Reviewerly/exHarmony huggingface dataset:
On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective

We consider training over-parameterized two-layer neural networks with Rectified Linear Unit (ReLU) using gradient descent (GD) method. Inspired by a recent line of work, we study the evolutions of network prediction errors across GD iterations, which can be neatly described in a matrix form. When the network is sufficiently over-parameterized, these matrices individually approximate {\em an} integral operator which is determined by the feature vector distribution $\rho$ only. Consequently, GD method can be viewed as {\em approximately} applying the powers of this integral operator on the underlying/target function $f^*$ that generates the responses/labels. We show that if $f^*$ admits a low-rank approximation with respect to the eigenspaces of this integral operator, then the empirical risk decreases to this low rank approximation error at a linear rate which is determined by $f^*$ and $\rho$ only, i.e., the rate is independent of the sample size $n$. Furthermore, if $f^*$ has zero low-rank approximation error, then, as long as the width of the neural network is $\Omega(n\log n)$, the empirical risk decreases to $\Theta(1/\sqrt{n})$. To the best of our knowledge, this is the first result showing the sufficiency of nearly-linear network over-parameterization. We provide an application of our general results to the setting where $\rho$ is the uniform distribution on the spheres and $f^*$ is a polynomial. Throughout this paper, we consider the scenario where the input dimension $d$ is fixed.
Gesture Recognition Using Spatiotemporal Deformable Convolutional Representation

Dynamic gesture recognition, which plays an essential role in human-computer interaction, has been widely investigated but not yet addressed. The interference of the varied and complex background makes the classifier easily be misguided due to the relatively smaller size of the hands and arms compared with the full scenes. In this paper, we address the problem by proposing a novel spatiotemporal deformable convolutional neural network for end-to-end learning. To eliminate the background interference, a light-weight spatiotemporal deformable convolution module is specially designed to augment the spatiotemporal sampling locations of 3D convolution by learning additional offsets according to the preceding feature map. The proposed method is evaluated on two challenging datasets, EgoGesture and Jester, and achieves the state-of-the-art performance on both of the two datasets. The code and trained models will be released for better communication and future work.
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis

Deep neural network models such as long short-term memory (LSTM) and tree-LSTM have been proven to be effective for sentiment analysis.However, sequential LSTM is a bias model wherein the words in the tail of a sentence are more heavily emphasized than those in the header for building sentence representations.Even tree-LSTM, with useful structural information, could not avoid the bias problem because the root node will be dominant and the nodes in the bottom of the parse tree will be less emphasized even though they may contain salient information.To overcome the bias problem, this study proposes a capsule tree-LSTM model, introducing a dynamic routing algorithm as an aggregation layer to build sentence representation by assigning different weights to nodes according to their contributions to prediction.Experiments on Stanford Sentiment Treebank (SST) for sentiment classification and EmoBank for regression show that the proposed method improved the performance of tree-LSTM and other neural network models.In addition, the deeper the tree structure, the bigger the improvement.
Neural Naturalist: Generating Fine-Grained Image Comparisons

We introduce the new Birds-to-Words dataset of 41k sentences describing fine-grained differences between photographs of birds.The language collected is highly detailed, while remaining understandable to the everyday observer (e.g., "heart-shaped face," "squat body").Paragraph-length descriptions naturally adapt to varying levels of taxonomic and visual distance-drawn from a novel stratified sampling approach-with the appropriate level of detail.We propose a new model called Neural Naturalist that uses a joint image encoding and comparative module to generate comparative language, and evaluate the results with humans who must use the descriptions to distinguish real images.Our results indicate promising potential for neural models to explain differences in visual embedding space using natural language, as well as a concrete path for machine learning to aid citizen scientists in their effort to preserve biodiversity. 🦉Work done during an internship at Google. 1 https://www.inaturalist.org"Animal 2 looks smaller and has a stouter, darker bill than Animal 1. Animal 2 has black spots on its wings.Animal 2 has a black hood that extends down onto its breast, and the rest of its breast is white with orange only on its sides.In comparison, Animal 1's breast is entirely orange.""Animal 2 is brightly red-colored all over, except for a black oval around its beak.Animal 1 has more muted red and grey colors."vs perceptual difficulty: medium highly
One Model to Learn Both: Zero Pronoun Prediction and Translation

