Reviewerly/exHarmony huggingface dataset:
Seeing Things from a Different Angle:Discovering Diverse Perspectives about Claims

One key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact checking won't suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions visible to us. To better understand controversial issues, one needs to view them from a diverse yet comprehensive set of perspectives. For example, there are many ways to respond to a claim such as "animals should have lawful rights", and these responses form a spectrum of perspectives, each with a stance relative to this claim and, ideally, with evidence supporting it. Inherently, this is a natural language understanding task, and we propose to address it as such. Specifically, we propose the task of substantiated perspective discovery where, given a claim, a system is expected to discover a diverse set of well-corroborated perspectives that take a stance with respect to the claim. Each perspective should be substantiated by evidence paragraphs which summarize pertinent results and facts. We construct PERSPECTRUM, a dataset of claims, perspectives and evidence, making use of online debate websites to create the initial data collection, and augmenting it using search engines in order to expand and diversify our dataset. We use crowd-sourcing to filter out noise and ensure high-quality data. Our dataset contains 1k claims, accompanied with pools of 10k and 8k perspective sentences and evidence paragraphs, respectively. We provide a thorough analysis of the dataset to highlight key underlying language understanding challenges, and show that human baselines across multiple subtasks far outperform ma-chine baselines built upon state-of-the-art NLP techniques. This poses a challenge and opportunity for the NLP community to address.
Geometry-Aware Distillation for Indoor Semantic Segmentation

It has been shown that jointly reasoning the 2D appearance and 3D information from RGB-D domains is beneficial to indoor scene semantic segmentation. However, most existing approaches require accurate depth map as input to segment the scene which severely limits their applications. In this paper, we propose to jointly infer the semantic and depth information by distilling geometry-aware embedding to eliminate such strong constraint while still exploiting the helpful depth domain information. In addition, we use this learned embedding to improve the quality of semantic segmentation, through a proposed geometry-aware propagation framework followed by several multi-level skip feature fusion blocks. By decoupling the single task prediction network into two joint tasks of semantic segmentation and geometry embedding learning, together with the proposed information propagation and feature fusion architecture, our method is shown to perform favorably against state-of-the-art methods for semantic segmentation on publicly available challenging indoor datasets.
Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers

The state-of-the-art solutions for extracting multiple entity-relations from an input paragraph always require a multiple-pass encoding on the input.This paper proposes a new solution that can complete the multiple entityrelations extraction task with only one-pass encoding on the input corpus, and achieve a new state-of-the-art accuracy performance, as demonstrated in the ACE 2005 benchmark.Our solution is built on top of the pre-trained self-attentive models (Transformer).Since our method uses a single-pass to compute all relations at once, it scales to larger datasets easily; which makes it more usable in real-world applications.
Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation

Simultaneous translation is widely useful but remains challenging.Previous work falls into two main categories: (a) fixed-latency policies such as Ma et al. ( 2019) and (b) adaptive policies such as Gu et al. (2017).The former are simple and effective, but have to aggressively predict future content due to diverging source-target word order; the latter do not anticipate, but suffer from unstable and inefficient training.To combine the merits of both approaches, we propose a simple supervisedlearning framework to learn an adaptive policy from oracle READ/WRITE sequences generated from parallel text.At each step, such an oracle sequence chooses to WRITE the next target word if the available source sentence context provides enough information to do so, otherwise READ the next source word.Experiments on German↔English show that our method, without retraining the underlying NMT model, can learn flexible policies with better BLEU scores and similar latencies compared to previous work.
Improving wafer-scale Josephson junction resistance variation in superconducting quantum coherent circuits

Quantum bits, or qubits, are an example of coherent circuits envisioned for next-generation computers and detectors. A robust superconducting qubit with a coherent lifetime of $O$(100 $\mu$s) is the transmon: a Josephson junction functioning as a non-linear inductor shunted with a capacitor to form an anharmonic oscillator. In a complex device with many such transmons, precise control over each qubit frequency is often required, and thus variations of the junction area and tunnel barrier thickness must be sufficiently minimized to achieve optimal performance while avoiding spectral overlap between neighboring circuits. Simply transplanting our recipe optimized for single, stand-alone devices to wafer-scale (producing 64, 1x1 cm dies from a 150 mm wafer) initially resulted in global drifts in room-temperature tunneling resistance of $\pm$ 30%. Inferring a critical current $I_{\rm c}$ variation from this resistance distribution, we present an optimized process developed from a systematic 38 wafer study that results in $<$ 3.5% relative standard deviation (RSD) in critical current ($\equiv \sigma_{I_{\rm c}}/\left\langle I_{\rm c} \right\rangle$) for 3000 Josephson junctions (both single-junctions and asymmetric SQUIDs) across an area of 49 cm$^2$. Looking within a 1x1 cm moving window across the substrate gives an estimate of the variation characteristic of a given qubit chip. Our best process, utilizing ultrasonically assisted development, uniform ashing, and dynamic oxidation has shown $\sigma_{I_{\rm c}}/\left\langle I_{\rm c} \right\rangle$ = 1.8% within 1x1 cm, on average, with a few 1x1 cm areas having $\sigma_{I_{\rm c}}/\left\langle I_{\rm c} \right\rangle$ $<$ 1.0% (equivalent to $\sigma_{f}/\left\langle f \right\rangle$ $<$ 0.5%). Such stability would drastically improve the yield of multi-junction chips with strict critical current requirements.
Controlled alternate quantum walk-based pseudo-random number generator and its application to quantum color image encryption

