Reviewerly/exHarmony huggingface dataset:
Stratified Learning: A General-Purpose Statistical Method for Improved Learning under Covariate Shift

We propose a simple, statistically principled, and theoretically justified method to improve supervised learning when the training set is not representative, a situation known as covariate shift. We build upon a well-established methodology in causal inference, and show that the effects of covariate shift can be reduced or eliminated by conditioning on propensity scores. In practice, this is achieved by fitting learners within strata constructed by partitioning the data based on the estimated propensity scores, leading to approximately balanced covariates and much-improved target prediction. We demonstrate the effectiveness of our general-purpose method on two contemporary research questions in cosmology, outperforming state-of-the-art importance weighting methods. We obtain the best reported AUC (0.958) on the updated "Supernovae photometric classification challenge", and we improve upon existing conditional density estimation of galaxy redshift from Sloan Data Sky Survey (SDSS) data.
Noncryogenic Quantum Repeaters with hot Hybrid Alkali-Noble Gases

We propose a quantum repeater architecture that can operate without cryogenics. Each node in our architecture builds on a cell of hot alkali atoms and noble-gas spins that offer an hours-long storage time. Such a cell of hybrid gases is placed in a ring cavity, which allows us to suppress the detrimental four-wave-mixing noise in the system. We investigate the protocol based on a single-photon source made of an ensemble of the same hot alkali atoms. A single photon emitted from the source is either stored in the memory or transmitted to the central station to be detected. We quantify the fidelity and success probability of generating entanglement between two remote ensembles of noble-gas spins by taking into account finite memory efficiency, channel loss, and dark counts in detectors. We describe how the entanglement can be extended to long distances via entanglement swapping operations by retrieving the stored signal. Moreover, we quantify the performance of this proposed repeater architecture in terms of repeater rates and overall entanglement fidelities and compare it to another recently proposed noncryogenic quantum repeater architecture based on nitrogen-vacancy (N-V) centers and optomechanical spin-photon interfaces. As the system requires a relatively simple setup, it is easier to perform multiplexing, which enables achieving rates comparable to the rates of repeaters with N-V centers and optomechanics, while the overall entanglement fidelities of the present scheme are higher than the fidelities of the previous scheme. Our work shows that a scalable long-distance quantum network made of hot hybrid atomic gases is within reach of current technological capabilities.
A Q-learning approach to generating behavior of emotional persuasion with adaptive time belief in decision-making of agent-based negotiation

There is an increasing research interest in agent-based automated negotiation. However, how to design an agent that can adaptively compete with its human counterpart is still in its early stage. This research makes efforts toward this end by modeling two essential elements of human-led negotiation: emotion and timing, to adapt to negotiation dynamics. In multi-issue-multi-round bilateral negotiation, the Web-Fechner law is applied to model an agent’s emotion generation and the emotion decay law is used to realize a decaying property of emotion. Meanwhile, rather than a fixed time belief, agents are endowed with time beliefs that can vary with selected negotiation tactics and be adjusted by agents’ emotions. A proposal updating algorithm integrating those two elements is consequently proposed based on Q-learning. Then, 135 numerical experiments were conducted with comparative analyses and statistical tests. The results show that the proposed model can significantly increase an individual agent’s utility, the joint utility, and the probability of successful negotiations by an average of 76.14%, 172.22%, and 66.10%, respectively, whereas reducing the utility difference and negotiation rounds by an average of 97.39% and 29.57%, respectively. These results imply that the proposed negotiation model has advantages in improving negotiation utility, efficiency, and outcome fairness.
Role of Education and Financial Development in Economic Growth of Selected Asian Countries

Education and financial development are key determinants of economic growth. This research makes an effort to highlight the role of education and financial development with urbanization in the economic growth of selected Asian countries. Economic growth is used as a dependent variable, and secondary school enrolment, financial development, life expectancy, and urban population are used as independent variables. Panel data set for the time 2002 to 2019 is used for the analysis. The random effect technique is used. The study results demonstrate that secondary school enrolment, financial development, life expectancy, and urban population increase economic growth in selected Asian countries. It is recommended that government should provide free of cost education facilities in selected economies. Moreover, the public should be provided with more financial debt facilities to increase per capita income, high living standards, and economic growth in these selected economies. The plan must be placed on education and health high on the agenda in these economies.
Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling

We introduce Reprompting, an iterative sampling algorithm that searches for the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, we infer CoT recipes that work consistently well for a set of training samples. Our method iteratively samples new recipes using previously sampled solutions as parent prompts to solve other training problems. On five Big-Bench Hard tasks that require multi-step reasoning, Reprompting achieves consistently better performance than the zero-shot, few-shot, and human-written CoT baselines. Reprompting can also facilitate transfer of knowledge from a stronger model to a weaker model leading to substantially improved performance of the weaker model. Overall, Reprompting brings up to +17 point improvements over the previous state-of-the-art method that uses human-written CoT prompts.
Learning Likelihood Ratios with Neural Network Classifiers

The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions, and more. Many modern scientific applications, however, make use of data- or simulation-driven models for which computing the likelihood ratio can be very difficult or even impossible. By applying the so-called ``likelihood ratio trick,'' approximations of the likelihood ratio may be computed using clever parametrizations of neural network-based classifiers. A number of different neural network setups can be defined to satisfy this procedure, each with varying performance in approximating the likelihood ratio when using finite training data. We present a series of empirical studies detailing the performance of several common loss functionals and parametrizations of the classifier output in approximating the likelihood ratio of two univariate and multivariate Gaussian distributions as well as simulated high-energy particle physics datasets.
Language Models Meet World Models: Embodied Experiences Enhance Language Models

While large language models (LMs) have shown remarkable capabilities across numerous tasks, they often struggle with simple reasoning and planning in physical environments, such as understanding object permanence or planning household activities. The limitation arises from the fact that LMs are trained only on written text and miss essential embodied knowledge and skills. In this paper, we propose a new paradigm of enhancing LMs by finetuning them with world models, to gain diverse embodied knowledge while retaining their general language capabilities. Our approach deploys an embodied agent in a world model, particularly a simulator of the physical world (VirtualHome), and acquires a diverse set of embodied experiences through both goal-oriented planning and random exploration. These experiences are then used to finetune LMs to teach diverse abilities of reasoning and acting in the physical world, e.g., planning and completing goals, object permanence and tracking, etc. Moreover, it is desirable to preserve the generality of LMs during finetuning, which facilitates generalizing the embodied knowledge across tasks rather than being tied to specific simulations. We thus further introduce the classical (EWC) for selective weight updates, combined with low-rank adapters (LoRA) for training efficiency. Extensive experiments show our approach substantially improves base LMs on 18 downstream tasks by 64.28% on average. In particular, the small LMs (1.3B, 6B, and 13B) enhanced by our approach match or even outperform much larger LMs (e.g., ChatGPT).
Integrating Item Relevance in Training Loss for Sequential Recommender Systems

Sequential Recommender Systems (SRSs) are a popular type of recommender system that learns from a user's history to predict the next item they are likely to interact with. However, user interactions can be affected by noise stemming from account sharing, inconsistent preferences, or accidental clicks. To address this issue, we (i) propose a new evaluation protocol that takes multiple future items into account and (ii) introduce a novel relevance-aware loss function to train a SRS with multiple future items to make it more robust to noise. Our relevance-aware models obtain an improvement of ~1.2% of NDCG@10 and 0.88% in the traditional evaluation protocol, while in the new evaluation protocol, the improvement is ~1.63% of NDCG@10 and ~1.5% of HR w.r.t the best performing models.
TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model

Language is by its very nature incremental in how it is produced and processed. This property can be exploited by NLP systems to produce fast responses, which has been shown to be beneficial for real-time interactive applications. Recent neural network-based approaches for incremental processing mainly use RNNs or Transformers. RNNs are fast but monotonic (cannot correct earlier output, which can be necessary in incremental processing). Transformers, on the other hand, consume whole sequences, and hence are by nature non-incremental. A restart-incremental interface that repeatedly passes longer input prefixes can be used to obtain partial outputs, while providing the ability to revise. However, this method becomes costly as the sentence grows longer. In this work, we propose the Two-pass model for AdaPtIve Revision (TAPIR) and introduce a method to obtain an incremental supervision signal for learning an adaptive revision policy. Experimental results on sequence labelling show that our model has better incremental performance and faster inference speed compared to restart-incremental Transformers, while showing little degradation on full sequences.
Anonymization of whole slide images in histopathology for research and education

