Reviewerly/exHarmony huggingface dataset:
Past and Present of Natural Language Processing

The use of computer technology to study and process the natural language started in the late 1940s and the early 1950s. Over the past 60 years, this research has made considerable progress and has become an important emerging discipline in the contemporary computer science. In the network information age, the natural language processing (NLP) has attracted an increasing attention, becoming a globally significant learning. Various theories and methods have been proposed. This handbook is a manual on formal analyses of the NLP, being devoted to a systematic summary of NLP theories and methods developed so far with emphasis on its formal models.
Formal Models Based on Unification

In the development of natural language processing, the earliest automatic grammatical analysis theory was phrase structure grammar proposed by American linguist N. Chomsky. Because phrase structure grammar makes the generation of natural language sentences computable, this language theory has been widely used in the computer processing of natural language. In this chapter, we shall introduce unification method based on complex features.
Formal Models Based on Lexicalism

Differences between languages are mostly found in lexis. Due to the importance of lexis in linguistic research, lexicalism began to emerge, and building lexical resources has gained wide attention in NLP.
A Mixed Precision Randomized Preconditioner for the LSQR Solver on GPUs

Randomized preconditioners for large-scale regression problems have become extremely popular over the past decade. Such preconditioners are known to accelerate large-scale regression solvers both from a theoretical and a practical perspective. In this paper, we present a mixed precision randomized preconditioner for LSQR solvers, focusing on overdetermined, dense least squares problems. We implement and evaluate our method on GPUs and we demonstrate that it outperforms the standard double precision version of randomized, preconditioned LSQR by up to $$20\%$$ on the NVIDIA A100. We present extensive numerical experiments utilizing the half-precision and tensorcore units to demonstrate that, in many cases, constructing the preconditioner in reduced precision does not affect the convergence of LSQR solvers. This leads to important speedups without loss of accuracy.
Efficient Large Scale DLRM Implementation on Heterogeneous Memory Systems

We propose a new data structure called CachedEmbeddings for training large scale deep learning recommendation models (DLRM) efficiently on heterogeneous (DRAM + non-volatile) memory platforms. CachedEmbeddings implements an implicit software-managed cache and data movement optimization that is integrated with the Julia programming framework to optimize the implementation of large scale DLRM implementations with multiple sparse embedded tables operations. In particular we show an implementation that is 1.4X to 2X better than the best known Intel CPU based implementations on state-of-the-art DLRM benchmarks on a real heterogeneous memory platform from Intel, and 1.32X to 1.45X improvement over Intel’s 2LM implementation that treats the DRAM as a hardware managed cache.
Knowledge Graphs

Knowledge graphs are a large-scale knowledge base designed by Google for intelligent search. Having become an important knowledge source in NLP research, knowledge graphs are now used by many internet companies to organize knowledge stored in the Semantic Web.
Formal Models of Probabilistic Grammar

To enable computers to process natural languages, Chomsky’s context-free grammar is often used for rule-based syntactic parsing. Based on context-free grammar, researchers have designed effective parsing techniques, such as the top-down parsing method, the bottom-up parsing method, the left-corner parsing method, the CYK algorithm, the Earley algorithm, and the chart parsing method. However, these methods can hardly deal with ambiguities in natural languages. In recent years, improvement in context-free grammar has been made primarily in two aspects. First, probabilistic context-free grammar has been proposed so that rules in context-free grammar can be assigned different probabilities. Second, probabilistic lexicalized context-free grammar has been proposed to take into account not only the probabilities of the rules but also the influence of head words on these probabilities. These methods combine the rule-based rationalist approach and the statistical empiricist approach in a skillful way and reflect a new trend in NLP. This chapter introduces probabilistic grammar, including probabilistic context-free grammar for syntactic ambiguity, the fundamentals of probabilistic context-free grammar, three hypotheses regarding probabilistic context-free grammar, and probabilistic lexicalized context-free grammar.
Formal Models of Neural Network and Deep Learning

Having been widely used in NLP in recent years, neural networks and deep learning have gradually become the mainstream technology in NLP research. Therefore, this chapter will present some details about models based on neural networks and deep learning, including the evolution of neural networks, neural networks of our brain, artificial neural networks, machine learning, deep learning, word vectors, word embedding, dense word vectors, perceptrons, feedforward neural networks, convolutional neural networks, recurrent neural networks, attention mechanisms, external memory, and pretrained models (such as Transformer and BERT).
Pioneers in the Study of Language Computing

Before the advent of electronic computers, there were some farsighted scholars who studied computational issues of the language. They studied this linguistic phenomenon from the perspective of computation, revealing the mathematical nature of the language.
Formal Models of Automatic Situation and Pragmatic Processing

