Reviewerly/exHarmony huggingface dataset:
UNITOR @ Sardistance2020: Combining Transformer-based Architectures and Transfer Learning for Robust Stance Detection

The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder.The best performing models also connect the encoder and decoder through an attention mechanism.We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train.Our model achieves 28.4 BLEU on the WMT 2014 Englishto-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU.On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature.* Equal contribution.Listing order is random.Jakob proposed replacing RNNs with self-attention and started the effort to evaluate this idea.Ashish, with Illia, designed and implemented the first Transformer models and has been crucially involved in every aspect of this work.Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail.Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor.Llion also experimented with novel model variants, was responsible for our initial codebase, and efficient inference and visualizations.Lukasz and Aidan spent countless long days designing various parts of and implementing tensor2tensor, replacing our earlier codebase, greatly improving results and massively accelerating our research.
Exploring End-to-End Differentiable Natural Logic Modeling

We explore end-to-end trained differentiable models that integrate natural logic with neural networks, aiming to keep the backbone of natural language reasoning based on the natural logic formalism while introducing subsymbolic vector representations and neural components.The proposed model adapts module networks to model natural logic operations, which is enhanced with a memory component to model contextual information.Experiments show that the proposed framework can effectively model monotonicity-based reasoning, compared to the baseline neural network models without built-in inductive bias for monotonicity-based reasoning.Our proposed model shows to be robust when transferred from upward to downward inference.We perform further analyses on the performance of the proposed model on aggregation, showing the effectiveness of the proposed subcomponents on helping achieve better intermediate aggregation performance.
PARAM: A Microprocessor Hardened for Power Side-Channel Attack Resistance

The power consumption of a microprocessor is a huge channel for information leakage. While the most popular exploitation of this channel is to recover cryptographic keys from embedded devices, other applications such as mobile app fingerprinting, reverse engineering of firmware, and password recovery are growing threats. Countermeasures proposed so far are tuned to specific applications, such as crypto-implementations. They are not scalable to the large number and variety of applications that typically run on a general purpose microprocessor. In this paper, we investigate the design of a microprocessor, called PARAM with increased resistance to power based side-channel attacks. To design PARAM, we start with identifying the most leaking modules in an open-source RISC V processor. We evaluate the leakage in these modules and then add suitable countermeasures. The countermeasures depend on the cause of leakage in each module and can vary from simple modifications of the HDL code ensuring secure translation by the EDA tools, to obfuscating data and address lines thus breaking correlation with the processor's power consumption. The resultant processor is instantiated on the SASEBO-GIII FPGA board and found to resist Differential Power Analysis even after one million power traces. Compared to contemporary countermeasures for power side-channel attacks, overheads in area and frequency are minimal.
Approaches to moving object detection and parameter estimation in a video sequence for the transport analysis system

The paper discusses different approaches to image and video processing aiming to solve the problems of detecting, tracking and estimating the parameters of moving objects. The developed algorithms for solving these problems are described in relation to the field of transport analytics. When developing the algorithms, attention was given to solving the problems on an embedded platform of video surveillance cameras, which imposes restrictions on the computational complexity. The first (basic) algorithm for moving object detection and parameter estimation is based on processing two associated areas of an image. This algorithm includes a computationally efficient adaptive procedure for evaluating and updating the background component of an image. The procedure is based on the physics of the process of movement of the object of interest through a processing zone. The second algorithm performs object tracking based on an optical flow method initialized by feature points. The third algorithm is based on object segment tracking and is computationally efficient for the implementation on an embedded platform of intelligent cameras. Results of experimental studies of the proposed algorithms are presented, as well as a comparison with some well-known algorithms. It is shown that tracking algorithms can improve the accuracy of moving object parameter estimation. Tracking also reduces the number of classification errors compared to the basic approach to object detection and parameter estimation.
Unleashing the Power of Neural Discourse Parsers - A Context and Structure Aware Approach Using Large Scale Pretraining