Zero pronouns (ZPs) are frequently omitted in pro-drop languages, but should be recalled in non-pro-drop languages.This discourse phenomenon poses a significant challenge for machine translation (MT) when translating texts from pro-drop to non-pro-drop languages.In this paper, we propose a unified and discourseaware ZP translation approach for neural MT models.Specifically, we jointly learn to predict and translate ZPs in an end-to-end manner, allowing both components to interact with each other.In addition, we employ hierarchical neural networks to exploit discourse-level context, which is beneficial for ZP prediction and thus translation.Experimental results on both Chinese⇒English and Japanese⇒English data show that our approach significantly and accumulatively improves both translation performance and ZP prediction accuracy over not only baseline but also previous works using external ZP prediction models.Extensive analyses confirm that the performance improvement comes from the alleviation of different kinds of errors especially caused by subjective ZPs.
Local privacy protection classification based on human-centric computing

Abstract Human-centric computing is becoming an important part of data-driven artificial intelligence (AI) and the importance of data mining under Human-centric computing is getting more and more attention. The rapid development of machine learning has gradually increased its ability to mine data. In this paper, privacy protection is combined with machine learning, in which a logistic regression is adopted for local differential privacy protection to achieves classification task utilizing noise addition and feature selection. The design idea is mainly divided into three parts: noise addition, feature selection and logistic regression. In the part of noise addition, the way of adding noise using Laplace mechanism to original data achieves the purpose of disturbing data. The part of feature selection is to highlight the impact of noised data on the classifier. The part of logistic regression is to use logistic regression to implement classification task. The experimental results show that an accuracy of 85.7% can be achieved for the privacy data by choosing appropriate regularization coefficients.
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations

Deep learning sequence models have led to a marked increase in performance for a range of Natural Language Processing tasks, but it remains an open question whether they are able to induce proper hierarchical generalizations for representing natural language from linear input alone.Work using artificial languages as training input has shown that LSTMs are capable of inducing the stack-like data structures required to represent context-free and certain mildly context-sensitive languages (Weiss et al., 2018)-formal language classes which correspond in theory to the hierarchical structures of natural language.Here we present a suite of experiments probing whether neural language models trained on linguistic data induce these stack-like data structures and deploy them while incrementally predicting words.We study two natural language phenomena: center embedding sentences and syntactic island constraints on the filler-gap dependency.In order to properly predict words in these structures, a model must be able to temporarily suppress certain expectations and then recover those expectations later, essentially pushing and popping these expectations on a stack.Our results provide evidence that models can successfully suppress and recover expectations in many cases, but do not fully recover their previous grammatical state.
A Lattice-Based Linkable Ring Signature Supporting Stealth Addresses

First proposed in CryptoNote, a collection of popular privacy-centric cryptocurrencies have employed Linkable Ring Signature and a corresponding Key Derivation Mechanism (KeyDerM) for keeping the payer and payee of a transaction anonymous and unlinkable. The KeyDerM is used for generating a fresh signing key and the corresponding public key, referred to as a stealth address, for the transaction payee. The stealth address will then be used in the linkable ring signature next time when the payee spends the coin. However, in all existing works, including Monero, the privacy model only considers the two cryptographic primitives separately. In addition, to be applied to cryptocurrencies, the security and privacy models for Linkable Ring Signature should capture the situation that the public key ring of a signature may contain keys created by an adversary (referred to as adversarially-chosen-key attack), since in cryptocurrencies, it is normal for a user (adversary) to create self-paying transactions so that some maliciously created public keys can get into the system without being detected . In this paper, we propose a new cryptographic primitive, referred to as Linkable Ring Signature Scheme with Stealth Addresses (SALRS), which comprehensively and strictly captures the security and privacy requirements of hiding the payer and payee of a transaction in cryptocurrencies, especially the adversarially-chosen-key attacks. We also propose a lattice-based SALRS construction and prove its security and privacy in the random oracle model. In other words, our construction provides strong confidence on security and privacy in twofolds, i.e., being proved under strong models which capture the practical scenarios of cryptocurrencies, and being potentially quantum-resistant. The efficiency analysis also shows that our lattice-based SALRS scheme is practical for real implementations.
Neural Word Decomposition Models for Abusive Language Detection

