Reviewerly/exHarmony huggingface dataset:
Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with Attention Mechanism

As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of prior knowledge, are time-consuming and ineffective for radar emitter signal recognition. In recent years, convolutional neural network (CNN) has shown its superiority in recognition so that experts have applied it in radar signal recognition. However, in the field of radar emitter signal recognition, the data are usually one-dimensional (1-D), which takes more time and storage space than by using the original two-dimensional CNN model directly. Moreover, the features extracted from convolutional layers are redundant so that the recognition accuracy is low. In order to solve these problems, this paper proposes a novel one-dimensional convolutional neural network with an attention mechanism (CNN-1D-AM) to extract more discriminative features and recognize the radar emitter signals. In this method, features of the given 1-D signal sequences are extracted directly by the 1-D convolutional layers and are weighted in accordance with their importance to recognition by the attention unit. The experiments based on seven different radar emitter signals indicate that the proposed CNN-1D-AM has the advantages of high accuracy and superior performance in radar emitter signal recognition.
Disability and the COVID-19 Pandemic

The COVID-19 pandemic has forced institutions to rapidly alter their behavior, which typically has disproportionate negative effects on people with disabilities as accessibility is overlooked. To investigate these issues, we analyzed Twitter data to examine accessibility problems surfaced by the crisis. We identified three key domains at the intersection of accessibility and technology: (i) the allocation of product delivery services, (ii) the transition to remote education, and (iii) the dissemination of public health information. We found that essential retailers expanded their high-risk customer shopping hours and pick-up and delivery services, but individuals with disabilities still lacked necessary access to goods and services. Long-experienced access barriers to online education were exacerbated by the abrupt transition of in-person to remote instruction. Finally, public health messaging has been inconsistent and inaccessible, which is unacceptable during a rapidly-evolving crisis. We argue that organizations should create flexible, accessible technology and policies in calm times to be adaptable in times of crisis to serve individuals with diverse needs.
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing

With the success of language pretraining, it is highly desirable to develop more efficient architectures of good scalability that can exploit the abundant unlabeled data at a lower cost. To improve the efficiency, we examine the much-overlooked redundancy in maintaining a full-length token-level presentation, especially for tasks that only require a single-vector presentation of the sequence. With this intuition, we propose Funnel-Transformer which gradually compresses the sequence of hidden states to a shorter one and hence reduces the computation cost. More importantly, by re-investing the saved FLOPs from length reduction in constructing a deeper or wider model, we further improve the model capacity. In addition, to perform token-level predictions as required by common pretraining objectives, Funnel-Transformer is able to recover a deep representation for each token from the reduced hidden sequence via a decoder. Empirically, with comparable or fewer FLOPs, Funnel-Transformer outperforms the standard Transformer on a wide variety of sequence-level prediction tasks, including text classification, language understanding, and reading comprehension. The code and pretrained checkpoints are available at this https URL.
Optimal-Time Dictionary-Compressed Indexes

We describe the first self-indexes able to count and locate pattern occurrences in optimal time within a space bounded by the size of the most popular dictionary compressors. To achieve this result, we combine several recent findings, including string attractors —new combinatorial objects encompassing most known compressibility measures for highly repetitive texts—and grammars based on locally consistent parsing . More in detail, letγ be the size of the smallest attractor for a text T of length n . The measureγ is an (asymptotic) lower bound to the size of dictionary compressors based on Lempel–Ziv, context-free grammars, and many others. The smallest known text representations in terms of attractors use space O (γ log ( n /γ)), and our lightest indexes work within the same asymptotic space. Let ε &gt; 0 be a suitably small constant fixed at construction time, m be the pattern length, and occ be the number of its text occurrences. Our index counts pattern occurrences in O ( m +log 2+ε n ) time and locates them in O ( m +( occ +1)log ε n ) time. These times already outperform those of most dictionary-compressed indexes, while obtaining the least asymptotic space for any index searching within O (( m + occ ),polylog, n ) time. Further, by increasing the space to O (γ log ( n /γ)log ε n ), we reduce the locating time to the optimal O ( m + occ ), and within O (γ log ( n /γ)log n ) space we can also count in optimal O ( m ) time. No dictionary-compressed index had obtained this time before. All our indexes can be constructed in O ( n ) space and O ( n log n ) expected time. As a by-product of independent interest, we show how to build, in O ( n ) expected time and without knowing the sizeγ of the smallest attractor (which is NP-hard to find), a run-length context-free grammar of size O (γ log ( n /γ)) generating (only) T . As a result, our indexes can be built without knowingγ.
How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks

