Reviewerly/exHarmony huggingface dataset:
Modeling Simulation of COVID-19 in Indonesia based on Early Endemic Data

The COVID-19 pandemic has recently caused so much anxiety and speculation around the world. This phenomenon was mainly driven by the drastic increase in the number of infected people with the COVID-19 virus worldwide. Here we propose a simple model to predict the endemic in Indonesia. The model is based on the Richardâ€™s Curve that represents a modified logistic equation. Based on the similar trends of initial data between Indonesia and South Korea, we use parameter values that are obtained through parameter estimation of the model to the data in South Korea. Further, we use a strict assumption that the implemented strategy in Indonesia is as effective as in South Korea. The results show that endemic will end in April 2020 with the total number of cases more than 8000.
Opening the black box of machine learning in radiology: can the proximity of annotated cases be a way?

Abstract Machine learning (ML) and deep learning (DL) systems, currently employed in medical image analysis, are data-driven models often considered as black boxes . However, improved transparency is needed to translate automated decision-making to clinical practice. To this aim, we propose a strategy to open the black box by presenting to the radiologist the annotated cases (ACs) proximal to the current case (CC), making decision rationale and uncertainty more explicit. The ACs, used for training, validation, and testing in supervised methods and for validation and testing in the unsupervised ones, could be provided as support of the ML/DL tool. If the CC is localised in a classification space and proximal ACs are selected by proper metrics, the latter ones could be shown in their original form of images, enriched with annotation to radiologists, thus allowing immediate interpretation of the CC classification. Moreover, the density of ACs in the CC neighbourhood, their image saliency maps, classification confidence, demographics, and clinical information would be available to radiologists. Thus, encrypted information could be transmitted to radiologists, who will know model output (what) and salient image regions (where) enriched by ACs, providing classification rationale (why). Summarising, if a classifier is data-driven, let us make its interpretation data-driven too.
A Controllable Model of Grounded Response Generation

Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses. Attempts to boost informativeness alone come at the expense of factual accuracy, as attested by pretrained language models' propensity to "hallucinate" facts. While this may be mitigated by access to background knowledge, there is scant guarantee of relevance and informativeness in generated responses. We propose a framework that we call controllable grounded response generation (CGRG), in which lexical control phrases are either provided by a user or automatically extracted by a control phrase predictor from dialogue context and grounding knowledge. Quantitative and qualitative results show that, using this framework, a transformer based model with a novel inductive attention mechanism, trained on a conversation-like Reddit dataset, outperforms strong generation baselines.
Correlation Paradox of the Dissociation Limit: A Quantum Information Perspective

The interplay between electron interaction and geometry in a molecular system can lead to rather paradoxical situations. The prime example is the dissociation limit of the hydrogen molecule. While a significant increase of the distance r between the two nuclei marginalizes the electron–electron interaction, the exact ground state does, however, not take the form of a single Slater determinant. By first reviewing and then employing concepts from quantum information theory, we resolve this paradox and its generalizations to more complex systems in a quantitative way. To be more specific, we illustrate and prove that thermal noise due to finite, possibly even just infinitesimally low, temperature T will destroy the entanglement beyond a critical separation distance rcrit(T) entirely. Our analysis is comprehensive in the sense that we simultaneously discuss both total correlation and entanglement in the particle picture as well as in the orbital/mode picture. Our results reveal a conceptually new characterization of static and dynamical correlation in ground states by relating them to the (non)robustness of correlation with respect to thermal noise.
Robust Detection of Adversarial Attacks on Medical Images

Although deep learning systems trained on medical images have shown state-of-the-art performance in many clinical prediction tasks, recent studies demonstrate that these systems can be fooled by carefully crafted adversarial images. It has raised concerns on the practical deployment of deep learning based medical image classification systems. To tackle this problem, we propose an unsupervised learning approach to detect adversarial attacks on medical images. Our approach is capable of detecting a wide range of adversarial attacks without knowing the attackers nor sacrificing the classification performance. More importantly, our approach can be easily embedded into any deep learning-based medical imaging system as a module to improve the system's robustness. Experiments on a public chest X-ray dataset demonstrate the strong performance of our approach in defending adversarial attacks under both white-box and black-box settings.
Experimental Reconstruction of the Few-Photon Nonlinear Scattering Matrix from a Single Quantum Dot in a Nanophotonic Waveguide

