework for incorporating the notion of skill assistance to aid novice operators to sustain these motion patterns whilst adhering to environmental constraints.Our shared control method uses streaming joystick data to estimate the model parameters that provide a description of the operator's intention.We introduce a novel parametrization for state and control that combines skill and underlying trajectory models, leveraging a special type of curve known as Clothoids.This new parameterization allows for efficient computation of skill-based short term horizon plans, enabling the use of a Model Predictive Control (MPC) loop.We perform experiments on a hardware mock-up, validating the effectiveness of our method to recognize a switch of intended skill, and showing an improved quality of output motion, even under dynamically changing obstacles.See our accompanying video here: https://youtu.be/TwhsgA6fw6M.
A Survey on Handwritten Character Recognition using Machine Learning Technique

Due to its broad range of applications, handwritten character recognition is widespread. Processing application forms, digitizing ancient articles, processing postal addresses, processing bank checks, and many other handwritten character processing fields are increasing in popularity. Since the last three decades, handwritten characters have drawn the attention of researchers. For successful recognition, several methods have been suggested. This paper presents a comprehensive overview of handwritten character recognition using a neural network as a machine learning tool.