Reviewerly/exHarmony huggingface dataset:
Characteristics of the IELTS Reading Comprehension: Implications for Development of EFL Reading Comprehension Instruction

Question type and format in reading tasks are essential characteristics that have been proven to affect students' performance. This study aims to investigate the characteristics of questions used in the IELTS reading comprehension tasks, including (1) question types and (2) question formats. The researchers collected 240 reading question items from six test modules in Barron's IELTS Preparation Test, authored by Lougheed (2016). The categorization criteria for different question types were adapted from Nuttall's (2005) taxonomy of questions. Question formats were categorized based on common question formats (Lougheed, 2013; Hughes, 2003). Data were analyzed using frequency and percentage. The findings revealed three common question types in the reading sections of IELTS preparation tests, including questions of literal comprehension (43.8%), questions of reinterpretation (43.3%), and questions of inference (12.9%). Five question formats were commonly used in the reading tests, including multiple-choice (33.3%), identifying information (28%), completion (25%), matching (11.7%), and short answer (2%). In addition, the study revealed four main patterns of question arrangement in the reading test as supplementary findings. This study discussed the characteristics of IELTS question items and patterns of question arrangement to contribute towards a more accurate interpretation of reading tasks and a prospective design for reading practice for EFL students.
Incoherent control of two-photon induced optical measurements in open quantum systems: Quantum heat engine perspective

We present a consistent optimization procedure for the optical measurements in open quantum systems using recently developed incoherent control protocol. Assigning an effective hot bath for the two-entangled-photon pump we recast the transmission of classical probe as a work in a quantum heat engine framework. We demonstrate that maximum work in such a heat engine can exceed that for the classical two-photon and one-photon pumps, while efficiency at maximum power can be attributed to conventional boundaries obtained for the three-level maser heat engine. Our results pave the way for incoherent control and optimization of optical measurements in open quantum systems that involve two-photon processes with quantum light.
The use of automated systems in computer forensics

The rapid development and spread of new information and telecommunication technologies is acquiring the character of a global information revolution today, which has an increasing im-pact on politics, economics, management, finance, science, culture and other spheres of society within national borders and in the world as a whole. This is used not only by conscientious users of communication networks, but also by malefactors pursuing various illegal goals – personal enrichment, discrediting citizens and state bodies, spreading illegal information, ideas of terrorism and extremism. There is also a rapid growth of new types of crimes related to the use of information systems. All this requires the creation of new computer forensics tools (forensics), which is able to process a large amount of information and help in the identification, dis-closure and investigation of crimes. The article analyzes the directions of modern development of automation of search and analysis of criminally significant information. Various types of electronic traces for different types of crimes are considered. New forms of using a number of elements of artificial intelligence and the use of mathematical apparatus within the automation of expert systems to obtain the necessary evidence in relevant criminal cases are proposed. A recommendation system is proposed, which, with the help of multifactor analysis, will allow to form a "portrait" of a person based on the data contained on the user's electronic media.
Investigation of noisy audio recordings of speech for processing in an automated system

This article discusses the possibility of processing sound from noise using a neural network that works with image recognition. To make sure of this, spectrograms of the recorded voice of the speaker with a duration of 10 seconds and spectrograms with white noise superimposed on the recorded audio track were considered. After analyzing the noisy audio track by a sub-jective method (listening to the audio track) and analyzing the spectrograms of the noisy audio track, it was revealed that the neural network will be able to recognize the differences in the images on which the noise is visible. This is necessary in order to further train the neural net-work to recognize the noise intensity of the audio track.
TTS-by-TTS 2: Data-Selective Augmentation for Neural Speech Synthesis Using Ranking Support Vector Machine with Variational Autoencoder

Recent advances in synthetic speech quality have enabled us to train text-to-speech (TTS) systems by using synthetic corpora. However, merely increasing the amount of synthetic data is not always advantageous for improving training efficiency. Our aim in this study is to selectively choose synthetic data that are beneficial to the training process. In the proposed method, we first adopt a variational autoencoder whose posterior distribution is utilized to extract latent features representing acoustic similarity between the recorded and synthetic corpora. By using those learned features, we then train a ranking support vector machine (RankSVM) that is well known for effectively ranking relative attributes among binary classes. By setting the recorded and synthetic ones as two opposite classes, RankSVM is used to determine how the synthesized speech is acoustically similar to the recorded data. Then, synthetic TTS data, whose distribution is close to the recorded data, are selected from large-scale synthetic corpora. By using these data for retraining the TTS model, the synthetic quality can be significantly improved. Objective and subjective evaluation results show the superiority of the proposed method over the conventional methods.
Deep Reinforcement Learning Policies Learn Shared Adversarial Features across MDPs

