Reviewerly/exHarmony huggingface dataset:
Control Systems and Electronic Instrumentation Applied to Autonomy in Wheelchair Mobility: The State of the Art

Automatic wheelchairs have evolved in terms of instrumentation and control, solving the mobility problems of people with physical disabilities. With this work it is intended to establish the background of the instrumentation and control methods of automatic wheelchairs and prototypes, as well as a classification in each category. To this end a search of specialised databases was carried out for articles published between 2012 and 2019. Out of these, 97 documents were selected based on the inclusion and exclusion criteria. The following categories were proposed for these articles: (a) wheelchair instrumentation and control methods, among which there are systems that implement micro-electromechanical sensors (MEMS), surface electromyography (sEMG), electrooculography (EOG), electroencephalography (EEG), and voice recognition systems; (b) wheelchair instrumentation, among which are found obstacle detection systems, artificial vision (image and video), as well as navigation systems (GPS and GSM). The results found in this review tend towards the use of EEG signals, head movements, voice commands, and algorithms to avoid obstacles. The most used techniques involve the use of a classic control and thresholding to move the wheelchair. In addition, the discussion was mainly based on the characteristics of the user and the types of control. To conclude, the articles exhibited the existing limitations and possible solutions in their designs, as well as informing the physically disabled community about the technological developments in this field.
Facilitating User Authorization from Imbalanced Data Logs of Credit Cards Using Artificial Intelligence

An effective machine learning implementation means that artificial intelligence has tremendous potential to help and automate financial threat assessment for commercial firms and credit agencies. The scope of this study is to build a predictive framework to help the credit bureau by modelling/assessing the credit card delinquency risk. Machine learning enables risk assessment by predicting deception in large imbalanced data by classifying the transaction as normal or fraudster. In case of fraud transaction, an alert can be sent to the related financial organization that can suspend the release of payment for particular transaction. Of all the machine learning models such as RUSBoost, decision tree, logistic regression, multilayer perceptron, K-nearest neighbor, random forest, and support vector machine, the overall predictive performance of customized RUSBoost is the most impressive. The evaluation metrics used in the experimentation are sensitivity, specificity, precision, F scores, and area under receiver operating characteristic and precision recall curves. Datasets used for training and testing of the models have been taken from kaggle.com.
Efficient MILP Modelings for Sboxes and Linear Layers of SPN ciphers

Mixed Integer Linear Programming (MILP) solvers are regularly used by designers for providing security arguments and by cryptanalysts for searching for new distinguishers. For both applications, bitwise models are more refined and permit to analyze properties of primitives more accurately than word-oriented models. Yet, they are much heavier than these last ones. In this work, we first propose many new algorithms for efficiently modeling any subset of Fn2 with MILP inequalities. This permits, among others, to model differential or linear propagation through Sboxes. We manage notably to represent the differential behaviour of the AES Sbox with three times less inequalities than before. Then, we present two new algorithms inspired from coding theory to model complex linear layers without dummy variables. This permits us to represent many diffusion matrices, notably the ones of Skinny-128 and AES in a much more compact way. To demonstrate the impact of our new models on the solving time we ran experiments for both Skinny-128 and AES. Finally, our new models allowed us to computationally prove that there are no impossible differentials for 5-round AES and 13-round Skinny-128 with exactly one input and one output active byte, even if the details of both the Sbox and the linear layer are taken into account.
The World is Not Binary: Learning to Rank with Grayscale Data for Dialogue Response Selection

Response selection plays a vital role in building retrieval-based conversation systems.Despite that response selection is naturally a learning-to-rank problem, most prior works take a point-wise view and train binary classifiers for this task: each response candidate is labeled either relevant (one) or irrelevant (zero).On the one hand, this formalization can be sub-optimal due to its ignorance of the diversity of response quality.On the other hand, annotating grayscale data for learning-to-rank can be prohibitively expensive and challenging.In this work, we show that grayscale data can be automatically constructed without human effort.Our method employs off-the-shelf response retrieval models and response generation models as automatic grayscale data generators.With the constructed grayscale data, we propose multi-level ranking objectives for training, which can (1) teach a matching model to capture more fine-grained context-response relevance difference and (2) reduce the traintest discrepancy in terms of distractor strength.Our method is simple, effective, and universal.Experiments on three benchmark datasets and four state-of-the-art matching models show that the proposed approach brings significant and consistent performance improvements.
Ultra-directional high-efficiency chiral silicon photonic circuits

