Reviewerly/exHarmony huggingface dataset:
Self-Supervised Monocular Depth Underwater

Depth estimation is critical for any robotic system. In the past years estimation of depth from monocular images have shown great improvement, however, in the underwater environment results are still lagging behind due to appearance changes caused by the medium. So far little effort has been invested on overcoming this. Moreover, underwater, there are more limitations for using high resolution depth sensors, this makes generating ground truth for learning methods another enormous obstacle. So far unsupervised methods that tried to solve this have achieved very limited success as they relied on domain transfer from dataset in air. We suggest training using subsequent frames self-supervised by a reprojection loss, as was demonstrated successfully above water. We suggest several additions to the self-supervised framework to cope with the underwater environment and achieve state-of-the-art results on a challenging forward-looking underwater dataset.
Paley-like graphs over finite fields from vector spaces

Motivated by the well-known Paley graphs over finite fields and their generalizations, in this paper we explore a natural multiplicative-additive analogue of such graphs arising from vector spaces over finite fields. Namely, if $n\ge 2$ and $U\subsetneq \mathbb F_{q^n}$ is an $\mathbb F_q$-vector space, $G_{U}$ is the (undirected) graph with vertex set $V(G_U)=\mathbb F_{q^n}$ and edge set $E(G_U)=\{(a, b)\in \mathbb F_{q^n}^2\,|\, a\ne b, ab\in U\}$. We describe the structure of an arbitrary maximal clique in $G_U$ and provide bounds on the clique number $\omega(G_U)$ of $G_U$. In particular, we compute the largest possible value of $\omega(G_U)$ for arbitrary $q$ and $n$. Moreover, we obtain the exact value of $\omega(G_U)$ when $U\subsetneq \mathbb F_{q^n}$ is any $\mathbb F_q$-vector space of dimension $d_U\in \{1, 2, n-1\}$.
HealthE: Classifying Entities in Online Textual Health Advice

The processing of entities in natural language is essential to many medical NLP systems. Unfortunately, existing datasets vastly under-represent the entities required to model public health relevant texts such as health advice often found on sites like WebMD. People rely on such information for personal health management and clinically relevant decision making. In this work, we release a new annotated dataset, HealthE, consisting of 6,756 health advice. HealthE has a more granular label space compared to existing medical NER corpora and contains annotation for diverse health phrases. Additionally, we introduce a new health entity classification model, EP S-BERT, which leverages textual context patterns in the classification of entity classes. EP S-BERT provides a 4-point increase in F1 score over the nearest baseline and a 34-point increase in F1 when compared to off-the-shelf medical NER tools trained to extract disease and medication mentions from clinical texts. All code and data are publicly available on Github.
Not another Negation Benchmark: The NaN-NLI Test Suite for Sub-clausal Negation

Negation is poorly captured by current language models, although the extent of this problem is not widely understood. We introduce a natural language inference (NLI) test suite to enable probing the capabilities of NLP methods, with the aim of understanding sub-clausal negation. The test suite contains premise--hypothesis pairs where the premise contains sub-clausal negation and the hypothesis is constructed by making minimal modifications to the premise in order to reflect different possible interpretations. Aside from adopting standard NLI labels, our test suite is systematically constructed under a rigorous linguistic framework. It includes annotation of negation types and constructions grounded in linguistic theory, as well as the operations used to construct hypotheses. This facilitates fine-grained analysis of model performance. We conduct experiments using pre-trained language models to demonstrate that our test suite is more challenging than existing benchmarks focused on negation, and show how our annotation supports a deeper understanding of the current NLI capabilities in terms of negation and quantification.
A Unified Framework for Multi-intent Spoken Language Understanding with prompting

Multi-intent Spoken Language Understanding has great potential for widespread implementation. Jointly modeling Intent Detection and Slot Filling in it provides a channel to exploit the correlation between intents and slots. However, current approaches are apt to formulate these two sub-tasks differently, which leads to two issues: 1) It hinders models from effective extraction of shared features. 2) Pretty complicated structures are involved to enhance expression ability while causing damage to the interpretability of frameworks. In this work, we describe a Prompt-based Spoken Language Understanding (PromptSLU) framework, to intuitively unify two sub-tasks into the same form by offering a common pre-trained Seq2Seq model. In detail, ID and SF are completed by concisely filling the utterance into task-specific prompt templates as input, and sharing output formats of key-value pairs sequence. Furthermore, variable intents are predicted first, then naturally embedded into prompts to guide slot-value pairs inference from a semantic perspective. Finally, we are inspired by prevalent multi-task learning to introduce an auxiliary sub-task, which helps to learn relationships among provided labels. Experiment results show that our framework outperforms several state-of-the-art baselines on two public datasets.
Geomagnetic Survey Interpolation with the Machine Learning Approach