Systemic functional grammar holds that language has both situational and internal contexts and that situation plays an important role in language understanding. Since different sentences are often used to express the same message due to different situations, systemic functional grammar is a kind of linguistic theory that studies different sentence expressions according to functional differences of situations. It has been widely used in the NLP. Pragmatics is the study of the relationship between the language and the context in which it is used. As the context of language use includes ontologies such as people and things, pragmatics involves studying how to use language to indicate or refer back to people and things. Pragmatics also involves the formation of the structure of an utterance and the study of how the listener understands the conversation because the context of language use also includes the context of the utterance. Research on formal models of automatic pragmatic processing has achieved preliminary results.
Concluding Remarks

The development of the Internet has led us into the era of big data, which has amazingly accelerated statistics-based research on NLP.
Spherical Earth Based Measurement Modeling for Practical OTHR Target Tracking

We revisit the problem of modeling of the over-the-horizon radar (OTHR) target tracking. The accuracy of target kinematic and measurement models are critical factors affecting tracking performance. In most of the existing literature, a flat Earth OTHR measurement model is used. This has two obvious drawbacks: (1) a state vector with components of ground range, range-rate, bearing, and bearing-rate cannot be used directly in common kinematic models such as the nearly constant velocity (NCV), nearly constant acceleration (NCA), nearly constant turn (NCT), and a combination of these. (2) The curvature of Earth is ignored, which affects the measurement accuracy of the OTHR. We develop a spherical Earth based measurement model. First, the target kinematic state is modeled in a two-dimensional tracker coordinate frame (T frame), which is suitable for the common kinematic models. Second, the measurement modeling is completed using the spherical Earth model, which fully considers the curvature of Earth among the receiver, transmitter, and target. Furthermore, the inverse mapping for track initiation is derived. Finally, the effectiveness of the developed model is demonstrated by a non-maneuvering target moving with the NCV motion and a maneuvering target moving with a sequence of NCV, NCT, and NCV motions. The interacting multiple model (IMM) algorithm is used for state estimation of the maneuvering target. The unscented Kalman filter (UKF), cubature Kalman filter (CKF), and quadrature Kalman filter (QKF) are compared for state estimation.
Learning Posterior Distributions in Underdetermined Inverse Problems

In recent years, classical knowledge-driven approaches for inverse problems have been complemented by data-driven methods exploiting the power of machine and especially deep learning. Purely data-driven methods, however, come with the drawback of disregarding prior knowledge of the problem even though it has shown to be beneficial to incorporate this knowledge into the problem-solving process. We thus introduce an unpaired learning approach for learning posterior distributions of underdetermined inverse problems. It combines advantages of deep generative modeling with established ideas of knowledge-driven approaches by incorporating prior information about the inverse problem. We develop a new neural network architecture 'UnDimFlow' (short for Unequal Dimensionality Flow) consisting of two normalizing flows, one from the data to the latent, and one from the latent to the solution space. Additionally, we incorporate the forward operator to develop an unpaired learning method for the UnDimFlow architecture and propose a tailored point estimator to recover an optimal solution during inference. We evaluate our method on the two underdetermined inverse problems of image inpainting and super-resolution.
Artificial Intelligence, Capabilities, Liabilities: Interactions in the Shadows of Regulation, Antitrust – And Family Law

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Proximal Residual Flows for Bayesian Inverse Problems

Normalizing flows are a powerful tool for generative modelling, density estimation and posterior reconstruction in Bayesian inverse problems. In this paper, we introduce proximal residual flows, a new architecture of normalizing flows. Based on the fact, that proximal neural networks are by definition averaged operators, we ensure invertibility of certain residual blocks. Moreover, we extend the architecture to conditional proximal residual flows for posterior reconstruction within Bayesian inverse problems. We demonstrate the performance of proximal residual flows on numerical examples.
A Closer Look at Video Sampling for Sequential Action Recognition

In recent years, sequential action recognition has attracted increasingly attention as it requires long-term sequential and compositional reasoning of human actions and object interactions. Existing methods perform reasoning either by using snippets that cover very short consecutive frames or key frames sampled from segments, which take a bias process of local and global temporal information. We also find ad-hoc training and ensembling of two separate networks using existing sampling strategies can easily outperform complex state-of-the-art methods, which reveals the complementary nature of current sampling strategies. Motivated by this observation, we propose a simple yet efficient strategy named Dense Segmental Sampling (DSS) and a novel network architecture named Temporal Dense Segment Network (TDSN) to capture the complementary information from DSS. Our TDSN achieves excellent results on benchmark action recognition datasets, which not only validate the proposed strategy but also help highlight the importance along this direction for sequential video reasoning.
Hybrid Training of Denoising Networks to Improve the Texture Acutance of Digital Cameras