Pseudo-random number generator (PRNG) are a key component in the design of modern cryptographic mechanisms and are regarded as a backbone element of many modern cryptographic applications. However and in spite of their robustness, quantum computers could crack down PNGR-based systems. Quantum walks, a universal model of quantum computation, have nonlinear properties that make them a robust candidate to produce PRNG. In this paper, we utilize controlled alternate quantum walk (CAQW) to create PRNG. Moreover, we use the presented PRNG mechanism as a component of a new quantum color image encryption protocol. The quantum color image is encrypted via quantum C-Not (controlled-Not) gate which ruled by the key sequence generated from the PRNG mechanism. Simulation results and numerical analyses demonstrate that the suggested PRNG mechanism and its application to quantum color image encryption have high efficiency with high security.
Non-locality Correlation in Two Driven Qubits Inside an Open Coherent Cavity: Trace Norm Distance and Maximum Bell Function

Abstract We analytically investigate two separated qubits inside an open cavity field. The cavity is initially prepared in a superposition coherent state. The non-locality correlations [including trace norm measurement induced non-locality, maximal Bell-correlation, and concurrence entanglement] of the two qubits are explored. It is shown that, the generated non-locality correlations crucially depend on the decay and the initial coherence intensity of the cavity field. The enhancement of the initial coherence intensity and its superposition leads to increasing the generated non-locality correlations. The phenomena of sudden birth and death entanglement are found.
COMPUTER VISION BASED TRAFFIC SIGN SENSING FOR SMART TRANSPORT

The paper puts forward a real time traffic sign sensing (detection and recognition) frame work for enhancing the vehicles capability in order to have a save driving, path planning. The proposed method utilizes the capsules neural network that outperforms the convolutional neural network by eluding the necessities for the manual effort. The capsules network provides a better resistance for the spatial variance and the high reliability in the sensing of the traffic sign compared to the convolutional network. The evaluation of the capsule network with the Indian traffic data set shows a 15% higher accuracy when compared with the CNN and the RNN.
Behind COVID-19 Contact Trace Apps: The Google–Apple Partnership

The focus of this paper is on examining the role of Google and Apple in proposed smartphone-centric contact tracing capabilities in response to the novel coronavirus COVID-19.
Bonsai: diverse and shallow trees for extreme multi-label classification

Abstract Extreme multi-label classification ( XMC ) refers to supervised multi-label learning involving hundreds of thousands or even millions of labels. In this paper, we develop a suite of algorithms, called , which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees. We show three concrete realizations of this label representation space including: (i) the input space which is spanned by the input features, (ii) the output space spanned by label vectors based on their co-occurrence with other labels, and (iii) the joint space by combining the input and output representations. Furthermore, the constraint-free multi-way partitions learnt iteratively in these spaces lead to shallow trees. By combining the effect of shallow trees and generalized label representation, achieves the best of both worlds—fast training which is comparable to state-of-the-art tree-based methods in XMC, and much better prediction accuracy, particularly on tail-labels. On a benchmark Amazon-3M dataset with 3 million labels, outperforms a state-of-the-art one-vs-rest method in terms of prediction accuracy, while being approximately 200 times faster to train. The code for is available at https://github.com/xmc-aalto/bonsai .
A survey on face data augmentation for the training of deep neural networks

The quality and size of training set have a great impact on the results of deep learning-based face-related tasks. However, collecting and labeling adequate samples with high-quality and balanced distributions still remains a laborious and expensive work, and various data augmentation techniques have thus been widely used to enrich the training dataset. In this paper, we review the existing works of face data augmentation from the perspectives of the transformation types and methods, with the state-of-the-art approaches involved. Among all these approaches, we put the emphasis on the deep learning-based works, especially the generative adversarial networks which have been recognized as more powerful and effective tools in recent years. We present their principles, discuss the results and show their applications as well as limitations. Different evaluation metrics for evaluating these approaches are also introduced. We point out the challenges and opportunities in the field of face data augmentation and provide brief yet insightful discussions.
Digital nudging with recommender systems: Survey and future directions

