Reviewerly/exHarmony huggingface dataset:
Self-testing Quantum States via Non-maximal Bell Violation

Self-testing protocols enable certification of quantum devices without demanding full knowledge about their inner workings. A typical approach in designing such protocols is based on observing nonlocal correlations which exhibit maximum violation in a Bell test. We show that in Bell experiment known as Hardy's test of nonlocality not only the maximally nonlocal correlation self-tests a quantum state, rather a non-maximal nonlocal behavior can serve the same purpose. We, in fact, completely characterize all such behaviors leading to self-test of every pure two qubit entangled state except the maximally entangled ones. Apart from originating a novel self-testing protocol, our method provides a powerful tool towards characterizing the complex boundary of the set of quantum correlations.
Comment on "Multi-output quantum teleportation of different quantum  information with an IBM quantum experience"

Recently, Yu et al., (Commun. Theor. Phys. 73 (2021) 085103) has proposed a scheme for "multi-output quantum teleportation" and has implemented the scheme using an IBM quantum computer. In their so called multicast-based quantum teleportation scheme, a sender (Alice) teleported two different quantum states (one of which is a m-qubit GHZ class state and the other is a (m+1)-qubit GHZ class state) to the two receivers. To perform the task, a five-qubit cluster state was used as a quantum channel, and the scheme was realized using IBM quantum computer for m = 1. In this comment, it is shown that the quantum resources used by Yu et al., was extensively high. One can perform the same task of two-party quantum teleportation using two Bell states only. The modified scheme for multi-output teleportation using optimal resources has also been implemented using IBM quantum computer for m = 1 and the obtained result is compared with the result of Yu et al.
Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS)

Repeated Cross-Sectional (RCS) data measures a phenomenon by repeatedly sampling new cases from a population at successive measurement moments. It allows for analyzing societal trends without the need to follow individuals. To gain a deeper understanding of these trends, we propose EMM-RCS, an Exceptional Model Mining instance designed to find subgroups displaying exceptional trend behavior in RCS data. We build quality measures on the standard error, finding various types of exceptionalities within trends (exceptional flattening, slope, deviation from the norm). Additionally, EMM-RCS can handle practical RCS data problems, including uneven spacing of measurements over time, fluctuating sample sizes, and missing data.
This is not the padding you are looking for! On the ineffectiveness of  QUIC PADDING against website fingerprinting

Website fingerprinting (WF) is a well-know threat to users' web privacy. New internet standards, such as QUIC, include padding to support defenses against WF. We study whether network-layer padding can indeed be used to construct effective WF defenses. We confirm previous claims that network-layer padding cannot provide good protection against powerful adversaries capable of observing all traffic traces. In contrast to prior work, we also demonstrate that such padding is ineffective even against adversaries with partial view of the traffic. Network-layer padding without application input is ineffective because it fails to hide information unique across different applications. We show that application-layer padding solutions need to be deployed by both first and third parties, and that they can only thwart traffic analysis in limited situations. We identify challenges to deploy effective WF defenses and provide recommendations to reduce these hurdles.
Forward Operator Estimation in Generative Models with Kernel Transfer  Operators

Generative models which use explicit density modeling (e.g., variational autoencoders, flow-based generative models) involve finding a mapping from a known distribution, e.g. Gaussian, to the unknown input distribution. This often requires searching over a class of non-linear functions (e.g., representable by a deep neural network). While effective in practice, the associated runtime/memory costs can increase rapidly, usually as a function of the performance desired in an application. We propose a much cheaper (and simpler) strategy to estimate this mapping based on adapting known results in kernel transfer operators. We show that our formulation enables highly efficient distribution approximation and sampling, and offers surprisingly good empirical performance that compares favorably with powerful baselines, but with significant runtime savings. We show that the algorithm also performs well in small sample size settings (in brain imaging).
Inference for ROC Curves Based on Estimated Predictive Indices

We provide a comprehensive theory of conducting in-sample statistical inference about receiver operating characteristic (ROC) curves that are based on predicted values from a first stage model with estimated parameters (such as a logit regression). The term "in-sample" refers to the practice of using the same data for model estimation (training) and subsequent evaluation, i.e., the construction of the ROC curve. We show that in this case the first stage estimation error has a generally non-negligible impact on the asymptotic distribution of the ROC curve and develop the appropriate pointwise and functional limit theory. We propose methods for simulating the distribution of the limit process and show how to use the results in practice in comparing ROC curves.
XAI and Strategy Extraction via Reward Redistribution

