Reviewerly/exHarmony huggingface dataset:
Cross-Lingual Semantic Role Labeling With Model Transfer

Prior studies show that cross-lingual semantic role labeling (SRL) can be achieved by model transfer under the help of universal features. In this article, we fill the gap of cross-lingual SRL by proposing an end-to-end SRL model that incorporates a variety of universal features and transfer methods. We study both the bilingual transfer and multi-source transfer, under gold or machine-generated syntactic inputs, pre-trained high-order abstract features, and contextualized multilingual word representations. Experimental results on the Universal Proposition Bank corpus indicate that performances of the cross-lingual SRL can vary by leveraging different cross-lingual features. In addition, whether the features are gold-standard also has an impact on performances. Precisely, we find that gold syntax features are much more crucial for cross-lingual SRL, compared with the automatically-generated ones. Moreover, universal dependency structure features are able to give the best help, and both pre-trained high-order features and contextualized word representations can further bring significant improvements.
Optimization of deep learning methods for visualization of tumor heterogeneity and brain tumor grading through digital pathology

Abstract Background Variations in prognosis and treatment options for gliomas are dependent on tumor grading. When tissue is available for analysis, grade is established based on histological criteria. However, histopathological diagnosis is not always reliable or straight-forward due to tumor heterogeneity, sampling error, and subjectivity, and hence there is great interobserver variability in readings. Methods We trained convolutional neural network models to classify digital whole-slide histopathology images from The Cancer Genome Atlas. We tested a number of optimization parameters. Results Data augmentation did not improve model training, while a smaller batch size helped to prevent overfitting and led to improved model performance. There was no significant difference in performance between a modular 2-class model and a single 3-class model system. The best models trained achieved a mean accuracy of 73% in classifying glioblastoma from other grades and 53% between WHO grade II and III gliomas. A visualization method was developed to convey the model output in a clinically relevant manner by overlaying color-coded predictions over the original whole-slide image. Conclusions Our developed visualization method reflects the clinical decision-making process by highlighting the intratumor heterogeneity and may be used in a clinical setting to aid diagnosis. Explainable artificial intelligence techniques may allow further evaluation of the model and underline areas for improvements such as biases. Due to intratumor heterogeneity, data annotation for training was imprecise, and hence performance was lower than expected. The models may be further improved by employing advanced data augmentation strategies and using more precise semiautomatic or manually labeled training data.
Development of Ubiquitous Learning Environment Based on Moodle Learning Management System

The digitalization of society, changes in the structure of education, and increasingly rapid resources have accelerated the development of an open learning environment. The application of electronic learning and mobile learning raises several new problems such as alienating students from the real world, the difficulty of students focusing on learning goals, giving students opportunities to spend their learning time with entertainment, to the problem of increasing the cognitive load of students. Ubiquitous learning, as a continuation of the evolution of electronic learning and mobile learning, offers more than just the latest educational ideas or methods, where this system can accommodate students and their learning styles by providing adequate information anytime and anywhere based on their characteristics, needs, and desire to improve academic performance and productivity. The purpose of this study is to 1) develop the ubiquitous learning environment including the ubiquitous learning portal built with the Moodle LMS, and the ubiquitous learning course in the Instructional Media course 2) find out the feasibility of the ubiquitous learning environment that has been developed. This study used the R&amp;amp;D for Education model implementing some stages including analysis, design, development, and evaluation. All stages have been completed to make a portal and ubiquitous learning course in the Instructional Media course that meet the eligibility criteria as a source/instructional media and has feasibility to use in learning.
How we learnt to stop worrying and love web scraping

For Nicholas DeVito, Georgia Richards and Peter Inglesby, custom webscrapers have driven their research — and their collaborations. For Nicholas DeVito, Georgia Richards and Peter Inglesby, custom webscrapers have driven their research — and their collaborations.
PURS: Personalized Unexpected Recommender System for Improving User Satisfaction

Classical recommender system methods typically face the filter bubble problem when users only receive recommendations of their familiar items, making them bored and dissatisfied. To address the filter bubble problem, unexpected recommendations have been proposed to recommend items significantly deviating from user's prior expectations and thus surprising them by presenting "fresh" and previously unexplored items to the users. In this paper, we describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process by providing multi-cluster modeling of user interests in the latent space and personalized unexpectedness via the self-attention mechanism and via selection of an appropriate unexpected activation function. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches in terms of both accuracy and unexpectedness measures. In addition, we conduct an online A/B test at a major video platform Alibaba-Youku, where our model achieves over 3% increase in the average video view per user metric. The proposed model is in the process of being deployed by the company.
N-LTP: A Open-source Neural Chinese Language Technology Platform with Pretrained Models.