In order to evaluate the capacity of a camera to render textures properly, the standard practice, used by classical scoring protocols, is to compute the frequential response to a dead leaves image target, from which is built a texture acutance metric. In this work, we propose a mixed training procedure for image restoration neural networks, relying on both natural and synthetic images, that yields a strong improvement of this acutance metric without impairing fidelity terms. The feasibility of the approach is demonstrated both on the denoising of RGB images and the full development of RAW images, opening the path to a systematic improvement of the texture acutance of real imaging devices.
ResNet-WGAN-Based End-to-End Learning for IoV Communication With Unknown Channels

An end-to-end learning framework is proposed to optimize each module jointly in the communication system. Recently, convolutional neural network (CNN) and conditional Generative Adversarial Network (cGAN) are used for end-to-end learning. However, deeper network layers will degrade the effect of CNN. cGAN suffers from unstable training and lacks generative diversity. In this article, we propose the end-to-end learning based on deep residual network (ResNet) and Wasserstein GAN (WGAN) for communication with unknown channels (ResNet-WGAN). First, ResNet is applied to solve the problem of network degradation to extract deeper data features. Second, for unknown channels, WGAN with conditional information is used to fit the channel effect to improve training stability and generative diversity. Finally, we present the simulation results of the ResNet-WGAN under additive white Gaussian noise (AWGN) channel, Rayleigh fading channel, and frequency selective channel. The results demonstrate that the ResNet-WGAN reduces the communication bit error rate (BER) and block error rate (BLER). In particular, this article applies ResNet-WGAN to the Internet of Vehicles (IoV) communication, and the results demonstrate that ResNet-WGAN is more effective.
Graph Laplacian and Neural Networks for Inverse Problems in Imaging: GraphLaNet

In imaging problems, the graph Laplacian is proven to be a very effective regularization operator when a good approximation of the image to restore is available. In this paper, we study a Tikhonov method that embeds the graph Laplacian operator in a $$\ell _1$$ –norm penalty term. The novelty is that the graph Laplacian is built upon a first approximation of the solution obtained as the output of a trained neural network. Numerical examples in 2D computerized tomography demonstrate the efficacy of the proposed method.
Piece-wise Constant Image Segmentation with a Deep Image Prior Approach

Image segmentation is a key topic in image processing and computer vision and several approaches have been proposed in the literature to address it. The formulation of the image segmentation problem as the minimization of the Mumford-Shah energy has been one of the most commonly used techniques in the last past decades. More recently, deep learning methods have yielded a new generation of image segmentation models with remarkable performance. In this paper we propose an unsupervised deep learning approach for piece-wise image segmentation based on the so called Deep Image Prior by parameterizing the Mumford-Shah functional in terms of the weights of a convolutional neural network. Several numerical experiments on both biomedical and natural images highlight the goodness of the suggested approach. The implicit regularization provided by the Deep Image Prior model allows to also consider noisy input images and to investigate the robustness of the proposed technique with respect to the level of noise.
VerFHS: Verifiable Image Retrieval on Forward Privacy in Blockchain-Enabled IoT

In the scenario of the Internet of Things (IoT) co-located with the cloud, many applications, such as face recognition, traffic monitoring, and medical diagnosis, usually outsource a large amount of generated image data to cloud servers (CSs) to reduce the burden of local storage. Many secure encrypted image retrieval schemes have been proposed to protect data privacy. However, existing work incurs storage and communication burdens, lacks verifiability of query results and has potential forward security threats. To solve these issues, we propose the VerFHS framework in this article, which can satisfy Verifiability, Feedback, and High-Security. Specifically, we first present an extended secure <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -NN algorithm to protect indexes, cleverly design ciphertext inner product for similarity comparison, and use the reward mechanism of blockchain to build a monitoring and feedback mechanism for CSs. Then we demonstrate an enhanced VerFHS scheme in the dynamic setting (VerFHSD) that uses a permutation matrix to process image encryption against adaptive attacks during dynamic updates. VerFHSD prevents CSs from making search queries over newly added images via previous tokens, thereby achieving forward security. The formal security analysis shows that our schemes protect the privacy of images, indexes and query tokens, and forward security. And extensive experiments using the real-world data set demonstrate that our scheme not only has the highest search accuracy all the time, but also achieves efficient queries at the millisecond level, when compared with other advanced image retrieval schemes.
Study of Cost Index System for Nuclear Power Plant

Cost index is an effective tool for the cost estimation of nuclear power plants (NPPs). Especially, during the early planning of a commercial NPP, the cost index is needed for the general design and investment. Based on several 1000mW commercial NPPs that were already set up in China resent years, a cost index system is established for the base construction cost. Focus on the design phase of general design, the details of establishing a cost index system is established in this study. A path way of using this cost index to cost estimation is alsodiscussed.
Perception-Aware Trajectory Planning for a Pair of Unicycle-Like Robots With Absolute and Relative Ranging Measurements