This paper portrays the method of UAV magnetometry survey data interpolation. The method accommodates the fact that this kind of data has a spatial distribution of the samples along a series of straight lines (similar to maritime tacks), which is a prominent characteristic of many kinds of UAV surveys. The interpolation relies on the very basic Nearest Neighbours algorithm, although augmented with a Machine Learning approach. Such an approach enables the error of less than 5 percent by intelligently adjusting the Nearest Neighbour algorithm parameters. The method was pilot tested on geomagnetic data with Borok Geomagnetic Observatory UAV aeromagnetic survey data.
A Keypoint Based Enhancement Method for Audio Driven Free View Talking Head Synthesis

Audio driven talking head synthesis is a challenging task that attracts increasing attention in recent years. Although existing methods based on 2D landmarks or 3D face models can synthesize accurate lip synchronization and rhythmic head pose for arbitrary identity, they still have limitations, such as the cut feeling in the mouth mapping and the lack of skin highlights. The morphed region is blurry compared to the surrounding face. A Keypoint Based Enhancement (KPBE) method is proposed for audio driven free view talking head synthesis to improve the naturalness of the generated video. Firstly, existing methods were used as the backend to synthesize intermediate results. Then we used keypoint decomposition to extract video synthesis controlling parameters from the backend output and the source image. After that, the controlling parameters were composited to the source keypoints and the driving keypoints. A motion field based method was used to generate the final image from the keypoint representation. With keypoint representation, we overcame the cut feeling in the mouth mapping and the lack of skin highlights. Experiments show that our proposed enhancement method improved the quality of talking-head videos in terms of mean opinion score.
PS-ARM: An End-to-End Attention-aware Relation Mixer Network for Person Search

Person search is a challenging problem with various real-world applications, that aims at joint person detection and re-identification of a query person from uncropped gallery images. Although, the previous study focuses on rich feature information learning, it is still hard to retrieve the query person due to the occurrence of appearance deformations and background distractors. In this paper, we propose a novel attention-aware relation mixer (ARM) module for person search, which exploits the global relation between different local regions within RoI of a person and make it robust against various appearance deformations and occlusion. The proposed ARM is composed of a relation mixer block and a spatio-channel attention layer. The relation mixer block introduces a spatially attended spatial mixing and a channel-wise attended channel mixing for effectively capturing discriminative relation features within an RoI. These discriminative relation features are further enriched by introducing a spatio-channel attention where the foreground and background discriminability is empowered in a joint spatio-channel space. Our ARM module is generic and it does not rely on fine-grained supervision or topological assumptions, hence being easily integrated into any Faster R-CNN based person search methods. Comprehensive experiments are performed on two challenging benchmark datasets: CUHKSYSU and PRW. Our PS-ARM achieves state-of-the-art performance on both datasets. On the challenging PRW dataset, our PS-ARM achieves an absolute gain of 5 in the mAP score over SeqNet, while operating at a comparable speed.
Adversarially Robust Prototypical Few-shot Segmentation with Neural-ODEs

Few-shot Learning (FSL) methods are being adopted in settings where data is not abundantly available. This is especially seen in medical domains where the annotations are expensive to obtain. Deep Neural Networks have been shown to be vulnerable to adversarial attacks. This is even more severe in the case of FSL due to the lack of a large number of training examples. In this paper, we provide a framework to make few-shot segmentation models adversarially robust in the medical domain where such attacks can severely impact the decisions made by clinicians who use them. We propose a novel robust few-shot segmentation framework, Prototypical Neural Ordinary Differential Equation (PNODE), that provides defense against gradient-based adversarial attacks. We show that our framework is more robust compared to traditional adversarial defense mechanisms such as adversarial training. Adversarial training involves increased training time and shows robustness to limited types of attacks depending on the type of adversarial examples seen during training. Our proposed framework generalises well to common adversarial attacks like FGSM, PGD and SMIA while having the model parameters comparable to the existing few-shot segmentation models. We show the effectiveness of our proposed approach on three publicly available multi-organ segmentation datasets in both in-domain and cross-domain settings by attacking the support and query sets without the need for ad-hoc adversarial training.
Introduction to R Software

R is a free software environment for statistical computing and graphics. It is a command line-driven program and runs on a wide variety of UNIX platforms, Windows and Mac OS. It is an interpreter, i.e., the user enters commands at the prompt, and each command is executed one at a time. R is very much a vehicle for newly developing methods of interactive data analysis. It has developed rapidly and has been extended by a large collection of packages. Over the last 10 years, R has become one of the most widely used statistical software packages among statisticians and researchers since it provides more than 4000 specialized packages.
Unsupervised Few-shot Learning via Deep Laplacian Eigenmaps

