Reviewerly/exHarmony huggingface dataset:
Accelerated Variational Quantum Eigensolver

The problem of finding the ground state energy of a Hamiltonian using a quantum computer is currently solved using either the quantum phase estimation (QPE) or variational quantum eigensolver (VQE) algorithms. For precision $\ensuremath{\epsilon}$, QPE requires $O(1)$ repetitions of circuits with depth $O(1/\ensuremath{\epsilon})$, whereas each expectation estimation subroutine within VQE requires $O(1/{\ensuremath{\epsilon}}^{2})$ samples from circuits with depth $O(1)$. We propose a generalized VQE algorithm that interpolates between these two regimes via a free parameter $\ensuremath{\alpha}\ensuremath{\in}[0,1]$, which can exploit quantum coherence over a circuit depth of $O(1/{\ensuremath{\epsilon}}^{\ensuremath{\alpha}})$ to reduce the number of samples to $O(1/{\ensuremath{\epsilon}}^{2(1\ensuremath{-}\ensuremath{\alpha})})$. Along the way, we give a new routine for expectation estimation under limited quantum resources that is of independent interest.
An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets

Learning and mining from imbalanced datasets gained increased interest in recent years. One simple but efficient way to increase the performance of standard machine learning techniques on imbalanced datasets is the synthetic generation of minority samples. In this paper, a detailed, empirical comparison of 85 variants of minority oversampling techniques is presented and discussed involving 104 imbalanced datasets for evaluation. The goal of the work is to set a new baseline in the field, determine the oversampling principles leading to the best results under general circumstances, and also give guidance to practitioners on which techniques to use with certain types of datasets.
Moderator engagement and community development in the age of algorithms

Online communities provide a forum for rich social interaction and identity development for billions of Internet users worldwide. In order to manage these communities, platform owners have increasingly turned to commercial content moderation, which includes both the use of moderation algorithms and the employment of professional moderators, rather than user-driven moderation, to detect and respond to anti-normative behaviors such as harassment and spread of offensive content. We present findings from semi-structured interviews with 56 volunteer moderators of online communities across three platforms (Twitch, Reddit, and Facebook), from which we derived a generalized model categorizing the ways moderators engage with their communities and explaining how these communities develop as a result. This model contains three processes: being and becoming a moderator; moderation tasks, actions, and responses; and rules and community development. In this work, we describe how moderators contribute to the development of meaningful communities, both with and without algorithmic support.
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing

Tal Schuster, Ori Ram, Regina Barzilay, Amir Globerson. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Design of task-specific optical systems using broadband diffractive neural networks

Deep learning has been transformative in many fields, motivating the emergence of various optical computing architectures. Diffractive optical network is a recently introduced optical computing framework that merges wave optics with deep-learning methods to design optical neural networks. Diffraction-based all-optical object recognition systems, designed through this framework and fabricated by 3D printing, have been reported to recognize hand-written digits and fashion products, demonstrating all-optical inference and generalization to sub-classes of data. These previous diffractive approaches employed monochromatic coherent light as the illumination source. Here, we report a broadband diffractive optical neural network design that simultaneously processes a continuum of wavelengths generated by a temporally incoherent broadband source to all-optically perform a specific task learned using deep learning. We experimentally validated the success of this broadband diffractive neural network architecture by designing, fabricating and testing seven different multi-layer, diffractive optical systems that transform the optical wavefront generated by a broadband THz pulse to realize (1) a series of tuneable, single-passband and dual-passband spectral filters and (2) spatially controlled wavelength de-multiplexing. Merging the native or engineered dispersion of various material systems with a deep-learning-based design strategy, broadband diffractive neural networks help us engineer the light-matter interaction in 3D, diverging from intuitive and analytical design methods to create task-specific optical components that can all-optically perform deterministic tasks or statistical inference for optical machine learning.
A Novel Deep Learning-Based Collaborative Filtering Model for Recommendation System

