Reviewerly/exHarmony huggingface dataset:
Towards Trustworthy Conversational Agents for Children

Conversational agents (CAs) such as Amazon Alexa, Google Assistant or website chatbots are becoming very popular in some countries, and despite their adult-centered design, these devices are becoming part of children’s lives, creating the need for children-centric and trustworthy systems. This thesis aims to study CAs, with a special focus on children and trustworthiness, in order to identify the main aspects and criteria for their evaluation and improvement, and creating methodologies contributing to the design of trustworthy, child-centred CAs.
Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

Predictive maintenance (PdM) is the task of scheduling maintenance operations based on a statistical analysis of the system's condition. We propose a human-in-the-loop PdM approach in which a machine learning system predicts future problems in sets of workstations (computers, laptops, and servers). Our system interacts with domain experts to improve predictions and elicit their knowledge. In our approach, domain experts are included in the loop not only as providers of correct labels, as in traditional active learning, but as a source of explicit decision rule feedback. The system is automated and designed to be easily extended to novel domains, such as maintaining workstations of several organizations. In addition, we develop a simulator for reproducible experiments in a controlled environment and deploy the system in a large-scale case of real-life workstations PdM with thousands of workstations for dozens of companies.
Limits of Short-Time Evolution of Local Hamiltonians

Evolutions of local Hamiltonians in short times are expected to remain local and thus limited. In this paper, we validate this intuition by proving some limitations on short-time evolutions of local time-dependent Hamiltonians. We show that the distribution of the measurement output of short-time (at most logarithmic) evolutions of local Hamiltonians are \emph{concentrated} and satisfy an \emph{isoperimetric inequality}. To showcase explicit applications of our results, we study the \textsc{MaxCut} problem and conclude that quantum annealing needs at least a run-time that scales logarithmically in the problem size to beat classical algorithms on \textsc{MaxCut}. To establish our results, we also prove a Lieb-Robinson bound that works for time-dependent Hamiltonians which might be of independent interest.
Eye tracking sentences in language education

The present study reports and discusses the use of eye tracking qualitative data (static and dynamic gaze plots and heatmaps) in reading workshops in a middle school and in Generative Syntax and Sentence Processing courses at the undergraduate and graduate levels. Both endeavors take the sentential level as the proper object to be metacognitively explored in language education in order to develop innate science forming capacity and knowledge of language. In both projects non-discrepant qualitative eye tracking data collected and quantitatively analyzed in psycholinguistic studies carried out in LAPEX-UFRJ (Experimental Psycholinguistics Laboratory of the Federal University of Rio de Janeiro) were displayed to students as a point of departure, triggering discussions. Active, problem-solving based methodologies were employed with the objective of stimulating student participation. The article also discusses the importance of developing full literacy, epistemic vigilance and intellectual self-defense in an infodemic world.
A Pair Measurement Surface Code on Pentagons

In this paper, I present a way to compile the surface code into two-body parity measurements ("pair measurements"), where the pair measurements run along the edges of a Cairo pentagonal tiling. The resulting circuit improves on prior work by Chao et al. by using fewer pair measurements per four-body stabilizer measurement (5 instead of 6) and fewer time steps per round of stabilizer measurement (6 instead of 10). Using Monte Carlo sampling, I show that these improvements increase the threshold of the surface code when compiling into pair measurements from $\approx 0.2\%$ to $\approx 0.4\%$, and also that they improve the teraquop footprint at a $0.1\%$ physical gate error rate from $\approx6000$ qubits to $\approx3000$ qubits. However, I also show that the teraquop footprint of Chao et al's construction improves more quickly than mine as physical error rate decreases, and is likely better below a physical gate error rate of $\approx 0.03\%$ (due to bidirectional hook errors in my construction). I also compare to the planar honeycomb code, showing that although this work does noticeably reduce the gap between the surface code and the honeycomb code (when compiling into pair measurements), the honeycomb code is still more efficient (threshold $\approx 0.8\%$, teraquop footprint at $0.1\%$ of $\approx 1000$).
Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting

Landslide hazards are complex nonlinear systems with a highly dynamic nature. Accurate forecasting of landslide displacement and evolution is crucial for the prevention and mitigation of landslide hazards. In this study, a probabilistic landslide displacement forecasting model based on the quantification of epistemic uncertainty is proposed. In particular, the displacement forecasting problem is cast as a time-series regression problem with limited training samples and must be solved by statistical inference. The epistemic uncertainty of the landslide displacement series is depicted by the statistical properties of the function space constituted by the nonlinear mappings generated by the sparse Gaussian process regression. Data for our study was collected from the study area located in northwestern China. Other state-of-the-art probabilistic forecasting models have also been utilized for comparative analysis. The experimental results confirmed the superiority of the sparse Gaussian process in the modeling of landslide displacement series in terms of forecasting accuracy, uncertainty quantification, and robustness to overfitting.
Latent Influence Based Self-Attention Framework for Heterogeneous Network Embedding