We study how neural networks trained by gradient descent extrapolate, i.e., what they learn outside the support of the training distribution. Previous works report mixed empirical results when extrapolating with neural networks: while feedforward neural networks, a.k.a. multilayer perceptrons (MLPs), do not extrapolate well in certain simple tasks, Graph Neural Networks (GNNs), a structured network with MLP modules, have shown some success in more complex tasks. Working towards a theoretical explanation, we identify conditions under which MLPs and GNNs extrapolate well. First, we quantify the observation that ReLU MLPs quickly converge to linear functions along any direction from the origin, which implies that ReLU MLPs do not extrapolate most nonlinear functions. But, they can provably learn a linear target function when the training distribution is sufficiently diverse. Second, in connection to analyzing the successes and limitations of GNNs, these results suggest a hypothesis for which we provide theoretical and empirical evidence: the success of GNNs in extrapolating algorithmic tasks to new data (e.g., larger graphs or edge weights) relies on encoding task-specific non-linearities in the architecture or features. Our theoretical analysis builds on a connection of over-parameterized networks to the neural tangent kernel. Empirically, our theory holds across different training settings.
Findings of the WMT 2023 Shared Task on Quality Estimation

Frederic Blain, Chrysoula Zerva, Ricardo Ribeiro, Nuno M. Guerreiro, Diptesh Kanojia, José G. C. de Souza, Beatriz Silva, Tânia Vaz, Yan Jingxuan, Fatemeh Azadi, Constantin Orasan, André Martins. Proceedings of the Eighth Conference on Machine Translation. 2023.
Pushed Across the Digital Divide: COVID-19 Accelerated Pathology Training onto a New Digital Learning Curve

Bringing digital teaching materials into residency training programs has seen slow adoption, expected for many new technologies. The COVID-19 pandemic dramatically shifted the paradigm for many resident teaching modalities as institutions instituted social distancing to prevent spread of the novel coronavirus. The impact of this shift on pathology trainee education has not been well studied. We conducted an online survey of pathology trainees, program directors, and faculty to assess pre- and post-COVID-19 use of, and response to, various digital pathology modalities. Responses were solicited through both social media and directed appeals. A total of 261 respondents (112 faculty, 52 program directors, and 97 trainees) reported a dramatic and significant increase in the use of digital pathology-related education tools. A significant majority of faculty and program directors agreed that this shift had adversely affected the quality (59% and 62%, respectively) and effectiveness (66%) of their teaching. This perception was similar among learners relative to the impact on quality (59%) and effectiveness (64%) of learning. Most respondents (70%-92%) anticipate that their use of digital pathology education tools will increase or remain the same post-COVID. The global COVID-19 pandemic created a unique opportunity and challenge for pathology training programs. Digital pathology resources were accordingly readily adopted to continue supporting educational activities. The learning curve and utilization of this technology was perceived to impair the quality and effectiveness of teaching and learning. Since the use of digital tools appears poised to continue to grow post-COVID19, challenges due to impaired quality and effectiveness will need to be addressed.
Security in Next Generation Mobile Payment Systems: A Comprehensive Survey

Cash payment is still king in several markets, accounting for more than 90% of the payments in almost all the developing countries.The usage of mobile phones is pretty ordinary in this present era.Mobile phones have become an inseparable friend for many users, serving much more than just communication tools.Every subsequent person is heavily relying on them due to multifaceted usage and affordability.Every person wants to manage his/her daily transactions and related issues by using his/her mobile phone.With the rise and advancements of mobile-specific security, threats are evolving as well.In this paper, we provide a survey of various security models for mobile phones.We explore multiple proposed models of the mobile payment system (MPS), their technologies and comparisons, payment methods, different security mechanisms involved in MPS, and provide analysis of the encryption technologies, authentication methods, and firewall in MPS.We also present current challenges and future directions of mobile phone security.
Fusing Context Into Knowledge Graph for Commonsense Question Answering

Commonsense question answering (QA) requires a model to grasp commonsense and factual knowledge to answer questions about world events.Many prior methods couple language modeling with knowledge graphs (KG).However, although a KG contains rich structural information, it lacks the context to provide a more precise understanding of the concepts.This creates a gap when fusing knowledge graphs into language modeling, especially when there is insufficient labeled data.Thus, we propose to employ external entity descriptions to provide contextual information for knowledge understanding.We retrieve descriptions of related concepts from Wiktionary and feed them as additional input to pretrained language models.The resulting model achieves state-of-the-art result in the Common-senseQA dataset and the best result among non-generative models in OpenBookQA.
Explainable Embodied Agents Through Social Cues