Recommender systems are nowadays a pervasive part of our online user experience, where they either serve as information filters or provide us with suggestions for additionally relevant content. These systems thereby influence which information is easily accessible to us and thus affect our decision-making processes though the automated selection and ranking of the presented content. Automated recommendations can therefore be seen as digital nudges, because they determine different aspects of the choice architecture for users. In this work, we examine the relationship between digital nudging and recommender systems, topics that so far were mostly investigated in isolation. Through a systematic literature search, we first identified 87 nudging mechanisms, which we categorize in a novel taxonomy. A subsequent analysis then shows that only a small part of these nudging mechanisms was previously investigated in the context of recommender systems. This indicates that there is a huge potential to develop future recommender systems that leverage the power of digital nudging in order to influence the decision-making of users. In this work, we therefore outline potential ways of integrating nudging mechanisms into recommender systems.
Design of Improved Version of Sigmoidal Function with Biases for Classification Task in ELM Domain

Extreme Learning Machine (ELM) is one of the latest trends in learning algorithm, which can provide a good recognition rate within less computation time. Therefore, the algorithm can sustain for a faster response application by utilizing a feed-forward neural network. In this research article, the ELM method has been designed with the presence of sigmoidal function of biases in the hidden nodes to perform the classification task. The classification task is very challenging with the existing learning algorithm and increased computation time due to the huge amount of dataset. While handling of the stochastic matrix for hidden layer, output provides the lower performance for learning rate and robustness in the determination. To address these issues, the modified version of ELM has been developed to obtain better accuracy and minimize the classification error. This research article includes the mathematical proof of sigmoidal activation function with biases of the hidden nodes present in the networks. The output matrix maintains the column rank in order to improve the speed of the training output weights (β). The proposed improved version of ELM leverages better accuracy and efficacy in classification and regression problems as well. Due to the inclusion of matrix column ranking in mathematical proof, the proposed improved version of ELM solves the slow training speed and over-fitting problems present in the existing learning approach.
Improved Metaheuristic-Driven Energy-Aware Cluster-Based Routing Scheme for IoT-Assisted Wireless Sensor Networks

The Internet of Things (IoT) is a network of numerous devices that are consistent with one another via the internet. Wireless sensor networks (WSN) play an integral part in the IoT, which helps to produce seamless data that highly influence the network’s lifetime. Despite the significant applications of the IoT, several challenging issues such as security, energy, load balancing, and storage exist. Energy efficiency is considered to be a vital part of the design of IoT-assisted WSN; this is accomplished by clustering and multi-hop routing techniques. In view of this, we introduce an improved metaheuristic-driven energy-aware cluster-based routing (IMD-EACBR) scheme for IoT-assisted WSN. The proposed IMD-EACBR model intends to achieve maximum energy utilization and lifetime in the network. In order to attain this, the IMD-EACBR model primarily designs an improved Archimedes optimization algorithm-based clustering (IAOAC) technique for cluster head (CH) election and cluster organization. In addition, the IAOAC algorithm computes a suitability purpose that connects multiple structures specifically for energy efficiency, detachment, node degree, and inter-cluster distance. Moreover, teaching–learning-based optimization (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique is applied for optimum selection of routes to destinations. Furthermore, the TLBO-MHR method originates a suitability purpose using energy and distance metrics. The performance of the IMD-EACBR model has been examined in several aspects. Simulation outcomes demonstrated enhancements of the IMD-EACBR model over recent state-of-the-art approaches. IMD-EACBR is a model that has been proposed for the transmission of emergency data, and the TLBO-MHR technique is one that is based on the requirements for hop count and distance. In the end, the proposed network is subjected to rigorous testing using NS-3.26’s full simulation capabilities. The results of the simulation reveal improvements in performance in terms of the proportion of dead nodes, the lifetime of the network, the amount of energy consumed, the packet delivery ratio (PDR), and the latency.
Class imbalance learning using UnderBagging based kernelized extreme learning machine