User generated text on social media often suffers from a lot of undesired characteristics including hatespeech, abusive language, insults etc. that are targeted to attack or abuse a specific group of people.Often such text is written differently compared to traditional text such as news involving either explicit mention of abusive words, obfuscated words and typological errors or implicit abuse i.e., indicating or targeting negative stereotypes.Thus, processing this text poses several robustness challenges when we apply natural language processing techniques developed for traditional text.For example, using word or token based models to process such text can treat two spelling variants of a word as two different words.Following recent work, we analyze how character, subword and byte pair encoding (BPE) models can be aid some of the challenges posed by user generated text.In our work, we analyze the effectiveness of each of the above techniques, compare and contrast various word decomposition techniques when used in combination with others.We experiment with finetuning large pretrained language models, and demonstrate their robustness to domain shift by studying Wikipedia attack, toxicity and Twitter hatespeech datasets.
Deep-cARe: Projection-Based Home Care Augmented Reality System with Deep Learning for Elderly

Developing innovative and pervasive smart technologies that provide medical support and improve the welfare of the elderly has become increasingly important as populations age. Elderly people frequently experience incidents of discomfort in their daily lives, including the deterioration of cognitive and memory abilities. To provide auxiliary functions and ensure the safety of the elderly in daily living situations, we propose a projection-based augmented reality (PAR) system equipped with a deep-learning module. In this study, we propose three-dimensional space reconstruction of a pervasive PAR space for the elderly. In addition, we propose the application of a deep-learning module to lay the foundation for contextual awareness. Performance experiments were conducted for grafting the deep-learning framework (pose estimation, face recognition, and object detection) onto the PAR technology through the proposed hardware for verification of execution possibility, real-time execution, and applicability. The precision of the face pose is particularly high by pose estimation; it is used to determine an abnormal user state. For face recognition results of whole class, the average detection rate (DR) was 74.84% and the precision was 78.72%. However, for face occlusions, the average DR was 46.83%. It was confirmed that the face recognition can be performed properly if the face occlusion situation is not frequent. By object detection experiment results, the DR increased as the distance from the system decreased for a small object. For a large object, the miss rate increased when the distance between the object and the system decreased. Scenarios for supporting the elderly, who experience degradation in movement and cognitive functions, were designed and realized, constructed using the proposed platform. In addition, several user interfaces (UI) were implemented according to the scenarios regardless of distance between users and the proposed system. In this study, we developed a bidirectional PAR system that provides the relevant information by understanding the user environment and action intentions instead of a unidirectional PAR system for simple information provision. We present a discussion of the possibility of care systems for the elderly through the fusion of PAR and deep-learning frameworks.
Error detection on quantum computers improving the accuracy of chemical calculations

A major milestone of quantum error correction is to achieve the fault-tolerance threshold beyond which quantum computers can be made arbitrarily accurate. This requires extraordinary resources and engineering efforts. We show that, even without achieving full fault tolerance, quantum error detection is already useful on the current generation of quantum hardware. We demonstrate this experimentally by executing an end-to-end chemical calculation for the hydrogen molecule encoded in the [[4, 2, 2]] quantum error-detecting code. The encoded calculation with logical qubits significantly improves the accuracy of the molecular ground-state energy.
Blind Visual Motif Removal From a Single Image

Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image. For example, decorative text that specifies where the image was taken, repeatedly appears across a variety of different images. Often, the reoccurring visual motif, is semantically similar, yet, differs in location, style and content (e.g. text placement, font and letters). This work proposes a deep learning based technique for blind removal of such objects. In the blind setting, the location and exact geometry of the motif are unknown. Our approach simultaneously estimates which pixels contain the visual motif, and synthesizes the underlying latent image. It is applied to a single input image, without any user assistance in specifying the location of the motif, achieving state-of-the-art results for blind removal of both opaque and semi-transparent visual motifs.
Unraveling Metric Vector Spaces With Factorization for Recommendation

Unlike all prior work, in this article, we investigate the notion of “unraveling metric vector spaces,” i.e., deriving meaning and low-rank structure from distance or metric space. Our new model bridges two commonly adopted paradigms for recommendations-metric learning approaches and factorization-based models, distinguishing itself accordingly. More concretely, we show that factorizing a metric vector space can be surprisingly efficacious. All in all, our proposed method, factorized metric learning, is highly effective for two classic recommendation tasks, possessing the potential of displacing many popular choices as an extremely strong baseline. We have done experiments on a number of real-world datasets, which show that our model performs better than recent state of the art largely on the rating prediction and item ranking tasks.
ElastImpact: 2.5D Multilevel Instant Impact Using Elasticity on Head-Mounted Displays

