Reviewerly/exHarmony huggingface dataset:
A novel reasoning mechanism for multi-label text classification

The aim in multi-label text classification is to assign a set of labels to a given document. Previous classifier-chain and sequence-to-sequence models have been shown to have a powerful ability to capture label correlations. However, they rely heavily on the label order, while labels in multi-label data are essentially an unordered set. The performance of these approaches is therefore highly variable depending on the order in which the labels are arranged. To avoid being dependent on label order, we design a reasoning-based algorithm named Multi-Label Reasoner (ML-Reasoner) for multi-label classification. ML-Reasoner employs a binary classifier to predict all labels simultaneously and applies a novel iterative reasoning mechanism to effectively utilize the inter-label information, where each instance of reasoning takes the previously predicted likelihoods for all labels as additional input. This approach is able to utilize information between labels, while avoiding the issue of label-order sensitivity. Extensive experiments demonstrate that our method outperforms state-of-the art approaches on the challenging AAPD dataset. We also apply our reasoning module to a variety of strong neural-based base models and show that it is able to boost performance significantly in each case.
Have Your Text and Use It Too! End-to-End Neural Data-to-Text Generation with Semantic Fidelity

End-to-end neural data-to-text (D2T) generation has recently emerged as an alternative to pipeline-based architectures. However, it has faced challenges generalizing to new domains and generating semantically consistent text. In this work, we present DataTuner, a neural, end-to-end data-to-text generation system that makes minimal assumptions about the data representation and target domain. We take a two-stage generation-reranking approach, combining a fine-tuned language model with a semantic fidelity classifier. Each component is learnt end-toe-nd without needing dataset-specific heuristics, entity delexicalization, or post-processing. We show that DataTuner achieves state of the art results on automated metrics across four major D2T datasets (LDC2017T10, WebNLG, ViGGO, and Cleaned E2E), with fluency assessed by human annotators as nearing or exceeding the human-written reference texts. Our generated text has better semantic fidelity than the state of the art on these datasets. We further demonstrate that our model-based semantic fidelity scorer is a better assessment tool compared to traditional heuristic-based measures of semantic accuracy.
M-learning in the COVID-19 era: physical vs digital class

Emerging technologies, such as the development of the Internet of Things and the transition to smart cities, and innovative handheld devices have led to big changes in many aspects of our lives, while more changes were imminent. Education is also a sector that has undergone huge changes due to the spreading of those devices. Even at the era of feature phones, it started to become clear that portable devices with access to the internet can be used for learning. The process of learning with the use of mobile phones was then in an early stage, due to the limitations of feature phones. Whereas, with the introduction of smartphones, education is expected to be drastically altered in the future, in most parts of the world. New, radical, and controversial in some cases, approaches have been developed, over the past years, in an effort to implement a mobile learning process in real life conditions. Intelligent tutoring systems have had rapid growth, especially in the COVID-19 era, while a significant increase in online courses via social networks has also been noted. This paper focuses on presenting the most important research parameters of m-learning during the last decade, while it also incorporates a novel empirical study in the domain. The utilization of educational data has been taken into consideration and is presented, aiming at ways to improve human interaction in the digital classroom.
Self-Supervised Learning for Few-Shot Image Classification

Few-shot image classification aims to classify unseen classes with limited labelled samples. Recent works benefit from the meta-learning process with episodic tasks and can fast adapt to class from training to testing. Due to the limited number of samples for each task, the initial embedding network for meta-learning becomes an essential component and can largely affect the performance in practice. To this end, most of the existing methods highly rely on the efficient embedding network. Due to the limited labelled data, the scale of embedding network is constrained under a supervised learning(SL) manner which becomes a bottleneck of the few-shot learning methods. In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself. We evaluate our work by extensive comparisons with previous baseline methods on two few-shot classification datasets (i.e., MiniImageNet and CUB) and achieve better performance over baselines. Tests on four datasets in cross-domain few-shot learning classification show that the proposed method achieves state-of-the-art results and further prove the robustness of the proposed model. Our code is available at https://github.com/phecy/SSL-FEW-SHOT.
SmBoP: Semi-autoregressive Bottom-up Semantic Parsing