The use of deep neural networks as function approximators has led to striking progress for reinforcement learning algorithms and applications. Yet the knowledge we have on decision boundary geometry and the loss landscape of neural policies is still quite limited. In this paper, we propose a framework to investigate the decision boundary and loss landscape similarities across states and across MDPs. We conduct experiments in various games from Arcade Learning Environment, and discover that high sensitivity directions for neural policies are correlated across MDPs. We argue that these high sensitivity directions support the hypothesis that non-robust features are shared across training environments of reinforcement learning agents. We believe our results reveal fundamental properties of the environments used in deep reinforcement learning training, and represent a tangible step towards building robust and reliable deep reinforcement learning agents.
When AI Difficulty Is Easy: The Explanatory Power of Predicting IRT Difficulty

One of challenges of artificial intelligence as a whole is robustness. Many issues such as adversarial examples, out of distribution performance, Clever Hans phenomena, and the wider areas of AI evaluation and explainable AI, have to do with the following question: Did the system fail because it is a hard instance or because something else? In this paper we address this question with a generic method for estimating IRT-based instance difficulty for a wide range of AI domains covering several areas, from supervised feature-based classification to automated reasoning. We show how to estimate difficulty systematically using off-the-shelf machine learning regression models. We illustrate the usefulness of this estimation for a range of applications.
Competing Mutual Information Constraints with Stochastic Competition-Based Activations for Learning Diversified Representations

This work aims to address the long-established problem of learning diversified representations. To this end, we combine information-theoretic arguments with stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA) units. In this context, we ditch the conventional deep architectures commonly used in Representation Learning, that rely on non-linear activations; instead, we replace them with sets of locally and stochastically competing linear units. In this setting, each network layer yields sparse outputs, determined by the outcome of the competition between units that are organized into blocks of competitors. We adopt stochastic arguments for the competition mechanism, which perform posterior sampling to determine the winner of each block. We further endow the considered networks with the ability to infer the sub-part of the network that is essential for modeling the data at hand; we impose appropriate stick-breaking priors to this end. To further enrich the information of the emerging representations, we resort to information-theoretic principles, namely the Information Competing Process (ICP). Then, all the components are tied together under the stochastic Variational Bayes framework for inference. We perform a thorough experimental investigation for our approach using benchmark datasets on image classification. As we experimentally show, the resulting networks yield significant discriminative representation learning abilities. In addition, the introduced paradigm allows for a principled investigation mechanism of the emerging intermediate network representations.
Efficient Causal Structure Learning from Multiple Interventional Datasets with Unknown Targets

We consider the problem of reducing the false discovery rate in multiple high-dimensional interventional datasets under unknown targets. Traditional algorithms merged directly multiple causal graphs learned, which ignores the contradictions of different datasets, leading to lots of inconsistent directions of edges. For reducing the contradictory information, we propose a new algorithm, which first learns an interventional Markov equivalence class (I-MEC) before merging multiple graphs. It utilizes the full power of the constraints available in interventional data and combines ideas from local learning, intervention, and search-and-score techniques in a principled and effective way in different intervention experiments. Specifically, local learning on multiple datasets is used to build a causal skeleton. Perfect intervention destroys some possible triangles, leading to the identification of more possible V-structures. And then a theoretically correct I-MEC is learned. Search and scoring techniques based on the learned I-MEC further identify the remaining unoriented edges. Both theoretical analysis and experiments on benchmark Bayesian networks with the number of variables from 20 to 724 validate that the effectiveness of our algorithm in reducing the false discovery rate in high-dimensional interventional data.
Training on the Test Set: Mapping the System-Problem Space in AI

Many present and future problems associated with artificial intelligence are not due to its limitations, but to our poor assessment of its behaviour. Our evaluation procedures produce aggregated performance metrics that lack detail and quantified uncertainty about the following question: how will an AI system, with a particular profile \pi, behave for a new problem, characterised by a particular situation \mu? Instead of just aggregating test results, we can use machine learning methods to fully capitalise on this evaluation information. In this paper, we introduce the concept of an assessor model, \hat{R}(r|\pi,\mu), a conditional probability estimator trained on test data. We discuss how these assessors can be built by using information of the full system-problem space and illustrate a broad range of applications that derive from varied inferences and aggregations from \hat{R}. Building good assessor models will change the predictive and explanatory power of AI evaluation and will lead to new research directions for building and using them. We propose accompanying every deployed AI system with its own assessor.
Diagnostics of mathematical motivation as a component of the process of teaching mathematics to students – future ecologists