Objective The exchange of health-related data is subject to regional laws and regulations, such as the General Data Protection Regulation (GDPR) in the EU or the Health Insurance Portability and Accountability Act (HIPAA) in the United States, resulting in non-trivial challenges for researchers and educators when working with these data. In pathology, the digitization of diagnostic tissue samples inevitably generates identifying data that can consist of sensitive but also acquisition-related information stored in vendor-specific file formats. Distribution and off-clinical use of these Whole Slide Images (WSIs) are usually done in these formats, as an industry-wide standardization such as DICOM is yet only tentatively adopted and slide scanner vendors currently do not provide anonymization functionality. Methods We developed a guideline for the proper handling of histopathological image data particularly for research and education with regard to the GDPR. In this context, we evaluated existing anonymization methods and examined proprietary format specifications to identify all sensitive information for the most common WSI formats. This work results in a software library that enables GDPR-compliant anonymization of WSIs while preserving the native formats. Results Based on the analysis of proprietary formats, all occurrences of sensitive information were identified for file formats frequently used in clinical routine, and finally, an open-source programming library with an executable CLI tool and wrappers for different programming languages was developed. Conclusions Our analysis showed that there is no straightforward software solution to anonymize WSIs in a GDPR-compliant way while maintaining the data format. We closed this gap with our extensible open-source library that works instantaneously and offline.
TECHNICAL UNIVERSITY STUDENTS’ SELF-REGULATION IN ONLINE ESP LEARNING

Facing restrictions during the Covid-19 global pandemic, university students are likely to experience difficulties in controlling their learning environments and taking right decisions.As a part of performance review, one of the most important soft skills, the ability to conduct self-evaluation will be needed for future professional growth of university graduates.Therefore, it is crucial to understand the issues related to self-regulation of technical university students learning English for Specific Purposes (ESP) online.The aim of the study is to reveal peculiarities of technical university students' self-regulation in online ESP learning.This study investigates aspects of technical university students' self-regulation regarding their ESP course learning remotely.The quantitative method was employed to collect and analyse the responses of bachelor students of the National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" using a 5-point Likert scale and calculating the median (Mdn) and Inter-Quartile Range (IQR).The qualitative data aimed at further exploration of the issue was conducted through interviews, as well as oral and written feedback of students during the online lessons.We consider the second year of the Covid-19 quarantine as emergency education as there were no other options except studying online.Though universities adjusted to the changed environment and were better equipped in order to provide technical and methodological support for studying online, it was not possible to have regular face-to-face classes.Журнал «Перспективи та інновації науки» (Серія «Педагогіка», Серія «Психологія», Серія «Медицина») № 9(27) 2023 26Reporting the findings of our research, most technical university students indicated agreement with all the statements on various aspects related to their selfregulation skills.The respondents generally hold strong opinions for the statements, especially about asking for help, listening actively and staying on the topic.However, one quarter of students who needed help, did not indicate that in class.In addition, some students underestimated their ability to help others.Therefore, ESP teachers should create supportive learning environment, provide additional materials for students and encourage them to help each other.Two thirds of the respondents in the research knew how to assess their performance at the lesson.Still, it is important for teachers to equip students with the necessary tools for self-assessment.Speaking was the most important skill for more than half of the respondents in our research, they also focused on Use of English in order to achieve good results at exams.
Machine Learning Models for Statistical Analysis

Compared to traditional statistical models, Machine Learning (ML) algorithms provide the ability to interpret, understand and summarize patterns and regularities in observed data for making predictions in an advanced and more sophisticated way. The main reasons for the advantage of ML methods in making predictions are a small number of significant predictors of the statistical models, which means limited informative capability, and pseudo-correct regular statistical patterns, used without previous understanding of the used data causality. Also, some ML methods, like Artificial Neural Networks, use non-linear algorithms, considering links and associations between parameters. On the other hand, statistical models use one-step-ahead linear processes to improve only short-term prediction accuracy by minimizing a cost function. Although designing an optimal ML model can be a very complex process, it can be used as a potential solution for making improved prediction models compared to statistical ones. However, ML models will not automatically improve prediction accuracy, so it is necessary to evaluate and analyze several statistical and ML methods, including some artificial neural networks, through accuracy measures for prediction purposes in various fields of applications. A couple of techniques for improving suggested ML methods and artificial neural networks are proposed to get better accuracy results
Protecting public clients using an authorization algorithm

