Reviewerly/exHarmony huggingface dataset:
New Protocols and Negative Results for Textual Entailment Data Collection

Natural language inference (NLI) data has proven useful in benchmarking and, especially, as pretraining data for tasks requiring language understanding.However, the crowdsourcing protocol that was used to collect this data has known issues and was not explicitly optimized for either of these purposes, so it is likely far from ideal.We propose four alternative protocols, each aimed at improving either the ease with which annotators can produce sound training examples or the quality and diversity of those examples.Using these alternatives and a fifth baseline protocol, we collect and compare five new 8.5k-example training sets.In evaluations focused on transfer learning applications, our results are solidly negative, with models trained on our baseline dataset yielding good transfer performance to downstream tasks, but none of our four new methods (nor the recent ANLI) showing any improvements over that baseline.In a small silver lining, we observe that all four new protocols, especially those where annotators edit pre-filled text boxes, reduce previously observed issues with annotation artifacts.
Iterative Feature Mining for Constraint-Based Data Collection to Increase Data Diversity and Model Robustness

Diverse data is crucial for training robust models, but crowdsourced text often lacks diversity as workers tend to write simple variations from prompts.We propose a general approach for guiding workers to write more diverse text by iteratively constraining their writing.We show how prior workflows are special cases of our approach, and present a way to apply the approach to dialog tasks such as intent classification and slot-filling.Using our method, we create more challenging versions of test sets from prior dialog datasets and find dramatic performance drops for standard models.Finally, we show that our approach is complementary to recent work on improving data diversity, and training on data collected with our approach leads to more robust models.
Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm

Facial expressions are important cues to observe human emotions. Facial expression recognition has attracted many researchers for years, but it is still a challenging topic since expression features vary greatly with the head poses, environments, and variations in the different persons involved. In this work, three major steps are involved to improve the performance of micro-facial expression recognition. First, an Adaptive Homomorphic Filtering is used for face detection and rotation rectification processes. Secondly, Micro-facial features were used to extract the appearance variations of a testing image-spatial analysis. The features of motion information are used for expression recognition in a sequence of facial images. An effective Micro-Facial Expression Based Deep-Rooted Learning (MFEDRL) classifier is proposed in this paper to better recognize spontaneous micro-expressions by learning parameters on the optimal features. This proposed method includes two loss functions such as cross entropy loss function and centre loss function. Then the performance of the algorithm will be evaluated using recognition rate and false measures. Simulation results show that the predictive performance of the proposed method outperforms that of the existing classifiers such as Convolutional Neural Network (CNN), Deep Neural Network (DNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and k-Nearest Neighbours (KNN) in terms of accuracy and Mean Absolute Error (MAE).
Higher Education Institutions Towards Digital Transformation—The WUT Case

Abstract New emerging digital technologies such as artificial intelligence, virtual reality, augmented reality, cloud computing, blockchain, robotization, the Internet of Things, big data, etc. have produced a powerful disruptive effect in almost all areas of our existence and have radically changed the way we live, work, learn or relax. Without consciously realizing it, everyone is adapting to the digital era. As nothing “escapes” the all-encompassing digital transformation, higher education follows track too. So, it is natural to ask ourselves: what are the higher education institutions doing to keep up with this rapidly evolving digital world? In this paper, we present the case of West University of Timişoara as an example of good practice in dealing with the effects of digital transformation on the university and its academic community (teachers, students, administrative staff). Our goal is to gain an understanding of what is being proposed through the institutional development strategy, and what is actually happening in our university from the digitalization perspective. Thus, we conduct an exploratory research using a quantitative approach that involves a survey applied to students enrolled in different study programs, at different levels. We focus on their opinion about how our university can prepare and transform in order to adopt an integrated digital approach, looking into topics like: technology-enabled services, digital enrollment of students, digitization of the administrative processes, implementation of digital procedures to offer recommendations or file complains, digital curricula, new modes of digital learning delivery, etc. Our findings reveal that West University of Timişoara must take significant steps towards the implementation of digital transformations, while, however, remaining watchful and cautious of the hidden implications of this process.
Quantum coherence in mutually unbiased bases

We investigate the $$l_{1}$$ norm of coherence of quantum states in mutually unbiased bases. We find that the sum of squared $$l_{1}$$ norm of coherence of single qubit mixed state is less than two. We derive that the $$l_{1}$$ norms of coherence of three classes of X states in nontrivial mutually unbiased bases for 4-dimensional Hilbert space are equal. We propose the concept of “autotensor of mutually unbiased basis (AMUB)” by the tensor of mutually unbiased bases and depict the level surface of the constant sum of the $$l_{1}$$ norm of coherence of Bell-diagonal states in AMUB. We find that the $$l_{1}$$ norms of coherence of Werner states and isotropic states in AMUB are equal, respectively.
Graph Fourier transform based on ℓ1 norm variation minimization