Chiral light matter interaction enables new fundamental researches and applications of light. The interaction has traditionally faced challenges in low directionality and efficiency based on spin orbit interaction of light in microscopic waveguides. It is pivotal to exploit photonic integrated circuits to efficiently engineer photonic chiral behavior. Here, we present ultra directional high efficient chiral coupling in silicon photonic circuits based on low order to high order mode conversion and interference. We show that the directionality of chiral coupling, in principle, can approach minus/plus 1 with circular polarization inputs, benefited from the underlying mechanism of complete destructive and constructive interference. The chiral coupling efficiency can exceed 70%, with negligible scattering to nonguided modes, much higher than conventional coupling mechanisms. Moreover, the chiral silicon photonic circuits can function as a perfect 3 dB power splitter for arbitrarily linear polarization inputs, and also open up the possibility of on chip chirality determination to further flourish the development of chiral optics.
Compositional Demographic Word Embeddings

Word embeddings are usually derived from corpora containing text from many individuals, thus leading to general purpose representations rather than individually personalized representations.While personalized embeddings can be useful to improve language model performance and other language processing tasks, they can only be computed for people with a large amount of longitudinal data, which is not the case for new users.We propose a new form of personalized word embeddings that use demographic-specific word representations derived compositionally from full or partial demographic information for a user (i.e., gender, age, location, religion).We show that the resulting demographic-aware word representations outperform generic word representations on two tasks for English: language modeling and word associations.We further explore the trade-off between the number of available attributes and their relative effectiveness and discuss the ethical implications of using them.
Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

Representing and analyzing the complexity of models constructed by data is a difficult and challenging task, hence the need for new, more effective techniques emerges, despite the numerous methodologies recently proposed in this field. In the present paper, the main idea is to systematically create a nested structure, based on a fuzzy cognitive map (FCM), in which each element/concept at a higher map level is decomposed into another FCM that provides a more detailed and precise representation of complex time series data. This nested structure is then optimized by applying evolutionary learning algorithms. Through the application of a dynamic optimization process, the whole nested structure based on FCMs is restructured in order to derive important relationships between map concepts at every nesting level as well as to determine the weights of these relationships on the basis of the available time series. This process allows discovering and describing hidden relationships among important map concepts. The paper proposes the application of the suggested nested approach for time series forecasting as well as for decision-making tasks regarding appliances’ energy consumption prediction.
A Class of Models with the Potential to Represent Fundamental Physics

A class of models intended to be as minimal and structureless as possible is introduced. Even in cases with simple rules, rich and complex behavior is found to emerge, and striking correspondences to some important core known features of fundamental physics are seen, suggesting the possibility that the models may provide a new approach to finding a fundamental theory of physics.
Circuit optimization of Hamiltonian simulation by simultaneous diagonalization of Pauli clusters

Many applications of practical interest rely on time evolution of Hamiltonians that are given by a sum of Pauli operators. Quantum circuits for exact time evolution of single Pauli operators are well known, and can be extended trivially to sums of commuting Paulis by concatenating the circuits of individual terms. In this paper we reduce the circuit complexity of Hamiltonian simulation by partitioning the Pauli operators into mutually commuting clusters and exponentiating the elements within each cluster after applying simultaneous diagonalization. We provide a practical algorithm for partitioning sets of Paulis into commuting subsets, and show that the proposed approach can help to significantly reduce both the number of CNOT operations and circuit depth for Hamiltonians arising in quantum chemistry. The algorithms for simultaneous diagonalization are also applicable in the context of stabilizer states; in particular we provide novel four- and five-stage representations, each containing only a single stage of conditional gates.
Consistency of a Recurrent Language Model With Respect to Incomplete Decoding

Despite strong performance on a variety of tasks, neural sequence models trained with maximum likelihood have been shown to exhibit issues such as length bias and degenerate repetition.We study the related issue of receiving infinite-length sequences from a recurrent language model when using common decoding algorithms.To analyze this issue, we first define inconsistency of a decoding algorithm, meaning that the algorithm can yield an infinite-length sequence that has zero probability under the model.We prove that commonly used incomplete decoding algorithms -greedy search, beam search, top-k sampling, and nucleus sampling -are inconsistent, despite the fact that recurrent language models are trained to produce sequences of finite length.Based on these insights, we propose two remedies which address inconsistency: consistent variants of top-k and nucleus sampling, and a selfterminating recurrent language model.Empirical results show that inconsistency occurs in practice, and that the proposed methods prevent inconsistency.
Quantum computing model of an artificial neuron with continuously valued input data

