Reviewerly/exHarmony huggingface dataset:
An overview of mixing augmentation methods and augmentation strategies

Abstract Deep Convolutional Neural Networks have made an incredible progress in many Computer Vision tasks. This progress, however, often relies on the availability of large amounts of the training data, required to prevent over-fitting, which in many domains entails significant cost of manual data labeling. An alternative approach is application of data augmentation (DA) techniques that aim at model regularization by creating additional observations from the available ones. This survey focuses on two DA research streams: image mixing and automated selection of augmentation strategies. First, the presented methods are briefly described, and then qualitatively compared with respect to their key characteristics. Various quantitative comparisons are also included based on the results reported in recent DA literature. This review mainly covers the methods published in the materials of top-tier conferences and in leading journals in the years 2017–2021.
On the Computation of PSNR for a Set of Images or Video

When comparing learned image/video restoration and compression methods, it is common to report peak-signal to noise ratio (PSNR) results. However, there does not exist a generally agreed upon practice to compute PSNR for sets of images or video. Some authors report average of individual image/frame PSNR, which is equivalent to computing a single PSNR from the geometric mean of individual image/frame mean-square error (MSE). Others compute a single PSNR from the arithmetic mean of frame MSEs for each video. Furthermore, some compute the MSE/PSNR of Y-channel only, while others compute MSE/PSNR for RGB channels. This paper investigates different approaches to computing PSNR for sets of images, single video, and sets of video and the relation between them. We show the difference between computing the PSNR based on arithmetic vs. geometric mean of MSE depends on the distribution of MSE over the set of images or video, and that this distribution is task-dependent. In particular, these two methods yield larger differences in restoration problems, where the MSE is exponentially distributed and smaller differences in compression problems, where the MSE distribution is narrower. We hope this paper will motivate the community to clearly describe how they compute reported PSNR values to enable consistent comparison.
Abstractive Text Summarization: Enhancing Sequence-to-Sequence Models Using Word Sense Disambiguation and Semantic Content Generalization

Abstract Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enhance their efficiency. In this direction, this work presents a novel framework that combines sequence-to-sequence neural-based text summarization along with structure and semantic-based methodologies. The proposed framework is capable of dealing with the problem of out-of-vocabulary or rare words, improving the performance of the deep learning models. The overall methodology is based on a well-defined theoretical model of knowledge-based content generalization and deep learning predictions for generating abstractive summaries. The framework is composed of three key elements: (i) a pre-processing task, (ii) a machine learning methodology, and (iii) a post-processing task. The pre-processing task is a knowledge-based approach, based on ontological knowledge resources, word sense disambiguation, and named entity recognition, along with content generalization, that transforms ordinary text into a generalized form. A deep learning model of attentive encoder-decoder architecture, which is expanded to enable a coping and coverage mechanism, as well as reinforcement learning and transformer-based architectures, is trained on a generalized version of text-summary pairs, learning to predict summaries in a generalized form. The post-processing task utilizes knowledge resources, word embeddings, word sense disambiguation, and heuristic algorithms based on text similarity methods in order to transform the generalized version of a predicted summary to a final, human-readable form. An extensive experimental procedure on three popular data sets evaluates key aspects of the proposed framework, while the obtained results exhibit promising performance, validating the robustness of the proposed approach.
Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo

Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes, and it underpins the only practical numerical approach to the study of complex high-dimensional supergravity theories. Understanding quantum black holes and the role of entanglement in a holographic setup is of paramount importance for the realization of a quantum theory of gravity. Moreover, a complete numerical understanding of the holographic duality and the emergence of geometric space-time features from microscopic degrees of freedom could pave the way for new discoveries in quantum information science. Euclidean lattice Monte Carlo simulations are the de facto numerical tool for understanding the spectrum of large matrix models and have been used to test the holographic duality. However, they are not tailored to extract dynamical properties or even the quantum wave function of the ground state of matrix models. Quantum computing and deep learning provide potentially useful approaches to study the dynamics of matrix quantum mechanics. If successful in the context of matrix models, these rapidly improving numerical techniques could become the new Swiss army knife of quantum gravity practitioners. In this paper, we perform the first systematic survey for quantum computing and deep-learning approaches to matrix quantum mechanics, comparing them to lattice Monte Carlo simulations. These provide baseline benchmarks before addressing more complicated problems. In particular, we test the performance of each method by calculating the low-energy spectrum.22 MoreReceived 23 August 2021Accepted 20 December 2021DOI:https://doi.org/10.1103/PRXQuantum.3.010324Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasLattice gauge theoryMachine learningQuantum algorithmsSupersymmetric field theoriesTechniquesHybrid Monte Carlo algorithmPath-integral Monte CarloQuantum InformationParticles & Fields
Effect of retrace error on stitching coherent scanning interferometry measurements of freeform optics