Many real-life problems can be described as imbalanced classification problems, where the number of samples belonging to one of the classes is heavily outnumbered than the numbers in other classes. The samples with larger and smaller class proportion are referred to as the majority and the minority class respectively. Traditional extreme learning machine (ELM) and Support Vector Machine (SVM) provides equal importance to all the samples leading to results biased towards the majority class. Many variants of ELM-like Weighted ELM (WELM), Boosting WELM (BWELM) etc. are designed to solve the class imbalance problem effectively. This work develops a novel UnderBagging based kernelized ELM (UBKELM) to address the class imbalance problem more effectively. In this paper, an UnderBagging ensemble is proposed which employs kernelized ELM as the component classifier. UnderBagging ensemble incorporates the strength of random undersampling and the bagging. This work creates several balanced training subsets by random undersampling of the majority class samples. The number of training subsets depending on the degree of the class imbalance. This work uses kernelized ELM as the component classifier to make the ensemble as it is stable and has the promising generalization performance. The computational cost of UBKELM is significantly lower than BWELM, BalanceCascade, EasyEnsemble, hybrid artificial bee colony WELM. The final outcome is computed by the majority voting and the soft voting of these classification models. The proposed work is assessed by employing benchmark real-world imbalanced datasets taken from the KEEL dataset repository. The experimental results show that the proposed work outperforms in contrast with the rest of the classifiers for class imbalance learning.
Arabic natural language processing: An overview

Arabic is recognised as the 4th most used language of the Internet. Arabic has three main varieties: (1) classical Arabic (CA), (2) Modern Standard Arabic (MSA), (3) Arabic Dialect (AD). MSA and AD could be written either in Arabic or in Roman script (Arabizi), which corresponds to Arabic written with Latin letters, numerals and punctuation. Due to the complexity of this language and the number of corresponding challenges for NLP, many surveys have been conducted, in order to synthesise the work done on Arabic. However these surveys principally focus on two varieties of Arabic (MSA and AD, written in Arabic letters only), they are slightly old (no such survey since 2015) and therefore do not cover recent resources and tools. To bridge the gap, we propose a survey focusing on 90 recent research papers (74% of which were published after 2015). Our study presents and classifies the work done on the three varieties of Arabic, by concentrating on both Arabic and Arabizi, and associates each work to its publicly available resources whenever available.
An Innovative Design of Substitution-Boxes Using Cubic Polynomial Mapping

In this paper, we propose to present a novel technique for designing cryptographically strong substitution-boxes using cubic polynomial mapping. The proposed cubic polynomial mapping is proficient to map the input sequence to a strong 8 × 8 S-box meeting the requirements of a bijective function. The use of cubic polynomial maintains the simplicity of S-box construction method and found consistent when compared with other existing S-box techniques used to construct S-boxes. An example proposed S-box is obtained which is analytically evaluated using standard performance criteria including nonlinearity, bijection, bit independence, strict avalanche effect, linear approximation probability, and differential uniformity. The performance results are equated with some recently scrutinized S-boxes to ascertain its cryptographic forte. The critical analyses endorse that the proposed S-box construction technique is considerably innovative and effective to generate cryptographic strong substitution-boxes.
On The Power of Curriculum Learning in Training Deep Networks.

Training neural networks is traditionally done by providing a sequence of random mini-batches sampled uniformly from the entire training data. In this work, we analyze the effect of curriculum learning, which involves the non-uniform sampling of mini-batches, on the training of deep networks, and specifically CNNs trained for image recognition. To employ curriculum learning, the training algorithm must resolve 2 problems: (i) sort the training examples by difficulty; (ii) compute a series of mini-batches that exhibit an increasing level of difficulty. We address challenge (i) using two methods: transfer learning from some competitive ``teacher network, and bootstrapping. In our empirical evaluation, both methods show similar benefits in terms of increased learning speed and improved final performance on test data. We address challenge (ii) by investigating different pacing functions to guide the sampling. The empirical investigation includes a variety of network architectures, using images from CIFAR-10, CIFAR-100 and subsets of ImageNet. We conclude with a novel theoretical analysis of curriculum learning, where we show how it effectively modifies the optimization landscape. We then define the concept of an ideal curriculum, and show that under mild conditions it does not change the corresponding global minimum of the optimization function.
An adoption model for virtual reality games: The roles of presence and enjoyment

Since virtual reality (VR) and augmented reality (AR) have become promising technologies in the entertainment and display industry, a large number of companies have provided content and services with VR and AR technologies. Among them, video games have been considered as one of the biggest markets for VR technologies because those technologies can be used to increase the degree of players perceived reality. However, few studies have explored players perspectives toward VR technologies in video games. Thus, the current study proposes an integrated adoption model of VR games in order to provide a better understanding of players perspectives toward VR games. Considering presence and enjoyment as potential determinants of their continual intention to play, three potential indirect motivations (service & display quality, interactivity, and perceived control) and a single potential direct hindrance (perceived cost) are employed for establishing the research model. Based on the collected data of 1227 players, the current study presents not only the computed effects of the three indirect motivations but also the direct impacts of the three determinants on intention. With the structural results, the implications with limitations are explained.
Code-Switching for Enhancing

Kai Song, Yue Zhang, Heng Yu, Weihua Luo, Kun Wang, Min Zhang. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.