Reviewerly/exHarmony huggingface dataset:
A sequence to sequence model for dialogue generation with gated mixture of topics

In this paper, we propose GMoT-Seq2Seq, a sequence to sequence (Seq2Seq) model with a gated mixture of topics (MoT) designed to utilize topic information to generate fluent and coherent responses. Seq2Seq model is good at capturing the local structure of word sequence which affects the fluency due to their sequential nature, but probably has difficulty to extract topic information from the utterance. In contrast, topic models are very capable of capturing global semantic information that has a direct impact on the coherence. Absorbing the advantages of both, the proposed GMoT-Seq2Seq model uses a Seq2Seq to capture the temporal dependencies, and an MoT layer to obtain the topic vector that provides global semantic dependencies in the conversation. The MoT layer can summarize the utterances into a proportion vector over several underlying topics. To balance the fluency and coherence, we utilize a topic gate to dynamically control the information from the inferred topic vector and the partially generated responses. Experiment results show that our proposed model outperforms the compared baselines, and can generate more fluent and coherent responses.
HW-TSC’s Participation in the WMT 2020 News Translation Shared Task

This paper presents our work in the WMT 2020 News Translation Shared Task. We participate in 3 language pairs including Zh/En, Km/En, and Ps/En and in both directions under the constrained condition. We use the standard Transformer-Big model as the baseline and obtain the best performance via two variants with larger parameter sizes. We perform detailed pre-processing and filtering on the provided large-scale bilingual and monolingual dataset. Several commonly used strategies are used to train our models such as Back Translation, Ensemble Knowledge Distillation, etc. We also conduct experiment with similar language augmentation, which lead to positive results, although not used in our submission. Our submission obtains remarkable results in the final evaluation.
Computational Separation Between Convolutional and Fully-Connected Networks

Convolutional neural networks (CNN) exhibit unmatched performance in a multitude of computer vision tasks. However, the advantage of using convolutional networks over fully-connected networks is not understood from a theoretical perspective. In this work, we show how convolutional networks can leverage locality in the data, and thus achieve a computational advantage over fully-connected networks. Specifically, we show a class of problems that can be efficiently solved using convolutional networks trained with gradient-descent, but at the same time is hard to learn using a polynomial-size fully-connected network.
Deep Sketch-Guided Cartoon Video Inbetweening

We propose a novel framework to produce cartoon videos by fetching the color information from two input keyframes while following the animated motion guided by a user sketch. The key idea of the proposed approach is to estimate the dense cross-domain correspondence between the sketch and cartoon video frames, and employ a blending module with occlusion estimation to synthesize the middle frame guided by the sketch. After that, the input frames and the synthetic frame equipped with established correspondence are fed into an arbitrary-time frame interpolation pipeline to generate and refine additional inbetween frames. Finally, a module to preserve temporal consistency is employed. Compared to common frame interpolation methods, our approach can address frames with relatively large motion and also has the flexibility to enable users to control the generated video sequences by editing the sketch guidance. By explicitly considering the correspondence between frames and the sketch, we can achieve higher quality results than other image synthesis methods. Our results show that our system generalizes well to different movie frames, achieving better results than existing solutions.
A critical overview on Quantum Computing

Quantum Computing and Quantum Information Science seem very promising and developing rapidly since its inception in early 1980s by Paul Benioff with the proposal of quantum mechanical model of the Turing machine and later By Richard Feynman and Yuri Manin for the proposal of a quantum computers for simulating various problems that classical computer could not. Quantum computers have a computational advantage for some problems, over classical computers and most applications are trying to use an efficient combination of classical and quantum computers like Shor’s factoring algorithm. Other areas that are expected to be benefitted from quantum computing are Machine Learning and deep learning, molecular biology, genomics and cancer research, space exploration, atomic and nuclear research and macro-economic forecasting. This paper represents a brief overview of the state of art of quantum computing and quantum information science with discussions of various theoretical and experimental aspects adopted by the researchers.
Calibration method of meteorological sensor based on enhanced BP network