The collaborative filtering (CF) based models are capable of grasping the interaction or correlation of users and items under consideration. However, existing CF-based methods can only grasp single type of relation, such as restricted Boltzmann machine which distinctly seize the correlation of user-user or item-item relation. On the other hand, matrix factorization explicitly captures the interaction between them. To overcome these setbacks in CF-based methods, we propose a novel deep learning method which imitates an effective intelligent recommendation by understanding the users and items beforehand. In the initial stage, corresponding low-dimensional vectors of users and items are learned separately, which embeds the semantic information reflecting the user-user and item-item correlation. During the prediction stage, a feed-forward neural networks is employed to simulate the interaction between user and item, where the corresponding pretrained representational vectors are taken as inputs of the neural networks. Several experiments based on two benchmark datasets (MovieLens 1M and MovieLens 10M) are carried out to verify the effectiveness of the proposed method, and the result shows that our model outperforms previous methods that used feed-forward neural networks by a significant margin and performs very comparably with state-of-the-art methods on both datasets.
Domain-specific knowledge graphs: A survey

Knowledge Graphs (KGs) have made a qualitative leap and effected a real revolution in knowledge representation. This is leveraged by the underlying structure of the KG which underpins a better comprehension, reasoning and interpretation of knowledge for both human and machine. Therefore, KGs continue to be used as the main means of tackling a plethora of real-life problems in various domains. However, there is no consensus in regard to a plausible and inclusive definition of a domain-specific KG. Further, in conjunction with several limitations and deficiencies, various domain-specific KG construction approaches are far from perfect. This survey is the first to offer a comprehensive definition of a domain-specific KG. Also, the paper presents a thorough review of the state-of-the-art approaches drawn from academic works relevant to seven domains of knowledge. An examination of current approaches reveals a range of limitations and deficiencies. At the same time, uncharted territories on the research map are highlighted to tackle extant issues in the literature and point to directions for future research.
Investigating the process of learning with desktop virtual reality: A structural equation modeling approach

Virtual reality (VR) is gaining attention for having the potential to enrich students’ educational experiences. However, few studies have investigated the process of learning with VR. With the use of structural equation modeling, this study investigated the affective and cognitive factors that play a role in learning with a desktop VR simulation when pre-to post-test changes in motivation, self-efficacy, and knowledge about genetics are used as outcomes. The sample consisted of 199 university students (120 females), who learned from a desktop VR genetics simulation as a mandatory part of an undergraduate medical genetics course. The results indicated that there were two general paths by which desktop VR led to increases in the amount of learning following a VR lesson: an affective path that went through VR features, presence, intrinsic motivation, and self-efficacy; and a cognitive path that went through VR features, usability, cognitive benefits, and self-efficacy. It is concluded that learners may benefit from desktop VR simulations in which efficacious VR features and a high level of usability are emphasized.
SRM: A Style-Based Recalibration Module for Convolutional Neural Networks

Following the advance of style transfer with Convolutional Neural Networks (CNNs), the role of styles in CNNs has drawn growing attention from a broader perspective. In this paper, we aim to fully leverage the potential of styles to improve the performance of CNNs in general vision tasks. We propose a Style-based Recalibration Module (SRM), a simple yet effective architectural unit, which adaptively recalibrates intermediate feature maps by exploiting their styles. SRM first extracts the style information from each channel of the feature maps by style pooling, then estimates per-channel recalibration weight via channel-independent style integration. By incorporating the relative importance of individual styles into feature maps, SRM effectively enhances the representational ability of a CNN. The proposed module is directly fed into existing CNN architectures with negligible overhead. We conduct comprehensive experiments on general image recognition as well as tasks related to styles, which verify the benefit of SRM over recent approaches such as Squeeze-and-Excitation (SE). To explain the inherent difference between SRM and SE, we provide an in-depth comparison of their representational properties.
Reinforcement Learning to Optimize Long-term User Engagement in Recommender Systems

Recommender systems play a crucial role in our daily lives. Feed streaming mechanism has been widely used in the recommender system, especially on the mobile Apps. The feed streaming setting provides users the interactive manner of recommendation in never-ending feeds. In such a manner, a good recommender system should pay more attention to user stickiness, which is far beyond classical instant metrics and typically measured by long-term user engagement. Directly optimizing long-term user engagement is a non-trivial problem, as the learning target is usually not available for conventional supervised learning methods. Though reinforcement learning~(RL) naturally fits the problem of maximizing the long term rewards, applying RL to optimize long-term user engagement is still facing challenges: user behaviors are versatile to model, which typically consists of both instant feedback (eg. clicks) and delayed feedback (eg. dwell time, revisit); in addition, performing effective off-policy learning is still immature, especially when combining bootstrapping and function approximation. To address these issues, in this work, we introduce a RL framework --- FeedRec to optimize the long-term user engagement. FeedRec includes two components: 1)~a Q-Network which designed in hierarchical LSTM takes charge of modeling complex user behaviors, and 2)~a S-Network, which simulates the environment, assists the Q-Network and voids the instability of convergence in policy learning. Extensive experiments on synthetic data and a real-world large scale data show that FeedRec effectively optimizes the long-term user engagement and outperforms state-of-the-arts.
Differential evolution: A recent review based on state-of-the-art works