We report on the effect of retrace error during measurement of freeform optics using a commercial coherence scanning interferometer (CSI), and its in-built stitching capabilities. It is shown that measuring segments of freeform optics under non-null conditions, results in artifacts on the measured zone, similar to the Seidel aberrations. An experimental approach is used to quantify the induced aberrations based on the local slopes of the surface. Simulation of surfaces containing different order aberrations is shown to have a significant effect on the measurement data. A correction method is proposed that uses experimental measurements to determine the required correction based on local slope and position in the aperture. These corrections reduce the measurement difference from a comparison measurement using a Fizeau interferometer.
Improving Accuracy and Diversity in Matching of Recommendation With Diversified Preference Network

Real-world recommendation systems need to deal with millions of item candidates. Therefore, most practical large-scale recommendation systems usually contain two modules. The matching module aims to efficiently retrieve hundreds of high-quality items from large corpora, while the ranking module aims to generate specific ranks for these items. Recommendation diversity is an essential factor that strongly impacts user experience. There are lots of efforts that have explored recommendation diversity in ranking, while the matching module should take more responsibility for diversity. In this article, we propose a novel Heterogeneous graph neural network framework for diversified recommendation (GraphDR) in matching to improve both recommendation accuracy and diversity. Specifically, GraphDR builds a huge heterogeneous preference network to record different types of user preferences, and conducts a field-level heterogeneous graph attention network for node aggregation. We conduct a neighbor-similarity based loss with a multi-channel matching to improve both accuracy and diversity. In experiments, we conduct extensive online and offline evaluations on a real-world recommendation system with various accuracy and diversity metrics and achieve significant improvements. GraphDR has been deployed on a well-known recommendation system named WeChat Top Stories, which affects millions of users. The source code will be released in <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lqfarmer/GraphDR</uri> .
Fusing Task-Oriented and Open-Domain Dialogues in Conversational Agents

The goal of building intelligent dialogue systems has largely been separately pursued under two paradigms: task-oriented dialogue (TOD) systems, which perform task-specific functions, and open-domain dialogue (ODD) systems, which focus on non-goal-oriented chitchat. The two dialogue modes can potentially be intertwined together seamlessly in the same conversation, as easily done by a friendly human assistant. Such ability is desirable in conversational agents, as the integration makes them more accessible and useful. Our paper addresses this problem of fusing TODs and ODDs in multi-turn dialogues. Based on the popular TOD dataset MultiWOZ, we build a new dataset FusedChat, by rewriting the existing TOD turns and adding new ODD turns. This procedure constructs conversation sessions containing exchanges from both dialogue modes. It features inter-mode contextual dependency, i.e., the dialogue turns from the two modes depend on each other. Rich dependency patterns such as co-reference and ellipsis are included. The new dataset, with 60k new human-written ODD turns and 5k re-written TOD turns, offers a benchmark to test a dialogue model's ability to perform inter-mode conversations. This is a more challenging task since the model has to determine the appropriate dialogue mode and generate the response based on the inter-mode context. However, such models would better mimic human-level conversation capabilities. We evaluate two baseline models on this task, including the classification-based two-stage models and the two-in-one fused models. We publicly release FusedChat and the baselines to propel future work on inter-mode dialogue systems.
IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effective Domain-Specific Vocabulary Initialization

We present INDOBERTWEET, the first largescale pretrained model for Indonesian Twitter that is trained by extending a monolinguallytrained Indonesian BERT model with additive domain-specific vocabulary.We focus in particular on efficient model adaptation under vocabulary mismatch, and benchmark different ways of initializing the BERT embedding layer for new word types.We find that initializing with the average BERT subword embedding makes pretraining five times faster, and is more effective than proposed methods for vocabulary adaptation in terms of extrinsic evaluation over seven Twitter-based datasets. 1
Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-training