Meteorological observation plays an important role in establishing atmospheric theory and improving the accuracy of weather forecasts. Sensors are the foundation and the core in the meteorological observation. Only the accurate calibration of the sensor can ensure the validity of measurement data. In most current methods of sensor calibration, the least squares method is used for calibration which results in low calibration accuracy. In this paper, a sensor calibration model is implemented by using artificial intelligence technology. By combining a back propagation (BP) neural network, a Gaussian function and the Levenberg-Marquardt (LM) algorithm, an enhanced BP network is realized for sensor calibration. The calibration model is transplanted to a Microcontroller unit (MCU). The Gaussian function is fitted by piecewise polynomials, which effectively reduces the computing resources and time use of the MCU. The experimental results show that: the traditional BP network reduces the mean squared error (MSE) of the atmospheric pressure sensor from 5.93 to 2.83. This reduces the measurement error by 52.28%. The enhanced BP network reduces the MSE to 0.77, which further reduces the measurement error by 34.74%. By fitting a Gaussian function to a polynomial, the computing time of MCU is reduced from 40 us to 0.1 us, which significantly reduces the complexity of the algorithm.
Imbalanced Thangka Image Classification research Based on the ResNet Network

Abstract Aiming at the problem of performance degradation caused by ignoring the softmax score of the wrong class in the training process of the unbalanced data set of Thangka images and the problem of the loss of negative feature information in the propagation process of the ReLU activation function, a new loss calculation method is proposed. Firstly, the parameters of the pre-training model on the COCO data set are used as the initial parameters. Secondly, CE and CCE are used to calculate the loss during the calculation of loss in the back propagation. Finally, AReLU activation function is used and a weight assigned to CE and CCE is added as final loss to update the parameters. The experimental results show that this algorithm improves the convergence speed and accuracy of the model with respect to imbalanced data. Compared with other loss functions, ours method performance is state-of-the-art, such as complement cross entropy.
Balancing Freedoms, Rights and Responsibilities during COVID in Us: A Study of Anti- and Pro-Restriction Discourse

Countries across the world have instituted unprecedented restrictions on freedom of movement, privacy and individual rights to control the spread of COVID-19. These measures tend to have been derived from communally orientated East Asian cultures. The way that culturally relevant concepts of rights and freedoms underpin COVID restrictions in democratic and individually orientated countries remains unknown. This data memo addresses this issue through analysis of pro- and anti-restriction discourse on social media in the US. It finds that anti-social and economic restriction discourse more frequently articulates rights and freedoms, based on ideas of inviolable rights to freedom of movement or freedom of economic activity or a cost-benefit analysis that places economic activity over public health. Pro-social and economic restriction discourse less frequently mentions rights and freedoms, instead supporting restrictions as following state and medical advice and out of deference and respect to medical professionals. Discourse is highly polarised and divisive and articulated largely through established political identity positions. It is suggested that more attention is paid to discussions of balancing rights and freedoms in COVID control restrictions. To convince opposers of restrictions, supporters of restrictions should base arguments around communal rights and positive freedoms. It is also important to critically evaluate whether and how these perspectives need to be adapted to be appropriate and resonant in democratic and individualistic countries.
Authors and Machines

Machines, by providing the means of mass production of works of authorship, engendered copyright law. Throughout history, the emergence of new technologies tested the concept of authorship, and courts in response endeavored to clarify copyright’s foundational principles. Today, developments in computer science have created a new form of machine — the “artificially intelligent” system apparently endowed with “computational creativity” — that introduces challenging variations on the perennial question of what makes one an “author” in copyright law: Is the creator of a generative program automatically the author of the works her process begets, even if she cannot anticipate the contents of those works? Does the user of the program become the (or an) author of an output whose content the user has at least in part defined? This article frames these and similar questionsthat generative machines provoke as an opportunity to revisit the concept of copyright authorship in general and to illuminate its murkier corners. This article examines several fundamental relationships (between author and amanuensis, between author and tool, and between author and co-author) as well as several authorship anomalies (including the problem of “accidental” or “indeterminate” authorship) to unearth the basic principles and latent ambiguities which have nourished debates over the meaning of the “author” in copyright. We present an overarching and internally consistent model of authorship based on two basic pillars: a mental step (the conception of a work) and a physical step (the execution of a work), and define the contours of these basic pillars to arrive at a cohesive definition of authorship. We then apply the conception-and-execution theory of authorship to reach a series of conclusions about the question of machine “authorship.” We contend that even the most technologically advanced machines of our era are little more than faithful agents of the humans who design or use them. Asking whether a computer can be an author therefore is the “wrong” question; the “right” question addresses how to evaluate the authorial claims of the humans involved in either preparing or using the machines that “create.” We argue that in many cases, either the upstream human being who programs and trains a machine to produce an output, or the downstream human being who requests the output, is sufficiently involved in the conception and execution of the resulting work to claim authorship. But in some instances, the contributions of the human designer and user will be too attenuated from the work’s creation for either to qualify as “authors” — leaving the work “authorless.”
Learning Associative Inference Using Fast Weight Memory