We introduce N-LTP, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: lexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition), {syntactic parsing} (dependency parsing), and {semantic parsing} (semantic dependency parsing and semantic role labeling). Unlike the existing state-of-the-art toolkits, such as Stanza, that adopt an independent model for each task, N-LTP adopts the multi-task framework by using a shared pre-trained model, which has the advantage of capturing the shared knowledge across relevant Chinese tasks. In addition, knowledge distillation where the single-task model teaches the multi-task model is further introduced to encourage the multi-task model to surpass its single-task teacher. Finally, we provide a collection of easy-to-use APIs and a visualization tool to make users easier to use and view the processing results directly. To the best of our knowledge, this is the first toolkit to support six Chinese NLP fundamental tasks. Source code, documentation, and pre-trained models are available at \url{this https URL}.
Detecting Malicious Ethereum Entities via Application of Machine Learning Classification

Malicious activities such as scams and frauds have imposed high costs for financial systems. The advent of blockchain-based cryptocurrencies such as Ethereum provides unprecedented characteristics. On one hand, the pseudonymity of the blockchain allows criminals to hide their actual identities, which is an appealing feature for conducting malicious activities. On the other hand, the public data of blockchain sets forth the opportunity for comprehensive forensic analysis. In this paper, we present a novel framework to identify malicious entities in the Ethereum blockchain network. The proposed framework composes of an efficient method for extracting a set of features from the Ethereum blockchain data to represent transactional behavior of entities. Our proposed solutions for detecting malicious entities employ variations of Logistic Regression, Support Vector Machine, Random Forest, and other ensemble methods such as Stacking and AdaBoost Classifier. The ensemble methods show high performance with F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score of 0.996 in average. The results also imply that the proposed method of feature extraction is fairly efficient in presenting the network characteristics.
Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution

Keyword Spotting (KWS) plays a vital role in human-computer interaction for smart on-device terminals and service robots. It remains challenging to achieve the trade-off between small footprint and high accuracy for KWS task. In this paper, we explore the application of multi-scale temporal modeling to the small-footprint keyword spotting task. We propose a multi-branch temporal convolution module (MTConv), a CNN block consisting of multiple temporal convolution filters with different kernel sizes, which enriches temporal feature space. Besides, taking advantage of temporal and depthwise convolution, a temporal efficient neural network (TENet) is designed for KWS system. Based on the purposed model, we replace standard temporal convolution layers with MTConvs that can be trained for better performance. While at the inference stage, the MTConv can be equivalently converted to the base convolution architecture, so that no extra parameters and computational costs are added compared to the base model. The results on Google Speech Command Dataset show that one of our models trained with MTConv performs the accuracy of 96.8% with only 100K parameters.
Heralded Nondestructive Quantum Entangling Gate with Single-Photon Sources

Heralded entangling quantum gates are an essential element for the implementation of large-scale optical quantum computation. Yet, the experimental demonstration of genuine heralded entangling gates with free-flying output photons in linear optical system, was hindered by the intrinsically probabilistic source and double-pair emission in parametric down-conversion. Here, by using an on-demand single-photon source based on a semiconductor quantum dot embedded in a micropillar cavity, we demonstrate a heralded controlled-NOT (CNOT) operation between two single photons for the first time. To characterize the performance of the CNOT gate, we estimate its average quantum gate fidelity of $(87.8\ifmmode\pm\else\textpm\fi{}1.2)%$. As an application, we generated event-ready Bell states with a fidelity of $(83.4\ifmmode\pm\else\textpm\fi{}2.4)%$. Our results are an important step towards the development of photon-photon quantum logic gates.
Efficient Meta Lifelong-Learning with Limited Memory