The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depthfirst traversal.In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SMBOP) that constructs at decoding step t the top-K sub-trees of height ≤ t.Our parser enjoys several benefits compared to top-down autoregressive parsing.From an efficiency perspective, bottom-up parsing allows to decode all sub-trees of a certain height in parallel, leading to logarithmic runtime complexity rather than linear.From a modeling perspective, a bottom-up parser learns representations for meaningful semantic sub-programs at each step, rather than for semantically-vacuous partial trees.We apply SMBOP on SPIDER, a challenging zero-shot semantic parsing benchmark, and show that SMBOP leads to a 2.2x speed-up in decoding time and a ∼5x speed-up in training time, compared to a semantic parser that uses autoregressive decoding.SMBOP obtains 71.1 denotation accuracy on SPIDER, establishing a new state-of-the-art, and 69.5 exact match, comparable to the 69.6 exact match of the autoregressive RAT-SQL+GRAPPA.
Object-Centric Image Generation from Layouts

We begin with the hypothesis that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes with multiple objects well. Our layout-to-image-generation method, which we call Object-Centric Generative Adversarial Network (or OC-GAN), relies on a novel Scene-Graph Similarity Module (SGSM). The SGSM learns representations of the spatial relationships between objects in the scene, which lead to our model's improved layout-fidelity. We also propose changes to the conditioning mechanism of the generator that enhance its object instance-awareness. Apart from improving image quality, our contributions mitigate two failure modes in previous approaches: (1) spurious objects being generated without corresponding bounding boxes in the layout, and (2) overlapping bounding boxes in the layout leading to merged objects in images. Extensive quantitative evaluation and ablation studies demonstrate the impact of our contributions, with our model outperforming previous state-of-the-art approaches on both the COCO-Stuff and Visual Genome datasets. Finally, we address an important limitation of evaluation metrics used in previous works by introducing SceneFID -- an object-centric adaptation of the popular Fréchet Inception Distance metric, that is better suited for multi-object images.
Shallow Feature Matters for Weakly Supervised Object Localization

Weakly supervised object localization (WSOL) aims to localize objects by only utilizing image-level labels. Class activation maps (CAMs) are the commonly used features to achieve WSOL. However, previous CAM-based methods did not take full advantage of the shallow features, despite their importance for WSOL. Because shallow features are easily buried in background noise through conventional fusion. In this paper, we propose a simple but effective Shallow feature-aware Pseudo supervised Object Localization (SPOL) model for accurate WSOL, which makes the utmost of low-level features embedded in shallow layers. In practice, our SPOL model first generates the CAMs through a novel element-wise multiplication of shallow and deep feature maps, which filters the background noise and generates sharper boundaries robustly. Besides, we further propose a general class-agnostic segmentation model to achieve the accurate object mask, by only using the initial CAMs as the pseudo label without any extra annotation. Eventually, a bounding box extractor is applied to the object mask to locate the target. Experiments verify that our SPOL outperforms the state-of-the-art on both CUB- 200 and ImageNet-1K benchmarks, achieving 93.44% and 67.15% (i.e., 3.93% and 2.13% improvement) Top-5 localization accuracy, respectively.
Digital Pathology Workflow Implementation at IPATIMUP

The advantages of the digital methodology are well known. In this paper, we provide a detailed description of the process for the digital transformation of the pathology laboratory at IPATIMUP, the major modifications that operate throughout the processing pipeline, and the advantages of its implementation. The model of digital workflow implementation at IPATIMUP demonstrates that careful planning and adoption of simple measures related to time, space, and sample management can be adopted by any pathology laboratory to achieve higher quality and easy digital transformation.
An Improved Wild Horse Optimizer for Solving Optimization Problems