Humans can quickly associate stimuli to solve problems in novel contexts. Our novel neural network model learns state representations of facts that can be composed to perform such associative inference. To this end, we augment the LSTM model with an associative memory, dubbed \textit{Fast Weight Memory} (FWM). Through differentiable operations at every step of a given input sequence, the LSTM \textit{updates and maintains} compositional associations stored in the rapidly changing FWM weights. Our model is trained end-to-end by gradient descent and yields excellent performance on compositional language reasoning problems, meta-reinforcement-learning for POMDPs, and small-scale word-level language modelling.
Decorrelated unbiased converted measurement for bistatic radar tracking

Bistatic radar target tracking is challenging due to the fact that the measurements are nonlinear functions of the Cartesian state. The converted measurement Kalman filter (CMKF) converts the raw measurement into Cartesian coordinates prior to tracking and is superior to the extended Kalman filter for certain problems. The challenges of CMKF are debiasing the converted measurement and approximating the converted measurement error covariance. Due to no closed form of biases, we utilize the second-order Taylor series expansion of the conventional measurement conversion to find the conversion bias in bistatic radar and propose the unbiased converted measurement (UCM). In order to decorrelate the converted measurement error covariance from the measurement noise, we evaluate the covariance using the prediction in Bayesian recursive filtering, designated as the decorrelated unbiased converted measurement (DUCM). Monte Carlo simulations show that the DUCM is unbiased and consistent, and the DUCM filter exhibits an improved performance compared with the conventional CMKF and the UCM filter in bistatic radar tracking.
Talking Realities: Audio Guides in Virtual Reality Visualizations

Building upon the ideas of storytelling and explorable explanations, we introduce Talking Realities, a concept for producing data-driven interactive narratives in virtual reality. It combines an audio narrative with an immersive visualization to communicate analysis results. The narrative is automatically produced using template-based natural language generation and adapts to data and user interactions. The synchronized animation of visual elements in accordance with the audio connects the two representations. In addition, we discuss various modes of explanation ranging from fully guided tours to free exploration of the data. We demonstrate the applicability of our concept by developing a virtual reality visualization for air traffic data. Furthermore, generalizability is exhibited by sketching mock-ups for two more application scenarios in the context of information and scientific visualization.
AttnGrounder: Talking to Cars with Attention

We propose the Attention Grounder (AttnGrounder), a single-stage end-to-end trainable model for the task of visual grounding. Visual grounding aims to localize a specific object in an image based on a given natural language text query. Unlike previous methods that use the same text representation for every image region, we use a visual-text attention module that relates each word in the given query with every region in the corresponding image, constructing a region dependent text representation. Furthermore, to improve the localization ability of our model, we use a visual-text attention module that generates an attention mask around the referred object. The attention mask is trained as an auxiliary task using a rectangular mask generated with the provided ground-truth coordinates. We evaluate the AttnGrounder on the Talk2Car dataset and show an improvement of 3.26% over the existing methods. The code is available at https://github.com/i-m-vivek/AttnGrounder .
Addressing Cybersecurity in the Next Generation Mobility Ecosystem with CARAMEL

The proliferation of next generation mobility, promotes the use of autonomous cars, connected vehicles and electromobility. It creates novel attack surfaces for high impact cyberattacks affecting the society. Addressing the cybersecurity challenges introduced by modern vehicles requires a proactive and multi-faceted approach combining techniques originating from various domains of ICT. Emerging technologies such as 5G, LiDAR, novel in-vehicle and roadside sensors and smart charging, used in modern cars, introduce new challenges and potential security gaps in the next generation mobility ecosystem. Thus, it is critical that the domain’s cybersecurity must be approached in a structured manner from a multi-domain and multi-technology perspective. The CARAMEL H2020 project aims to address the cybersecurity challenges on the pillars upon which the next generation mobility is constructed (i.e., autonomous mobility, connected mobility, electromobility). To achieve that, advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques will be utilized for the identification of anomalies and the classification of incoming signals indicating a cyber-attack or a cybersecurity risk. Apart from risk detection, methods for the mitigation of the identified risks will also be continuously incorporated to the CARAMEL solution. The final goal of CARAMEL is to create an anti-hacking platform for the European automotive cybersecurity and to demonstrate its value through extensive attack and penetration scenarios. In this paper we will expand on the unique cybersecurity-relevant characteristics of the pillars upon which the CARAMEL solution is built. Next, a number of use cases emerging from such analysis will be extracted in order to form the basis upon which the CARAMEL platform will be evaluated. Finally, we will conclude with an overview of the platform’s architectural composition.
Coherent-driving-assisted quantum speedup in Markovian channels*