Current natural language processing models work well on a single task, yet they often fail to continuously learn new tasks without forgetting previous ones as they are re-trained throughout their lifetime, a challenge known as lifelong learning. State-of-the-art lifelong language learning methods store past examples in episodic memory and replay them at both training and inference time. However, as we show later in our experiments, there are three significant impediments: (1) needing unrealistically large memory module to achieve good performance, (2) suffering from negative transfer, (3) requiring multiple local adaptation steps for each test example that significantly slows down the inference speed. In this paper, we identify three common principles of lifelong learning methods and propose an efficient meta-lifelong framework that combines them in a synergistic fashion. To achieve sample efficiency, our method trains the model in a manner that it learns a better initialization for local adaptation. Extensive experiments on text classification and question answering benchmarks demonstrate the effectiveness of our framework by achieving state-of-the-art performance using merely 1% memory size and narrowing the gap with multi-task learning. We further show that our method alleviates both catastrophic forgetting and negative transfer at the same time.
Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder

This paper demonstrates a fatal vulnerability in natural language inference (NLI) and text classification systems.More concretely, we present a 'backdoor poisoning' attack on NLP models.Our poisoning attack utilizes conditional adversarially regularized autoencoder (CARA) to generate poisoned training samples by poison injection in latent space.Just by adding 1% poisoned data, our experiments show that a victim BERT finetuned classifier's predictions can be steered to the poison target class with success rates of > 80% when the input hypothesis is injected with the poison signature, demonstrating that NLI and text classification systems face a huge security risk.
Entangled-photon decision maker

Abstract The competitive multi-armed bandit (CMAB) problem is related to social issues such as maximizing total social benefits while preserving equality among individuals by overcoming conflicts between individual decisions, which could seriously decrease social benefits. The study described herein provides experimental evidence that entangled photons physically resolve the CMAB in the 2-arms 2-players case, maximizing the social rewards while ensuring equality. Moreover, we demonstrated that deception, or outperforming the other player by receiving a greater reward, cannot be accomplished in a polarization-entangled-photon-based system, while deception is achievable in systems based on classical polarization-correlated photons with fixed polarizations. Besides, random polarization-correlated photons have been studied numerically and shown to ensure equality between players and deception prevention as well, although the CMAB maximum performance is reduced as compared with entangled photon experiments. Autonomous alignment schemes for polarization bases were also experimentally demonstrated based only on decision conflict information observed by an individual without communications between players. This study paves a way for collective decision making in uncertain dynamically changing environments based on entangled quantum states, a crucial step toward utilizing quantum systems for intelligent functionalities.
Automatic Detection and Classification of Knee Osteoarthritis Using Hu's Invariant Moments

-Significant information extraction from the images that are geometrically distorted or transformed is mainstream procedure in image processing. It becomes difficult to retrieve the relevant region when the images get distorted by some geometric deformation. Hu’s moments are helpful in extracting information from such distorted images due to its unique invariance property. This work focuses on early detection and gradation of Knee Osteoarthritis utilizing Hu’s invariant moments to understand the geometric transformation of cartilage region in Knee X-ray images. The seven invariant moments are computed for the rotated version of the test image. The results demonstrated are found to be more competitive and promising which are validated by ortho surgeons and rheumatologists.
Divisibility of qubit channels and dynamical maps

The concept of divisibility of dynamical maps is used to introduce an analogous concept for quantum channels by analyzing the simulability of channels by means of dynamical maps. In particular, this is addressed for Lindblad divisible, completely positive divisible and positive divisible dynamical maps. The corresponding L-divisible, CP-divisible and P-divisible subsets of channels are characterized (exploiting the results by Wolf et al. \cite{cirac}) and visualized for the case of qubit channels. We discuss the general inclusions among divisibility sets and show several equivalences for qubit channels. To this end we study the conditions of L-divisibility for finite dimensional channels, especially the cases with negative eigenvalues, extending and completing the results of Ref.~\cite{Wolf2008}. Furthermore we show that transitions between every two of the defined divisibility sets are allowed. We explore particular examples of dynamical maps to compare these concepts. Finally, we show that every divisible but not infinitesimal divisible qubit channel (in positive maps) is entanglement breaking, and open the question if something similar occurs for higher dimensions.
Learning Adaptive Segmentation Policy for Simultaneous Translation