Wild horse optimizer (WHO) is a recently proposed metaheuristic algorithm that simulates the social behavior of wild horses in nature. Although WHO shows competitive performance compared to some algorithms, it suffers from low exploitation capability and stagnation in local optima. This paper presents an improved wild horse optimizer (IWHO), which incorporates three improvements to enhance optimizing capability. The main innovation of this paper is to put forward the random running strategy (RRS) and the competition for waterhole mechanism (CWHM). The random running strategy is employed to balance exploration and exploitation, and the competition for waterhole mechanism is proposed to boost exploitation behavior. Moreover, the dynamic inertia weight strategy (DIWS) is utilized to optimize the global solution. The proposed IWHO is evaluated using twenty-three classical benchmark functions, ten CEC 2021 test functions, and five real-world optimization problems. High-dimensional cases (D = 200, 500, 1000) are also tested. Comparing nine well-known algorithms, the experimental results of test functions demonstrate that the IWHO is very competitive in terms of convergence speed, precision, accuracy, and stability. Further, the practical capability of the proposed method is verified by the results of engineering design problems.
Risks and Benefits of Large Language Models for the Environment

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTRisks and Benefits of Large Language Models for the EnvironmentMatthias C. Rillig*Matthias C. RilligFreie Universität Berlin, Institute for Biology, 14195 Berlin, GermanyBerlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, Germany*[email protected]More by Matthias C. RilligView Biographyhttps://orcid.org/0000-0003-3541-7853, Marlene ÅgerstrandMarlene ÅgerstrandDepartment of Environmental Science, Stockholm University, SE-106 91 Stockholm, SwedenMore by Marlene Ågerstrand, Mohan BiMohan BiFreie Universität Berlin, Institute for Biology, 14195 Berlin, GermanyBerlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), 14195 Berlin, GermanyMore by Mohan Bi, Kenneth A. GouldKenneth A. GouldDepartment of Sociology, Brooklyn College, City University of New York, 2900 Bedford Avenue, Brooklyn, New York 11210, United StatesMore by Kenneth A. Gould, and Uli SauerlandUli SauerlandLeibniz-Centre General Linguistics (ZAS), Schützenstrasse 18, 10117 Berlin, GermanyMore by Uli SauerlandCite this: Environ. Sci. Technol. 2023, 57, 9, 3464–3466Publication Date (Web):February 23, 2023Publication History Received9 February 2023Published online23 February 2023Published inissue 7 March 2023https://doi.org/10.1021/acs.est.3c01106Copyright © 2023 American Chemical SocietyRequest reuse permissions This publication is free to access through this site. Learn MoreArticle Views8084Altmetric-Citations1LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (2 MB) Get e-Alertsclose Get e-Alerts
The Influence of Teachers’ Professional Development Activities on the Factors Promoting ICT Integration in Primary Schools in Mongolia

This paper examines the influences of professional development activities on important teacher-level factors that are important for the use of Information and Communication Technology (ICT) in education for primary school teachers in Mongolia. The study utilizes the survey data collected in 2012 (n = 826) and 2016 (n = 1161) to identify the changes in factors that are important to the use of ICT in education. The study result shows that six teacher level factors that are important for the ICT integration have been improved over time through professional development activities. These are professional competency in educational use of ICT, collaboration for ICT integration, benefits on use of ICT, autonomy to innovate, recognition as a professional, and skills and practices in educational use of ICT. This provides supporting evidence to educational practitioners for the implementation of effective professional development programs to promote ICT integration in education, especially in the developing country’s context.
A complete formal semantics of x86-64 user-level instruction set architecture

We present the most complete and thoroughly tested formal semantics of x86-64 to date. Our semantics faithfully formalizes all the non-deprecated, sequential user-level instructions of the x86-64 Haswell instruction set architecture. This totals 3155 instruction variants, corresponding to 774 mnemonics. The semantics is fully executable and has been tested against more than 7,000 instruction-level test cases and the GCC torture test suite. This extensive testing paid off, revealing bugs in both the x86-64 reference manual and other existing semantics. We also illustrate potential applications of our semantics in different formal analyses, and discuss how it can be useful for processor verification.
A theoretical attempt to revive the serial/parallel-search dichotomy