In recent years, Graph Neural Networks has received enormous attention from academia for its huge potential of modeling the network traits such as macrostructure and single node attributes. However, prior mainstream works mainly focus on homogeneous network and lack the capacity to characterize the network heterogeneous property. Besides, most previous literature cannot model the influence under microscope vision, making it infeasible to model the joint relation between the heterogeneity and mutual interaction within multiple relation type. In this paper, we propose an Influence Self-attention network to address the difficulties mentioned above. To model heterogeneity and mutual interaction, we redesign attention mechanism with influence factor on the single-type relation level, which learns the importance coefficient from its adjacent neighbors under the same meta-path based patterns. To incorporate the heterogeneous meta-path in a unified dimension, we developed a self-attention based framework for meta-path relation fusion according to the learned meta-path coefficient. Our experimental results demonstrate that our framework not only achieve higher results than current state-of-the-art baselines, but also show promising vision on depicting heterogeneous interactive relations under complicated network structure.
Quantitative characterization of several entanglement detection criteria

Quantitative characterization of different entanglement detection criteria for bipartite systems is presented. We review the implication sequence of these criteria and then numerically estimate volume ratios between criteria non-violating quantum states and all quantum states. The numerical approach is based on the hit-and-run algorithm, which is applied to the convex set of all quantum states embedded into a Euclidean vector space of the Hilbert-Schmidt inner product. We demonstrate that reduction, majorization, and the R\'enyi-entropy-based criteria are very ineffective compared to the positive partial transpose. In the case of the R\'enyi-entropy-based criterion, we show that the ratio of detectable entanglement increases with the order of the R\'enyi entropy.
Robot path planning based on electrostatic field

The traditional artificial potential field method often falls into the local minimum point, resulting in the inability to reach the target point. In order to solve this problem, an electrostatic field algorithm is proposed, which provides a new framework for the trajectory of mobile robot. The electrostatic field algorithm uses the electrostatic field model to generate a gradient equipotential surface under the action of a uniform electric field to maintain the distance from the obstacle. Trajectory optimization reduces path planning based on sensor information and related motion constraint penalty function. The electrostatic field algorithm parallels the robot kinematics model and optimizes the candidate trajectory subset, and considers the allowable speed of the robot to find the optimal trajectory. The robot is closely combined with the environment and retains the global nature of the planned path. Compared with the traditional artificial potential field method, the advantage of path planning based on electrostatic field algorithm is confirmed.
A paradigm for secure object access and unrestricted mobility in distributed systems

In distributed architectures consisting of processing nodes, we associate a node chain with each object. The node chain connects a node named in the object identifier to the nodes reserving secondary and primary memory space for the object. No restriction exists on object movements across the network. A process can access a given object only if it holds a security gate referencing this object. The gate includes a password and the specification of an access authorisation. Gate weakening and revocation are fully supported.
Short-Term and Working Memory Capacity and the Language Device

Many general linguistic theories and language processing frameworks have assumed that language processing is largely a chunking procedure and that it is underpinned and constrained by our memory limitations. Despite this general consensus, the distinction between short-term memory (STM) and working memory (WM) limitations as they relate to language processing has remained elusive. To resolve this issue, we propose an integrated memory- and chunking-based metric of parsing complexity, in which STM limitations of 7 ± 2 (Miller, 1956a) are relevant to the Momentary Chunk Number (MCN), while WM limitations of 4 ± 1 (Cowan, 2001) are relevant to the Mean Momentary Chunk Number (MMCN). Examples of concrete calculations of our new metric are presented vis-à-vis Liu’s MDD metric and Hawkins’ IC-to-word Ratio metric. Related methodology issues are also discussed. We conclude the paper by echoing some recently repeated calls -(O'Grady, 2012 & 2017; Gómez-Rodríguez et al., 2019; Wen, 2019) to include STM and WM limitations as part and parcel of the language device (LD; cf. Chomsky, 1957) in that their impacts are ubiquitous and permeating in all essential linguistic domains ranging from phonology to grammar, discourse comprehension and production.
Non-Linear Pairwise Language Mappings for Low-Resource Multilingual Acoustic Model Fusion