Differential evolution (DE) is a popular evolutionary algorithm inspired by Darwin’s theory of evolution and has been studied extensively to solve different areas of optimisation and engineering applications since its introduction by Storn in 1997. This study aims to review the massive progress of DE in the research community by analysing the 192 articles published on this subject from 1997 to 2021, particularly studies in the past five years. The methodology used to search for relevant DE papers and an overview of the original DE are firstly explained. Recent advances in the modifications proposed to enhance the effectiveness and efficiency of the original DE are reviewed by analysing the strengths and weaknesses of each published work, followed by the potential applications of these DE variants in solving different real-world engineering problems. In contrast to most existing DE review papers, additional analyses are performed in this survey by investigating the impacts of various parameter settings on given DE variants to identify their optimal values required for solving certain problem classes. The qualities of modifications incorporated into selected DE variants are also evaluated by measuring the performance gains achieved in terms of search accuracy and/or efficiency against the original DE. The additional surveys conducted in this study are anticipated to provide more insightful perspectives for both beginners and experts of DE research, enabling their better understanding about current research trends and new motivations to outline appropriate strategic planning for future development works.
Perceptual-Sensitive GAN for Generating Adversarial Patches

Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Recently, adversarial patch, with noise confined to a small and localized patch, emerged for its easy accessibility in real-world. However, existing attack strategies are still far from generating visually natural patches with strong attacking ability, since they often ignore the perceptual sensitivity of the attacked network to the adversarial patch, including both the correlations with the image context and the visual attention. To address this problem, this paper proposes a perceptual-sensitive generative adversarial network (PS-GAN) that can simultaneously enhance the visual fidelity and the attacking ability for the adversarial patch. To improve the visual fidelity, we treat the patch generation as a patch-to-patch translation via an adversarial process, feeding any types of seed patch and outputting the similar adversarial patch with high perceptual correlation with the attacked image. To further enhance the attacking ability, an attention mechanism coupled with adversarial generation is introduced to predict the critical attacking areas for placing the patches, which can help producing more realistic and aggressive patches. Extensive experiments under semi-whitebox and black-box settings on two large-scale datasets GTSRB and ImageNet demonstrate that the proposed PS-GAN outperforms state-of-the-art adversarial patch attack methods.
Multi-Task Deep Reinforcement Learning with PopArt

The reinforcement learning (RL) community has made great strides in designing algorithms capable of exceeding human performance on specific tasks. These algorithms are mostly trained one task at the time, each new task requiring to train a brand new agent instance. This means the learning algorithm is general, but each solution is not; each agent can only solve the one task it was trained on. In this work, we study the problem of learning to master not one but multiple sequentialdecision tasks at once. A general issue in multi-task learning is that a balance must be found between the needs of multiple tasks competing for the limited resources of a single learning system. Many learning algorithms can get distracted by certain tasks in the set of tasks to solve. Such tasks appear more salient to the learning process, for instance because of the density or magnitude of the in-task rewards. This causes the algorithm to focus on those salient tasks at the expense of generality. We propose to automatically adapt the contribution of each task to the agent’s updates, so that all tasks have a similar impact on the learning dynamics. This resulted in state of the art performance on learning to play all games in a set of 57 diverse Atari games. Excitingly, our method learned a single trained policy - with a single set of weights - that exceeds median human performance. To our knowledge, this was the first time a single agent surpassed human-level performance on this multi-task domain. The same approach also demonstrated state of the art performance on a set of 30 tasks in the 3D reinforcement learning platform DeepMind Lab.
Evaluating Probabilistic Forecasts with <b>scoringRules</b>

Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography.In typical applications, many alternative statistical models and data sources can be used to produce probabilistic forecasts.Hence, evaluating and selecting among competing methods is an important task.The scoringRules package for R provides functionality for comparative evaluation of probabilistic models based on proper scoring rules, covering a wide range of situations in applied work.This paper discusses implementation and usage details, presents case studies from meteorology and economics, and points to the relevant background literature.
Fused Matrix Factorization with Geographical and Social Influence in Location-Based Social Networks