Abstract In reinforcement learning, an agent interacts with an environment from which it receives rewards, that are then used to learn a task. However, it is often unclear what strategies or concepts the agent has learned to solve the task. Thus, interpretability of the agent’s behavior is an important aspect in practical applications, next to the agent’s performance at the task itself. However, with the increasing complexity of both tasks and agents, interpreting the agent’s behavior becomes much more difficult. Therefore, developing new interpretable RL agents is of high importance. To this end, we propose to use Align-RUDDER as an interpretability method for reinforcement learning. Align-RUDDER is a method based on the recently introduced RUDDER framework, which relies on contribution analysis of an LSTM model, to redistribute rewards to key events. From these key events a strategy can be derived, guiding the agent’s decisions in order to solve a certain task. More importantly, the key events are in general interpretable by humans, and are often sub-tasks; where solving these sub-tasks is crucial for solving the main task. Align-RUDDER enhances the RUDDER framework with methods from multiple sequence alignment (MSA) to identify key events from demonstration trajectories. MSA needs only a few trajectories in order to perform well, and is much better understood than deep learning models such as LSTMs. Consequently, strategies and concepts can be learned from a few expert demonstrations, where the expert can be a human or an agent trained by reinforcement learning. By substituting RUDDER’s LSTM with a profile model that is obtained from MSA of demonstration trajectories, we are able to interpret an agent at three stages: First, by extracting common strategies from demonstration trajectories with MSA. Second, by encoding the most prevalent strategy via the MSA profile model and therefore explaining the expert’s behavior. And third, by allowing the interpretation of an arbitrary agent’s behavior based on its demonstration trajectories.
Multi-Attention Multiple Instance Learning

A new multi-attention based method for solving the MIL problem (MAMIL), which takes into account the neighboring patches or instances of each analyzed patch in a bag, is proposed. In the method, one of the attention modules takes into account adjacent patches or instances, several attention modules are used to get a diverse feature representation of patches, and one attention module is used to unite different feature representations to provide an accurate classification of each patch (instance) and the whole bag. Due to MAMIL, a combined representation of patches and their neighbors in the form of embeddings of a small dimensionality for simple classification is realized. Moreover, different types of patches are efficiently processed, and a diverse feature representation of patches in a bag by using several attention modules is implemented. A simple approach for explaining the classification predictions of patches is proposed. Numerical experiments with various datasets illustrate the proposed method.
Georacle: Enabling Geospatially Aware Smart Contracts

Smart contracts have enabled a paradigm shift in computing by leveraging decentralized networks of trust to achieve consensus at scale. Oracle networks further extend the power of smart contracts by solving the so-called "oracle problem". Such networks enable smart contracts to make use of the vast amount pre-existing data available on the web today without jeopardizing the integrity of the underlying network of trust. By leveraging oracle networks, smart contracts can make decisions based on data corresponding to the physical world. To this end, we introduce Georacle - an oracle service that enables geospatially aware smart contracts in a way that respects the space constrained nature of blockchain environments. Contracts can query the location of objects in a given area, map between street addresses and coordinates, and retrieve the geometry of a desired region of space while conserving gas consumption and avoiding unnecessary data processing.
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from Demonstrations, Offline RL or Imitation Learning. RLDS enables not only reproducibility of existing research and easy generation of new datasets, but also accelerates novel research. By providing a standard and lossless format of datasets it enables to quickly test new algorithms on a wider range of tasks. The RLDS ecosystem makes it easy to share datasets without any loss of information and to be agnostic to the underlying original format when applying various data processing pipelines to large collections of datasets. Besides, RLDS provides tools for collecting data generated by either synthetic agents or humans, as well as for inspecting and manipulating the collected data. Ultimately, integration with TFDS facilitates the sharing of RL datasets with the research community.
PAN-DOMAIN: Privacy-preserving Sharing and Auditing of Infection  Identifier Matching

The spread of COVID-19 has highlighted the need for a robust contact tracing infrastructure that enables infected individuals to have their contacts traced, and followed up with a test. The key entities involved within a contact tracing infrastructure may include the Citizen, a Testing Centre (TC), a Health Authority (HA), and a Government Authority (GA). Typically, these different domains need to communicate with each other about an individual. A common approach is when a citizen discloses his personally identifiable information to both the HA a TC, if the test result comes positive, the information is used by the TC to alert the HA. Along with this, there can be other trusted entities that have other key elements of data related to the citizen. However, the existing approaches comprise severe flaws in terms of privacy and security. Additionally, the aforementioned approaches are not transparent and often being questioned for the efficacy of the implementations. In order to overcome the challenges, this paper outlines the PAN-DOMAIN infrastructure that allows for citizen identifiers to be matched amongst the TA, the HA and the GA. PAN-DOMAIN ensures that the citizen can keep control of the mapping between the trusted entities using a trusted converter, and has access to an audit log.
A Research on Handwritten Text Recognition (HTR) Strategies for English Words