Multilingual speech recognition has drawn significant attention as an effective way to compensate data scarcity for low-resource languages. End-to-end (e2e) modelling is preferred over conventional hybrid systems, mainly because of no lexicon requirement. However, hybrid DNN-HMMs still outperform e2e models in limited data scenarios. Furthermore, the problem of manual lexicon creation has been alleviated by publicly available trained models of grapheme-to-phoneme (G2P) and text to IPA transliteration for a lot of languages. In this paper, a novel approach of hybrid DNN-HMM acoustic models fusion is proposed in a multilingual setup for the low-resource languages. Posterior distributions from different monolingual acoustic models, against a target language speech signal, are fused together. A separate regression neural network is trained for each source-target language pair to transform posteriors from source acoustic model to the target language. These networks require very limited data as compared to the ASR training. Posterior fusion yields a relative gain of 14.65% and 6.5% when compared with multilingual and monolingual baselines respectively. Cross-lingual model fusion shows that the comparable results can be achieved without using posteriors from the language dependent ASR.
Drils revisited

Designed for graybox optimization problems, the state-of-the-art Drils algorithm (Deterministic Recombination and Iterated Local Search) follows the framework of a hybrid iterated local search by combining the efficient identification of improving moves and the fast recombination of local optima. The Drils algorithm uses a perturbation mechanism in order to feed the graybox crossover with promising local optima. The perturbation is a key element to avoid that the search gets trapped. In this paper, we revisit the Drils algorithm by focusing on two main questions: (i) how the perturbation is performed, and (ii) how strong it should be. We propose two alternative designs of the perturbation within the framework of Drils. The so-obtained algorithms are proved to provide substantial improvements. This is demonstrated based on extensive experiments using a diverse set of NKQ-landscapes, with different degrees of ruggedness, as well as, different dimensions ranging from relatively small to very large. Besides, we provide a comprehensive analysis on the impact of the proposed mechanisms allowing us to highlight the guiding principles for an accurate design and configuration of the perturbation as a function of landscape characteristics.
An exploration of how training set composition bias in machine learning affects identifying rare objects

When training a machine learning classifier on data where one of the classes is intrinsically rare, the classifier will often assign too few sources to the rare class. To address this, it is common to up-weight the examples of the rare class to ensure it isn't ignored. It is also a frequent practice to train on restricted data where the balance of source types is closer to equal for the same reason. Here we show that these practices can bias the model toward over-assigning sources to the rare class. We also explore how to detect when training data bias has had a statistically significant impact on the trained model's predictions, and how to reduce the bias's impact. While the magnitude of the impact of the techniques developed here will vary with the details of the application, for most cases it should be modest. They are, however, universally applicable to every time a machine learning classification model is used, making them analogous to Bessel's correction to the sample variance.
AVL Trees

In the previous chapter, we introduced the binary search tree. In such a tree, each node contains a key that is larger than all the keys in its left subtree and smaller than all the keys in its right subtree. Duplicate keys are not allowed.
Correlation Analysis of Japanese Literature and Psychotherapy Effects Based on an Equation Diagnosis Algorithm

Objective. On the basis of inheriting the classical aesthetic orientation of Japanese literature, contemporary Japanese literature changes the creative methods of traditional Japanese writers, strives to transcend the national limitations of Japanese literature, and strives to capture literary materials with a “modern feel.” Combined with modern western literary techniques to express the “modern self,” and integrating popular literature with pure literature, Japanese writers created a “healing” literature that is popular all over the world. There is some value in exploring the correlation between Japanese literature and the influence of psychotherapy. Method. In this paper, an equation diagnosis algorithm based on iterative learning for a class of linear discrete systems is studied, and the existing discrete iterative learning diagnostics are improved. We exchange the order of time domain and iterative domain to achieve the purpose of point-by-point equation diagnosis. On this basis, a virtual influence initial value estimation algorithm with a sliding window is proposed. The impact that has not been diagnosed is estimated and used as the initial value of the impact, so as to reduce the number of iterations and improve the efficiency of equation diagnosis. This paper takes the public and readers as different perspectives, based on the use and satisfaction theory and prospect theory, and uses interviews and questionnaires to study the communication effect of Japanese literature from the three dimensions of cognition, emotion, and behavior. The cognitive part includes the public’s understanding and readers’ understanding and reading; the emotional part includes the public’s and readers’ impressions and emotional attitudes; the behavioral part includes the public’s reading situation and readers’ behavior changes. Results/Discussion. In the survey of loyal readers, although readers’ understanding of Japanese literature has improved compared with the general public, it is not high, and their liking and influence on behavior are slightly lower than the preset level. This paper believes that the existing problems of Japanese literature dissemination are mainly concentrated in three aspects: the market is somewhat biased, which narrows the cognitive scope of users; as a cross-cultural literary work, Japanese literature still makes readers feel that it exists; there is some sense of estrangement, which will affect their enjoyment in reading; as a niche interest, readers lack timely communication and sharing objects after reading, so it is difficult to enhance their sense of identity.
On the Evaluation of the Plausibility and Faithfulness of Sentiment Analysis Explanations