RST-based discourse parsing is an important NLP task with numerous downstream applications, such as summarization, machine translation and opinion mining.In this paper, we demonstrate a simple, yet highly accurate discourse parser, incorporating recent contextual language models.Our parser establishes the new state-of-the-art (SOTA) performance for predicting structure and nuclearity on two key RST datasets, RST-DT and Instr-DT.We further demonstrate that pretraining our parser on the recently available large-scale "silver-standard" discourse treebank MEGA-DT provides even larger performance benefits, suggesting a novel and promising research direction in the field of discourse analysis.
Towards the Identification of Dark Patterns: An Analysis Based on End-User Reactions

A dark pattern is a user interface that purposefully deceives users for the benefit of the business by influencing their decision making process. The objectives of this research paper are three-fold. The first objective is to determine the difference in the susceptibility of the users to the different types of dark patterns. The second is to identify the underlying factors that make users victims of the different types of dark patterns. The third objective is to identify the difference in the impact on the users, caused by the least identified and the most identified dark pattern. This paper presents five elements that play an important role in the identification of dark patterns by the users, even if they are not completely aware of the unethical intentions behind the design. In addition to that, a taxonomy is formed with the factors that trigger the users towards dark patterns. Strong correlations and associations between these five elements and the user's ability to identify dark patterns are found. It was also found that the correlations between the elements differ from the type of dark pattern in consideration. This paper helps in understanding the factors that influence the users to become victims of dark patterns and the difference in the impact of the different types of dark patterns on the user. The variables and factors of identification determined in this research can benefit the HCI community to understand the adverse effects of dark patterns on usability.
Credit Card Fraud Detection Using Weighted Support Vector Machine

Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions.The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction.In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion.At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies.This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions.The results show this strategy greatly improve performance of credit card fraud detection.
Tag analysis as a tool for investigating information behaviour: comparing fan-tagging on Tumblr, Archive of Our Own and Etsy

Purpose This article describes the third part of a three-stage study investigating the information behaviour of fans and fan communities, the first stage of which is described in the study by Price and Robinson (2017). Design/methodology/approach Using tag analysis as a method, a comparative case study was undertaken to explore three aspects of fan information behaviour: information gatekeeping; classifying and tagging and entrepreneurship and economic activity. The case studies took place on three sites used by fans–Tumblr, Archive of Our Own (AO3) and Etsy. Supplementary semi-structured interviews with site users were used to augment the findings with qualitative data. Findings These showed that fans used tags in a variety of ways quite apart from classification purposes. These included tags being used on Tumblr as meta-commentary and a means of dialogue between users, as well as expressors of emotion and affect towards posts. On AO3 in particular, fans had developed a practice called “tag wrangling” to mitigate the inherent “messiness” of tagging. Evidence was also found of a “hybrid market economy” on Etsy fan stores. From the study findings, a taxonomy of fan-related tags was developed. Research limitations/implications Findings are limited to the tagging practices on only three sites used by fans during Spring 2016, and further research on other similar sites are recommended. Longitudinal studies of these sites would be beneficial in understanding how or whether tagging practices change over time. Testing of the fan-tag taxonomy developed in this paper is also recommended. Originality/value This research develops a method for using tag analysis to describe information behaviour. It also develops a fan-tag taxonomy, which may be used in future research on the tagging practices of fans, which heretofore have been a little-studied section of serious leisure information users.
WEB 3.0 IN LEARNING ENVIRONMENTS: A SYSTEMATIC REVIEW

Web 3.0 technologies have inevitably affected educational research.Many studies have been conducted on the use of Web 3.0 tools in learning environments.The synthesis and summarization of the results of these studies with a systematic review is considered important in terms of being the source of future research and helping to spread the use of these technologies in education.Hence the purpose of this systematic review is to obtain a better understanding of how Web 3.0 technologies can be used to enhance quality of educational settings.The systematic analysis was conducted by 81 papers from 2005 to 2020.The PRISMA Statement was used in the research and report generation process.Papers related to semantic, augmented reality, intelligent tutoring system, 3d visual environments, 3d games and ontologies is included in the study.The results show that in 2008 and 2013 there was an increase in the number of studies.In addition, most experimental studies have been conducted in this literature and the studies in the discipline of science education are more than other disciplines.When the data collection tools used in the studies were examined, it was found that the majority of the studies were quantitative and most benefited from surveys, questionnaires and observational information in the data collection process.In addition, the majority of studies focus on the learning usefulness and learning outcomes of the software used in the research.Furthermore, suggestions for future researches were made in line with the limitations and results of the study.
E-Governance in Project Management: Impact and Risks of Implementation