A core distinction in Anne Treisman’s feature-integration theory (FIT) is in that between parallel and serial search. We outline this dichotomy and selectively review the reasons why it has largely been abandoned in the visual-search community—namely, its theoretical dispensability, failure to find reliable yardsticks for differentiating parallel and serial search, and falsification of core predictions. We then go on to introduce a new theoretical framework that, we argue, clears up some of the theoretical confusion by merging FIT with various competing theories. This framework’s core feature is the distinction between and characterization of two fundamentally different search modes: one in which attention is guided to a single item via a priority map (priority guidance), and one in which clumps of multiple items are scanned in parallel in a spatially systematic order (clump scanning). Finally, we will elaborate how this new theoretical framework can resolve current controversies in the literature and how it relates to other existing theories. We (somewhat optimistically) believe that the outcome of this theoretical exercise is a unification of theories of visual search that can explain, or at least is consistent with, all phenomena reported in the visual-search literature that have previously been accounted for by various conflicting theories.
Demystifying differentiable programming: shift/reset the penultimate backpropagator

Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay. This success rests crucially on gradient-descent optimization and the ability to “learn” parameters of a neural network by backpropagating observed errors. However, neural network architectures are growing increasingly sophisticated and diverse, which motivates an emerging quest for even more general forms of differentiable programming, where arbitrary parameterized computations can be trained by gradient descent. In this paper, we take a fresh look at automatic differentiation (AD) techniques, and especially aim to demystify the reverse-mode form of AD that generalizes backpropagation in neural networks. We uncover a tight connection between reverse-mode AD and delimited continuations, which permits implementing reverse-mode AD purely via operator overloading and without managing any auxiliary data structures. We further show how this formulation of AD can be fruitfully combined with multi-stage programming (staging), leading to an efficient implementation that combines the performance benefits of deep learning frameworks based on explicit reified computation graphs (e.g., TensorFlow) with the expressiveness of pure library approaches (e.g., PyTorch).
IMaT: Unsupervised Text Attribute Transfer via Iterative Matching and Translation

Text attribute transfer aims to automatically rewrite sentences such that they possess certain linguistic attributes, while simultaneously preserving their semantic content.This task remains challenging due to a lack of supervised parallel data.Existing approaches try to explicitly disentangle content and attribute information, but this is difficult and often results in poor content-preservation and ungrammaticality.In contrast, we propose a simpler approach, Iterative Matching and Translation (IMaT), which: (1) constructs a pseudoparallel corpus by aligning a subset of semantically similar sentences from the source and the target corpora; (2) applies a standard sequence-to-sequence model to learn the attribute transfer; (3) iteratively improves the learned transfer function by refining imperfections in the alignment.In sentiment modification and formality transfer tasks, our method outperforms complex state-of-the-art systems by a large margin.As an auxiliary contribution, we produce a publicly-available test set with human-generated transfer references. 1 .
Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes

The goal of this paper is to design image classification systems that, after an initial multi-task training phase, can automatically adapt to new tasks encountered at test time. We introduce a conditional neural process based approach to the multi-task classification setting for this purpose, and establish connections to the meta- and few-shot learning literature. The resulting approach, called CNAPs, comprises a classifier whose parameters are modulated by an adaptation network that takes the current task's dataset as input. We demonstrate that CNAPs achieves state-of-the-art results on the challenging Meta-Dataset benchmark indicating high-quality transfer-learning. We show that the approach is robust, avoiding both over-fitting in low-shot regimes and under-fitting in high-shot regimes. Timing experiments reveal that CNAPs is computationally efficient at test-time as it does not involve gradient based adaptation. Finally, we show that trained models are immediately deployable to continual learning and active learning where they can outperform existing approaches that do not leverage transfer learning.
An Optimized Steganography Hiding Capacity and Imperceptibly Using Genetic Algorithms

In stenography, embedding data within an image has a trade-off between image quality and embedding capacity. Specifically, the more data are concealed within a carrier image, the further distortion the image suffers, causing a decline in the resultant stego image quality. Embedding high capacity of data into an image while preserving the quality of the carrier image can be seen as an optimization problem. In this paper, we propose a novel spatial steganography scheme using genetic algorithms (GAs). Our scheme utilizes new operations to increase least significant bits (LSB) matching between the carrier and the stego image which results in increased embedding capacity and reduced distortion. These operations are optimized pixel scanning in vertical and horizontal directions, circular shifting, flipping secret bits and secret data transposing. We formulate a general GA-based steganography model to search for the optimum solutions. Finally, we use LSB substitution for data embedding. We conduct extensive experimental testing of the proposed scheme and compare it to the state-of-art steganography schemes. The proposed scheme outperforms the relevant GA-based steganography methodologies.