The goal of stance detection is to determine the viewpoint expressed in a piece of text towards a target. These viewpoints or contexts are often expressed in many different languages depending on the user and the platform, which can be a local news outlet, a social media platform, a news forum, etc. Most research on stance detection, however, has been limited to working with a single language and on a few limited targets, with little work on cross-lingual stance detection. Moreover, non-English sources of labelled data are often scarce and present additional challenges. Recently, large multilingual language models have substantially improved the performance on many non-English tasks, especially such with a limited number of examples. This highlights the importance of model pre-training and its ability to learn from few examples. In this paper, we present the most comprehensive study of cross-lingual stance detection to date: we experiment with 15 diverse datasets in 12 languages from 6 language families, and with 6 low-resource evaluation settings each. For our experiments, we build on pattern-exploiting training (PET), proposing the addition of a novel label encoder to simplify the verbalisation procedure. We further propose sentiment-based generation of stance data for pre-training, which shows sizeable improvement of more than 6% F1 absolute in few-shot learning settings compared to several strong baselines.
Problemy pidhotovky vchyteliv pochatkovykh klasiv u zakladakh vyshchoi osvity v umovakh dystantsiinoho ta zmishanoho navchannia [The problems of primary school teachers’ training in the conditions of distance and blended teaching in higher education establishments]

Background: The relevance of the study is caused by the implementation of modern information and communication technologies into the educational process of higher education institutions, that induces the appearance of new kinds and forms of its organization. Purpose: To analyze of the interpretation of the definitions of “distance learning” and “blended learning”; to identify of problems of their use in professional training of future primary school teachers at the V. O. Sukhomlynskyi Mykolaiv National University. Materials and Methods: Theoretical and empirical methods are used in the work: analysis, classification and generalization of the source base of the research; normative documentation regulating the activity of higher education institutions (educational and professional programs, curricula); experience in the educational process organizing; diagnostics of students majoring in 013 Primary education (questionnaires, observations, introspection). Results: The structure and content of “distance learning” and “blended-learning” are disclosed, considers its advantages and its efficiency. The scientific approaches principles mean and forms of organization of the process of distance learning and blended-learning while training of elementary school teachers are investigated. The advantages of using the Moodle platform at the V. O. Sukhomlynskyi Mykolaiv National University are proved. The possibility and expediency of using distance and blended learning in the process of professional training of future primary school teachers is proved. Conclusions: The development of computer and information and communication technologies, the use of distance and blended learning have significantly changed the role and place of teachers and students in the educational process in higher education institutions. Their implementation promotes the formation of professional competence of future primary school teachers.
Tracking Cells and Their Lineages Via Labeled Random Finite Sets

Determining the trajectories of cells and their lineages or ancestries in live-cell experiments are fundamental to the understanding of how cells behave and divide. This paper proposes novel online algorithms for jointly tracking and resolving lineages of an unknown and time-varying number of cells from time-lapse video data. Our approach involves modeling the cell ensemble as a labeled random finite set with labels representing cell identities and lineages. A spawning model is developed to take into account cell lineages and changes in cell appearance prior to division. We then derive analytic filters to propagate multi-object distributions that contain information on the current cell ensemble including their lineages. We also develop numerical implementations of the resulting multi-object filters. Experiments using simulation, synthetic cell migration video, and real time-lapse sequence, are presented to demonstrate the capability of the solutions.
Exploring the English Learning Strategies of an Indigenous Papuan Student of Indonesia

Language learning strategies are crucial for learning English as a foreign language (EFL). This study sought to reveal language learning strategies used by Daniela, an Indigenous Papuan student, in developing her knowledge and skills in English. To gain this objective, a case study (Kohlbacher, 2006) using an in-depth interview was employed. Collected data were qualitatively analyzed using descriptive analysis techniques. Results of data analysis revealed two main categories of learning strategies used by Daniela in solving her learning problems, namely direct and indirect learning strategies. In direct learning strategies Daniela dealt with memory, cognition, and compensation strategies, while in indirect learning strategies she dealt more with metacognitive, affective, and social strategies. Apart from these findings, this study also revealed Daniela’s performance in English is mostly influenced by her reading hobby, internal motivation, positive attitude towards English, her personality (diligent and independent), and her dream to become a vocational English teacher. Results of this study may be useful for the head of school to encourage English teachers to provide learners with diverse learning tasks to elicit the use of various learning strategies.
Examining Deep Learning Architectures for Crime Classification and Prediction