The issue of how to make embodied agents explainable has experienced a surge of interest over the past 3 years, and there are many terms that refer to this concept, such as transparency and legibility. One reason for this high variance in terminology is the unique array of social cues that embodied agents can access in contrast to that accessed by non-embodied agents. Another reason is that different authors use these terms in different ways. Hence, we review the existing literature on explainability and organize it by (1) providing an overview of existing definitions, (2) showing how explainability is implemented and how it exploits different social cues, and (3) showing how the impact of explainability is measured. Additionally, we present a list of open questions and challenges that highlight areas that require further investigation by the community. This provides the interested reader with an overview of the current state of the art.
Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling

An extraordinary challenge for real-world applications is traffic sign recognition, which plays a crucial role in driver guidance. Traffic signals are very difficult to detect using an extremely precise, real-time approach in practical autonomous driving scenes. This article reviews several object detection methods, including Yolo V3 and Densenet, in conjunction with spatial pyramid pooling (SPP). The SPP principle is employed to boost the Yolo V3 and Densenet backbone networks to extract the features. Moreover, we adopt spatial pyramid pooling to learn object features more completely. These models are measured and compared with key measurement parameters such as average accuracy (mAP), working area size, detection time, and billion floating-point number (BFLOPS). Based on the experimental results, Yolo V3 SPP outperforms state-of-the-art systems. Specifically, Yolo V3 SPP obtains 87.8% accuracy for small (S) target, 98.0% for medium (M) target, and 98.6% for large target groups in the BTSD dataset. Our results have shown that Yolo V3 SPP obtains the highest total BFLOPS (66.111), and mAP (99.28%). Consequently, SPP upgrades the achievement of all experimental models.
A study on development of a hybrid aerial terrestrial robot system for avoiding ground obstacles by flight

To date, many studies related to robots have been performed around the world. Many of these studies have assumed operation at locations where entry is difficult, such as disaster sites, and have focused on various terrestrial robots, such as snake-like, humanoid, spider-type, and wheeled units. Another area of active research in recent years has been aerial robots with small helicopters for operation indoors and outdoors. However, less research has been performed on robots that operate both on the ground and in the air. Accordingly, in this paper, we propose a hybrid aerial/terrestrial robot system. The proposed robot system was developed by equipping a quadcopter with a mechanism for ground movement. It does not use power dedicated to ground movement, and instead uses the flight mechanism of the quadcopter to achieve ground movement as well. Furthermore, we addressed the issue of obstacle avoidance as part of studies on autonomous control. Thus, we found that autonomous control of ground movement and flight was possible for the hybrid aerial/terrestrial robot system, as was autonomous obstacle avoidance by flight when an obstacle appeared during ground movement.
Two Classes of Entropy Measures for Complex Fuzzy Sets

Complex fuzzy sets are characterized by complex-valued membership functions, whose range is extended from the traditional fuzzy range of [0,1] to the unit circle in the complex plane. In this paper, we define two kinds of entropy measures for complex fuzzy sets, called type-A and type-B entropy measures, and analyze their rotational invariance properties. Among them, two formulas of type-A entropy measures possess the attribute of rotational invariance, whereas the other two formulas of type-B entropy measures lack this characteristic.
Feasibility of using a fully immersive virtual reality system for kinematic data collection

Commercially-available Virtual Reality (VR) systems have the potential to be effective tools for simultaneous visual manipulation and kinematic data collection. Previously, these systems have been integrated with research-grade motion capture systems to provide both functionalities; however, they are yet to be used as stand-alone systems for kinematic data collection. The present study aimed to validate the HTC VIVE VR system for kinematic data collection by evaluating the accuracy of its position and orientation signals. The VIVE controller and tracker were each compared to a Polhemus Liberty magnetic tracking system sensor for angular and translational measurement error and signal drift. A sensor from each system was mounted to opposite ends of a rigid segment which was driven through fifty rotations and fifty translations. Mean angular errors for both the VIVE tracker and controller were below 0.4°. Mean translational error for both sensors was below 3 mm. Drift in the Liberty signal components was consistently lower than drift in VIVE components. However, all mean rotational drift measures were below 0.1° and all mean translational measures were below 0.35 mm. These data indicate that the HTC VIVE system has the potential to be a valid and reliable means of kinematic data collection. However, further investigation is necessary to determine the VIVE's suitability for capturing extremely minute or high-volume movements.
Abstract Text Summarization: A Low Resource Challenge

Shantipriya Parida, Petr Motlicek. 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.
Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter With Both Adaptivity and Robustness

