Reviewerly/exHarmony huggingface dataset:
Improving Transparency, Falsifiability, and Rigor by Making Hypothesis Tests Machine-Readable

Making scientific information machine-readable greatly facilitates its reuse. Many scientific articles have the goal to test a hypothesis, so making the tests of statistical predictions easier to find and access could be very beneficial. We propose an approach that can be used to make hypothesis tests machine-readable. We believe there are two benefits to specifying a hypothesis test in such a way that a computer can evaluate whether the statistical prediction is corroborated or not. First, hypothesis tests become more transparent, falsifiable, and rigorous. Second, scientists benefit if information related to hypothesis tests in scientific articles is easily findable and reusable, for example, to perform meta-analyses, conduct peer review, and examine metascientific research questions. We examine what a machine-readable hypothesis test should look like and demonstrate the feasibility of machine-readable hypothesis tests in a real-life example using the fully operational prototype R package scienceverse.
Bilevel Parameter Learning for Nonlocal Image Denoising Models

We propose a bilevel optimization approach for the estimation of parameters in nonlocal image denoising models. The parameters we consider are both the fidelity weight and weights within the kernel of the nonlocal operator. In both cases, we investigate the differentiability of the solution operator in function spaces and derive a first-order optimality system that characterizes local minima. For the numerical solution of the problems, we use a second-order trust-region algorithm in combination with a finite element discretization of the nonlocal denoising models and introduce a computational strategy for the solution of the resulting dense linear systems. Several experiments illustrate the applicability and effectiveness of our approach.
Rethinking deep active learning: Using unlabeled data at model training

Active learning typically focuses on training a model on few labeled examples alone, while unlabeled ones are only used for acquisition. In this work we depart from this setting by using both labeled and unlabeled data during model training across active learning cycles. We do so by using unsupervised feature learning at the beginning of the active learning pipeline and semi-supervised learning at every active learning cycle, on all available data. The former has not been investigated before in active learning, while the study of latter in the context of deep learning is scarce and recent findings are not conclusive with respect to its benefit. Our idea is orthogonal to acquisition strategies by using more data, much like ensemble methods use more models. By systematically evaluating on a number of popular acquisition strategies and datasets, we find that the use of unlabeled data during model training brings a surprising accuracy improvement in image classification, compared to the differences between acquisition strategies. We thus explore smaller label budgets, even one label per class.
Deep Ordinal Regression with Label Diversity

Regression via classification (RvC) is a common method used for regression problems in deep learning, where the target variable belongs to a set of continuous values. By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compared to using a standard regression approach. However, it is not clear how the set of discrete classes should be chosen and how it affects the overall solution. In this work, we propose that using several discrete data representations simultaneously can improve neural network learning compared to a single representation. Our approach is end-to-end differentiable and can be added as a simple extension to conventional learning methods, such as deep neural networks. We test our method on three challenging tasks and show that our method reduces the prediction error compared to a baseline RvC approach while maintaining a similar model complexity.
HEAL: an automated deep learning framework for cancer histopathology image analysis

Digital pathology supports analysis of histopathological images using deep learning methods at a large-scale. However, applications of deep learning in this area have been limited by the complexities of configuration of the computational environment and of hyperparameter optimization, which hinder deployment and reduce reproducibility.Here, we propose HEAL, a deep learning-based automated framework for easy, flexible and multi-faceted histopathological image analysis. We demonstrate its utility and functionality by performing two case studies on lung cancer and one on colon cancer. Leveraging the capability of Docker, HEAL represents an ideal end-to-end tool to conduct complex histopathological analysis and enables deep learning in a broad range of applications for cancer image analysis.The docker image of HEAL is available at https://hub.docker.com/r/docurdt/heal and related documentation and datasets are available at http://heal.erc.monash.edu.au.Supplementary data are available at Bioinformatics online.
Saliency-Driven Versatile Video Coding for Neural Object Detection

Saliency-driven image and video coding for humans has gained importance in the recent past. In this paper, we propose such a saliency-driven coding framework for the video coding for machines task using the latest video coding standard Versatile Video Coding (VVC). To determine the salient regions before encoding, we employ the real-time-capable object detection network You Only Look Once~(YOLO) in combination with a novel decision criterion. To measure the coding quality for a machine, the state-of-the-art object segmentation network Mask R-CNN was applied to the decoded frame. From extensive simulations we find that, compared to the reference VVC with a constant quality, up to 29 % of bitrate can be saved with the same detection accuracy at the decoder side by applying the proposed saliency-driven framework. Besides, we compare YOLO against other, more traditional saliency detection methods.
Voltage-controlled superparamagnetic ensembles for low-power reservoir computing