The definition of graph Fourier transform is a fundamental issue in graph signal processing. Conventional graph Fourier transform is defined through the eigenvectors of the graph Laplacian matrix, which minimize the ℓ2 norm signal variation. In this paper, we propose a generalized definition of graph Fourier transform based on the ℓ1 norm variation minimization. We obtain a necessary condition satisfied by the ℓ1 Fourier basis, and provide a fast greedy algorithm to approximately construct the ℓ1 Fourier basis. Numerical experiments are also provided to compare the differences between the Laplacian basis, ℓ1 basis and greedy basis.
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining

Pre-trained neural language models bring significant improvement for various NLP tasks, by fine-tuning the models on task-specific training sets.During fine-tuning, the parameters are initialized from pre-trained models directly, which ignores how the learning process of similar NLP tasks in different domains is correlated and mutually reinforced.In this paper, we propose an effective learning procedure named Meta Fine-Tuning (MFT), serving as a meta-learner to solve a group of similar NLP tasks for neural language models.Instead of simply multi-task training over all the datasets, MFT only learns from typical instances of various domains to acquire highly transferable knowledge.It further encourages the language model to encode domaininvariant representations by optimizing a series of novel domain corruption loss functions.After MFT, the model can be fine-tuned for each domain with better parameter initialization and higher generalization ability.We implement MFT upon BERT to solve several multi-domain text mining tasks.Experimental results confirm the effectiveness of MFT and its usefulness for few-shot learning.
Training Discriminative Models to Evaluate Generative Ones

Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks (GANs), produce good visual samples of varied categories of images. However, the validation of their quality is still difficult to define and there is no existing agreement on the best evaluation process. This paper aims at making a step toward an objective evaluation process for generative models. It presents a new method to assess a trained generative model by evaluating the test accuracy of a classifier trained with generated data. The test set is composed of real images. Therefore, The classifier accuracy is used as a proxy to evaluate if the generative model fit the true data distribution. By comparing results with different generated datasets we are able to classify and compare generative models. The motivation of this approach is also to evaluate if generative models can help discriminative neural networks to learn, i.e., measure if training on generated data is able to make a model successful at testing on real settings. Our experiments compare different generators from the Variational Auto-Encoders (VAE) and Generative Adversarial Network (GAN) frameworks on MNIST and fashion MNIST datasets. Our results show that none of the generative models is able to replace completely true data to train a discriminative model. But they also show that the initial GAN and WGAN are the best choices to generate on MNIST database (Modified National Institute of Standards and Technology database) and fashion MNIST database.
Uncertainty relation for angle from a quantum-hydrodynamical perspective

We revisit the problem of the uncertainty relation for angle by using quantum hydrodynamics formulated in terms of the stochastic variational method (SVM), where we need not define the angle operator. We derive both the Kennard and Robertson–Schrödinger inequalities for canonical variables in polar coordinates. The inequalities have state-dependent minimum values which can be smaller than ħ∕2 and then permit a finite uncertainty of angle for the eigenstate of the angular momentum. The present approach provides a useful methodology to study quantum behaviors in arbitrary coordinates.
Using Type Information to Improve Entity Coreference Resolution

Coreference resolution (CR) is an essential part of discourse analysis.Most recently, neural approaches have been proposed to improve over SOTA models from earlier paradigms.So far none of the published neural models leverage external semantic knowledge such as type information.This paper offers the first such model and evaluation, demonstrating modest gains in accuracy by introducing either gold standard or predicted types.In the proposed approach, type information serves both to (1) improve mention representation and (2) create a soft type consistency check between coreference candidate mentions.Our evaluation covers two different grain sizes of types over four different benchmark corpora.
A Two-Phase Distributed Filtering Algorithm for Networked Uncertain Systems with Fading Measurements under Deception Attacks

