Reviewerly/exHarmony huggingface dataset:
A Design of Optimized Colour Image Interpolation Algorithm Based on Edge Gradient

Most of the popular digital cameras are equipped with image sensors of Complementary Metal Oxide Semiconductor(CMOS), in which the Bayer filter is used to store the information of the image. The original image obtained from Bayer filter is then restored to colour by interpolation algorithms. However, there has been low quality with recovered image. In order to solve this problem, an optimization based on the edge gradient interpolation algorithm is proposed in this paper. Firstly, more accurate edges for interpolation are obtained by re-optimizing the calculation of the horizontal and vertical gradients. Then, the missing channels are recovered by the method of intra-neighbourhood averaging, mainly 3x3 neighbourhood. Several sets of comparison experiments based on MATLAB were conducted and the colour peak signal-to-noise ratio was introduced to evaluate the image quality. It has proved that the optimized algorithm proposed in this paper recovers colour images with higher quality compared to the traditional edge gradient interpolation algorithm
Less is More: Fairness in Wide-Area Proof-of-Work Blockchain Networks

Blockchain is rapidly emerging as an important class of network application, with a unique set of trust, security and transparency properties. In a blockchain system, participants record and update the `server-side' state of an application as blocks of a replicated, immutable ledger using a consensus protocol over the Internet. Mining blocks has become lucrative in recent years; e.g., a miner receives over USD 200,000 per mined block in Bitcoin today. A key factor affecting mining rewards, is the latency of broadcasting blocks over the network. In this paper, we consider the problem of topology design for optimizing mining rewards in a wide-area blockchain network that uses a Proof-of-Work protocol for consensus. Contrary to general wisdom that a faster network is always better for miners, we show a counter intuitive result where a slower network is actually beneficial to some miners. This is because competing miners must choose neighbors that not only decrease their own latency to others, but also ensure that the latency between other miners do not decrease because of itself. We formalize this problem, and provide both theoretical analysis and experimental results to support our claim.
Professional self-determination of students with disabilities

The article presents the results of the development and implementation of effective practices aimed at assisting professional self-determination of disabled children with and children with physical condition (PC) in the continuing education system. The effectiveness of this work in the conditions of distance learning is shown. The professional self-determination of students with PC is complicated by a number of conditions, both related to health restrictions available to the student and a limited range of possible professions. Making a choice that meets the desires and opportunities of such a student is very important, since this ensures his independence and full life in the future. The article analyses the conditions of professional self-determination of children who are unable to attend educational institutions systematically and stay there or at home; the need and possibility of providing them with assistance in a distance school are justified. The results of diagnosis of parental position on vocational guidance of a disabled child and results of questionnaire of students are presented. An additional general-education program developed for children with disabilities and children with physical condition of 8 9 classes is described in order to update the process of professional self-determination of children with disabilities and children with PC, prepare them for making informed professional choices taking into account the individual characteristics and capabilities of students, implemented as part of distance learning in the context of continuing education.
Using Shor's algorithm on near term Quantum computers: a reduced version

Considering its relevance in the field of cryptography, integer factorization is a prominent application where Quantum computers are expected to have a substantial impact. Thanks to Shor's algorithm this peculiar problem can be solved in polynomial time. However, both the number of qubits and applied gates detrimentally affect the ability to run a particular quantum circuit on the near term Quantum hardware. In this work, we help addressing both these problems by introducing a reduced version of Shor's algorithm that proposes a step forward in increasing the range of numbers that can be factorized on noisy Quantum devices. The implementation presented in this work is general and does not use any assumptions on the number to factor. In particular, we have found noteworthy results in most cases, often being able to factor the given number with only one iteration of the proposed algorithm. Finally, comparing the original quantum algorithm with our version on simulator, the outcomes are identical for some of the numbers considered.
Multifractal Spin-Glass Chaos Projection and Interrelation of Multicultural Music and Brain Signals

A complexity classification scheme is developed from the fractal spectra of spin-glass chaos and demonstrated with multigeographic multicultural music and brain electroencephalogram signals. Systematic patterns are found to emerge. Chaos under scale change is the essence of spin-glass ordering and can be obtained, continuously tailor-made, from the exact renormalization-group solution of Ising models on frustrated hierarchical lattices. The music pieces are from Turkish music, namely Arabesque, Rap, Pop, Classical, and Western music, namely Blues, Jazz, Pop, Classical. A surprising group defection occurs.
AugESC: Dialogue Augmentation with Large Language Models for Emotional Support Conversation

