Reviewerly/exHarmony huggingface dataset:
New Conservation Laws Based on Generalised Reynolds Transport Theorems

The Reynolds transport theorem gives a generic conservation law for a conserved quantity carried by fluid flow through a continuous control volume, providing the foundation for all conservation laws of fluid mechanics.We present new spatial and parametric multivariate generalisations of this theorem, each of which provides a continuous mapping of the contents of a domain within a coordinate space.The most general form involves mappings on a manifold, presented in a multivariate extension of exterior calculus.These theorems are used to generate tables of new conservation laws in integral form in different spaces, including Eulerian velocity space and velocity-volume phase space, based on fluid densities defined in these spaces.
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity

We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. It improves on the previous state-of-the-art by typically being orders of magnitude more robust toward noise and bad data, and also by discovering many formulas that stumped previous methods. We develop a method for discovering generalized symmetries (arbitrary modularity in the computational graph of a formula) from gradient properties of a neural network fit. We use normalizing flows to generalize our symbolic regression method to probability distributions from which we only have samples, and employ statistical hypothesis testing to accelerate robust brute-force search.
Classification of Diabetic Rat Histopathology Images Using Convolutional Neural Networks

Diabetes mellitus is a common disease worldwide.In progressive diabetes patients, deterioration of kidney histology tissue begins.Currently, the histopathologic examination of kidney tissue samples has been performed manually by pathologists.This examination process is time-consuming and requires pathologists' expertise.Thus, automatic detection methods are crucial for early detection and also treatment planning.Computer-aided diagnostic systems based on deep learning show high success rates in classifying medical images if a large and diverse image set is available during the training process.Herein, transfer learningbased convolutional neural network model was proposed for the automatic detection of diabetes mellitus using only rat kidney histopathology images.The model monitors structural changes, especially in the glomerulus and also other parts of the kidney caused by the damages of diabetes.According to the simulation results, the proposed model has reached 97.5% accuracy.As a result, the recommended model can quickly and accurately classify histopathology images and helps pathologists as the second reader in critical situations
Incorporating Stylistic Lexical Preferences in Generative Language Models

While recent advances in language modeling has resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate a specific target style.Leveraging the generative capabilities of a transformer-based language models, we present an approach to induce certain target-author attributes by incorporating continuous multi-dimensional lexical preferences of an author into generative language models.We introduce rewarding strategies in a reinforcement learning framework that encourages the use of words across multiple categorical dimensions, to varying extents.Our experiments demonstrate that the proposed approach can generate text that distinctively aligns with a given target author's lexical style.We conduct quantitative and qualitative comparisons with competitive and relevant baselines to illustrate the benefits of the proposed approach.
What do we expect from Multiple-choice QA Systems?

The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities.However, using various perturbations, multiple recent works have shown that good performance on a dataset might not indicate performance that correlates well with human's expectations from models that "understand" language.In this work we consider a top performing model on several Multiple Choice Question Answering (MCQA) datasets, and evaluate it against a set of expectations one might have from such a model, using a series of zero-information perturbations of the model's inputs.Our results show that the model clearly falls short of our expectations, and motivates a modified training approach that forces the model to better attend to the inputs.We show that the new training paradigm leads to a model that performs on par with the original model while better satisfying our expectations. 1
Improving Generalization in Reinforcement Learning with Mixture Regularization

Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments. To improve their generalizability, data augmentation approaches (e.g. cutout and random convolution) are previously explored to increase the data diversity. However, we find these approaches only locally perturb the observations regardless of the training environments, showing limited effectiveness on enhancing the data diversity and the generalization performance. In this work, we introduce a simple approach, named mixreg, which trains agents on a mixture of observations from different training environments and imposes linearity constraints on the observation interpolations and the supervision (e.g. associated reward) interpolations. Mixreg increases the data diversity more effectively and helps learn smoother policies. We verify its effectiveness on improving generalization by conducting extensive experiments on the large-scale Procgen benchmark. Results show mixreg outperforms the well-established baselines on unseen testing environments by a large margin. Mixreg is simple, effective and general. It can be applied to both policy-based and value-based RL algorithms. Code is available at this https URL .
Eye tracking in the wild: Piloting a real-life assessment paradigm for older adults