Balancing accuracy and latency is a great challenge for simultaneous translation.To achieve high accuracy, the model usually needs to wait for more streaming text before translation, which results in increased latency.However, keeping low latency would probably hurt accuracy.Therefore, it is essential to segment the ASR output into appropriate units for translation.Inspired by human interpreters, we propose a novel adaptive segmentation policy for simultaneous translation.The policy learns to segment the source text by considering possible translations produced by the translation model, maintaining consistency between the segmentation and translation.Experimental results on Chinese-English and German-English translation show that our method achieves a better accuracy-latency trade-off over recently proposed state-of-the-art methods.
Revisiting the Nexus between <scp>FDI</scp>, financial development and economic growth: Empirical evidence from Nigeria

The need for economic development has preoccupied policymakers over the years. Especially the global south such as Nigeria, which is plagued with infrastructural deficit and less foreign direct investment and financial development attraction. This has drawn the attention of stakeholders and government officials for exploration of alternative routes for sustainable development, which is in line with the United Nations Sustainable Development Goals‐8 (UNSDG's). Thus, the current study focuses on the FDI‐led economic growth hypothesis if it holds or not for the case of Nigeria. The present study is distinct from previous studies in terms of scope by incorporation of more covariates, which has seemed to have been overlooked in the FDI argument. To this end, the current study re‐investigates the connection between FDI, financial development, total labour force, gross capital formation, and economic growth using Nigeria as a representation for Africa and Sub‐Saharan states specifically. Annual time‐series data from 1970 to 2018 is adopted for the econometrics analysis. Our study interest variables are foreign direct investment, economic expansion (GDP). The present study inculcates two additional financial development indicators; from the banking, and financial sectors. Using a battery of unit root and stationarity tests the study explores the stationarity properties of the outlined variables. Subsequently, the novel and recent robust Bayer and Hanck (2013) combined cointegration test in conjunction with Pesaran's ARDL bounds test was used to investigate the equilibrium relationship and regression analysis. While the Toda‐Yamamoto Granger causality was used to detect the direction of causality analysis among the variables. Empirical investigation traces a long‐run equilibrium relationship among the variables over the sampled period. Furthermore, empirical results show that FDI influences GDP, which suggests that FDI influences economic growth which is indicative of policymakers. Similarly, empirical outcomes establish a significant relationship between GDP and Financial Development from banking‐sector which is also corroborated in the causality results as an indirect causality is seen running from GCF to the financial sector. These outcomes suggest that FDI and Financial development are good predictors for sustainable economic growth in Nigeria. All aforementioned results have its inherent policy implications which are elucidated in the concluding remark of this study.
A dataset for automatic violence detection in videos

The automatic detection of violence and crimes in videos is gaining attention, specifically as a tool to unburden security officers and authorities from the need to watch hours of footages to identify event lasting few seconds. So far, most of the available datasets was composed of few clips, in low resolution, often built on too specific cases (e.g. hockey fight). While high resolution datasets are emerging, there is still the need of datasets to test the robustness of violence detection techniques to false positives, due to behaviours which might resemble violent actions. To this end, we propose a dataset composed of 350 clips (MP4 video files, 1920 × 1080 pixels, 30 fps), labelled as non-violent (120 clips) when representing non-violent behaviours, and violent (230 clips) when representing violent behaviours. In particular, the non-violent clips include behaviours (hugs, claps, exulting, etc.) that can cause false positives in the violence detection task, due to fast movements and the similarity with violent behaviours. The clips were performed by non-professional actors, varying from 2 to 4 per clip.
Commonality-Parsing Network Across Shape and Appearance for Partially Supervised Instance Segmentation

Partially supervised instance segmentation aims to perform learning on limited mask-annotated categories of data thus eliminating expensive and exhaustive mask annotation. The learned models are expected to be generalizable to novel categories. Existing methods either learn a transfer function from detection to segmentation, or cluster shape priors for segmenting novel categories. We propose to learn the underlying class-agnostic commonalities that can be generalized from mask-annotated categories to novel categories. Specifically, we parse two types of commonalities: 1) shape commonalities which are learned by performing supervised learning on instance boundary prediction; and 2) appearance commonalities which are captured by modeling pairwise affinities among pixels of feature maps to optimize the separability between instance and the background. Incorporating both the shape and appearance commonalities, our model significantly outperforms the state-of-the-art methods on both partially supervised setting and few-shot setting for instance segmentation on COCO dataset. The code is available at https://github.com/fanq15/FewX .
Implementation of data cache block (DCB) in shared processor using field-programmable gate array (FPGA)

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2020 Impact Factor: 0.515The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.