These days Hand composed Text Recognition (HTR) is major surprising & troublesome examination space for space of Photo handling. By and by different acknowledgment procedures are in notable used for acknowledgment of manually written English letters in order (character). Application area of HCR is computerized record preparing like mining data from information passage, check, applications for advances, Visas, charge, medical coverage structures and so forth During this overview we present a layout of ebb and flow research work led for acknowledgment of manually written English letter sets. In Handwritten composition there is no limitation on the composing procedure. Manually written letters in order are muddled to perceive due to different human penmanship procedure, distinction fit as a fiddle of letters, point. An assortment of acknowledgment techniques for transcribed English letters in order are consulted here close by its exhibition.
Deep Translation Prior: Test-time Training for Photorealistic Style  Transfer

Recent techniques to solve photorealistic style transfer within deep convolutional neural networks (CNNs) generally require intensive training from large-scale datasets, thus having limited applicability and poor generalization ability to unseen images or styles. To overcome this, we propose a novel framework, dubbed Deep Translation Prior (DTP), to accomplish photorealistic style transfer through test-time training on given input image pair with untrained networks, which learns an image pair-specific translation prior and thus yields better performance and generalization. Tailored for such test-time training for style transfer, we present novel network architectures, with two sub-modules of correspondence and generation modules, and loss functions consisting of contrastive content, style, and cycle consistency losses. Our framework does not require offline training phase for style transfer, which has been one of the main challenges in existing methods, but the networks are to be solely learned during test-time. Experimental results prove that our framework has a better generalization ability to unseen image pairs and even outperforms the state-of-the-art methods.
Fast Yet Effective Machine Unlearning

Unlearning the data observed during the training of a machine learning (ML) model is an important task that can play a pivotal role in fortifying the privacy and security of ML-based applications. This paper raises the following questions: (i) can we unlearn a class/classes of data from a ML model without looking at the full training data even once? (ii) can we make the process of unlearning fast and scalable to large datasets, and generalize it to different deep networks? We introduce a novel machine unlearning framework with error-maximizing noise generation and impair-repair based weight manipulation that offers an efficient solution to the above questions. An error-maximizing noise matrix is learned for the class to be unlearned using the original model. The noise matrix is used to manipulate the model weights to unlearn the targeted class of data. We introduce impair and repair steps for a controlled manipulation of the network weights. In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model. Thereafter, the repair step is used to regain the overall performance. With very few update steps, we show excellent unlearning while substantially retaining the overall model accuracy. Unlearning multiple classes requires a similar number of update steps as for the single class, making our approach scalable to large problems. Our method is quite efficient in comparison to the existing methods, works for multi-class unlearning, doesn't put any constraints on the original optimization mechanism or network design, and works well in both small and large-scale vision tasks. This work is an important step towards fast and easy implementation of unlearning in deep networks. We will make the source code publicly available.
Causal Regularization Using Domain Priors

Neural networks leverage both causal and correlation-based relationships in data to learn models that optimize a given performance criterion, such as classification accuracy. This results in learned models that may not necessarily reflect the true causal relationships between input and output. When domain priors of causal relationships are available at the time of training, it is essential that a neural network model maintains these relationships as causal, even as it learns to optimize the performance criterion. We propose a causal regularization method that can incorporate such causal domain priors into the network and which supports both direct and total causal effects. We show that this approach can generalize to various kinds of specifications of causal priors, including monotonicity of causal effect of a given input feature or removing a certain influence for purposes of fairness. Our experiments on eleven benchmark datasets show the usefulness of this approach in regularizing a learned neural network model to maintain desired causal effects. On most datasets, domain-prior consistent models can be obtained without compromising on accuracy.
EXPERIMENTAL RESEARCH OF THE EFFECTIVENESS OF PROJECT- RESEARCH TRAINING IN COMPUTATIONAL INFORMATICS FOR FUTURE COMPUTER SCIENCE TEACHERS