Coherent photon-emitter interfaces offer a way to mediate efficient nonlinear photon-photon interactions, much needed for quantum information processing. Here we experimentally study the case of a two-level emitter, a quantum dot, coupled to a single optical mode in a nanophotonic waveguide. We carry out few-photon transport experiments and record the statistics of the light to reconstruct the scattering matrix elements of one- and two-photon components. This provides direct insight to the complex nonlinear photon interaction that contains rich many-body physics.
Robustness study of noisy annotation in deep learning based medical image segmentation

Partly due to the use of exhaustive-annotated data, deep networks have achieved impressive performance on medical image segmentation. Medical imaging data paired with noisy annotation are, however, ubiquitous, but little is known about the effect of noisy annotation on deep learning based medical image segmentation. We studied the effect of noisy annotation in the context of mandible segmentation from CT images. First, 202 images of head and neck cancer patients were collected from our clinical database, where the organs-at-risk were annotated by one of twelve planning dosimetrists. The mandibles were roughly annotated as the planning avoiding structure. Then, mandible labels were checked and corrected by a head and neck specialist to get the reference standard. At last, by varying the ratios of noisy labels in the training set, deep networks were trained and tested for mandible segmentation. The trained models were further tested on other two public datasets. Experimental results indicated that the network trained with noisy labels had worse segmentation than that trained with reference standard, and in general, fewer noisy labels led to better performance. When using 20% or less noisy cases for training, no significant difference was found on the segmentation results between the models trained by noisy or reference annotation. Cross-dataset validation results verified that the models trained with noisy data achieved competitive performance to that trained with reference standard. This study suggests that the involved network is robust to noisy annotation to some extent in mandible segmentation from CT images. It also highlights the importance of labeling quality in deep learning. In the future work, extra attention should be paid to how to utilize a small number of reference standard samples to improve the performance of deep learning with noisy annotation.
Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering

Evidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the corresponding QA method.We introduce a simple, fast, and unsupervised iterative evidence retrieval method, which relies on three ideas: (a) an unsupervised alignment approach to soft-align questions and answers with justification sentences using only GloVe embeddings, (b) an iterative process that reformulates queries focusing on terms that are not covered by existing justifications, which (c) a stopping criterion that terminates retrieval when the terms in the given question and candidate answers are covered by the retrieved justifications.Despite its simplicity, our approach outperforms all the previous methods (including supervised methods) on the evidence selection task on two datasets: MultiRC and QASC.When these evidence sentences are fed into a RoBERTa answer classification component, we achieve state-of-the-art QA performance on these two datasets.
Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization

Cross-lingual summarization is the task of generating a summary in one language given a text in a different language.Previous works on cross-lingual summarization mainly focus on using pipeline methods or training an endto-end model using the translated parallel data.However, it is a big challenge for the model to directly learn cross-lingual summarization as it requires learning to understand different languages and learning how to summarize at the same time.In this paper, we propose to ease the cross-lingual summarization training by jointly learning to align and summarize.We design relevant loss functions to train this framework and propose several methods to enhance the isomorphism and cross-lingual transfer between languages.Experimental results show that our model can outperform competitive models in most cases.In addition, we show that our model even has the ability to generate cross-lingual summaries without access to any cross-lingual corpus.
Predicting Performance for Natural Language Processing Tasks

Given the complexity of combinations of tasks, languages, and domains in natural language processing (NLP) research, it is computationally prohibitive to exhaustively test newly proposed models on each possible experimental setting.In this work, we attempt to explore the possibility of gaining plausible judgments of how well an NLP model can perform under an experimental setting, without actually training or testing the model.To do so, we build regression models to predict the evaluation score of an NLP experiment given the experimental settings as input.Experimenting on 9 different NLP tasks, we find that our predictors can produce meaningful predictions over unseen languages and different modeling architectures, outperforming reasonable baselines as well as human experts.Going further, we outline how our predictor can be used to find a small subset of representative experiments that should be run in order to obtain plausible predictions for all other experimental settings.
Multi-Cell Compositional LSTM for NER Domain Adaptation

Cross-domain NER is a challenging yet practical problem.Entity mentions can be highly different across domains.However, the correlations between entity types can be relatively more stable across domains.We investigate a multi-cell compositional LSTM structure for multi-task learning, modeling each entity type using a separate cell state.With the help of entity typed units, cross-domain knowledge transfer can be made in an entity type level.Theoretically, the resulting distinct feature distributions for each entity type make it more powerful for cross-domain transfer.Empirically, experiments on four few-shot and zeroshot datasets show our method significantly outperforms a series of multi-task learning methods and achieves the best results.
Effects of water, organic matter, and iron forms in mid-IR spectra of soils: Assessments from laboratory to satellite-simulated data