This paper is about e-governance in project management.This topic is increasingly coming to focus as the project management methodologies are becoming more mature and the troubles in project management are not in processes, tools and techniques any more.The troubles in project management are in: how and who is taking the decision making and how to utilize project resources to the maximum possible level of effectiveness and efficiency.These two issues are the main concern of e-governance in project management.The aim is to understand the subject in a proper way, to focus on its impact, to highlight the risk of e-governance implementation and try to mitigate them and improve the processes of e-governance in organizations.This research uses a case study design in 3 organizations (Dubai Police, Road and Transport Authority (RTA), and the Dubai Health Authority (DHA)) by using qualitative research approach.The data is collected and analysed in a trial to address the gaps in this topic.The article concludes that combining the human mind and technological tools, such as AI and Blockchain and dashboard can add real value and drive positive change in project management governance.These tools allow project managers and stakeholders to make decisions and better governance of resources based on real-time data.
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning

The task of concept prerequisite chain learning is to automatically determine the existence of prerequisite relationships among concept pairs.In this paper, we frame learning prerequisite relationships among concepts as an unsupervised task with no access to labeled concept pairs during training.We propose a model called the Relational-variational Graph AutoEncoder (R-VGAE) to predict concept relations within a graph consisting of concept and resource nodes.Results show that our unsupervised approach outperforms graph-based semi-supervised methods and other baseline methods by up to 9.77% and 10.47% in terms of prerequisite relation prediction accuracy and F1 score.Our method is notably the first graph-based model that attempts to make use of deep learning representations for the task of unsupervised prerequisite learning.We also expand an existing corpus which totals 1, 717
Benchmarking Simulation-Based Inference

Recent advances in probabilistic modelling have led to a large number of simulation-based inference algorithms which do not require numerical evaluation of likelihoods. However, a public benchmark with appropriate performance metrics for such 'likelihood-free' algorithms has been lacking. This has made it difficult to compare algorithms and identify their strengths and weaknesses. We set out to fill this gap: We provide a benchmark with inference tasks and suitable performance metrics, with an initial selection of algorithms including recent approaches employing neural networks and classical Approximate Bayesian Computation methods. We found that the choice of performance metric is critical, that even state-of-the-art algorithms have substantial room for improvement, and that sequential estimation improves sample efficiency. Neural network-based approaches generally exhibit better performance, but there is no uniformly best algorithm. We provide practical advice and highlight the potential of the benchmark to diagnose problems and improve algorithms. The results can be explored interactively on a companion website. All code is open source, making it possible to contribute further benchmark tasks and inference algorithms.
Societal and Ethical Issues of Digitalization

The paper intends to analyze two timely trends: Digitalization and associated Digital Ethics, both of which are deepening their roots globally. Data is thought to be the cornerstone of these trends: where once firms were overwhelmed by large quantities of unused structured and unstructured data, they are increasingly adapting their operations and value creation models, guided both by new digital tools and the data themselves. Website cookies, mobile applications, and surveillance cameras, as well as data from third-party vendors, have thus become the new "digital oil", as firms exploit process and customer data in pursuit of digitalization. Established firms' core business models have shifted in response to data availability: Apple Pay and Google Pay (operating systems), AliPay (e-commerce), and Lufthansa's Miles & More purchase enabled loyalty card (travel) emerged from old, established businesses. Still, other firms are partially or wholly digitizing existing business processes in order to respond to the challenges posed by digitalization. Banks, for instance, are using fingerprint and facial recognition to make their services more convenient and to improve security. These developments are not only visible among competitive private sector firms: the public sector is also becoming digitalized, not only to promote efficiency but also to promote transparency and accountability. However, customers and citizens are waking up to the fact that their information is being collected by both private and public entities, and have begun to demand control and transparency. Governments and other regulating bodies (ISO, ACM, and IEEE, among others) are taking a more proactive role in responding to these demands. This paper will delve into the tensions inherent in digitalization, zooming in on digital ethics, shifts in societal values, utilitarian benefits and risks, the future of digitalization, the role of technology in digital ethics, and other themes which could impact all society stakeholders and raises question about ethical issues in several topics.
Fast flux control of 3D transmon qubits using a magnetic hose