Previous research showed associations between personality traits and eye movements of young adults in the laboratory. However, less is known about these associations in real life and in older age. Primarily, there seems to be no paradigm to assess eye movements of older adults in real life. The present feasibility study thus aimed to test grocery shopping as a real-life assessment paradigm with older adults. Additionally, possible links between personality traits and eye movements were explored. The sample consisted of 38 older individuals (M = 72.85 years). Participants did their grocery shopping in a supermarket while wearing an eye tracker. Three key feasibility issues were examined, that is (1) wearability of the eye tracker during grocery shopping, (2) recording, and (3) evaluation of eye movements in a real-life context. Our real-life assessment paradigm showed to be feasible to implement and acceptable to older adults. This feasibility study provides specific practical recommendations which may be useful for fu-ture studies that plan to innovatively expand the traditional methods repertoire of personality science and aging research by using eye tracking in real life.
Opinion-Based Co-Occurrence Network for Identifying the Most Influential Product Features

Nowadays, social networking sites such as Facebook, Twitter, LinkedIn, YouTube, and other e-commerce websites produce a large number of text reviews. These text reviews mostly describe the product features and their opinions, which are the most important to the product developers, launchers, or buyers for business development and decisionmaking processes. Therefore, we present an opinion-based co-occurrence network for product reviews. The main aim of this research is to identify the popularity of product features or popular terms, the number of connections of a term, the strong relationship between terms, grouping the product terms, and the sentiment polarity links between terms in both positive sentiment and negative sentiment. Also, we employed the Harel-Koren fast multiscale layout algorithm and CNM (Clauset-Newman-Moore) algorithm for visualizing and grouping the network. We then measured the overall graph metrics and vertex metrics to characterize the network. Additionally, the experimental result shows the ranked product features and their social strength between product features and sentiments.
Deep anomaly detection for industrial systems: a case study

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation is used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are categorical in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.
TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks

As different genres are known to differ in their communicative properties and as previously, for Chinese, discourse relations have only been annotated over news text, we have created the TED-CDB dataset.TED-CDB comprises a large set of TED talks in Chinese that have been manually annotated according to the goals and principles of Penn Discourse Treebank, but adapted to features that are not present in English.It serves as a unique Chinese corpus of spoken discourse.Benchmark experiments show that TED-CDB poses a challenge for state-of-the-art discourse relation classifiers, whose F1 performance on 4way classification is <60%.This is a dramatic drop of 35% from performance on the news text in the Chinese Discourse Treebank.Transfer learning experiments have been carried out with the TED-CDB for both same-language cross-domain transfer and same-domain crosslanguage transfer.Both demonstrate that the TED-CDB can improve the performance of systems being developed for languages other than Chinese and would be helpful for insufficient or unbalanced data in other corpora.The dataset and our Chinese annotation guidelines has been made freely available. 1
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference

The state of the art on many NLP tasks is currently achieved by large pre-trained language models, which require a considerable amount of computation. We aim to reduce the inference cost in a setting where many different predictions are made on a single piece of text. In that case, computational cost during inference can be amortized over the different predictions (tasks) using a shared text encoder. We compare approaches for training such an encoder and show that encoders pre-trained over multiple tasks generalize well to unseen tasks. We also compare ways of extracting fixed- and limited-size representations from this encoder, including pooling features extracted from multiple layers or positions. Our best approach compares favorably to knowledge distillation, achieving higher accuracy and lower computational cost once the system is handling around 7 tasks. Further, we show that through binary quantization, we can reduce the size of the extracted representations by a factor of 16 to store them for later use. The resulting method offers a compelling solution for using large-scale pre-trained models at a fraction of the computational cost when multiple tasks are performed on the same text.
Modular Meta-Learning with Shrinkage