The article is the result of an experimental study on teaching computational informatics to future teachers of informatics in a pedagogical university. The experiment was carried out on the basis of the Institute of Mathematics, Physics and Informatics of the Kazakh National Pedagogical University named after Abai. The methodology and methods for organizing and conducting experimental work in the field of computational informatics are described. The analysis of the results of the experimental work is presented graphically in the form of tables and diagrams. The reliability of the obtained results was checked in MS Excel. The difference between the results of the experimental group and the results obtained in the control group is significant and statistically significant. Pearson's χ2 was used to assess the significance of differences. As a result of the experimental work, the effectiveness of the developed methodology for teaching computational informatics to future teachers of informatics is tested in the context of a competency-based approach.
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme  Learning Machine with a New Weighting Scheme and Spectro-Temporal Features  Along with Classical Feature Selection and A New Quantum-Inspired Dimension  Reduction Method

Affective computing is very important in the relationship between man and machine. In this paper, a system for speech emotion recognition (SER) based on speech signal is proposed, which uses new techniques in different stages of processing. The system consists of three stages: feature extraction, feature selection, and finally feature classification. In the first stage, a complex set of long-term statistics features is extracted from both the speech signal and the glottal-waveform signal using a combination of new and diverse features such as prosodic, spectral, and spectro-temporal features. One of the challenges of the SER systems is to distinguish correlated emotions. These features are good discriminators for speech emotions and increase the SER's ability to recognize similar and different emotions. This feature vector with a large number of dimensions naturally has redundancy. In the second stage, using classical feature selection techniques as well as a new quantum-inspired technique to reduce the feature vector dimensionality, the number of feature vector dimensions is reduced. In the third stage, the optimized feature vector is classified by a weighted deep sparse extreme learning machine (ELM) classifier. The classifier performs classification in three steps: sparse random feature learning, orthogonal random projection using the singular value decomposition (SVD) technique, and discriminative classification in the last step using the generalized Tikhonov regularization technique. Also, many existing emotional datasets suffer from the problem of data imbalanced distribution, which in turn increases the classification error and decreases system performance. In this paper, a new weighting method has also been proposed to deal with class imbalance, which is more efficient than existing weighting methods. The proposed method is evaluated on three standard emotional databases.
Tree-Based Dynamic Classifier Chains

Classifier chains are an effective technique for modeling label dependencies in multi-label classification. However, the method requires a fixed, static order of the labels. While in theory, any order is sufficient, in practice, this order has a substantial impact on the quality of the final prediction. Dynamic classifier chains denote the idea that for each instance to classify, the order in which the labels are predicted is dynamically chosen. The complexity of a naive implementation of such an approach is prohibitive, because it would require to train a sequence of classifiers for every possible permutation of the labels. To tackle this problem efficiently, we propose a new approach based on random decision trees which can dynamically select the label ordering for each prediction. We show empirically that a dynamic selection of the next label improves over the use of a static ordering under an otherwise unchanged random decision tree model. % and experimental environment. In addition, we also demonstrate an alternative approach based on extreme gradient boosted trees, which allows for a more target-oriented training of dynamic classifier chains. Our results show that this variant outperforms random decision trees and other tree-based multi-label classification methods. More importantly, the dynamic selection strategy allows to considerably speed up training and prediction.
Modifiers: Back to Basics

Modifiers provide a means of conveying additional information on a claim in lieu of or in conjunction with a narrative statement addressing special circumstances that may affect coverage and payment.
Segmentation and Sentiment Word Categorization Using Feature Extraction—A Novel ASFW Framework

Sentiment analysis (SA) is essential for classifying people’s thoughts about whatever they submit as reviews online. Because the content on these media is unstructured, the segmentation of the sentiment word, which is critical for detecting attitudes, must be done properly to overcome the problem of missing data, which can lead to erroneous criticism classifications and render the SA approach useless. This study provides a novel framework called ASFW for automatically segmenting the sentiment word in order to categorise the sentiment of “reviews”. This framework contains a pre-processing technique, feature extraction with characteristics such as terms of presence and frequency (TPF), parts of speech (POS), opinion words and phrases (OWP), and negations, and word segmentation using the RBDT algorithm. Experiments show that our proposed framework ASFW is successful and efficient in segmenting the words necessary for sentiment classification without incurring data loss, with 92% accuracy and a time complexity of 0.0008 ms. Furthermore, with a time complexity of only 0.0006 ms, the classification of sentiment words according to the category has obtained excellent accuracy of 94%.