With the pervasive use of Sentiment Analysis (SA) models in financial and social settings, performance is no longer the sole concern for reliable and accountable deployment. SA models are expected to explain their behavior and highlight textual evidence of their predictions. Recently, Explainable AI (ExAI) is enabling the “third AI wave” by providing explanations for the highly non-linear black-box deep AI models. Nonetheless, current ExAI methods, especially in the NLP field, are conducted on various datasets by employing different metrics to evaluate several aspects. The lack of a common evaluation framework is hindering the progress tracking of such methods and their wider adoption. In this work, inspired by offline information retrieval, we propose different metrics and techniques to evaluate the explainability of SA models from two angles. First, we evaluate the strength of the extracted “rationales” in faithfully explaining the predicted outcome. Second, we measure the agreement between ExAI methods and human judgment on a homegrown dataset (Dataset and code available at https://gitlab.com/awadailab/exai-nlp-eval ) to reflect on the rationales plausibility. Our conducted experiments comprise four dimensions: (1) the underlying architectures of SA models, (2) the approach followed by the ExAI method, (3) the reasoning difficulty, and (4) the homogeneity of the ground-truth rationales. We empirically demonstrate that anchors explanations are more aligned with the human judgment and can be more confident in extracting supporting rationales. As can be foreseen, the reasoning complexity of sentiment is shown to thwart ExAI methods from extracting supporting evidence. Moreover, a remarkable discrepancy is discerned between the results of different explainability methods on the various architectures suggesting the need for consolidation to observe enhanced performance. Predominantly, transformers are shown to exhibit better explainability than convolutional and recurrent architectures. Our work paves the way towards designing more interpretable NLP models and enabling a common evaluation ground for their relative strengths and robustness.
Components of Didactic Complexity of Mathematical Problem and Their Evaluation

The problem of estimating the didactic complexity of a mathematical task, which together with the solution is a multidimensional object characterized by a set of applied methods, is studied. The formation degree of the student's mathematical thinking is also determined by the set of mathematical methods he/she has mastered.&#x0D; Therefore, it is logical to consider the complexity of using certain methods of solution as the didactic complexity components of a particular task. It has been established that: 1) each mathematical problem can be characterized by a one-dimensional matrix, the components of which are proportional to the complexity of applying the corresponding method to solve it; 2) as the main methods for solving a mathematical problem, we can choose: methods of reading text, arithmetic calculations, algebraic transformations, method of the geometric constructions and operations with vectors use, combinatorial method, the trigonometric method, the method of logarithms and exponential functions, the differentiation and integration method, the operator method; 3) the complexity of applying a particular method in solving given task can be determined using a special computer program by counting the number of corresponding marker-terms and taking into account their complexity. The article evaluates the various components of the didactic complexity of 9 mathematical tasks on various topics and determines their general didactic complexity. It has been established that task 9 for calculating the divergence and rotor of a vector field is about 20 times more difficult than task 1 for solving a first-degree equation with one variable.
Detection of Face Mask in Thermal Images Using Deep CNN

Coronavirus (COVID-19) is a major health crisis across the globe, and one of the effective methods against the transmission of virus is wearing a face mask. It has become extremely important to monitor if people are wearing face mask when moving out. The aim of project is to automate the detection of face mask using images captured from a thermal camera. The problem is posed as a binary classification problem, and the input face image is classified as with mask or without mask. Transfer learning is used for classification, wherein deep CNN model, MobileNetV2, is used as a base model for feature extraction. A dataset of face images with and without mask is prepared using lepton FLIR camera interfaced to a Raspberry Pi board. The built model is able to detect people who are wearing a face mask and not wearing with an accuracy of 98%.
Quantum Computing With Trapped Ions: An overview

An array of trapped atomic ions that are laser cooled and isolated in an ultrahigh-vacuum environment presents one of the most advanced physical platforms for realizing a practical quantum computer. Small-scale ion quantum computers up to tens of ions have been built and achieved the highest fidelities on elementary quantum operations and overall quantum volume (QV). This article provides an overview of the elements of trapped-ion quantum computing (TIQC), current achievements in the field, and future perspectives.
A Deep Learning Inverse Hessian for Least-Squares Migration

Summary A new deep-learning (DL) approach for approximating the inverse of the Hessian as part of least-squares migration is proposed. This approach aims to compute complex non-linear non-stationary matching filters in the form of a convolutional neural network. This choice is motivated by the similarities between the conventional least-squares filtering method and DL methods. The aim is to train the network using a random selection of overlapping patches extracted from two migrated images that theoretically embed the effects of the Hessian.