Crowdsourced dialogue corpora are usually limited in scale and topic coverage due to the expensive cost of data curation. This would hinder the generalization of downstream dialogue models to open-domain topics. In this work, we leverage large language models for dialogue augmentation in the task of emotional support conversation (ESC). By treating dialogue augmentation as a dialogue completion task, we prompt a fine-tuned language model to complete full dialogues from available dialogue posts of various topics, which are then postprocessed based on heuristics. Applying this approach, we construct AugESC, an augmented dataset for the ESC task, which largely extends the scale and topic coverage of the crowdsourced ESConv corpus. Through comprehensive human evaluation, we demonstrate that our approach is superior to strong baselines of dialogue augmentation and that AugESC has comparable dialogue quality to the crowdsourced corpus. We also conduct human interactive evaluation and prove that post-training on AugESC improves downstream dialogue models' generalization ability to open-domain topics. These results suggest the utility of AugESC and highlight the potential of large language models in improving data-scarce dialogue generation tasks.
AnswerSumm: A Manually-Curated Dataset and Pipeline for Answer Summarization

Community Question Answering (CQA) fora such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of community-based questions. Each question thread can receive a large number of answers with different perspectives. One goal of answer summarization is to produce a summary that reflects the range of answer perspectives. A major obstacle for this task is the absence of a dataset to provide supervision for producing such summaries. Recent works propose heuristics to create such data, but these are often noisy and do not cover all answer perspectives present. This work introduces a novel dataset of 4,631 CQA threads for answer summarization curated by professional linguists. Our pipeline gathers annotations for all subtasks of answer summarization, including relevant answer sentence selection, grouping these sentences based on perspectives, summarizing each perspective, and producing an overall summary. We analyze and benchmark state-of-the-art models on these subtasks and introduce a novel unsupervised approach for multi-perspective data augmentation that boosts summarization performance according to automatic evaluation. Finally, we propose reinforcement learning rewards to improve factual consistency and answer coverage and analyze areas for improvement.
Multi-Granularity Alignment Domain Adaptation for Object Detection

Domain adaptive object detection is challenging due to distinctive data distribution between source domain and target domain. In this paper, we propose a unified multi-granularity alignment based object detection framework towards domain-invariant feature learning. To this end, we encode the dependencies across different granularity perspectives including pixel-, instance-, and category-levels simultaneously to align two domains. Based on pixel-level feature maps from the backbone network, we first develop the omni-scale gated fusion module to aggregate discriminative representations of instances by scale-aware convolutions, leading to robust multi-scale object detection. Meanwhile, the multi-granularity discriminators are proposed to identify which domain different granularities of samples(i.e., pixels, instances, and categories) come from. Notably, we leverage not only the instance discriminability in different categories but also the category consistency between two domains. Extensive experiments are carried out on multiple domain adaptation scenarios, demonstrating the effectiveness of our framework over state-of-the-art algorithms on top of anchor-free FCOS and anchor-based Faster RCNN detectors with different backbones.
DIGITAL TECHNOLOGIES AS A MEANS OF ADAPTIVE LEARNING FOR HIGHER EDUCATION INFORMATICS AND MATHEMATICS

Formulation of the problem. In the context of online / blended learning, the relevance of research on the use of digital technologies as a means of adaptive learning for higher education in computer science and mathematics is undeniable. Implementation of the principles of adaptive learning with the help of digital technologies provides significant opportunities for individualization and differentiation of the educational process by the cognitive characteristics and educational needs of each graduate. Materials and Methods. The study is based on the work of domestic and foreign scientists. Theoretical and practical methods are used. Using the methods of system analysis and theoretical generalization a study of the implementation of adaptive learning in distance learning at Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University. Results. It is substantiated that with the development of e-learning it became possible to effectively ensure the adaptability of the learning process of higher education in computer science and mathematics. Adaptation involves the analysis of individual characteristics and the initial level of training of students, the implementation of the process of interactive learning, evaluation of success criteria for the transition to the next level of education, and more. Methodical aspects of adaptive learning in the information educational system of a higher education institution are considered. Conclusions. The system of adaptive teaching of mathematics and computer science provides the optimal adaptation of the educational process to individual characteristics and personal preferences of students, promotes their cognitive activity, increases motivation to learn, provides monitoring of learning, and adjusts it to achieve planned individual results. The adaptive learning system has the potential to ensure the full involvement of students in the process of building their individual educational trajectory, developing their activities, improving the individualization of the educational process, and more.
Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty

We develop a non-parametric multivariate time series model that remains agnostic on the precise relationship between a (possibly) large set of macroeconomic time series and their lagged values. The main building block of our model is a Gaussian process prior on the functional relationship that determines the conditional mean of the model, hence the name of Gaussian process vector autoregression (GP-VAR). A flexible stochastic volatility specification is used to provide additional flexibility and control for heteroskedasticity. Markov chain Monte Carlo (MCMC) estimation is carried out through an efficient and scalable algorithm which can handle large models. The GP-VAR is illustrated by means of simulated data and in a forecasting exercise with US data. Moreover, we use the GP-VAR to analyze the effects of macroeconomic uncertainty, with a particular emphasis on time variation and asymmetries in the transmission mechanisms.
Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning

The advent of large pre-trained generative language models has provided a common framework for AI story generation via sampling the model to create sequences that continue the story. However, sampling alone is insufficient for story generation. In particular, it is hard to direct a language model to create stories to reach a specific goal event. We present two automated techniques grounded in deep reinforcement learning and reward shaping to control the plot of computer-generated stories. The first utilizes proximal policy optimization to fine-tune an existing transformer-based language model to generate text continuations but also be goal-seeking. The second extracts a knowledge graph from the unfolding story, which is used by a policy network with graph attention to select a candidate continuation generated by a language model. We report on automated metrics pertaining to how often stories achieve a given goal event as well as human participant rankings of coherence and overall story quality compared to baselines and ablations.
Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators

Heavily pre-trained transformers for language modelling, such as BERT, have shown to be remarkably effective for Information Retrieval (IR) tasks, typically applied to re-rank the results of a first-stage retrieval model. IR benchmarks evaluate the effectiveness of retrieval pipelines based on the premise that a single query is used to instantiate the underlying information need. However, previous research has shown that (I) queries generated by users for a fixed information need are extremely variable and, in particular, (II) neural models are brittle and often make mistakes when tested with modified inputs. Motivated by those observations we aim to answer the following question: how robust are retrieval pipelines with respect to different variations in queries that do not change the queries' semantics? In order to obtain queries that are representative of users' querying variability, we first created a taxonomy based on the manual annotation of transformations occurring in a dataset (UQV100) of user-created query variations. For each syntax-changing category of our taxonomy, we employed different automatic methods that when applied to a query generate a query variation. Our experimental results across two datasets for two IR tasks reveal that retrieval pipelines are not robust to these query variations, with effectiveness drops of $\approx20\%$ on average. The code and datasets are available at https://github.com/Guzpenha/query_variation_generators.
Scalable randomized benchmarking of quantum computers using mirror circuits

The performance of quantum gates is often assessed using some form of randomized benchmarking. However, the existing methods become infeasible for more than approximately five qubits. Here we show how to use a simple and customizable class of circuits -- randomized mirror circuits -- to perform scalable, robust, and flexible randomized benchmarking of Clifford gates. We show that this technique approximately estimates the infidelity of an average many-qubit logic layer, and we use simulations of up to 225 qubits with physically realistic error rates in the range 0.1-1% to demonstrate its scalability. We then use up to 16 physical qubits of a cloud quantum computing platform to demonstrate that our technique can reveal and quantify crosstalk errors in many-qubit circuits.
Downloading R and RStudio and Setting Up a File System