Abstract Artificial neural networks have been proposed as potential algorithms that could benefit from being implemented and run on quantum computers. In particular, they hold promise to greatly enhance Artificial Intelligence tasks, such as image elaboration or pattern recognition. The elementary building block of a neural network is an artificial neuron, i.e. a computational unit performing simple mathematical operations on a set of data in the form of an input vector. Here we show how the design for the implementation of a previously introduced quantum artificial neuron [ npj Quant. Inf. 5 , 26], which fully exploits the use of superposition states to encode binary valued input data, can be further generalized to accept continuous- instead of discrete-valued input vectors, without increasing the number of qubits. This further step is crucial to allow for a direct application of gradient descent based learning procedures, which would not be compatible with binary-valued data encoding.
Developing a Predictive Model for Asthma-Related Hospital Encounters in Patients With Asthma in a Large, Integrated Health Care System: Secondary Analysis

Background Asthma causes numerous hospital encounters annually, including emergency department visits and hospitalizations. To improve patient outcomes and reduce the number of these encounters, predictive models are widely used to prospectively pinpoint high-risk patients with asthma for preventive care via care management. However, previous models do not have adequate accuracy to achieve this goal well. Adopting the modeling guideline for checking extensive candidate features, we recently constructed a machine learning model on Intermountain Healthcare data to predict asthma-related hospital encounters in patients with asthma. Although this model is more accurate than the previous models, whether our modeling guideline is generalizable to other health care systems remains unknown. Objective This study aims to assess the generalizability of our modeling guideline to Kaiser Permanente Southern California (KPSC). Methods The patient cohort included a random sample of 70.00% (397,858/568,369) of patients with asthma who were enrolled in a KPSC health plan for any duration between 2015 and 2018. We produced a machine learning model via a secondary analysis of 987,506 KPSC data instances from 2012 to 2017 and by checking 337 candidate features to project asthma-related hospital encounters in the following 12-month period in patients with asthma. Results Our model reached an area under the receiver operating characteristic curve of 0.820. When the cutoff point for binary classification was placed at the top 10.00% (20,474/204,744) of patients with asthma having the largest predicted risk, our model achieved an accuracy of 90.08% (184,435/204,744), a sensitivity of 51.90% (2259/4353), and a specificity of 90.91% (182,176/200,391). Conclusions Our modeling guideline exhibited acceptable generalizability to KPSC and resulted in a model that is more accurate than those formerly built by others. After further enhancement, our model could be used to guide asthma care management. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039
UniConv: A Unified Conversational Neural Architecture for Multi-domain Task-oriented Dialogues

Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons.First, tracking dialogue states of multiple domains is non-trivial as the dialogue agent must obtain complete states from all relevant domains, some of which might have shared slots among domains as well as unique slots specifically for one domain only.Second, the dialogue agent must also process various types of information across domains, including dialogue context, dialogue states, and database, to generate natural responses to users.Unlike the existing approaches that are often designed to train each module separately, we propose "UniConv" -a novel unified neural architecture for end-to-end conversational systems in multi-domain task-oriented dialogues, which is designed to jointly train (i) a Bi-level State Tracker which tracks dialogue states by learning signals at both slot and domain level independently, and (ii) a Joint Dialogue Act and Response Generator which incorporates information from various input components and models dialogue acts and target responses simultaneously.We conduct comprehensive experiments in dialogue state tracking, contextto-text, and end-to-end settings on the Multi-WOZ2.1 benchmark, achieving superior performance over competitive baselines.
Quantum computation of molecular response properties

Accurately predicting response properties of molecules such as the dynamic polarizability and hyperpolarizability using quantum mechanics has been a long-standing challenge with widespread applications in material and drug design. Classical simulation techniques in quantum chemistry are hampered by the exponential growth of the many-electron Hilbert space as the system size increases. In this work, we propose an algorithm for computing linear and nonlinear molecular response properties on quantum computers, by first reformulating the target property into a symmetric expression more suitable for quantum computation via introducing a set of auxiliary quantum states, and then determining these auxiliary states via solving the corresponding linear systems of equations on quantum computers. On one hand, we prove that using the quantum linear system algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] as a subroutine the proposed algorithm scales only polynomially in the system size instead of the dimension of the exponentially large Hilbert space, and hence achieves an exponential speedup over existing classical algorithms. On the other hand, we introduce a variational hybrid quantum-classical variant of the proposed algorithm, which is more practical for near-term quantum devices.
Pre-training Entity Relation Encoder with Intra-span and Inter-span Information

