Reviewerly/exHarmony huggingface dataset:
Experimental reservoir computing using VCSEL polarization dynamics

We realize an experimental setup of a time-delay reservoir using a VCSEL with optical feedback and optical injection.The VCSEL is operated in the injection-locking regime.This allows us to solve different information processing tasks, such as chaotic time-series prediction with a NMSE of 1.6 × 10 -2 and nonlinear channel equalization with a SER of 1.5 × 10 -2 , improving state-of-the-art performance.We also demonstrate experimentally, through a careful statistical analysis, the impact of the VCSEL polarization dynamics on the performance of our architecture.More specifically, we confirm recent theoretical prediction stating that a polarization rotated feedback allows for the enhancement of the calculation performance compared to an isotropic feedback.
Literary Event Detection

In this work we present a new dataset of literary events—events that are depicted as taking place within the imagined space of a novel. While previous work has focused on event detection in the domain of contemporary news, literature poses a number of complications for existing systems, including complex narration, the depiction of a broad array of mental states, and a strong emphasis on figurative language. We outline the annotation decisions of this new dataset and compare several models for predicting events; the best performing model, a bidirectional LSTM with BERT token representations, achieves an F1 score of 73.9. We then apply this model to a corpus of novels split across two dimensions—prestige and popularity—and demonstrate that there are statistically significant differences in the distribution of events for prestige.
A Knowledge Discovery in Motion Sickness: A Comprehensive Literature Review

Motion sickness is a common perturbation experienced by humans in response to motion stimuli. The motion can happen in either real or virtual environments perceived by the vestibular system and visual illusion. The extensive varieties of research studies have been conducted in order to determine and evaluate aspects of motion sickness and its symptoms. To provide insights upon physiological changes in regards to motion sickness, researchers have used subjects from different ages, gender in addition to electrode positions and environmental conditions. The main purpose of this study is to provide a comprehensive review and comparison of the existing research studies regarding aspects of interference of the existence and augmentation of motion sickness. In this paper, we discuss the appearance of symptoms after motion sickness and summarize the physiological behaviors and emotions via a range of scenarios. In addition, the existing methods for measuring motion sickness levels are compared and discussed in detail. This study considers a number of important factors such as age, gender, health condition, participants (non/fatigue or non/drowsiness), road conditions, and different experimental set-ups impacting the results of motion sickness. Finally, this paper presents a range of practical methods to minimize and prevent the unpleasant side effects of motion sickness. This includes air ventilation, homogenized road/virtual environment features, and providing comfortable set-up and pre-movement before visual acceleration. A deeper understanding of changes in physiological signals during vection helps us to confirm the traditional subjective report and also improves our knowledge in the concept the vection.
Dynamics of reverse annealing for the fully connected <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>p</mml:mi></mml:math> -spin model

Reverse annealing, a variant of quantum annealing, is investigated by probing quantum transitions in a $p$-spin model and comparing it with the predictions offered by equilibrium statistical mechanics. It is shown that reverse annealing can provide an exponential speedup over conventional quantum annealing for this particular problem.
Spatially Attentive Visual Tracking Using Multi-Model Adaptive Response Fusion

Recent years have witnessed the top performances of integrating multi-level features from the pre-trained convolutional neural network (CNN) into correlation filters framework. However, they still suffer from background interference in detection stage due to the large search region and contamination of training samples caused by inaccurate tracking. In this paper, to suppress the interference of background features in target detection stage, an effective spatial attention map (SAM) is proposed to differently weight the multi-hierarchical convolutional features from search region to obtain the attentional features. This way helps to reduce the filter values corresponding to background features. Moreover, we construct multiple elementary correlation filter (ECF) models on multi-hierarchical deep features from CNN to track the target in parallel. To further improve the tracking stability, a multi-model adaptive response fusion (MAF) mechanism is presented. The mechanism can adaptively choose the outputs of reliable ECF models for adaptive weighted fusion by evaluating the confidences of response maps generated by attentional features convolved with ECF models. Finally, to adapt the target appearance changes in the following frames and avoid model corruption, we propose an adaptive updating strategy for the updates of the SAM and ECF models. We perform comprehensive experiments on OTB-2013 and OTB-2015 datasets and the experimental results show the superiority of our algorithm over other 12 state-of-the-art approaches.
Joint Link Prediction and Network Alignment via Cross-graph Embedding

Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondence as seeds, or from scratch. By extensive experiments on public benchmark, we show that link prediction and network alignment can benefit to each other especially for improving the recall for both tasks.
Detecting Robust Co-Saliency with Recurrent Co-Attention Neural Network

Effective feature representations which should not only express the images individual properties, but also reflect the interaction among group images are essentially crucial for robust co-saliency detection. This paper proposes a novel deep learning co-saliency detection approach which simultaneously learns single image properties and robust group feature in a recurrent manner. Specifically, our network first extracts the semantic features of each image. Then, a specially designed Recurrent Co-Attention Unit (RCAU) will explore all images in the group recurrently to generate the final group representation using the co-attention between images, and meanwhile suppresses noisy information. The group feature which contains complementary synergetic information is later merged with the single image features which express the unique properties to infer robust co-saliency. We also propose a novel co-perceptual loss to make full use of interactive relationships of whole images in the training group as the supervision in our end-to-end training process. Extensive experimental results demonstrate the superiority of our approach in comparison with the state-of-the-art methods.
Quantifying magic for multi-qubit operations

The development of a framework for quantifying 'non-stabilizerness' of quantum operations is motivated by the magic state model of fault-tolerant quantum computation and by the need to estimate classical simulation cost for noisy intermediate-scale quantum (NISQ) devices. The robustness of magic was recently proposed as a well-behaved magic monotone for multi-qubit states and quantifies the simulation overhead of circuits composed of Clifford + T gates, or circuits using other gates from the Clifford hierarchy. Here we present a general theory of the 'non-stabilizerness' of quantum operations rather than states, which are useful for classical simulation of more general circuits. We introduce two magic monotones, called channel robustness and magic capacity, which are well-defined for general n-qubit channels and treat all stabilizer-preserving CPTP maps as free operations. We present two complementary Monte Carlo-type classical simulation algorithms with sample complexity given by these quantities and provide examples of channels where the complexity of our algorithms is exponentially better than previously known simulators. We present additional techniques that ease the difficulty of calculating our monotones for special classes of channels.
Topology Optimization based Graph Convolutional Network

In the past few years, semi-supervised node classification in attributed network has been developed rapidly. Inspired by the success of deep learning, researchers adopt the convolutional neural network to develop the Graph Convolutional Networks (GCN), and they have achieved surprising classification accuracy by considering the topological information and employing the fully connected network (FCN). However, the given network topology may also induce a performance degradation if it is directly employed in classification, because it may possess high sparsity and certain noises. Besides, the lack of learnable filters in GCN also limits the performance. In this paper, we propose a novel Topology Optimization based Graph Convolutional Networks (TO-GCN) to fully utilize the potential information by jointly refining the network topology and learning the parameters of the FCN. According to our derivations, TO-GCN is more flexible than GCN, in which the filters are fixed and only the classifier can be updated during the learning process. Extensive experiments on real attributed networks demonstrate the superiority of the proposed TO-GCN against the state-of-the-art approaches.
A Surprisingly Effective Fix for Deep Latent Variable Modeling of Text

When trained effectively, the Variational Autoencoder (VAE) is both a powerful language model and an effective representation learning framework.In practice, however, VAEs are trained with the evidence lower bound (ELBO) as a surrogate objective to the intractable marginal data likelihood.This approach to training yields unstable results, frequently leading to a disastrous local optimum known as posterior collapse.In this paper, we investigate a simple fix for posterior collapse which yields surprisingly effective results.The combination of two known heuristics, previously considered only in isolation, substantially improves held-out likelihood, reconstruction, and latent representation learning when compared with previous state-of-theart methods.More interestingly, while our experiments demonstrate superiority on these principle evaluations, our method obtains a worse ELBO.We use these results to argue that the typical surrogate objective for VAEs may not be sufficient or necessarily appropriate for balancing the goals of representation learning and data distribution modeling.
Self-Assembling Modular Networks for Interpretable Multi-Hop Reasoning

Yichen Jiang, Mohit Bansal. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Online UAV Path Planning for Joint Detection and Tracking of Multiple Radio-Tagged Objects