Aim and Objective. In the conditions of modern reality, the importance of solving environmental problems of a global and regional nature makes it relevant to search for ways to improve the process of mathematical training of future ecologists. One of such directions is the motivation to study mathematics and its applications. The purpose of the study is to identify and test the methodological conditions that ensure the strengthening of the mathematical motivation of students – future ecologists in the process of teaching mathematics at a university. Materials and methods. The Mathematics Motivation Questionnaire (MMQ) was chosen as the main diagnostic tool, and the author's questionnaire compiled on the basis of this questionnaire, taking into account the specifics of teaching mathematics to future ecologists, was chosen as an additional diagnostic tool. 205 students of environmental training directions of Vyatka State University took part in the implemented survey. Statistical analysis of the obtained results was performed using the Mann-Whitney U test. Results. For the first time, a technology for diagnosing the mathematical motivation of students – future ecologists based on modern research is presented. A number of methodological conditions have been formulated that ensure the strengthening of the motivation under study, which clarifies and expands the ideas about the system of mathematical training of future ecologists. The analysis of motivation for admission to the university revealed a number of problems, which the intervention was aimed at overcoming. Its effectiveness was confirmed by significant differences in the MMQ questionnaire (Uemp = 100 &lt; Ucr = 113; p &lt; 0,05) and according to the author's questionnaire (Uemp = 72 &lt; Ucr = 88; p &lt; 0,01). Based on the re-evaluation, the transformation of the motivational attitudes of students - future ecologists from static beliefs about the nature of mathematics to dynamic ideas was revealed. Conclusion. The practical significance of the study is determined by the possibilities of using its results to improve the mathematical training of future ecologists, as well as by optimizing the process of teaching mathematics to maintain productive motivation.
An Axiomatic Approach to Revising Preferences

We study a model of preference revision in which a prior preference over a set of alternatives is adjusted in order to accommodate input from an authoritative source, while maintaining certain structural constraints (e.g., transitivity, completeness), and without giving up more information than strictly necessary. We analyze this model under two aspects: the first allows us to capture natural distance-based operators, at the cost of a mismatch between the input and output formats of the revision operator. Requiring the input and output to be aligned yields a second type of operator, which we characterize using preferences on the comparisons in the prior preference Prefence revision is set in a logic-based framework and using the formal machinery of belief change, along the lines of the well-known AGM approach: we propose rationality postulates for each of the two versions of our model and derive representation results, thus situating preference revision within the larger family of belief change operators.
Attention Biasing and Context Augmentation for Zero-Shot Control of Encoder-Decoder Transformers for Natural Language Generation

Controlling neural network-based models for natural language generation (NLG) to realize desirable attributes in the generated outputs has broad applications in numerous areas such as machine translation, document summarization, and dialog systems. Approaches that enable such control in a zero-shot manner would be of great importance as, among other reasons, they remove the need for additional annotated data and training. In this work, we propose novel approaches for controlling encoder-decoder transformer-based NLG models in zero shot. While zero-shot control has previously been observed in massive models (e.g., GPT3), our method enables such control for smaller models. This is done by applying two control knobs, attention biasing and context augmentation, to these models directly during decoding and without additional training or auxiliary models. These knobs control the generation process by directly manipulating trained NLG models (e.g., biasing cross-attention layers). We show that not only are these NLG models robust to such manipulations but also their behavior could be controlled without an impact on their generation performance.
Knowledge Distillation via Constrained Variational Inference

Knowledge distillation has been used to capture the knowledge of a teacher model and distill it into a student model with some desirable characteristics such as being smaller, more efficient, or more generalizable. In this paper, we propose a framework for distilling the knowledge of a powerful discriminative model such as a neural network into commonly used graphical models known to be more interpretable (e.g., topic models, autoregressive Hidden Markov Models). Posterior of latent variables in these graphical models (e.g., topic proportions in topic models) is often used as feature representation for predictive tasks. However, these posterior-derived features are known to have poor predictive performance compared to the features learned via purely discriminative approaches. Our framework constrains variational inference for posterior variables in graphical models with a similarity preserving constraint. This constraint distills the knowledge of the discriminative model into the graphical model by ensuring that input pairs with (dis)similar representation in the teacher model also have (dis)similar representation in the student model. By adding this constraint to the variational inference scheme, we guide the graphical model to be a reasonable density model for the data while having predictive features which are as close as possible to those of a discriminative model. To make our framework applicable to a wide range of graphical models, we build upon the Automatic Differentiation Variational Inference (ADVI), a black-box inference framework for graphical models. We demonstrate the effectiveness of our framework on two real-world tasks of disease subtyping and disease trajectory modeling.
Unsupervised Reinforcement Learning in Multiple Environments