Impact is a common effect in both daily life and virtual reality (VR) experiences, e.g., being punched, hit or bumped. Impact force is instantly produced, which is distinct from other force feedback, e.g., push and pull. We propose ElastImpact to provide 2.5D instant impact on a head-mounted display (HMD) for realistic and versatile VR experiences. ElastImpact consists of three impact devices, also called impactors. Each impactor blocks an elastic band with a mechanical brake using a servo motor and extending it using a DC motor to store the impact power. When releasing the brake, it provides impact instantly. Two impactors are affixed on both sides of the head and connected with the HMD to provide the normal direction impact toward the face (i.e., 0.5D in z-axis). The other impactor is connected with a proxy collider in a barrel in front of the HMD and rotated by a DC motor in the tangential plane of the face to provide 2D impact (i.e., xy-plane). By performing a just-noticeable difference (JND) study, we realize users' impact force perception distinguishability on the heads in the normal direction and tangential plane, separately. Based on the results, we combine normal and tangential impact as 2.5D impact, and performed a VR experience study to verify that the proposed 2.5D impact significantly enhances realism.
Green Finance for Sustainable Development in Pakistan

Over the past few decades, technological advancements have caused various environmental issues.To contend with them, the concept of Green Finance was introduced which envisages that both public and private sectors should establish linkages between technological development, innovation, and the greening of the economy to explore untapped opportunities for economic growth.Subsequently, every country has focused on establishing green finance policies.However, the processes associated with following the new methods of financing in developed countries are easier to follow when compared with developing countries.The State Bank of Pakistan has recently introduced green banking policies with an aim to reduce the vulnerability of banks from risks arising from the environment, fulfill their responsibility for protecting the environment, and to provide financing to transform the economy.This article analyses the green finance mechanism for sustainable development in Pakistan by exploring the supply side of the green banking approach, which includes various issues faced by banks/DFIs, and the demand side, which refers to the compliance of green banking practices by the borrowers.It also reviews the policies that developing countries have implemented to initiate Green Finance and report that China and India are far better at formulating and implementing green banking practices.However, Pakistan is in the early stages of developing and formulating its Green Banking Strategy and this study provides suggestions for the implementation of such practices.
Efficient verification of quantum gates with local operations

Efficient verification of the functioning of quantum devices is a key to the development of quantum technologies, but is a daunting task as the system size increases. Here we propose a simple and general framework for verifying unitary transformations that can be applied to both individual quantum gates and gate sets, including quantum circuits. This framework enables efficient verification of many important unitary transformations, including but not limited to all bipartite unitaries, Clifford unitaries, generalized controlled-$Z$ gates, generalized controlled-not gates, the controlled-swap gate, and permutation transformations. For all these unitaries, the sample complexity increases at most linearly with the system size and is often independent of the system size. Moreover, little overhead is incurred even if one can only prepare Pauli eigenstates and perform local measurements. Our approach is applicable in many scenarios in which randomized benchmarking (RB) does not apply and is thus instrumental to quantum computation and many other applications in quantum information processing.
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge

Deep learning based general language models have achieved state-of-the-art results in many popular tasks such as sentiment analysis and QA tasks.Text in domains like social media has its own salient characteristics.Domain knowledge should be helpful in domain relevant tasks.In this work, we devise a simple method to obtain domain knowledge and further propose a method to integrate domain knowledge with general knowledge based on deep language models to improve performance of emotion classification.Experiments on Twitter data show that even though a deep language model fine-tuned by a target domain data has attained comparable results to that of previous state-of-the-art models, this fine-tuned model can still benefit from our extracted domain knowledge to obtain more improvement.This highlights the importance of making use of domain knowledge in domain-specific applications.
Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness

We share a French-English parallel corpus of Foursquare restaurant reviews, and define a new task to encourage research on Neural Machine Translation robustness and domain adaptation, in a real-world scenario where betterquality MT would be greatly beneficial.We discuss the challenges of such user-generated content, and train good baseline models that build upon the latest techniques for MT robustness.We also perform an extensive evaluation (automatic and human) that shows significant improvements over existing online systems.Finally, we propose task-specific metrics based on sentiment analysis or translation accuracy of domain-specific polysemous words.