Learning a new task from a handful of examples remains an open challenge in machine learning. Despite the recent progress in few-shot learning, most methods rely on supervised pretraining or meta-learning on labeled meta-training data and cannot be applied to the case where the pretraining data is unlabeled. In this study, we present an unsupervised few-shot learning method via deep Laplacian eigenmaps. Our method learns representation from unlabeled data by grouping similar samples together and can be intuitively interpreted by random walks on augmented training data. We analytically show how deep Laplacian eigenmaps avoid collapsed representation in unsupervised learning without explicit comparison between positive and negative samples. The proposed method significantly closes the performance gap between supervised and unsupervised few-shot learning. Our method also achieves comparable performance to current state-of-the-art self-supervised learning methods under linear evaluation protocol.
C2KD: Cross-Lingual Cross-Modal Knowledge Distillation for Multilingual Text-Video Retrieval

Multilingual text-video retrieval methods have improved significantly in recent years, but the performance for other languages lags behind English. We propose a Cross-Lingual Cross-Modal Knowledge Distillation method to improve multilingual text-video retrieval. Inspired by the fact that English text-video retrieval outperforms other languages, we train a student model using input text in different languages to match the cross-modal predictions from teacher models using input text in English. We propose a cross entropy based objective which forces the distribution over the student's text-video similarity scores to be similar to those of the teacher models. We introduce a new multilingual video dataset, Multi-YouCook2, by translating the English captions in the YouCook2 video dataset to 8 other languages. Our method improves multilingual text-video retrieval performance on Multi-YouCook2 and several other datasets such as Multi-MSRVTT and VATEX. We also conducted an analysis on the effectiveness of different multilingual text models as teachers. The code, models, and dataset are available at https://github.com/roudimit/c2kd.
IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty

Single image surface normal estimation and depth estimation are closely related problems as the former can be calculated from the latter. However, the surface normals computed from the output of depth estimation methods are significantly less accurate than the surface normals directly estimated by networks. To reduce such discrepancy, we introduce a novel framework that uses surface normal and its uncertainty to recurrently refine the predicted depth-map. The depth of each pixel can be propagated to a query pixel, using the predicted surface normal as guidance. We thus formulate depth refinement as a classification of choosing the neighboring pixel to propagate from. Then, by propagating to sub-pixel points, we upsample the refined, low-resolution output. The proposed method shows state-of-the-art performance on NYUv2 and iBims-1 - both in terms of depth and normal. Our refinement module can also be attached to the existing depth estimation methods to improve their accuracy. We also show that our framework, only trained for depth estimation, can also be used for depth completion. The code is available at https://github.com/baegwangbin/IronDepth.
Randomized channel-state duality

Channel-state duality is a central result in quantum information science. It refers to the correspondence between a dynamical process (quantum channel) and a static quantum state in an enlarged Hilbert space. Since the corresponding dual state is generally mixed, it is described by a Hermitian matrix. In this article, we present a randomized channel-state duality. In other words, a quantum channel is represented by a collection of $N$ pure quantum states that are produced from a random source. The accuracy of this randomized duality relation is given by $1/N$, with regard to an appropriate distance measure. For large systems, $N$ is much smaller than the dimension of the exact dual matrix of the quantum channel. This provides a highly accurate low-rank approximation of any quantum channel, and, as a consequence of the duality relation, an efficient data compression scheme for mixed quantum states. We demonstrate these two immediate applications of the randomized channel-state duality with a chaotic $1$-dimensional spin system.
Lexical Complexity in The Writings of Iraqi, English L2, and English L1 Writers

: Lexical complexity is an essential construct in L2 writing studies. It has been widely utilized to evaluate writing quality and writers’ proficiency. Measures of lexical complexity were considered reliable indicators of writing quality. The more lexical complexity measures score, the more high-quality rating writing. Since the measures of lexical complexity are correlated with writing quality, investigating lexical complexity in the writing of Iraqi writers, and comparing it with the writing of other groups of writers, such as English L2 and L1 writers, intends to describe the current situation of Iraqi writers in terms of using the English language for research publication purposes. This study utilizes the corpus-based method to analyze and compare lexical complexity in the texts of linguistics research articles RAs written by Iraqi, English L2, and English L1 writers and published in international journals indexed in the Scopus database. Eight lexical complexity measures were calculated by using Lexical Complexity Analyzer LCA. The findings revealed a significant difference between Iraqi writers and other groups of writers. There is a gap between the writings of Iraqi writers and English L2 and L1 writers in terms of using complex lexical items. Iraqi writers utilized the lowest amount of lexical complexity in their texts of linguistics RAs.
Predicting Handwritten Devanagari Characters using modified-Lenet Model Architecture