Many real-world problems, including multi-speaker text-to-speech synthesis, can greatly benefit from the ability to meta-learn large models with only a few task-specific components. Updating only these task-specific modules then allows the model to be adapted to low-data tasks for as many steps as necessary without risking overfitting. Unfortunately, existing meta-learning methods either do not scale to long adaptation or else rely on handcrafted task-specific architectures. Here, we propose a meta-learning approach that obviates the need for this often sub-optimal hand-selection. In particular, we develop general techniques based on Bayesian shrinkage to automatically discover and learn both task-specific and general reusable modules. Empirically, we demonstrate that our method discovers a small set of meaningful task-specific modules and outperforms existing meta-learning approaches in domains like few-shot text-to-speech that have little task data and long adaptation horizons. We also show that existing meta-learning methods including MAML, iMAML, and Reptile emerge as special cases of our method.
Learning to ground medical text in a 3D human atlas

In this paper, we develop a method for grounding medical text into a physically meaningful and interpretable space corresponding to a human atlas.We build on text embedding architectures such as BERT and introduce a loss function that allows us to reason about the semantic and spatial relatedness of medical texts by learning a projection of the embedding into a 3D space representing the human body.We quantitatively and qualitatively demonstrate that our proposed method learns a context sensitive and spatially aware mapping, in both the inter-organ and intra-organ sense, using a large scale medical text dataset from the "Large-scale online biomedical semantic indexing" track of the 2020 BioASQ challenge.We extend our approach to a self-supervised setting, and find it to be competitive with a classification based method, and a fully supervised variant of approach.
Dual Modulated QR Codes for Proximal Privacy and Security

The ubiquitous presence of surveillance cameras severely compromises the security of private information (e.g. passwords) entered via a conventional keyboard interface in public places. We address this problem by proposing dual modulated QR (DMQR) codes, a novel QR code extension via which users can securely communicate private information in public places using their smartphones and a camera interface. Dual modulated QR codes use the same synchronization patterns and module geometry as conventional monochrome QR codes. Within each module, primary data is embedded using intensity modulation compatible with conventional QR code decoding. Specifically, depending on the bit to be embedded, a module is either left white or an elliptical black dot is placed within it. Additionally, for each module containing an elliptical dot, secondary data is embedded by orientation modulation; that is, by using different orientations for the elliptical dots. Because the orientation of the elliptical dots can only be reliably assessed when the barcodes are captured from a close distance, the secondary data provides “proximal privacy” and can be effectively used to communicate private information securely in public settings. Tests conducted using several alternative parameter settings demonstrate that the proposed DMQR codes are effective in meeting their objective- the secondary data can be accurately decoded for short capture distances (6 in.) but cannot be recovered from images captured over long distances (>12 in.). Furthermore, the proximal privacy can be adapted to application needs by varying the eccentricity of the elliptical dots used.
A Novel Clustering Scheme For Heterogeneous Cognitive Radio Wireless Sensor Networks

Introduction: This article is the product of the research “Learning-based Spectrum Analysis and Prediction in Cognitive Radio Sensor Networks”, developed at Sejong University in the year 2019.&#x0D; Problem: Most of the clustering schemes for distributed cognitive radio-enabled wireless sensor networks consider homogeneous cognitive radio-enabled wireless sensors. Many clustering schemes for such homogeneouscognitive radio-enabled wireless sensor networks waste resources and suffer from energy inefficiency because of the unnecessary overheads.&#x0D; Objective: The objective of the research is to propose a node clustering scheme that conserves energy and prolongs network lifetime.&#x0D; Methodology: A heterogeneous cognitive radio-enabled wireless sensor network in which only a few nodes have a cognitive radio module and the other nodes are normal sensor nodes. Along with the hardware cost, theproposed scheme is efficient in energy consumption.&#x0D; Results: We simulated the proposed scheme and compared it with the homogeneous cognitive radio-enabled wireless sensor networks. The results show that the proposed scheme is efficient in terms of energyconsumption.&#x0D; Conclusion: The proposed node clustering scheme performs better in terms of network energy conservation and network partition.&#x0D; Originality: There are heterogeneous node clustering schemes in the literature for cooperative spectrum sensing and energy efficiency, but to the best of our knowledge, there is no study that proposes a non-cognitiveradio-enabled sensor clustering for energy conservation along with cognitive radio-enabled wireless sensors.&#x0D; Limitations: The deployment of the proposed special device for cognitive radio-enabled wireless sensors is complicated and requires special hardware with better battery powered cognitive sensor nodes.
Improving and Measuring Spatial Skills with Augmented Reality and Gamification