We propose thermally driven, voltage-controlled superparamagnetic ensembles as low-energy platforms for hardware-based reservoir computing. In the proposed devices, thermal noise is used to drive the ensembles' magnetization dynamics, while control of their net magnetization states is provided by strain-mediated voltage inputs. Using an ensemble of CoFeB nanodots as an example, we use analytical models and micromagnetic simulations to demonstrate how such a device can function as a reservoir and perform two benchmark machine learning tasks (spoken digit recognition and chaotic time series prediction) with competitive performance. Our results indicate robust performance on timescales from microseconds to milliseconds, potentially allowing such a reservoir to be tuned to perform a wide range of real-time tasks, from decision making in driverless cars (fast) to speech recognition (slow). The low energy consumption expected for such a device makes it an ideal candidate for use in edge computing applications that require low latency and power.
The formation of a successful personality of a pupil in Ukrainian primary school during media education implementation

The article substantiates the relevance of implementing the technology of formation of a successful personality of a primary school age pupil during media education implementation at primary school. A technology model is developed. The necessity of solving problems of success simultaneously with increasing the level of media culture of a pupil, the formation of key competencies for life, preparation of a child for the life’s self-realization on the basis of the partnership implementation of schoolchildren, parents and teachers is proved. The need of the embodiment of the pedagogy of heart and the pedagogy of success is shown. The diagnostic toolkit for determining the levels of formation of the successful personality of primary school pupils is specified. The effective forms and methods of the schoolchildren education are substantiated: the creation of electronic books, projects “Rules of Success Achievement”, “Stories of Success”, watching movies about successful people with special needs, analysis of media products on the topic of success. The ways of educating parents about the problems of children’s success are determined. The results of experimental research are analyzed. The necessity of improving the content of textbooks in the context of achieving success, increasing interest to children’s periodicals is revealed. The formation of a successful pupil is considered as one of the ways to strengthen the Ukrainian state. The necessity of raising the authority of the teaching profession is proved.
Improved barnacles mating optimizer algorithm for feature selection and support vector machine optimization

With the rapid development of computer technology, data collection becomes easier, and data object presents more complex. Data analysis method based on machine learning is an important, active, and multi-disciplinarily research field. Support vector machine (SVM) is one of the most powerful and fast classification models. The main challenges SVM faces are the selection of feature subset and the setting of kernel parameters. To improve the performance of SVM, a metaheuristic algorithm is used to optimize them simultaneously. This paper first proposes a novel classification model called IBMO-SVM, which hybridizes an improved barnacle mating optimizer (IBMO) with SVM. Three strategies, including Gaussian mutation, logistic model, and refraction-learning, are used to improve the performance of BMO from different perspectives. Through 23 classical benchmark functions, the impact of control parameters and the effectiveness of introduced strategies are analyzed. The convergence accuracy and stability are the main gains, and exploration and exploitation phases are more properly balanced. We apply IBMO-SVM to 20 real-world datasets, including 4 extremely high-dimensional datasets. Experimental results are compared with 6 state-of-the-art methods in the literature. The final statistical results show that the proposed IBMO-SVM achieves a better performance than the standard BMO-SVM and other compared methods, especially on high-dimensional datasets. In addition, the proposed model also shows significant superiority compared with 4 other classifiers.
MABAN: Multi-Agent Boundary-Aware Network for Natural Language Moment Retrieval

The amount of videos over the Internet and electronic surveillant cameras is growing dramatically, meanwhile paired sentence descriptions are significant clues to select attentional contents from videos. The task of natural language moment retrieval (NLMR) has drawn great interests from both academia and industry, which aims to associate specific video moments with the text descriptions figuring complex scenarios and multiple activities. In general, NLMR requires temporal context to be properly comprehended, and the existing studies suffer from two problems: (1) limited moment selection and (2) insufficient comprehension of structural context. To address these issues, a multi-agent boundary-aware network (MABAN) is proposed in this work. To guarantee flexible and goal-oriented moment selection, MABAN utilizes multi-agent reinforcement learning to decompose NLMR into localizing the two temporal boundary points for each moment. Specially, MABAN employs a two-phase cross-modal interaction to exploit the rich contextual semantic information. Moreover, temporal distance regression is considered to deduce the temporal boundaries, with which the agents can enhance the comprehension of structural context. Extensive experiments are carried out on two challenging benchmark datasets of ActivityNet Captions and Charades-STA, which demonstrate the effectiveness of the proposed approach as compared to state-of-the-art methods. The project page can be found in https://mic.tongji.edu.cn/e5/23/c9778a189731/page.htm.
SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval

We introduce SPARTA, a novel neural retrieval method that shows great promise in performance, generalization, and interpretability for open-domain question answering.Unlike many neural ranking methods that use dense vector nearest neighbor search, SPARTA learns a sparse representation that can be efficiently implemented as an Inverted Index.The resulting representation enables scalable neural retrieval that does not require expensive approximate vector search and leads to better performance than its dense counterpart.We validated our approaches on 4 opendomain question answering (OpenQA) tasks and 11 retrieval question answering (ReQA) tasks.SPARTA achieves new state-of-the-art results across a variety of open-domain question answering tasks in both English and Chinese datasets, including open SQuAD, CMRC and etc. Analysis also confirms that the proposed method creates human interpretable representation and allows flexible control over the trade-off between performance and efficiency.
Model Extraction and Adversarial Transferability, Your BERT is Vulnerable!

Natural language processing (NLP) tasks, ranging from text classification to text generation, have been revolutionised by the pretrained language models, such as BERT.This allows corporations to easily build powerful APIs by encapsulating fine-tuned BERT models for downstream tasks.However, when a fine-tuned BERT model is deployed as a service, it may suffer from different attacks launched by the malicious users.In this work, we first present how an adversary can steal a BERT-based API service (the victim/target model) on multiple benchmark datasets with limited prior knowledge and queries.We further show that the extracted model can lead to highly transferable adversarial attacks against the victim model.Our studies indicate that the potential vulnerabilities of BERT-based API services still hold, even when there is an architectural mismatch between the victim model and the attack model.Finally, we investigate two defence strategies to protect the victim model, and find that unless the performance of the victim model is sacrificed, both model extraction and adversarial transferability can effectively compromise the target models.
Diagnostic digital pathology implementation: Learning from the digital health experience

Digital Pathology (also referred to as Telepathology and Whole Slide Imaging) is the process of producing high resolution digital images from tissue sections on glass slides. These glass slides are normally examined under a microscope by a pathologist as part of the diagnostic process. The emergence of digital pathology now means that digital images are stored on secure servers and can be viewed on computer monitors; enabling pathologists to work remotely and to collaborate with other colleagues when second opinions are needed. The implementation of digital pathology into clinical practice has many potential benefits. Although this has been long recognised, its adoption as a diagnostic tool remains low and pathologists’ projections about its future deployment are cautious. Notable early digital pathology adopters have led the way. The challenge now is to scale-up digital pathology beyond the relatively few large networks and centres of excellence. Many other areas of healthcare have accumulated experience about optimising approaches to digital health/healthcare technology deployment and sustainability. This has been done in a multi-disciplinary context and has applied theoretical/conceptual frameworks. Thus far there has been little use of similar frameworks in the planning of digital pathology deployment in clinical practice. In this essay, I will explore the scope of digital pathology implementation approaches that have been deployed in clinical practice and examine what can be learned from the wider healthcare experience of adopting, scaling-up and sustaining innovative healthcare solutions.
Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization

Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content.A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style.Previous methods in filtering style completely remove tokens with style at the token level, which incurs the loss of content information.In this paper, we propose to enhance content preservation by implicitly removing the style information of each token with reverse attention, and thereby retain the content.Furthermore, we fuse content information when building the target style representation, making it dynamic with respect to the content.Our method creates not only styleindependent content representation, but also content-dependent style representation in transferring style.Empirical results show that our method outperforms the state-of-the-art baselines by a large margin in terms of content preservation.In addition, it is also competitive in terms of style transfer accuracy and fluency.
A Hybrid Encryption Solution to Improve Cloud Computing Security using Symmetric and Asymmetric Cryptography Algorithms