R is an open source statistical programming language based on the commercial programming language S. The S language was developed at Bell Laboratories in the 1970s and 1980s, mainly by John Chambers. The last version of S, called S4, came out in 1998. The R language was initially developed by Ross Ihaka and Robert Gentleman at the University of Auckland (in New Zealand) through “clean-room” methods. R was written to mimic S, but with its own code. The first official version of the R language was released in 1995 and version v1.0 in 2000. The current version of R is v4.1.1 (as of September 2021). R is a GNU package and is released under the GNU General Public License. The Comprehensive R Archive Network (CRAN) makes the language and packages available and is a network of mirrors at institutions. CRAN was first deployed in 1997. (The preceding information is from the Wikipedia pages S (programming language) and R (programming language).) The citation for R is
LEVERAGING LOCAL VARIATION IN DATA: SAMPLING AND WEIGHTING SCHEMES FOR SUPERVISED DEEP LEARNING

In the context of supervised learning of a function by a neural network, we claim and empirically verify that the neural network yields better results when the distribution of the data set focuses on regions where the function to learn is steep. We first traduce this assumption in a mathematically workable way using Taylor expansion and emphasize a new training distribution based on the derivatives of the function to learn. Then, theoretical derivations allow constructing a methodology that we call Variance Based Samples Weighting (VBSW). VBSW uses labels local variance to weight the training points. This methodology is general, scalable, cost-effective, and significantly increases the performances of a large class of neural networks for various classification and regression tasks on image, text, and multivariate data. We highlight its benefits with experiments involving neural networks from linear models to ResNet and Bert.
Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios

Overlapping speech diarization has been traditionally treated as a multi-label classification problem. In this paper, we reformulate this task as a single-label prediction problem by encoding multiple binary labels into a single label with the power set, which represents the possible combinations of target speakers. This formulation has two benefits. First, the overlaps of target speakers are explicitly modeled. Second, threshold selection is no longer needed. Through this formulation, we propose the speaker embedding-aware neural diarization (SEND) framework, where a speech encoder, a speaker encoder, two similarity scorers, and a post-processing network are jointly optimized to predict the encoded labels according to the similarities between speech features and speaker embeddings. Experimental results show that SEND has a stable learning process and can be trained on highly overlapped data without extra initialization. More importantly, our method achieves the state-of-the-art performance in real meeting scenarios with fewer model parameters and lower computational complexity.
Sense Embeddings are also Biased--Evaluating Social Biases in Static and Contextualised Sense Embeddings

Sense embedding learning methods learn different embeddings for the different senses of an ambiguous word. One sense of an ambiguous word might be socially biased while its other senses remain unbiased. In comparison to the numerous prior work evaluating the social biases in pretrained word embeddings, the biases in sense embeddings have been relatively understudied. We create a benchmark dataset for evaluating the social biases in sense embeddings and propose novel sense-specific bias evaluation measures. We conduct an extensive evaluation of multiple static and contextualised sense embeddings for various types of social biases using the proposed measures. Our experimental results show that even in cases where no biases are found at word-level, there still exist worrying levels of social biases at sense-level, which are often ignored by the word-level bias evaluation measures.
Hybrid Privacy Protection of IoT Using Reinforcement Learning

The smart mobile device, as an indispensable component of IoT, has been growing in volume and diversity in recent years. Along with this, cyber-physical social network (CPSN) experiences fast booming, in which users publish their posts or data for sharing. However, since the published data is usually public to all, adversaries can crawl data or launch attacks without much efforts. Existing research usually considers a static adversary where the attack is launched once to steal a type of sensitive information like identity or location. This is not practical in real-world scenarios. To release this assumption, we develop a hybrid privacy-preserving model that protects identity and location privacy at the same time against a dynamic adversary who actively launches attacks. In the proposed model, the privacy protection problem is also considered as a trade-off optimization model that users target on maximizing data utility with high-level privacy protection but adversaries try to achieve a opposite target. To make this happen, we model a multi-stage game built upon Markov Decision Process (GMDP). The user and the adversaries are regarded as two players (also known as parties) in this dynamic zero-sum game. The output of this game should be the optimal actions of users that an adversary cannot breach more privacy no matter how the actions changed. An improved reinforcement learning algorithm based on state-action-reward-state-action (SARSA) is developed, which reduces the cardinality from n to 2. This can significantly improve the convergence efficiency. At last, we conduct experimental simulations on real-world datasets to testify the superior performance on efficiency and feasibility over existing research. This paper is mainly based on our research on privacy protection using reinforcement learning [1–5].