Recently, location-based social networks (LBSNs), such as Gowalla, Foursquare, Facebook, and Brightkite, etc., have attracted millions of users to share their social friendship and their locations via check-ins. The available check-in information makes it possible to mine users’ preference on locations and to provide favorite recommendations. Personalized Point-of-interest (POI) recommendation is a significant task in LBSNs since it can help targeted users explore their surroundings as well as help third-party developers to provide personalized services. To solve this task, matrix factorization is a promising tool due to its success in recommender systems. However, previously proposed matrix factorization (MF) methods do not explore geographical influence, e.g., multi-center check-in property, which yields suboptimal solutions for the recommendation. In this paper, to the best of our knowledge, we are the first to fuse MF with geographical and social influence for POI recommendation in LBSNs. We first capture the geographical influence via modeling the probability of a user’s check-in on a location as a Multi-center Gaussian Model (MGM). Next, we include social information and fuse the geographical influence into a generalized matrix factorization framework. Our solution to POI recommendation is efficient and scales linearly with the number of observations. Finally, we conduct thorough experiments on a large-scale real-world LBSNs dataset and demonstrate that the fused matrix factorization framework with MGM utilizes the distance information sufficiently and outperforms other state-of-the-art methods significantly.
Chinese Text Sentiment Analysis Based on Extended Sentiment Dictionary

The method of text sentiment analysis based on sentiment dictionary often has the problems that the sentiment dictionary doesn't contain enough sentiment words or omits some field sentiment words. In addition, due to the existence of some polysemic sentiment words with positivity, negativity, and neutrality, the words' polarity cannot be accurately expressed, so the accuracy of text sentiment analysis is reduced to some extent. In this paper, an extended sentiment dictionary is constructed. The extended sentiment dictionary contains the basic sentiment words, the field sentiment words, and the polysemic sentiment words, which improves the accuracy of sentiment analysis. The naive Bayesian classifier is used to determine the field of the text in which the polysemic sentiment word is. Thus, the sentiment value of the polysemic sentiment word in the field is obtained. By utilizing the extended sentiment dictionary and the designed sentiment score rules, the sentiment of the text is achieved. The experimental results prove that the proposed sentiment analysis method based on extended sentiment dictionary has certain feasibility and accuracy. The research is meaningful for the sentiment recognition of the comment texts.
MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

Factory machinery is prone to failure or breakdown, resulting in significant expenses for companies.Hence, there is a rising interest in machine monitoring using different sensors including microphones.In scientific community, the emergence of public datasets has been promoting the advancement in acoustic detection and classification of scenes and events, but there are no public datasets that focus on the sound of industrial machines under normal and anomalous operating conditions in real factory environments.In this paper, we present a new dataset of industrial machine sounds which we call a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset).Normal and anomalous sounds were recorded for different types of industrial machines, i.e. valves, pumps, fans and slide rails.To resemble the real-life scenario, various anomalous sounds have been recorded, for instance, contamination, leakage, rotating unbalance, rail damage, etc.The purpose of releasing the MIMII dataset is to help the machine-learning and signal-processing community to advance the development of automated facility maintenance.
Studying human behavior with virtual reality: The Unity Experiment Framework

Virtual reality (VR) systems offer a powerful tool for human behavior research. The ability to create three-dimensional visual scenes and to measure responses to the visual stimuli enables the behavioral researcher to test hypotheses in a manner and scale that were previously unfeasible. For example, a researcher wanting to understand interceptive timing behavior might wish to violate Newtonian mechanics so that objects can move in novel 3-D trajectories. The same researcher might wish to collect such data with hundreds of participants outside the laboratory, and the use of a VR headset makes this a realistic proposition. The difficulty facing the researcher is that sophisticated 3-D graphics engines (e.g., Unity) have been created for game designers rather than behavioral scientists. To overcome this barrier, we have created a set of tools and programming syntaxes that allow logical encoding of the common experimental features required by the behavioral scientist. The Unity Experiment Framework (UXF) allows researchers to readily implement several forms of data collection and provides them with the ability to easily modify independent variables. UXF does not offer any stimulus presentation features, so the full power of the Unity game engine can be exploited. We use a case study experiment, measuring postural sway in response to an oscillating virtual room, to show that UXF can replicate and advance upon behavioral research paradigms. We show that UXF can simplify and speed up the development of VR experiments created in commercial gaming software and facilitate the efficient acquisition of large quantities of behavioral research data.