This paper focuses on solving the problems of unknown measurement noise covariance and measurement outliers, which occurs in the vision/dual-IMU integrated attitude determination system. Although many adaptive filters and robust filters have been proposed to deal with the unknown measurement noise covariance or measurement outliers, most of them cannot handle both the unknown noise covariance and outliers. The adaptive filters assume no outliers in measurements and the robust filters assume accurate measurement noise covariance matrices. In this paper, we propose an adaptive and robust cubature Kalman filter, which achieves the adaptivity by estimating the measurement noise covariance through the variational Bayesian (VB) method, and achieves the robustness by suppressing the outliers based on the maximum correntropy criterion (MCC). The robustness and adaptivity of the proposed filter are verified through a typical tracking simulation example. Furthermore, the experimental results show that the proposed filter can obtain higher estimation accuracy than other filters in the vision/dual-IMU integrated system.
Probabilistic Future Prediction for Video Scene Understanding

We present a novel deep learning architecture for probabilistic future prediction from video. We predict the future semantics, geometry and motion of complex real-world urban scenes and use this representation to control an autonomous vehicle. This work is the first to jointly predict ego-motion, static scene, and the motion of dynamic agents in a probabilistic manner, which allows sampling consistent, highly probable futures from a compact latent space. Our model learns a representation from RGB video with a spatio-temporal convolutional module. The learned representation can be explicitly decoded to future semantic segmentation, depth, and optical flow, in addition to being an input to a learnt driving policy. To model the stochasticity of the future, we introduce a conditional variational approach which minimises the divergence between the present distribution (what could happen given what we have seen) and the future distribution (what we observe actually happens). During inference, diverse futures are generated by sampling from the present distribution.
Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images

Instance segmentation is an important task for biomedical and biological image analysis. Due to the complicated background components, the high variability of object appearances, numerous overlapping objects, and ambiguous object boundaries, this task still remains challenging. Recently, deep learning based methods have been widely employed to solve these problems and can be categorized into proposal-free and proposal-based methods. However, both proposal-free and proposal-based methods suffer from information loss, as they focus on either global-level semantic or local-level instance features. To tackle this issue, we present a Panoptic Feature Fusion Net (PFFNet) that unifies the semantic and instance features in this work. Specifically, our proposed PFFNet contains a residual attention feature fusion mechanism to incorporate the instance prediction with the semantic features, in order to facilitate the semantic contextual information learning in the instance branch. Then, a mask quality sub-branch is designed to align the confidence score of each object with the quality of the mask prediction. Furthermore, a consistency regularization mechanism is designed between the semantic segmentation tasks in the semantic and instance branches, for the robust learning of both tasks. Extensive experiments demonstrate the effectiveness of our proposed PFFNet, which outperforms several state-of-the-art methods on various biomedical and biological datasets.
Sudden death and revival of Gaussian Einstein–Podolsky–Rosen steering in noisy channels

Abstract Einstein–Podolsky–Rosen (EPR) steering is a useful resource for secure quantum information tasks. It is crucial to investigate the effect of inevitable loss and noise in quantum channels on EPR steering. We analyze and experimentally demonstrate the influence of purity of quantum states and excess noise on Gaussian EPR steering by distributing a two-mode squeezed state through lossy and noisy channels, respectively. We show that the impurity of state never leads to sudden death of Gaussian EPR steering, but the noise in quantum channel can. Then we revive the disappeared Gaussian EPR steering by establishing a correlated noisy channel. Different from entanglement, the sudden death and revival of Gaussian EPR steering are directional. Our result confirms that EPR steering criteria proposed by Reid and I. Kogias et al. are equivalent in our case. The presented results pave way for asymmetric quantum information processing exploiting Gaussian EPR steering in noisy environment.
Inferring Camouflaged Objects by Texture-Aware Interactive Guidance Network

Camouflaged objects, similar to the background, show indefinable boundaries and deceptive textures, which increases the difficulty of detection task and makes the model rely on features with more information. Herein, we design a texture label to facilitate our network for accurate camouflaged object segmentation. Motivated by the complementary relationship between texture labels and camouflaged object labels, we propose an interactive guidance framework named TINet, which focuses on finding the indefinable boundary and the texture difference by progressive interactive guidance. It maximizes the guidance effect of refined multi-level texture cues on segmentation. Specifically, texture perception decoder (TPD) makes a comprehensive analysis of texture information in multiple scales. Feature interaction guidance decoder (FGD) interactively refines multi-level features of camouflaged object detection and texture detection level by level. Holistic perception decoder (HPD) enhances FGD results by multi-level holistic perception. In addition, we propose a boundary weight map to help the loss function pay more attention to the object boundary. Sufficient experiments conducted on COD and SOD datasets demonstrate that the proposed method performs favorably against 23 state-of-the-art methods.