Abstract Handwritten character recognition has always been an interesting area of pattern recognition for research in the field of image processing. Unlike its counterpart of English Character Recognition (CR), Devanagari Optical Character Recognition poses its own complications (DOCR). With 36 consonants, 14 vowels and 10 digits, it’s only generic that Nepali Devanagari Optical Character Recognition models tend to go bigger hence less efficient, but, as with all other problems, non efficient solutions are of no use to the real world. This paper includes novel research on accurate yet efficient Convolutional Neural Network (CNN) architecture for DOCR. Dataset from Devanagari Handwritten Character Dataset (DHCD) was used to train the model to result in the highest accuracy achieved on the dataset along with ensured efficiency.
Component Metadata Infrastructure

At the start of CLARIN, metadata for language resources faced various problems, for example, different communities using different terminology, which made interoperability difficult.The Component Metadata (CMD) Infrastructure (CMDI) was developed as a solution and is based on specifying reusable components, each of which contains other components and elements.Components are assembled into profiles, the schema for the metadata description of a specific type of language resources.The CMD Infrastructure consists of many interacting parts, including a Component Registry, several semantic registries, a metadata harvester, and a central catalogue (the VLO).It is supported by repository systems and metadata editors developed and maintained by various stakeholders across the CLARIN network.The CMDI landscape has expanded throughout the years, and has remained sustainable by adapting itself, as it will continue to do in the future.
CLARIN – How It Started

This chapter describes the genesis of CLARIN, from the point of departure in the growing understanding of language resources as important building blocks, through the European political agreement that research infrastructures are essential for the European Research Area, and finally focussing on the actual creation of CLARIN as a language research infrastructure, serving communities that deal with language data.
CLARIN’s Support for Research into the Acquisition of Lexical Properties

Odijk (2011) sketched a research question on the acquisition of lexical properties of words, and illustrated it with some concrete examples, in particular with respect to the lexical properties of the Dutch synonyms heel, erg, and zeer (all meaning ‘very’). This work also indicated what the CLARIN infrastructure should offer to make it possible to address this research question. In this contribution I sketch to what extent the CLARIN infrastructure has achieved these requirements and desiderata. The resulting picture is mixed: (1) some have been implemented; (2) some have not been implemented and are still highly desirable; (3) some have not been implemented but turned out to be not so urgent; (4) new requirements and desiderata have arisen in the last 10 years, only some of which have been implemented. In this way, I evaluate the development of the CLARIN infrastructure (mainly its Netherlands part) over the past 10 years, and sketch the requirements and desiderata for the CLARIN infrastructure to address this research question for the next 10 years.
Training of Digital Language Resources Skills in South Africa

South Africa recognizes eleven official languages, although more languages are spoken in the country.Most of these languages are considered underresourced: there is only a limited set of computational resources available.This includes linguistic data collections as well as computational linguistic tools.This scarcity of resources limits the computational linguistic and more applied (e.g., digital humanities) work on these languages.However, in South Africa there is currently also a lack of people who know how to use these resources.The South African Centre for Digital Language Resources (SADiLaR) is a government-funded research infrastructure that aims to tackle both problems.First, it runs a digitization programme, which develops new digital language resources.This programme digitizes analogue linguistic data collections, but also develops new computational linguistic tools.Second, a digital humanities programme aims to build research capacity in the field of digital humanities.This is done through training events, among other initiatives, which have recently been clustered in the SADiLaR-run "Escalator project".Escalator aims to develop a community of practice in the field of digital humanities.By taking a comprehensive approach to training events with follow-ups, combined with the development of a Champions Initiative programme consisting of the training of experts, Escalator aims to make it easier for researchers to transition into more computational types of research in the humanities and social sciences.This chapter will provide a historical overview of the field of natural language processing and digital humanities in South Africa.In particular, it will focus on the development of computational linguistic resources and their application.Additionally, an overview of activities in this area performed by SADiLaR will be
Integration of Blockchain Technology with Deep Learning Approaches: Vision and Future Opportunities

Abstract A recent study has successfully focused on Deep Learning and Blockchain technologies because of their tremendous potential for producing reliable decisions. The bulk of contemporary deep learning techniques implemented on centralized servers gives operational transparency, traceability, reliability, security, and reliable data provenance qualities. Deep Learning models are therefore susceptible to the single point of failure problem since they are trained using centralized data. The advantages of merging Blockchain with Deep Learning algorithms are examined in this paper. Additionally, current research is examined to determine the best strategy to combine these two technologies for the best outcome.