As is well known, the quantum evolution speed of quantum state can never be accelerated in the Markovian regime without any operators on the system. The Hamiltonian corrections induced by the action of coherent driving forces are often used to fight dissipative and decoherence mechanisms in experiments. For this reason, considering three noisy channels (the phase-flip channel, the amplitude damping channel and the depolarizing channel), we propose a scheme of speedup evolution of an open system by controlling an external unitary coherent driving operator on the system. It is shown that, in the presence of the coherent driving, no-speedup evolution can be transformed into quantum speedup evolution in the Markovian dynamics process. Additionally, under the fixed coherent driving strength in the above three noisy channels, the best way to achieve the most degree of quantum speedup for the system has been acquired by rotating the system with appropriate driving direction angles, respectively. Finally, we conclude that the reason for this acceleration is not the non-Markovian dynamical behavior of the system but due to the oscillation of geometric distance between the initial state and the target final state.
A Novel Method for Multiple Biomedical Events Extraction with Reinforcement Learning and Knowledge Bases

Biomedical event extraction is usually modeled as two sub-tasks: trigger identification and argument detection. Most existing methods perform these two sub-tasks sequentially but ignore the interaction between them. This paper proposes a novel method for multiple biomedical events extraction, in which the task of event extraction is modeled under a framework of reinforcement learning (RL). We treat the trigger identification and argument detection as main-task and subsidiary-task, respectively. And the result of argument detection is modeled as environmental information. In this way, the proposed method can capture the interaction between two sub-tasks, and the semantic associations among multiple biomedical events are also utilized effectively. Moreover, external biomedical knowledge bases are employed for representation learning of biomedical text. Comprehensive experiments are conducted on two widely used biomedical corpora, and results demonstrate that our method gains better performance compared to existing methods, especially in multiple biomedical events extraction.
Maintenance cost in the processing of subject–verb dependencies.

Although research in sentence comprehension has suggested that processing long-distance dependencies involves maintenance between the elements that form the dependency, studies on maintenance of long-distance subject-verb (SV) dependencies are scarce. The few relevant studies have delivered mixed results using self-paced reading or phoneme-monitoring tasks. In the current study, we used eye tracking during reading to test whether maintaining a long-distance SV dependency results in a processing cost on an intervening adverbial clause. In Experiment 1, we studied this question in Spanish and found that both go-past reading times and regressions out of an adverbial clause to the previous regions were significantly increased when the clause interrupts a SV dependency compared to when the same clause doesn't interrupt this dependency. We then replicated these findings in English (Experiment 2), observing significantly increased go-past reading times on a clause interrupting a SV dependency. The current study provides the first eye-tracking data showing a maintenance cost in the processing of SV dependencies cross-linguistically. Sentence comprehension models should account for the maintenance cost generated by SV dependency processing, and future research should focus on the nature of the maintained representation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Online clustering-based multi-camera vehicle tracking in scenarios with overlapping FOVs

Abstract Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring, one of the main research fields of Intelligent Transportation Systems. Several offline approaches have been proposed to address this task; however, they are not compatible with real-world applications due to their high latency and post-processing requirements. This lack of suitable approaches motivates our proposal: A new low-latency online approach for MTMC tracking in scenarios with partially overlapping fields of view (FOVs), such as road intersections. Firstly, the proposed approach detects vehicles at each camera. Then, the detections are merged between cameras by applying cross-camera clustering based on appearance and location. Lastly, the clusters containing different detections of the same vehicle are temporally associated to compute the tracks on a frame-by-frame basis. The experiments show promising low-latency results while addressing real-world challenges such as the a priori unknown and time-varying number of targets and the continuous state estimation of them without performing any post-processing of the trajectories. Our code is available at http://www-vpu.eps.uam.es/publications/Online-MTMC-Tracking .
Short Text Clustering with Transformers

Recent techniques for the task of short text clustering often rely on word embeddings as a transfer learning component. This paper shows that sentence vector representations from Transformers in conjunction with different clustering methods can be successfully applied to address the task. Furthermore, we demonstrate that the algorithm of enhancement of clustering via iterative classification can further improve initial clustering performance with different classifiers, including those based on pre-trained Transformer language models.