In this paper, the distributed filtering problem is addressed for a class of discrete-time stochastic systems over a sensor network with a given topology, susceptible to suffering deception attacks, launched by potential adversaries, which can randomly succeed or not with a known success probability, which is not necessarily the same for the different sensors. The system model integrates some random imperfections and features that are frequently found in real networked environments, namely: (1) fading measurements; (2) multiplicative noises in both the state and measurement equations; and (3) sensor additive noises cross-correlated with each other and with the process noise. According to the network communication scheme, besides its own local measurements, each sensor receives the measured outputs from its adjacent nodes. Based on such measurements, a recursive algorithm is designed to obtain the least-squares linear filter of the state. Thereafter, each sensor receives the filtering estimators previously obtained by its adjacent nodes, and these estimators are all fused to obtain the desired distributed filter as the minimum mean squared error matrix-weighted linear combination of them. The theoretical results are illustrated by a simulation example, where the efficiency of the developed distributed estimation strategy is discussed in terms of the error variances.
ISAAQ - Mastering Textbook Questions with Pre-trained Transformers and Bottom-Up and Top-Down Attention

Textbook Question Answering is a complex task in the intersection of Machine Comprehension and Visual Question Answering that requires reasoning with multimodal information from text and diagrams.For the first time, this paper taps on the potential of transformer language models and bottom-up and top-down attention to tackle the language and visual understanding challenges this task entails.Rather than training a language-visual transformer from scratch we rely on pretrained transformers, fine-tuning and ensembling.We add bottom-up and top-down attention to identify regions of interest corresponding to diagram constituents and their relationships, improving the selection of relevant visual information for each question and answer options.Our system ISAAQ reports unprecedented success in all TQA question types, with accuracies of 81.36%, 71.11% and 55.12% on true/false, text-only and diagram multiple choice questions.ISAAQ also demonstrates its broad applicability, obtaining state-of-theart results in other demanding datasets.
On the effectiveness of small, discriminatively pre-trained language representation models for biomedical text mining

Neural language representation models such as BERT (Devlin et al., 2019) have recently shown state of the art performance in downstream NLP tasks and bio-medical domain adaptation of BERT (Bio-BERT (Lee et al., 2019)) has shown same behavior on biomedical text mining tasks.However, due to their large model size and resulting increased computational need, practical application of models such as BERT is challenging making smaller models with comparable performance desirable for real word applications.Recently, a new language transformers based language representation model named ELECTRA (Clark et al., 2020) is introduced, that makes efficient usage of training data in a generative-discriminative neural model setting that shows performance gains over BERT.These gains are especially impressive for smaller models.Here, we introduce two small ELECTRA based model named Bio-ELECTRA and Bio-ELECTRA++ that are eight times smaller than BERT Base and Bio-BERT and achieves comparable or better performance on biomedical question answering, yes/no question answer classification, question answer candidate ranking and relation extraction tasks.Bio-ELECTRA is pre-trained from scratch on PubMed abstracts using a consumer grade GPU with only 8GB memory.Bio-ELECTRA++ is the further pre-trained version of Bio-ELECTRA trained on a corpus of open access full papers from PubMed Central.While, for biomedical named entity recognition, large BERT Base model outperforms Bio-ELECTRA++, Bio-ELECTRA and ELECTRA-Small++, with hyperparameter tuning Bio-ELECTRA++ achieves results comparable to BERT.
Quantum process identification: a method for characterizing non-markovian quantum dynamics

Abstract Established methods for characterizing quantum information processes do not capture non-Markovian (history-dependent) behaviors that occur in real systems. These methods model a quantum process as a fixed map on the state space of a predefined system of interest. Such a map averages over the system’s environment, which may retain some effect of its past interactions with the system and thus have a history-dependent influence on the system. Although the theory of non-Markovian quantum dynamics is currently an active area of research, a systematic characterization method based on a general representation of non-Markovian dynamics has been lacking. In this article we present a systematic method for experimentally characterizing the dynamics of open quantum systems. Our method, which we call quantum process identification (QPI), is based on a general theoretical framework which relates the (non-Markovian) evolution of a system over an extended period of time to a time-local (Markovian) process involving the system and an effective environment. In practical terms, QPI uses time-resolved tomographic measurements of a quantum system to construct a dynamical model with as many dynamical variables as are necessary to reproduce the evolution of the system. Through numerical simulations, we demonstrate that QPI can be used to characterize qubit operations with non-Markovian errors arising from realistic dynamics including control drift, coherent leakage, and coherent interaction with material impurities.
Laser-Based People Detection and Obstacle Avoidance for a Hospital Transport Robot

