Reviewerly/exHarmony huggingface dataset:
Capitalization Normalization for Language Modeling with an Accurate and Efficient Hierarchical RNN Model

Capitalization normalization (truecasing) is the task of restoring the correct case (uppercase or lowercase) of noisy text. We propose a fast, accurate and compact two-level hierarchical word-and-character-based recurrent neural network model. We use the truecaser to normalize user-generated text in a Federated Learning framework for language modeling. A case-aware language model trained on this normalized text achieves the same perplexity as a model trained on text with gold capitalization. In a real user A/B experiment, we demonstrate that the improvement translates to reduced prediction error rates in a virtual keyboard application. Similarly, in an ASR language model fusion experiment, we show reduction in uppercase character error rate and word error rate.
DebtFree: minimizing labeling cost in self-admitted technical debt identification using semi-supervised learning

Keeping track of and managing Self-Admitted Technical Debts (SATDs) is important for maintaining a healthy software project. Current active-learning SATD recognition tool involves manual inspection of 24% of the test comments on average to reach 90% of the recall. Among all the test comments, about 5% are SATDs. The human experts are then required to read almost a quintuple of the SATD comments which indicates the inefficiency of the tool. Plus, human experts are still prone to error: 95% of the false-positive labels from previous work were actually true positives. To solve the above problems, we propose DebtFree, a two-mode framework based on unsupervised learning for identifying SATDs. In mode1, when the existing training data is unlabeled, DebtFree starts with an unsupervised learner to automatically pseudo-label the programming comments in the training data. In contrast, in mode2 where labels are available with the corresponding training data, DebtFree starts with a pre-processor that identifies the highly prone SATDs from the test dataset. Then, our machine learning model is employed to assist human experts in manually identifying the remaining SATDs. Our experiments on 10 software projects show that both models yield statistically significant improvement in effectiveness over the state-of-the-art automated and semi-automated models. Specifically, DebtFree can reduce the labeling effort by 99% in mode1 (unlabeled training data), and up to 63% in mode2 (labeled training data) while improving the current active learner's F1 relatively to almost 100%.
elBERto: Self-supervised commonsense learning for question answering

Commonsense question answering requires reasoning about everyday situations and causes and effects implicit in context. Typically, existing approaches first retrieve external evidence and then perform commonsense reasoning using these evidence. In this paper, we propose a Self-supervised Bidirectional Encoder Representation Learning of Commonsense (elBERto) framework, which is compatible with off-the-shelf QA model architectures. The framework comprises five self-supervised tasks to force the model to fully exploit the additional training signals from contexts containing rich commonsense. The tasks include a novel Contrastive Relation Learning task to encourage the model to distinguish between logically contrastive contexts, a new Jigsaw Puzzle task that requires the model to infer logical chains in long contexts, and three classic self-supervised learning(SSL) tasks to maintain pre-trained models’ language encoding ability. On the representative WIQA, CosmosQA, and ReClor datasets, elBERto outperforms all other methods using the same backbones and the same training set, including those utilizing explicit graph reasoning and external knowledge retrieval. Moreover, elBERto achieves substantial improvements on out-of-paragraph and no-effect questions where simple lexical similarity comparison does not help, indicating that it successfully learns commonsense and is able to leverage it when given dynamic context.
Evaluation of Runtime Monitoring for UAV Emergency Landing

To certify UAV operations in populated areas, risk mitigation strategies -- such as Emergency Landing (EL) -- must be in place to account for potential failures. EL aims at reducing ground risk by finding safe landing areas using on-board sensors. The first contribution of this paper is to present a new EL approach, in line with safety requirements introduced in recent research. In particular, the proposed EL pipeline includes mechanisms to monitor learning based components during execution. This way, another contribution is to study the behavior of Machine Learning Runtime Monitoring (MLRM) approaches within the context of a real-world critical system. A new evaluation methodology is introduced, and applied to assess the practical safety benefits of three MLRM mechanisms. The proposed approach is compared to a default mitigation strategy (open a parachute when a failure is detected), and appears to be much safer.
Head and Eye Egocentric Gesture Recognition for Human-Robot Interaction Using Eyewear Cameras