Several recent works have been dedicated to unsupervised reinforcement learning in a single environment, in which a policy is first pre-trained with unsupervised interactions, and then fine-tuned towards the optimal policy for several downstream supervised tasks defined over the same environment. Along this line, we address the problem of unsupervised reinforcement learning in a class of multiple environments, in which the policy is pre-trained with interactions from the whole class, and then fine-tuned for several tasks in any environment of the class. Notably, the problem is inherently multi-objective as we can trade off the pre-training objective between environments in many ways. In this work, we foster an exploration strategy that is sensitive to the most adverse cases within the class. Hence, we cast the exploration problem as the maximization of the mean of a critical percentile of the state visitation entropy induced by the exploration strategy over the class of environments. Then, we present a policy gradient algorithm, alphaMEPOL, to optimize the introduced objective through mediated interactions with the class. Finally, we empirically demonstrate the ability of the algorithm in learning to explore challenging classes of continuous environments and we show that reinforcement learning greatly benefits from the pre-trained exploration strategy w.r.t. learning from scratch.
Flex Distribution for Bounded-Suboptimal Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents that minimize the sum of path costs. EECBS is a leading two-level algorithm that solves MAPF bounded-suboptimally, that is, within some factor w of the minimum sum of path costs C*. It uses focal search to find bounded-suboptimal paths on the low level and Explicit Estimation Search (EES) to resolve collisions on the high level. EES keeps track of a lower bound LB on C* to find paths whose sum of path costs is at most w LB in order to solve MAPF bounded-suboptimally. However, the costs of many paths are often much smaller than w times their minimum path costs, meaning that the sum of path costs is much smaller than w C*. In this paper, we therefore propose Flexible EECBS (FEECBS), which uses a flex(ible) distribution of the path costs (that relaxes the requirement to find bounded-suboptimal paths on the low level) in order to reduce the number of collisions that need to be resolved on the high level while still guaranteeing to solve MAPF bounded suboptimally. We address the drawbacks of flex distribution via techniques such as restrictions on the flex distribution, restarts of the high-level search with EECBS, and low-level focal-A* search. Our empirical evaluation shows that FEECBS substantially improves the efficiency of EECBS on MAPF instances with large maps and large numbers of agents.
Predicting Physical World Destinations for Commands Given to Self-Driving Cars

In recent years, we have seen significant steps taken in the development of self-driving cars. Multiple companies are starting to roll out impressive systems that work in a variety of settings. These systems can sometimes give the impression that full self-driving is just around the corner and that we would soon build cars without even a steering wheel. The increase in the level of autonomy and control given to an AI provides an opportunity for new modes of human-vehicle interaction. However, surveys have shown that giving more control to an AI in self-driving cars is accompanied by a degree of uneasiness by passengers. In an attempt to alleviate this issue, recent works have taken a natural language-oriented approach by allowing the passenger to give commands that refer to specific objects in the visual scene. Nevertheless, this is only half the task as the car should also understand the physical destination of the command, which is what we focus on in this paper. We propose an extension in which we annotate the 3D destination that the car needs to reach after executing the given command and evaluate multiple different baselines on predicting this destination location. Additionally, we introduce a model that outperforms the prior works adapted for this particular setting.
On the Viability of Quantum Annealers to Solve Fluid Flows

This paper explores the suitability of upcoming novel computing technologies, particularly adiabatic annealing based quantum computers, to solve fluid dynamics problems that form a critical component of several science and engineering applications. For our experiments, we start with a well-studied one-dimensional simple flow problem, and provide a framework to convert such problems in continuum to a form amenable for deployment on such quantum annealers. Since the DWave annealer returns multiple states sampling the energy landscape of the problem, we explore multiple solution selection strategies to approximate the solution of the problem. We analyze the continuum solutions obtained both qualitatively and quantitatively as well as their sensitivities to the particular solution selection scheme.