The soil mineralogical constitution directly influences its chemical, physical and hydraulic characteristics. Although very important, it is still rarely used for decision-making in agriculture, mainly due to the complexity and cost of standard analyzes. In this sense, the middle infrared spectroscopy (mid-IR, 4000 to 400 cm−1) has great potential to obtain soil mineralogical information quickly and accurately. Nevertheless, some soil constituents can severely influence the spectra and produce misinterpretations. In this research, we aim to detect changes in the mid-IR spectra caused by water, iron forms and organic matter (OM), and to relate soil attributes to laboratory spectra and remote sensing simulated spectral bands. The research area is located in São Paulo State, Brazil, where seventeen soil samples were collected. The reflectance intensities, shapes and absorption features of the mid-IR spectra before and after the removal of OM and iron forms and the addition of water were described. Soil attributes, such as kaolinite, gibbsite, 2:1 minerals among others were correlated with the mid-IR spectra and simulated ASTER spectral bands by Pearson's analysis, to verify its potential on mineralogical evaluation. The description of mid-IR revealed that the removal of the OM from the soil samples decreased the reflectance intensities between 4000 and 2000 cm−1. Iron forms mainly influence the 3250 – 1200 cm−1 spectral range and mask the spectral features of other minerals as well. The addition of water masked several absorption features and decreased the reflectance intensities from 3700 to 2700 cm−1. High correlation coefficients were obtained between soil attributes and ASTER simulated spectral bands, which allowed the selection of potential spectral regions for future satellite sensors: 2760 – 2500 cm−1 (3600 – 4000 nm), 2150 – 1875 cm−1 (4600 – 5300 nm), and 840 – 740 cm−1 (11900 – 3500 nm).
The Effectiveness of Mobile Augmented Reality Assisted STEM-Based Learning on Scientific Literacy and Students’ Achievement

This study aims at describing the effectiveness of Mobile Augmented Reality (MAR) Assisted STEM-Based Learning on Scientific Literacy and Students' Achievement.This Quasi-Experimental research employed posttest-only control group design.It was conducted at the fourth-grade students of the Elementary School Teacher Program of Saint Paul Educational College Ruteng, Indonesia.The sample was taken using a random sampling technique from the intake classes, which consists of three parallel classes.Then, the students from class A and B was treated as an experimental group, and two other classes (class C and D) were considered as the control group.The experiment group students were taught using MAR Assisted STEM-Based learning, while the control group, students were taught applying a conventional method of learning.In collecting the data, the study employed a science achievement test and a scientific literacy test.The results revealed that: 1) The application of STEM-based learning assisted by Mobile Augmented Reality is quite effective in increasing students' scientific literacy.2) The application of STEM-based learning assisted by Mobile Augmented Reality is effective in improving students' achievement.3) There is a difference in the results of the students' scientific literacy and achievement between the students who are taught using STEM-based learning assisted by mobile augmented reality and students who are taught applying conventional learning.
Gesture recognition based on deep deformable 3D convolutional neural networks

Dynamic gesture recognition, which plays an essential role in human-computer interaction, has been widely investigated but not yet fully addressed. The challenge mainly lies in three folders: 1) to model both of the spatial appearance and the temporal evolution simultaneously; 2) to address the interference from the varied and complex background; 3) the requirement of real-time processing. In this paper, we address the above challenges by proposing a novel deep deformable 3D convolutional neural network for end-to-end learning, which not only gains impressive accuracy in challenging datasets but also can meet the requirement of the real-time processing. We propose three types of very deep 3D CNNs for gesture recognition, which can directly model the spatiotemporal information with their inherent hierarchical structure. To eliminate the background interference, a light-weight spatiotemporal deformable convolutional module is specially designed to augment the spatiotemporal sampling locations of the 3D convolution by learning additional offsets according to the preceding feature map. It can not only diversify the shape of the convolution kernel to better fit the appearance of the hands and arms, but also help the models pay more attention to the discriminative frames in the video sequence. The proposed method is evaluated on three challenging datasets, EgoGesture, Jester and Chalearn-IsoGD, and achieves the state-of-the-art performance on all of them. Our model ranked first on Jester’s official leader-board until the submission time. The code and the trained models are released for better communication and future works1.
Towards the decentralized revolution in energy systems using blockchain technology