Ensuring the security of cloud computing is one of the most critical challenges facing the cloud services sector.Dealing with data in a cloud environment, which uses shared resources, and providing reliable and secure cloud services, requires a robust encryption solution with no or low negative impact on performance.Thus, this study proposes an effective cryptography solution to improve security in cloud computing with a very low impact on performance.A complex cryptography algorithm is not helpful in cloud computing as computing speed is essential in this environment.Therefore, this solution uses an improved Blowfish algorithm in combination with an ellipticcurve-based algorithm.Blowfish will encrypt the data, and the elliptic curve algorithm will encrypt its key, which will increase security and performance.Moreover, a digital signature technique is used to ensure data integrity.The solution is evaluated, and the results show improvements in throughput, execution time, and memory consumption parameters.
Path Analysis of Self-Regulation, Social Skills, Critical Thinking and Problem-Solving Ability on Social Studies Learning Outcomes

This study aims to analyze the effect of Self-Regulation, Social Skills, Critical Thinking, and Problem-Solving Skills on Social Learning Outcomes.This research used a quantitative approach, survey methods, and path analysis techniques.The participants were class V students throughout Jakarta from November 2015 to October 2016, with a total of 250 students.The research instrument to measure students' critical thinking skills, problem-solving skills, and social learning test results was multiple-choice tests, while to measure self-regulation and social skills were using a non-test instrument in the form of a questionnaire.The data analysis technique used descriptive analysis and path analysis by testing the significance of the path coefficient using the independent sample t-test.The results showed there was a positive direct effect of self-regulation, social skills, critical thinking skills, and problem-solving skills on social studies learning outcomes on learning outcomes.This research implied that efforts to improve social studies learning outcomes began with improving self-regulation strategies, practicing social skills, developing critical thinking skills, and problem-solving skills.
Self-Supervised Sketch-to-Image Synthesis

Imagining a colored realistic image from an arbitrary-drawn sketch is one of human capabilities that we eager machines to mimic. Unlike previous methods that either require the sketch-image pairs or utilize low-quantity detected edges as sketches, we study the exemplar-based sketch-to-image (s2i) synthesis task in a self-supervised learning manner, eliminating the necessity of the paired sketch data. To this end, we first propose an unsupervised method to efficiently synthesize line-sketches for general RGB-only datasets. With the synthetic paired-data, we then present a self-supervised Auto-Encoder (AE) to decouple the content/style features from sketches and RGB-images, and synthesize images both content-faithful to the sketches and style-consistent to the RGB-images. While prior works employ either the cycle-consistence loss or dedicated attentional modules to enforce the content/style fidelity, we show AE's superior performance with pure self-supervisions. To further improve the synthesis quality in high resolution, we also leverage an adversarial network to refine the details of synthetic images. Extensive experiments on $1024^2$ resolution demonstrate a new state-of-art-art performance of the proposed model on CelebA-HQ and Wiki-Art datasets. Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which the synthesized images are not only style-consistent but also semantically meaningful.
Multi-modal Multi-label Emotion Recognition with Heterogeneous Hierarchical Message Passing

As an important research issue in affective computing community, multi-modal emotion recognition has become a hot topic in the last few years. However, almost all existing studies perform multiple binary classification for each emotion with focus on complete time series data. In this paper, we focus on multi-modal emotion recognition in a multi-label scenario. In this scenario, we consider not only the label-to-label dependency, but also the feature-to-label and modality-to-label dependencies. Particularly, we propose a heterogeneous hierarchical message passing network to effectively model above dependencies. Furthermore, we propose a new multi-modal multi-label emotion dataset based on partial time-series content to show predominant generalization of our model. Detailed evaluation demonstrates the effectiveness of our approach.
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning

Geometry problem solving has attracted much attention in the NLP community recently.The task is challenging as it requires abstract problem understanding and symbolic reasoning with axiomatic knowledge.However, current datasets are either small in scale or not publicly available.Thus, we construct a new largescale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language.We further propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem Solver (Inter-GPS).Inter-GPS first parses the problem text and diagram into formal language automatically via rule-based text parsing and neural object detecting, respectively.Unlike implicit learning in existing methods, Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step.Also, a theorem predictor is designed to infer the theorem application sequence fed to the symbolic solver for the more efficient and reasonable searching path.Extensive experiments on the Geometry3K and GEOS datasets demonstrate that Inter-GPS achieves significant improvements over existing methods.