We consider two “kidnapped” unicycle vehicles released in an unknown environment. Each one is in sight of a ranging sensor (anchor) and has to choose a trajectory that enables it to localise itself relying only on its anchor measurements, on its odometry and on the information (state and mutual distance) that it can exchange with the other vehicle when they are sufficiently close. We propose a motion planning algorithm that solves the simultaneous localisation problem in this challenging scenario.
LOGEN: Few-Shot Logical Knowledge-Conditioned Text Generation With Self-Training

Natural language generation from structured data mainly focuses on surface-level descriptions, suffering from uncontrollable content selection and low fidelity. Previous works leverage logical forms to facilitate logical knowledge-conditioned text generation. Though achieving remarkable progress, they are data-hungry, which makes the adoption for real-world applications challenging with limited data. To this end, this paper proposes a unified framework for logical knowledge-conditioned text generation in the few-shot setting. With only a few seeds logical forms (e.g., 20/100 shot), our approach leverages self-training and samples pseudo logical forms based on content and structure consistency. Experimental results demonstrate that our approach can obtain better few-shot performance than baselines.
Semantic Enhancement Framework for Robust Speech Recognition

Auto speech recognition (ASR) has been widely used in dialogue systems of various domains, performing as a crucial part of technology. Since the output of the ASR system will provide input to the subsequent system, the semantic intelligibility problem of the recognition results draws wide attention, yet remains unsolved. We propose a semantic enhancement framework to extract global semantic information from the audio to guide the recognition results. We evaluate our method on the Wall Street Journal (WSJ) dataset. The proposed framework gain relative 5.9% and 9.1% improvement of the WER on dev93 set and eval92 set compared to the baseline model.
Mongolian Text-to-Speech Challenge Under Low-Resource Scenario for NCMMSC2022

Mongolian Text-to-Speech (TTS) Challenge under Low-Resource Scenario is a special session for National Conference on Man-Machine Speech Communication 2022 (NCMMSC2022), termed as NCMMSC2022-MTTSC. A Mongolian TTS dataset was provided to participants this year, and a low-resource Mongolian TTS task was designed. Specifically, the task is to synthesize high-quality Mongolian speech with given Mongolian scripts. Thirteen teams submitted their results for final evaluation. Mean opinion score (MOS) listening tests were conducted online to measure the naturalness, intelligibility of the synthetic speech. In addition, the word error rate (WER) of automatic speech recognition was further treated as the objective metric for intelligibility evaluation. The evaluation results show that the top system achieved comparable naturalness and intelligibility with the ground truth speech.
Resolution-Invariant Image Classification Based on Fourier Neural Operators

In this paper we investigate the use of Fourier Neural Operators (FNOs) for image classification in comparison to standard Convolutional Neural Networks (CNNs). Neural operators are a discretization-invariant generalization of neural networks to approximate operators between infinite dimensional function spaces. FNOs—which are neural operators with a specific parametrization—have been applied successfully in the context of parametric PDEs. We derive the FNO architecture as an example for continuous and Fréchet-differentiable neural operators on Lebesgue spaces. We further show how CNNs can be converted into FNOs and vice versa and propose an interpolation-equivariant adaptation of the architecture.
Transformer-Based Potential Emotional Relation Mining Network for Emotion Recognition in Conversation

Emotion recognition in conversation (ERC) has attracted much attention due to its widespread applications in the field of human communication analysis. Compared with the vanilla sentiment analysis of the single utterance, the ERC task which aims to judge the emotion labels of utterances in the conversation requires modeling both the contextual information and the speaker dependency. However, previous models are limited in exploring the potential emotional relation of the utterances. To address the problem, we propose a novel transformer-based potential emotion relation mining network (TPERMN) to better explore the potential emotional relation and integrate the emotional clues. First, we utilize the global gated recurrence unit to extract the situation-level emotion vector. Then, different speaker GRU are assigned to different speakers to capture the intra-speaker dependency of the utterances and obtain the speaker-level emotion vector. Second, a potential relation mining transformer called PERformer is devised to extract the potential emotional relation and integrate emotional clues for the situation-level and speaker-level emotion vector. In PERformer, we combine graph attention and multi-head attention mechanism to explore the deep semantic information and potential emotional relation. And an emotion augment block is designed to enhance and complement the inherent characteristics. After multi-layer accumulation, the updated representation is obtained for emotion classification. Detailed experiments on two public ERC datasets demonstrate our model outperforms the state-of-the-art models.