This paper describes the development of a laser-based people detection and obstacle avoidance algorithm for a differential-drive robot, which is used for transporting materials along a reference path in hospital domains. Detecting humans from laser data is an important functionality for the safety of navigation in the shared workspace with people. Nevertheless, traditional methods normally utilize machine learning techniques on hand-crafted geometrical features extracted from individual clusters. Moreover, the datasets used to train the models are usually small and need to manually label every laser scan, increasing the difficulty and cost of deploying people detection algorithms in new environments. To tackle these problems, (1) we propose a novel deep learning-based method, which uses the deep neural network in a sliding window fashion to effectively classify every single point of a laser scan. (2) To increase the speed of inference without losing performance, we use a jump distance clustering method to decrease the number of points needed to be evaluated. (3) To reduce the workload of labeling data, we also propose an approach to automatically annotate datasets collected in real scenarios. In general, the proposed approach runs in real-time and performs much better than traditional methods. Secondly, conventional pure reactive obstacle avoidance algorithms can produce inefficient and oscillatory behaviors in dynamic environments, making pedestrians confused and possibly leading to dangerous reactions. To improve the legibility and naturalness of obstacle avoidance in human crowded environments, we introduce a sampling-based local path planner, similar to the method used in autonomous driving cars. The key idea is to avoid obstacles by switching lanes. We also adopt a simple rule to decrease the number of unnecessary deviations from the reference path. Experiments carried out in real-world environments confirmed the effectiveness of the proposed algorithms.
Human Aspects of Agile Transition in Traditional Organizations

The agile transition is crucial for traditional organizations to remain competitive in the current market, which is characterized by a fast-pace and a constant need for innovation. In order to implement this transition, organizations must adjust their mindset to the new agile values. Despite its evident benefits, the transition to agile model is complex and time-consuming, posing many challenges to organizations. Since the agile philosophy is people-centered, rather than process-centered, most challenges are related to the human aspects of this transition and they demand a large transformation in all areas of the organization. This article provides a Systematic Literature Review (SLR) of the human aspects of agile transition and summarizes the existing literature into three categories: People, Management and Organization. Its main objective is to assist organizations undergoing agile transition in reducing risks related to this process, by acquiring knowledge on the challenges involved and implementing the proposed recommendations.
Deep reinforcement learning for universal quantum state preparation via dynamic pulse control

Abstract Accurate and efficient preparation of quantum state is a core issue in building a quantum computer. In this paper, we investigate how to prepare a certain single- or two-qubit target state from arbitrary initial states in semiconductor double quantum dots with only a few discrete control pulses by leveraging the deep reinforcement learning. Our method is based on the training of the network over numerous preparing tasks. The results show that once the network is well trained, it works for any initial states in the continuous Hilbert space. Thus repeated training for new preparation tasks is avoided. Our scheme outperforms the traditional optimization approaches based on gradient with both the higher efficiency and the preparation quality in discrete control space. Moreover, we find that the control trajectories designed by our scheme are robust against stochastic fluctuations within certain thresholds, such as the charge and nuclear noises.
General error mitigation for quantum circuits

Abstract A general method to mitigate the effect of errors in quantum circuits is outlined. The method is developed in sight of characteristics that an ideal method should possess and to ameliorate an existing method which only mitigates state preparation and measurement errors. The method is tested on different IBM Q quantum devices, using randomly generated circuits with up to four qubits. A large majority of results show significant error mitigation.
How educational technology innovates distance learning during pandemic crisis in remote areas in Indonesia?

This study presents the results of a review of seven international publications that discuss how technology can innovate distance learning during a pandemic crisis in remote areas in Indonesia. It cannot be denied that technology services are quite large in helping the learning of the nation's children all over Indonesia. However, we should cool off on educational researchers' findings, especially technology-based learning innovations, which are now increasingly being used in all business and educational activities of the younger generation. We have conducted a review of various literature. Then we continue with a review of ten publications on the role and impact of technology applications on the sustainability of learning in areas where internet networks are still tricky issues. To prove the claim, we conducted a qualitative analysis in a phenomenological and in-depth understanding to ensure that our findings were based on validity and reliability.
Implementation of OJS Based iJC Media E-Journal System at University of Pramita Indonesia

The increasing development of information technology in the era of 5.0 can provide information that is informative and very easy to obtain in the form of electronics. But not at Pramita University of Indonesia in managing journals or other scientific works still using conventional or manual methods that cause the main problem that is causing increased costs and time, the data can be separated because the old data is not archived properly and the management of scientific works also does not meet the specified requirements by Ristekdikti with regard to Journal Accreditation which requires that journals be managed online because that will be easier and faster. With the iJC (iLearning Journal Center) can publish journals online, make it easier for users to submit journals, change the management of ejournal to be more distributed and can simplify the process of submitting manuscripts, reviewing, editing, until the publication stage and by using iLearning Journal Center will produce e quality journal. In this study, researchers used several methods, namely the method of observation and study of literature and using Flowcharts.