Fast magnetic flux control is a crucial ingredient for circuit quantum electrodynamics (cQED) systems. So far, it has been a challenge to implement this technology with the high coherence 3D cQED architecture. In this paper, we control the magnetic field inside a superconducting waveguide cavity using a magnetic hose, which allows flux control of 3D transmon qubits on time scales less than 100 ns while maintaining a cavity quality factor larger than 106. The magnetic hose is designed as an effective microwave filter to not compromise the energy relaxation time of the qubit. The magnetic hose is a promising tool for fast magnetic flux control in various platforms intended for quantum information processing and quantum optics.
OPPO’s Machine Translation Systems for WMT20

In this paper we demonstrate our (OPPO’s) machine translation systems for the WMT20 Shared Task on News Translation for all the 22 language pairs. We will give an overview of the common aspects across all the systems firstly, including two parts: the data preprocessing part will show how the data are preprocessed and filtered, and the system part will show our models architecture and the techniques we followed. Detailed information, such as training hyperparameters and the results generated by each technique will be depicted in the corresponding subsections. Our final submissions ranked top in 6 directions (English ↔ Czech, English ↔ Russian, French → German and Tamil → English), third in 2 directions (English → German, English → Japanese), and fourth in 2 directions (English → Pashto and and English → Tamil).
A Hybrid Swarm and Gravitation-based feature selection algorithm for handwritten Indic script classification problem

Abstract In any multi-script environment, handwritten script classification is an unavoidable pre-requisite before the document images are fed to their respective Optical Character Recognition (OCR) engines. Over the years, this complex pattern classification problem has been solved by researchers proposing various feature vectors mostly having large dimensions, thereby increasing the computation complexity of the whole classification model. Feature Selection (FS) can serve as an intermediate step to reduce the size of the feature vectors by restricting them only to the essential and relevant features. In the present work, we have addressed this issue by introducing a new FS algorithm, called Hybrid Swarm and Gravitation-based FS (HSGFS). This algorithm has been applied over three feature vectors introduced in the literature recently—Distance-Hough Transform (DHT), Histogram of Oriented Gradients (HOG), and Modified log-Gabor (MLG) filter Transform. Three state-of-the-art classifiers, namely, Multi-Layer Perceptron (MLP), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM), are used to evaluate the optimal subset of features generated by the proposed FS model. Handwritten datasets at block, text line, and word level, consisting of officially recognized 12 Indic scripts, are prepared for experimentation. An average improvement in the range of 2–5% is achieved in the classification accuracy by utilizing only about 75–80% of the original feature vectors on all three datasets. The proposed method also shows better performance when compared to some popularly used FS models. The codes used for implementing HSGFS can be found in the following Github link: https://github.com/Ritam-Guha/HSGFS .
Question Answering Over Knowledge Base: A Scheme for Integrating Subject and the Identified Relation to Answer Simple Questions

Abstract Answering natural language question over a knowledge base is an important and challenging task with a wide range of application in natural language processing and information retrieval. Several existing knowledge-based question answering systems exploit complex end-to-end neural network approaches that are computationally expensive and take long to execute when training the neural network. More importantly, such an end-to-end approach makes it difficult to examine the process of query processing. In this study, we decompose the question answering problem in a three-step pipeline of entity detection, entity linking, and relation prediction, and solve each component separately. We explore basic neural network and non-neural network methods for entity detection and relation prediction plus a few heuristics for entity linking. We also introduce a method to identify ambiguity in the data and show that ambiguity in the data bounds the performance of the question answering system. The experiment on the SimpleQuestions benchmark data set shows that a combination of basic LSTMs, GRUs, and non-neural network techniques achieve reasonable performance while providing an opportunity to understand the question answering problem structure.