The paper focuses on authorization in public clients and provides a secure authorization model as an alternative to costly Microsoft Duende BFF solution. After providing a brief overview of confidential and public clients in terms of authorization, we have analyzed problems and potential attack vectors associated with the authorization process in public clients due to their inability to hold credentials securely. Confidential clients are implemented on secure servers or able to facilitate secure authentication by other means, while public clients lack this security. Our research discovered algorithms, models, and methods for secure authorization in public clients. As a part of our model, we have implemented high entropy Proof Key for Code Exchange generator in C# .NET 6.0. In addition, we have provided a solution to a problem of storing sensitive information in public clients using the Backend for Frontend concept. This concept leverages a reverse proxy pattern where a backend application acts as a proxy and handles all client requests. Having a proxy backend application significantly tightens security model for public clients, while restricting possible attack vectors. The authorization model being researched was based on Proof Key for Code Exchange and Backend for Frontend approach. During the testing phase of our research, we have confirmed that the model was not vulnerable to Cross-Site-Scripting and Auth Code Interception attacks. A sequence diagram outlining main actors and interactions among them in context of authorization has been designed. The diagram stands as the visual representation of the model that uses proposed methods and algorithms. As a result, we have managed to build an alternative to secure authorization solutions for public clients that do not rely on the client secret. We have summarized our key findings in a Blazor Web Assembly application, which is classified as public and uses the described authentication model.
Principles and models of expert-analytical methodology for adaptive organizational decisions forming under deep uncertainty

The paper depicts and analyzes Expert-Analytical Methodology named EAM DMDU to support Proactive Anti-crisis Decisions within Organizational Systems under deep uncertainty. Complex tools are proposed for Decisions Domain Knowledge analytical operation. The Benefit is no essential resource demands while keeping the basic principles to deal with deep uncertainty (uncertainties and inconsisten- cies eliciting; Decision vulnerabilities searching instead prediction; threats resilience priority over effectiveness). EAM DMDU enables Deliberative multi-staged Process for Adaptive Decision forming aimed at expected future conflict situation solving. The Process stages are: Problem situation Analysis, Impact on Problem Situation Goal Proposition, Goal proposals Assessment; Efforts for Goal achieving Proposals; Effort Proposals Assessment; reference Proposal option Selection and Decision adaptations accordingly to Decision Frame changes Recommendation. Knowledge operation is enabled with the procedures such as: formal analysis, individual expert assessment, Decision elements deliberative forming. EAM DMDU common information space of is based on Domain Ontology and ensures equal participants’ awareness, expert judgments with their arguments constructive representation and knowledge reuse. Expert-analytical Selection of Proposals uses their Perspectivity Model. It is a sub-goals hierarchy to achieve the goal being formed over previous Process stages. Hierarchy knot is represented with ontologically formalized definition for State of the Art corresponding sub-goal achievement. Leaf node depicts State of the Art with explicit expert Estimates of Certainty factor (from the Stanford model) being provided concerning its implementation through Decision element Proposal being assessed. The Estimate’s arguments are elements of information space used by expert. Under incomplete certainty of element expert provides its boundary values and State of the Art estimates both pessimistic and optimistic. Perspectivity Model contains also conditions for goal achievement violation being caused with environmental threats. Procedures for Estimates formal integration up to Model provide extreme estimates of Proposals Perspectivity and Robustness regarding current uncertainty. Under unsatisfactory properties of integrated Estimates their deliberative adjustment is carried out using Uncertainty Map and arguments provided. The final reference Decision contains selected Goal-Means option and guides to adapt it when decision frame changes. Further research is carried out for EAM DMDU instrumental tools development and its usage for defense resource management.
Basketball Action Recognition Based on the Combination of YOLO and a Deep Fuzzy LSTM Network

Abstract The ability to identify human actions in uncontrolled surroundings is important for human-computer interaction (HCI), especially in the sports areas to offer athletes, coaches, and analysts valuable knowledge about movement techniques and aid referees in making well-informed decisions regarding sports movements. Noteworthy, recognizing human actions in the context of basketball sports remains a difficult task due to issues like intricate backgrounds, obstructed actions, and inconsistent lighting conditions. Accordingly, a method based on the combination of YOLO and deep fuzzy LSTM network is proposed in this paper. YOLO is utilized for detecting players in the frame and the combination of LSTM and fuzzy logic is used to perform the final classification. The reason behind using LSTM along with fuzzy logic refers to its inability in coping with uncertainty which led to the creation of a more transparent, interpretable, and accurate predictive system. The proposed model was validated on SpaceJam and Basketball-51 datasets. Based on the empirical results, the proposed model outperformed all baseline models on both datasets which obviously confirms the priority of our combinational model for basketball action recognition.