Non-verbal communication plays a particularly important role in a wide range of scenarios in Human-Robot Interaction (HRI). Accordingly, this work addresses the problem of human gesture recognition. In particular, we focus on head and eye gestures, and adopt an egocentric (first-person) perspective using eyewear cameras. We argue that this egocentric view may offer a number of conceptual and technical benefits over scene- or robot-centric perspectives. A motion-based recognition approach is proposed, which operates at two temporal granularities. Locally, frame-to-frame homographies are estimated with a convolutional neural network (CNN). The output of this CNN is input to a long short-term memory (LSTM) to capture longer-term temporal visual relationships, which are relevant to characterize gestures. Regarding the configuration of the network architecture, one particularly interesting finding is that using the output of an internal layer of the homography CNN increases the recognition rate with respect to using the homography matrix itself. While this work focuses on action recognition, and no robot or user study has been conducted yet, the system has been designed to meet real-time constraints. The encouraging results suggest that the proposed egocentric perspective is viable, and this proof-of-concept work provides novel and useful contributions to the exciting area of HRI.
Improving the Fusion of Acoustic and Text Representations in RNN-T

The recurrent neural network transducer (RNN-T) has recently become the mainstream end-to-end approach for streaming automatic speech recognition (ASR). To estimate the output distributions over subword units, RNN-T uses a fully connected layer as the joint network to fuse the acoustic representations extracted using the acoustic encoder with the text representations obtained using the prediction network based on the previous subword units. In this paper, we propose to use gating, bilinear pooling, and a combination of them in the joint network to produce more expressive representations to feed into the output layer. A regularisation method is also proposed to enable better acoustic encoder training by reducing the gradients back-propagated into the prediction network at the beginning of RNN-T training. Experimental results on a multilingual ASR setting for voice search over nine languages show that the joint use of the proposed methods can result in 4%–5% relative word error rate reductions with only a few million extra parameters.
Inverted Pyramid Multi-task Transformer for Dense Scene Understanding

Multi-task dense scene understanding is a thriving research domain that requires simultaneous perception and reasoning on a series of correlated tasks with pixel-wise prediction. Most existing works encounter a severe limitation of modeling in the locality due to heavy utilization of convolution operations, while learning interactions and inference in a global spatial-position and multi-task context is critical for this problem. In this paper, we propose a novel end-to-end Inverted Pyramid multi-task Transformer (InvPT) to perform simultaneous modeling of spatial positions and multiple tasks in a unified framework. To the best of our knowledge, this is the first work that explores designing a transformer structure for multi-task dense prediction for scene understanding. Besides, it is widely demonstrated that a higher spatial resolution is remarkably beneficial for dense predictions, while it is very challenging for existing transformers to go deeper with higher resolutions due to huge complexity to large spatial size. InvPT presents an efficient UP-Transformer block to learn multi-task feature interaction at gradually increased resolutions, which also incorporates effective self-attention message passing and multi-scale feature aggregation to produce task-specific prediction at a high resolution. Our method achieves superior multi-task performance on NYUD-v2 and PASCAL-Context datasets respectively, and significantly outperforms previous state-of-the-arts. The code is available at https://github.com/prismformore/InvPT .
Modeling User Behavior with Graph Convolution for Personalized Product Search

User preference modeling is a vital yet challenging problem in personalized product search. In recent years, latent space based methods have achieved state-of-the-art performance by jointly learning semantic representations of products, users, and text tokens. However, existing methods are limited in their ability to model user preferences. They typically represent users by the products they visited in a short span of time using attentive models and lack the ability to exploit relational information such as user-product interactions or item co-occurrence relations. In this work, we propose to address the limitations of prior arts by exploring local and global user behavior patterns on a user successive behavior graph, which is constructed by utilizing short-term actions of all users. To capture implicit user preference signals and collaborative patterns, we use an efficient jumping graph convolution to explore high-order relations to enrich product representations for user preference modeling. Our approach can be seamlessly integrated with existing latent space based methods and be potentially applied in any product retrieval method that uses purchase history to model user preferences. Extensive experiments on eight Amazon benchmarks demonstrate the effectiveness and potential of our approach. The source code is available at https://github.com/floatSDSDS/SBG .
On the c-differential spectrum of power functions over finite fields

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. Inspired by this pioneering work, power functions with low c-differential uniformity were constructed. Wang and Zheng (Several classes of PcN power functions over finite fields, arXiv:2104.12942 ) defined the c-differential spectrum of a power function. In this paper, we present some properties of the c-differential spectrum of a power function. Then we apply these properties to investigate the c-differential spectra of some power functions. A new class of APcN function is also obtained.
Self-Promoted Supervision for Few-Shot Transformer