We consider the problem of online path planning for joint detection and tracking of multiple unknown radio-tagged objects. This is a necessary task for gathering spatio-temporal information using UAVs with on-board sensors in a range of monitoring applications. In this paper, we propose an online path planning algorithm with joint detection and tracking because signal measurements from these objects are inherently noisy. We derive a partially observable Markov decision process with a random finite set track-before-detect (TBD) multi-object filter, which also maintains a safe distance between the UAV and the objects of interest using a void probability constraint. We show that, in practice, the multi-object likelihood function of raw signals received by the UAV in the time-frequency domain is separable and results in a numerically efficient multi-object TBD filter. We derive a TBD filter with a jump Markov system to accommodate maneuvering objects capable of switching between different dynamic modes. Our evaluations demonstrate the capability of the proposed approach to handle multiple radio-tagged objects subject to birth, death, and motion modes. Moreover, this online planning method with the TBD-based filter outperforms its detection-based counterparts in detection and tracking, especially in low signal-to-noise ratio environments.
The effect of blended learning on the achievement of ninth grade students in science and their attitudes towards its use

This study aimed to investigate the effects of blended learning on ninth grade students' achievement in science and their attitudes towards using it. It compares the results of various ways of teaching science topics, and students' attitudes towards their use. The study was conducted using a quasi-experimental design case study. The participants of the study were 112 students, divided into two groups: one an experimental group (n = 61) and the other a control group (n = 51). An achievement test and questionnaire were designed to confirm the study's validity and reliability. SPSS was used to analyze the data. The findings revealed that there were statistically significant differences between the experimental and the control groups, in favor of the experimental group, and the experimental group's attitudes were also more positive towards the using of blended learning. Their attitudes were in favor of students with academic performance in a science subject of the Performance level (Pass). The study recommends further research into the use of blended learning in higher education institutions.
ACMM: Aligned Cross-Modal Memory for Few-Shot Image and Sentence Matching

Image and sentence matching has drawn much attention recently, but due to the lack of sufficient pairwise data for training, most previous methods still cannot well associate those challenging pairs of images and sentences containing rarely appeared regions and words, i.e., few-shot content. In this work, we study this challenging scenario as few-shot image and sentence matching, and accordingly propose an Aligned Cross-Modal Memory (ACMM) model to memorize the rarely appeared content. Given a pair of image and sentence, the model first includes an aligned memory controller network to produce two sets of semantically-comparable interface vectors through cross-modal alignment. Then the interface vectors are used by modality-specific read and update operations to alternatively interact with shared memory items. The memory items persistently memorize cross-modal shared semantic representations, which can be addressed out to better enhance the representation of few-shot content. We apply the proposed model to both conventional and few-shot image and sentence matching tasks, and demonstrate its effectiveness by achieving the state-of-the-art performance on two benchmark datasets.
Accurate Markov Boundary Discovery for Causal Feature Selection

Causal feature selection has achieved much attention in recent years, which discovers a Markov boundary (MB) of the class attribute. The MB of the class attribute implies local causal relations between the class attribute and the features, thus leading to more interpretable and robust prediction models than the features selected by the traditional feature selection algorithms. Many causal feature selection methods have been proposed, and almost all of them employ conditional independence (CI) tests to identify MBs. However, many datasets from real-world applications may suffer from incorrect CI tests due to noise or small-sized samples, resulting in lower MB discovery accuracy for these existing algorithms. To tackle this issue, in this article, we first introduce a new concept of PCMasking to explain a type of incorrect CI tests in the MB discovery, then propose a cross-check and complement MB discovery (CCMB) algorithm to repair this type of incorrect CI tests for accurate MB discovery. To improve the efficiency of CCMB, we further design a pipeline machine-based CCMB (PM-CCMB) algorithm. Using benchmark Bayesian network datasets, the experiments demonstrate that both CCMB and PM-CCMB achieve significant improvements on the MB discovery accuracy compared with the existing methods, and PM-CCMB further improves the computational efficiency. The empirical study in the real-world datasets validates the effectiveness of CCMB and PM-CCMB against the state-of-the-art causal and traditional feature selection algorithms.
sCompile: Critical Path Identification and Analysis for Smart Contracts

Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is important for smart contracts to be checked against potential vulnerabilities. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise present them with user friendly warnings for user inspection. We identify paths which involve monetary transaction as critical paths, and prioritize those which potentially violate important properties. For scalability, symbolic execution techniques are only applied to top ranked critical paths. Our approach has been implemented in a tool called sCompile, which has been applied to 36,099 smart contracts. The experiment results show that sCompile is efficient, i.e., 5 s on average for one smart contract. Furthermore, we show that many known vulnerabilities can be captured if user inspects as few as 10 program paths generated by sCompile. Lastly, sCompile discovered 224 unknown vulnerabilities with a false positive rate of 15.4% before user inspection.
Long- and Short-term Preference Learning for Next POI Recommendation

Next POI recommendation has been studied extensively in recent years. The goal is to recommend next POI for users at specific time given users' historical check-in data. Therefore, it is crucial to model users' general taste and recent sequential behavior. Moreover, the context information such as the category and check-in time is also important to capture user preference. To this end, we propose a long- and short-term preference learning model (LSPL) considering the sequential and context information. In long-term module, we learn the contextual features of POIs and leverage attention mechanism to capture users' preference. In the short-term module, we utilize LSTM to learn the sequential behavior of users. Specifically, to better learn the different influence of location and category of POIs, we train two LSTM models for location-based sequence and category-based sequence, respectively. Then we combine the long and short-term results to recommend next POI for users. At last, we evaluate the proposed model on two real-world datasets. The experiment results demonstrate that our method outperforms the state-of-art approaches for next POI recommendation.
A Large-Scale Dataset for Argument Quality Ranking: Construction and Analysis

Identifying the quality of free-text arguments has become an important task in the rapidly expanding field of computational argumentation. In this work, we explore the challenging task of argument quality ranking. To this end, we created a corpus of 30,497 arguments carefully annotated for point-wise quality, released as part of this work. To the best of our knowledge, this is the largest dataset annotated for point-wise argument quality, larger by a factor of five than previously released datasets. Moreover, we address the core issue of inducing a labeled score from crowd annotations by performing a comprehensive evaluation of different approaches to this problem. In addition, we analyze the quality dimensions that characterize this dataset. Finally, we present a neural method for argument quality ranking, which outperforms several baselines on our own dataset, as well as previous methods published for another dataset.
Hierarchical Online Instance Matching for Person Search

Person Search is a challenging task which requires to retrieve a person's image and the corresponding position from an image dataset. It consists of two sub-tasks: pedestrian detection and person re-identification (re-ID). One of the key challenges is to properly combine the two sub-tasks into a unified framework. Existing works usually adopt a straightforward strategy by concatenating a detector and a re-ID model directly, either into an integrated model or into separated models. We argue that simply concatenating detection and re-ID is a sub-optimal solution, and we propose a Hierarchical Online Instance Matching (HOIM) loss which exploits the hierarchical relationship between detection and re-ID to guide the learning of our network. Our novel HOIM loss function harmonizes the objectives of the two sub-tasks and encourages better feature learning. In addition, we improve the loss update policy by introducing Selective Memory Refreshment (SMR) for unlabeled persons, which takes advantage of the potential discrimination power of unlabeled data. From the experiments on two standard person search benchmarks, i.e. CUHK-SYSU and PRW, we achieve state-of-the-art performance, which justifies the effectiveness of our proposed HOIM loss on learning robust features.
Attention Based Data Hiding with Generative Adversarial Networks

Recently, the generative adversarial network is the hotspot in research and industrial areas. Its application on data generation is the most common usage. In this paper, we propose the novel end-to-end framework to extend its application to data hiding area. The discriminative model simulates the detection process, which can help us understand the sensitivity of the cover image to semantic changes. The generative model is to generate the target image which is aligned with the original cover image. An attention model is introduced to generate the attention mask. This mask can help to generate a better target image without perturbation of the spotlight. The introduction of cycle discriminative model and inconsistent loss can help to enhance the quality of the generated target image in the iterative training process. The training dataset is mixed with intact images and attacked images. The mix training process can further improve robustness. Through the qualitative, quantitative experiments and analysis, this novel framework shows compelling performance and advantages over the current state-of-the-art methods in data hiding applications.