In this paper, we integrate span-related information into pre-trained encoder for entity relation extraction task.Instead of using generalpurpose sentence encoder (e.g., existing universal pre-trained models), we introduce a span encoder and a span pair encoder to the pre-training network, which makes it easier to import intra-span and inter-span information into the pre-trained model.To learn the encoders, we devise three customized pretraining objectives from different perspectives, which target on tokens, spans, and span pairs.In particular, a span encoder is trained to recover a random shuffling of tokens in a span, and a span pair encoder is trained to predict positive pairs that are from the same sentences and negative pairs that are from different sentences using contrastive loss.Experimental results show that the proposed pre-training method outperforms distantly supervised pretraining, and achieves promising performance on two entity relation extraction benchmark datasets (ACE05, SciERC).
Self-Supervised Knowledge Triplet Learning for Zero-Shot Question Answering

The aim of all Question Answering (QA) systems is to generalize to unseen questions.Current supervised methods are reliant on expensive data annotation.Moreover, such annotations can introduce unintended annotator bias, making systems focus more on the bias than the actual task.This work proposes Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs.We propose heuristics to create synthetic graphs for commonsense and scientific knowledge.We propose using KTL to perform zero-shot question answering, and our experiments show considerable improvements over large pre-trained transformer language models.
Improving Neural Topic Models using Knowledge Distillation

Topic models are often used to identify humaninterpretable topics to help make sense of large document collections.We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers.Our modular method can be straightforwardly applied with any neural topic model to improve topic quality, which we demonstrate using two models having disparate architectures, obtaining state-of-the-art topic coherence.We show that our adaptable framework not only improves performance in the aggregate over all estimated topics, as is commonly reported, but also in head-to-head comparisons of aligned topics.
Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling

Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles.Even though most semantic-role formalisms are built upon constituent syntax, and only syntactic constituents can be labeled as arguments (e.g., FrameNet and PropBank), all the recent work on syntaxaware SRL relies on dependency representations of syntax.In contrast, we show how graph convolutional networks (GCNs) can be used to encode constituent structures and inform an SRL system.Nodes in our SpanGCN correspond to constituents.The computation is done in 3 stages.First, initial node representations are produced by 'composing' word representations of the first and last words in the constituent.Second, graph convolutions relying on the constituent tree are performed, yielding syntacticallyinformed constituent representations.Finally, the constituent representations are 'decomposed' back into word representations, which are used as input to the SRL classifier.We evaluate SpanGCN against alternatives, including a model using GCNs over dependency trees, and show its effectiveness on standard English SRL benchmarks CoNLL-2005, CoNLL-2012, and FrameNet.
Multi-Instance Multi-Label Learning Networks for Aspect-Category Sentiment Analysis

Aspect-category sentiment analysis (ACSA) aims to predict sentiment polarities of sentences with respect to given aspect categories.To detect the sentiment toward a particular aspect category in a sentence, most previous methods first generate an aspect categoryspecific sentence representation for the aspect category, then predict the sentiment polarity based on the representation.These methods ignore the fact that the sentiment of an aspect category mentioned in a sentence is an aggregation of the sentiments of the words indicating the aspect category in the sentence, which leads to suboptimal performance.In this paper, we propose a Multi-Instance Multi-Label Learning Network for Aspect-Category sentiment analysis (AC-MIMLLN), which treats sentences as bags, words as instances, and the words indicating an aspect category as the key instances of the aspect category.Given a sentence and the aspect categories mentioned in the sentence, AC-MIMLLN first predicts the sentiments of the instances, then finds the key instances for the aspect categories, finally obtains the sentiments of the sentence toward the aspect categories by aggregating the key instance sentiments.Experimental results on three public datasets demonstrate the effectiveness of AC-MIMLLN 1 .
Complex conjugation supermap of unitary quantum maps and its universal implementation protocol

Advances in quantum computing technology lead to more elaborate means of manipulating quantum states and quantum dynamics. This work discusses ``universal complex conjugation'' of unitary quantum dynamics, which may be interpreted as a task of implementing a ``time-reversed'' dynamics on a given quantum system undergoing a unitary evolution without identifying the dynamics itself.