In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms in this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from police reports. Having time-series of crime types per location as training data, a comparative study of 10 state-of-the-art methods against 3 different deep learning configurations is conducted. In our experiments with 5 publicly available datasets, we demonstrate that the deep learning-based methods consistently outperform the existing best-performing methods. Moreover, we evaluate the effectiveness of different parameters in the deep learning architectures and give insights for configuring them to achieve improved performance in crime classification and finally crime prediction.
Evolution of histopathological breast cancer images classification using stochastic dilated residual ghost model

Breast cancer detection is a complex problem to solve, and it is a topic that is still being studied.Deep learning-based models aid medical science by helping to classify benign and malignant cancers and saving lives.Breast cancer histopathological image classification (BreakHis) and breast cancer histopathological annotation and diagnosis (BreCaHAD) datasets are used in the proposed model.The study led to the resolution of four essential issues: 1) Addresses the color divergence issue caused by strain normalization during image generation 2) Data augmentation uses several factors like as flip, rotation, shift, resize, and gamma value in order to overcome overfitting concerns caused by a lack of histopathology images.3) Using the proposed stochastic dilated convolution (SDC) model, able to find missing features and improve tiny and low-level features such as edge, contour, and color correctness.This model effectively solves depth issues with stochastic pooling and max pooling by combining three proposed units: spatial dilation convolution, channel dilation convolution, and dilated convolution to detect missing features and obtain more information on images.Stochastic pooling may choose any value since it is classified as a heterogeneous approach that can determine tiny and large values.When the max pooling and stochastic pooling are combined, a decent feature map is produced, processed by the dilatation unit, detecting cancer cells without adding to the complexity.4) To correctly identify breast cancer and extract depth characteristics, the stochastic dilated residual ghost (SDRG) model uses the proposed SDC model, a ghost unit, stochastic upsampling, and downsampling units.The proposed feature selection unit employs linear transformations to produce feature maps to show information based on intrinsic characteristics to eliminate redundant or similar features in convolutional neural networks.Convolution and identity mapping are used to construct and retain intrinsic feature mappings in this skip unit while upsampling with stochastic pooling is used to minimize feature dimensions.The proposed model's performance was assessed with 150,271 augmented and original histopathology images.The proposed method's findings compare favorably to eight popular approaches.In addition, the suggested model outperforms the competition in terms of accuracy, average precision score, precision, sensitivity, and f1 score.With an accuracy of 98.41% with BreakHis and 98.60% with the BreCaHAD dataset with enhanced images, the proposed method exceeds numerous state-of-the-art methods.
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering

Current Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module, where the retriever selects potentially relevant passages from open-source documents for a given question, and the reader produces an answer based on the retrieved passages.The recently proposed Fusion-in-Decoder (FiD) framework is a representative example, which is built on top of a dense passage retriever and a generative reader, achieving the state-of-the-art performance.In this paper we further improve the FiD approach by introducing a knowledgeenhanced version, namely KG-FiD.Our new model uses a knowledge graph to establish the structural relationship among the retrieved passages, and a graph neural network (GNN) to re-rank the passages and select only a top few for further processing.Our experiments on common ODQA benchmark datasets (Natural Questions and TriviaQA) demonstrate that KG-FiD can achieve comparable or better performance in answer prediction than FiD, with less than 40% of the computation cost.
Learning to disentangle scenes for person re-identification

There are many challenging problems in the person re-identification (ReID) task, such as the occlusion and scale variation. Existing works usually tried to solve them by employing a one-branch network. This one-branch network needs to be robust to various challenging problems, which makes this network overburdened. This paper proposes to divide-and-conquer the ReID task. For this purpose, we employ several self-supervision operations to simulate different challenging problems and handle each challenging problem using different networks. Concretely, we use the random erasing operation and propose a novel random scaling operation to generate new images with controllable characteristics. A general multi-branch network, including one master branch and two servant branches, is introduced to handle different scenes. These branches learn collaboratively and achieve different perceptive abilities. In this way, the complex scenes in the ReID task are effectively disentangled, and the burden of each branch is relieved. The results from extensive experiments demonstrate that the proposed method achieves state-of-the-art performances on three ReID benchmarks and two occluded ReID benchmarks. Ablation study also shows that the proposed scheme and operations significantly improve the performance in various scenes.