The few-shot learning ability of vision transformers (ViTs) is rarely investigated though heavily desired. In this work, we empirically find that with the same few-shot learning frameworks, replacing the widely used CNN feature extractor with a ViT model often severely impairs few-shot classification performance. Moreover, our empirical study shows that in the absence of inductive bias, ViTs often learn the low-qualified token dependencies under few-shot learning regime where only a few labeled training data are available, which largely contributes to the above performance degradation. To alleviate this issue, we propose a simple yet effective few-shot training framework for ViTs, namely Self-promoted sUpervisioN (SUN). Specifically, besides the conventional global supervision for global semantic learning, SUN further pretrains the ViT on the few-shot learning dataset and then uses it to generate individual location-specific supervision for guiding each patch token. This location-specific supervision tells the ViT which patch tokens are similar or dissimilar and thus accelerates token dependency learning. Moreover, it models the local semantics in each patch token to improve the object grounding and recognition capability which helps learn generalizable patterns. To improve the quality of location-specific supervision, we further propose: 1) background patch filtration to filtrate background patches out and assign them into an extra background class; and 2) spatial-consistent augmentation to introduce sufficient diversity for data augmentation while keeping the accuracy of the generated local supervisions. Experimental results show that SUN using ViTs significantly surpasses other few-shot learning frameworks with ViTs and is the first one that achieves higher performance than those CNN state-of-the-arts. Our code is publicly available at https://github.com/DongSky/few-shot-vit .
Single UHD Image Dehazing Via Interpretable Pyramid Network

Currently, most dehazing-based deep approaches are developed as an end-to-end manner to run a degraded image in a wooden fashion. For these dehazing models, the absence of 'Credible Modeling' to guide the network design is a barrier to be applied commercially in the open-world. We introduce the Taylor approximation principle as the soul and materialize this principle with the help of the Laplacian Pyramid. Specifically, the N paths of Laplacian pyramid model correspond to the N terms (sub-functions) in Taylor's theorem. We attempt to use bottom paths and top paths to reconstruct the low-/high-frequency information of the clear image, respectively. Further, we develop a T-Unet module that focuses on regularizing the feature maps generated at bottom paths and design an attention sharing weight K to help approximate the Taylor high-order terms. Extensive experimental results demonstrate that our approach can run a 4K image on a single GPU with 24G RAM in real-time (80fps) and have unparalleled interpretability.
CARAMUEL: The future of Space Quantum Key Distribution in GEO

The usage of QKD protocols through the current terrestrial infrastructure is limited to a hundred kilometers due to attenuation problems with fiber. This implies that the satellite will be necessary for the transmission of quantum keys in order to avoid these terrestrial limitations. Therefore, the current debate does not lie in the need for the satellite but rather in what type of infrastructure will be best suited, and specifically the usage of GEO vs LEO satellites. This analysis is not trivial and should not consider only the link budgets and the physical layer as it has been done up to date. It should also consider even more important factors, as the service KPIs that the end user will request such as key rate, availability, security of the keys and their maintenance, etc., as well as the possible communications architectures to provide the service. Therefore, the analysis should introduce the market oriented component, and provide the optimal solutions based on the scenarios and requirements requested by the customer. In this frame, and under ESA ARTES funding, a Spanish consortium led by Hispasat is defining an operational GEO QKD mission that shall fly as hosted payload in 2025. The quantum communications payload proposed in this mission represents a step beyond the state of the art since it is the first implementation of a quantum key distribution channel from geostationary orbit. This new service will involve the implementation of a complete communications system not currently available that will require the development of new technological elements and the adaptation for their operation in space of technologies such as photon sources, telescopes for optical communications compatible with quantum state transmission or processors for the distillation of quantum keys, as well as a whole new ground segment to control and manage the new quantum key distribution service and its integration into a future European secure quantum communications network. Details of the challenges and approach of this mission shall be presented
Text Extraction and Detection from Images using Machine Learning Techniques: A Research Review

Machine Learning is subset of Artificial Intelligence and there is lots of research growth across the world. It has capability to learn by its own without seeking any help from human beings or without any explicit programming based on its previous experience and knowledge. Mainly it can make its own decisions or can predict in performing certain tasks based on the res pective input dataset and its training set. Machine Learning is applicable in various real-time applications in our daily life. Text detection and text extraction is one of the important applications which contain valuable information from images captured from various sources. Text with different variations differs in their size, orientation, alignment, style, low brightness or contrast in images with complex background. Many facing issues in reading due to various variations in text on images. So, text detection and extraction is very important and challenging now a days. The objective here is to help human beings with a different language from different parts of the world so that they can easily read and understand any language written. Researchers use various machine learning algorithms and tools to recognize handwritten text and text captured from images to convert them into digital format. Optical Character Recognition (OCR) is a Machine learning technique that helps us to detect and extract text data or information of a document and turn it into editable and searchable data. This research paper mainly focusses on various machine learning algorithms that are applicable for text extraction of handwritten documents, images and detect them into digital format also translate it according to the user's requirements.
SOKS: Automatic Searching of the Optimal Kernel Shapes for Stripe-Wise Network Pruning