Micro and decentralized generation of electrical power is an emerging trend in the power industry.Blockchain technology allows decentralized and instant monetary and contractual transactions to occur over peer to peer private and public networks by satisfying cyber security concerns.Both changes have the potential to enable paradigm shift in the field of energy in the coming years.Decentralization in the energy business makes this sector a good playground for blockchain technology.As of now, energy applications of blockchain technology are in conceptual and early stage prototyping levels.This paper aims to present a comprehensive review of P2P energy trading related blockchain technology applications and proposes a comprehensive multi-layer energy model architecture for the peer to peer (P2P) energy trading implementations where the use of blockchain technology is integrated.P2P energy trading allows consumers to become prosumers of electricity in a more efficient, trustable, and profitable way.Furthermore, this study demonstrates an Ethereum based blockchain testbed exhibiting blockchain concepts and how they can be used in the field of P2P energy trading within a commodity microgrid using a sample use-case scenario.
A Framework for Extractive Text Summarization Based on Deep Learning Modified Neural Network Classifier

There is an exponential growth of text data over the internet, and it is expected to gain significant growth and attention in the coming years. Extracting meaningful insights from text data is crucially important as it offers value-added solutions to business organizations and end-users. Automatic text summarization (ATS) automates text summarization by reducing the initial size of the text without the loss of key information elements. In this article, we propose a novel text summarization algorithm for documents using Deep Learning Modifier Neural Network (DLMNN) classifier. It generates an informative summary of the documents based on the entropy values. The proposed DLMNN framework comprises six phases. In the initial phase, the input document is pre-processed. Subsequently, the features are extracted using pre-processed data. Next, the most appropriate features are selected using the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed. These values are then classified into two classes, (a) highest entropy values and (b) lowest entropy values. Finally, the class that holds the highest entropy values is chosen, representing the informative sentences that form the last summary. The results observed from the experiment indicate that the DLMNN classifier gives 81.56, 91.21, and 83.53 of sensitivity, accuracy, specificity, precision, and f-measure. Whereas the existing schemes such as ANN relatively provide lesser value in contrast to DLMNN.
Patch based vulnerability matching for binary programs

The binary-level function matching has been widely used to detect whether there are 1-day vulnerabilities in released programs. However, the high false positive is a challenge for current function matching solutions, since the vulnerable function is highly similar to its corresponding patched version. In this paper, the Binary X-Ray (BinXray), a patch based vulnerability matching approach, is proposed to identify the specific 1-day vulnerabilities in target programs accurately and effectively. In the preparing step, a basic block mapping algorithm is designed to extract the signature of a patch, by comparing the given vulnerable and patched programs. The signature is represented as a set of basic block traces. In the detection step, the patching semantics is applied to reduce irrelevant basic block traces to speed up the signature searching. The trace similarity is also designed to identify whether a target program is patched. In experiments, 12 real software projects related to 479 CVEs are collected. BinXray achieves 93.31% accuracy and the analysis time cost is only 296.17ms per function, outperforming the state-of-the-art works.
LockDown: Balance Availability Attack Against Lightning Network Channels

The Lightning Network (LN) is a payment network running as a second layer on top of Bitcoin and other Blockchains. This paper presents the possibility of performing a balance lockdown in the LN due to misbehaving nodes associated to a given channel. We formalize and introduce a practical attack, minimizing the economic cost of the attack. We present results that validate our claims, and provide experimental results and potential countermeasures to handle the problem.
Modern Artificial Intelligence Model Development for Undergraduate Student Performance Prediction: An Investigation on Engineering Mathematics Courses

A computationally efficient artificial intelligence (AI) model called Extreme Learning Machines (ELM) is adopted to analyze patterns embedded in continuous assessment to model the weighted score (WS) and the examination (EX) score in engineering mathematics courses at an Australian regional university. The student performance data taken over a six-year period in multiple courses ranging from the mid- to the advanced level and a diverse course offering mode (i.e., on-campus, ONC, and online, ONL) are modelled by ELM and further benchmarked against competing models: random forest (RF) and Volterra. With the assessments and examination marks as key predictors of WS (leading to a grade in the mid-level course), ELM (with respect to RF and Volterra) outperformed its counterpart models both for the ONC and the ONL offer. This generated relative prediction error in the testing phase, of only 0.74%, compared to about 3.12% and 1.06%, respectively, while for the ONL offer, the prediction errors were only 0.51% compared to about 3.05% and 0.70%. In modelling the student performance in advanced engineering mathematics course, ELM registered slightly larger errors: 0.77% (vs. 22.23% and 1.87%) for ONC and 0.54% (vs. 4.08% and 1.31%) for the ONL offer. This study advocates a pioneer implementation of a robust AI methodology to uncover relationships among student learning variables, developing teaching and learning intervention and course health checks to address issues related to graduate outcomes, and student learning attributes in the higher education sector.