We started to develop an Android application which implements tasks in Augmented Reality, analogous to the classic Mental Cutting Test. Our goal is to offer practicing exercises to our users with the support of gamified elements and AR technology. Thus, users can switch their 2D scenarios into the AR, rotate and scale them to determine the current answer of the scenario. As a motivation we also implemented the most important and popular elements of gamification principles. Thus, students complete their missions, claim rewards, get experimental points and achieve levels. Our application offers different surfaces and tools to users with different roles; thus, it is capable to measure the efficiency of gamification and AR with the use of four user groups such as users who can only access the 2D scenarios, users who can access the AR technology or the gamified elements and users who can access both of them. Besides, we also developed a repository which contains the 3D models (GLB files) and 2D images (SVG files) of the scenarios and various exercises can be composed or generated via its interface. As the result of our development, a prototype of application is ready to use and collect data about the activities of the users in June 2020.
Improved Differential Fault Attack on LEA by Algebraic Representation of Modular Addition

Recently, as the number of IoT (Internet of Things) devices has increased, the use of lightweight cryptographic algorithms that are suitable for environments with scarce resources has also increased. Consequently, the safety of such cryptographic algorithms is becoming increasingly important. Among them, side-channel analysis methods are very realistic threats. In this paper, we propose a novel differential fault attack method on the Lightweight Encryption Algorithm (LEA) cipher which became the ISO/IEC international standard lightweight cryptographic algorithm in 2019. Previously proposed differential fault attack methods on the LEA used the Single Bit Flip model, making it difficult to apply to real devices. The proposed attack method uses a more realistic attacker assumption, the Random Word Error model. We demonstrate that the proposed attack method can be implemented on real devices using an electromagnetic fault injection setup. Our attack method has the weakest attacker assumption among attack methods proposed to date. In addition, the number of required fault-injected ciphertexts and the number of key candidates for which exhaustive search is performed are the least among all existing methods. Therefore, when implementing the LEA cipher on IoT deivces, designers must apply appropriate countermeasures against fault injection attacks.
Reflective-net: learning from explanations

Abstract We examine whether data generated by explanation techniques, which promote a process of self-reflection, can improve classifier performance. Our work is based on the idea that humans have the ability to make quick, intuitive decisions as well as to reflect on their own thinking and learn from explanations. To the best of our knowledge, this is the first time that the potential of mimicking this process by using explanations generated by explainability methods has been explored. We found that combining explanations with traditional labeled data leads to significant improvements in classification accuracy and training efficiency across multiple image classification datasets and convolutional neural network architectures. It is worth noting that during training, we not only used explanations for the correct or predicted class, but also for other classes. This serves multiple purposes, including allowing for reflection on potential outcomes and enriching the data through augmentation.
Find research data repositories for the humanities - the data deposit recommendation service

Abstract How can researchers identify suitable research data repositories for the deposit of their research data? Which repository matches best the technical and legal requirements of a specific research project? For this end and with a humanities perspective the Data Deposit Recommendation Service (DDRS) has been developed as a prototype. It not only serves as a functional service for selecting humanities research data repositories but it is particularly a technical demonstrator illustrating the potential of re-using an already existing infrastructure - in this case re3data - and the feasibility to set up this kind of service for other research disciplines. The documentation and the code of this project can be found in the DARIAH GitHub repository: https://dariah-eric.github.io/ddrs/ .