In spite of the remarkable performance, deep convolutional neural networks (CNNs) are typically over-parameterized and computationally expensive. Network pruning has become a popular approach to reducing the storage and calculations of CNN models, which commonly prunes filters in a structured way or discards single weights without structural constraints. However, the redundancy in convolution kernels and the influence of kernel shapes on the performance of CNN models have attracted little attention. In this article, we develop a framework, termed searching of the optimal kernel shape (SOKS), to automatically search for the optimal kernel shapes and perform stripe-wise pruning (SWP). To be specific, we introduce coefficient matrices regularized by a variety of regularization terms to locate important kernel positions. The optimal kernel shapes not only provide appropriate receptive fields for each convolution layer, but also remove redundant parameters in convolution kernels. SWP is also achieved by utilizing these irregular kernels and actual inference speedups on the graphics processing unit (GPU) are obtained. Comprehensive experimental results demonstrate that SOKS searches high-efficiency kernel shapes and achieves superior performance in terms of both compression ratio and inference latency. Embedding the searched kernels into VGG-16 increases the accuracy from 93.53% to 94.26% on CIFAR-10, while pruning 59.27% model parameters and reducing 27.07% inference latency.
A High-Quality Entropy Source Using van der Waals Heterojunction for True Random Number Generation

Generators of random sequences used in high-end applications such as cryptography rely on entropy sources for their indeterminism. Physical processes governed by the laws of quantum mechanics are excellent sources of entropy available in nature. However, extracting enough entropy from such systems for generating truly random sequences is challenging while maintaining the feasibility of the extraction procedure for real-world applications. Here, we present a compact and an all-electronic van der Waals (vdW) heterostructure-based device capable of detecting discrete charge fluctuations for extracting entropy from physical processes and use it for the generation of independent and identically distributed (IID) true random sequences. We extract a record high value ($>0.98~bits/bit$) of min-entropy using the proposed scheme. We demonstrate an entropy generation rate tunable over multiple orders of magnitude and show the persistence of the underlying physical process for temperatures ranging from cryogenic to ambient conditions. We verify the random nature of the generated sequences using tests such as NIST SP 800-90B standard and other statistical measures and verify the suitability our random sequence for cryptographic applications using the NIST SP 800-22 standard. The generated random sequences are then used in implementing various randomized algorithms without any preconditioning steps.
swift-emulator: A Python package for emulation of simulated scaling relations

swift-emulator is a Python toolkit for using Gaussian processes machine learning to emulate scaling relations from cosmological simulations.swift-emulator focusses on implementing a clear, easy to use design and API to remove the barrier to entry for using emulator techniques.swift-emulator provides tools for every step: the design of the parameter sampling, the training of the Gaussian process model, and validating and anaylsing the trained emulators.By making these techniques easier to use, in particular in combination with the SWIFT code (Borrow & Borrisov, 2020;Schaller et al., 2018), it will be possible use fitting methods (like MCMC) to calibrate and better understand theoretical simulation models.
Design and Development of Conversational Chatbot for Covid-19 using NLP: an AI application

ince the discovery of corona virus (nCOV-19), and its subsequent progression into a global pandemic, an enormous hurdle faced by hospitals and their healthcare staff has been to streamline, and look after the huge flow of cases.It has become increasingly difficult to consult a Covid specialist when the first wave occurred in rural and areas not connected as well to modern amenities.Thus, it has become obvious that an interactive Chatbot with efficient execution can help patients living in such areas by educating on the appropriate preventive measures, news on virus strains, reducing the psychological damage caused by the fear of the virus and mental effects of solitary isolation.This study displays and discusses the schematics of an artificial intelligence (AI) chatbot for the purpose of evaluation, diagnosis and recommending immediate preventive as well as safety measures for patients who have been exposed to nCOV-19, and doubles as a virtual assistant to aid in measuring the severity of the infection via symptom analysis and connects with the authorised medical facilities when it progresses to a serious stage.