Reviewerly/exHarmony huggingface dataset:
The impact of education on the economic growth of Kazakhstan

Object: To examine the factors influencing the development of human capital as the driving force of the economy, identify trends in relations, dynamics, prospects, and results in which human capital affects the economic development of Kazakhstan.Methods: Methods of dialectics, a systematic approach, analysis and synthesis, typology, generalization, concretization, economic and statistical, factor analysis, institutional and cluster analysis.Results: The obtained results show the relationship between human capital and economic growth.Using and the proponent of the endogenous growth model of R. Barro proved that human capital is directly related to economic growth.Human capital is a factor influencing growth, but the degree of impact varies depending on the initial level of accumulated human capital.For example, it is easier for a state with well-developed human capital to adapt future technologies, compared to a state with poor human capital.Conclusions: Based on the results of regression analysis and the criteria of human capital, the following assumptions about the development of human capital are obtained.The knowledge gained from primary and secondary education lags behind, and the overall effect manifests itself after higher education.Higher education is a variable of human capital that affects GDP growth and investments in education, research and development and healthcare are needed, which lead to the accumulation of human capital in the country.Statistics show that primary and secondary education is available to the majority of the population of Kazakhstan.The situation with higher education is different.The acquisition of higher education disappears about 51% of the total population.This means that 49% of the population does not have a higher education.In conclusion, all three types of human capital (education, R&D, and healthcare) are underinvested, which negatively affects the development of human capital in Kazakhstan.
Potential Topics as Discourse Shapers Argumentation The Forum Holds a Talk With the Theme of the Covid-19 Pandemic: Pragma-dialectical Studies

The purpose of writing this article is to find potential topics with dialectical objectives and potential topics with rhetorical objectives as argumentation makers at each stage of critical discussion at the COVID-19-themed talk forum. The method used is descriptive qualitative with data analysis techniques emphasizing pragma-dialectical studies focusing on argumentative discourse. Data analysis techniques emphasize pragma-dialectical studies focusing on argumentative discourse. The results showed that potential topics with dialectical and rhetorical functions were found at each stage of the argumentation. Each function found has a different role as an argumentative. Potential topics with an analytic function in the confrontation stage are used to explain the issues discussed. The opening stage ensures that disputes exist and are unambiguous. At the argumentation stage, it is used to test the acceptance of a descriptive, evaluative, or prescriptive point of view. The closing stage ensures that everything that has been said is concluded with sufficient evidence. At the same time, the potential topics with a rhetorical function in the confrontation stage are used to ensure that the issues in the discussion degree are helpful. In the opening stage, it is used to select arguments that do not involve the burden of proof. At the argumentation stage, it is used to apply persuasion tools that will make someone win the argument. The closing stage is used to ensure the argument ends.
CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior

Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors' emotions. The reason is that considering both interlocutors' emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models in both automatic and manual evaluation.
Security, Privacy, and Decentralization in Web3

Much of the recent excitement around decentralized finance (DeFi) comes from hopes that DeFi can be a secure, private, less centralized alternative to traditional finance systems. However, people moving to DeFi sites in hopes of improving their security and privacy may end up with less of both as recent attacks have demonstrated. In this work, we improve the understanding of DeFi by conducting the first Web measurements of the security, privacy, and decentralization properties of popular DeFi front ends. We find that DeFi applications -- or dapps -- suffer from the same security and privacy risks that frequent other parts of the Web but those risks are greatly exacerbated considering the money that is involved in DeFi. Our results show that a common tracker can observe user behavior on over 56% of websites we analyzed and many trackers on DeFi sites can trivially link a user's Ethereum address with PII (e.g., user name or demographic information), or phish users by initiating fake Ethereum transactions. Lastly, we establish that despite claims to the opposite, because of companies like Amazon and Cloudflare operating significant Web infrastructure, DeFi as a whole is considerably less decentralized than previously believed.
Less, but Stronger: On the Value of Strong Heuristics in Semi-supervised Learning for Software Analytics

In many domains, there are many examples and far fewer labels for those examples; e.g. we may have access to millions of lines of source code, but access to only a handful of warnings about that code. In those domains, semi-supervised learners (SSL) can extrapolate labels from a small number of examples to the rest of the data. Standard SSL algorithms use ``weak'' knowledge (i.e. those not based on specific SE knowledge) such as (e.g.) co-train two learners and use good labels from one to train the other. Another approach of SSL in software analytics is potentially use ``strong'' knowledge that use SE knowledge. For example, an often-used heuristic in SE is that unusually large artifacts contain undesired properties (e.g. more bugs). This paper argues that such ``strong'' algorithms perform better than those standard, weaker, SSL algorithms. We show this by learning models from labels generated using weak SSL or our ``stronger'' FRUGAL algorithm. In four domains (distinguishing security-related bug reports; mitigating bias in decision-making; predicting issue close time; and (reducing false alarms in static code warnings), FRUGAL required only 2.5% of the data to be labeled yet out-performed standard semi-supervised learners that relied on (e.g.) some domain-independent graph theory concepts. Hence, for future work, we strongly recommend the use of strong heuristics for semi-supervised learning for SE applications. To better support other researchers, our scripts and data are on-line at https://github.com/HuyTu7/FRUGAL.
Zero-Shot Robot Manipulation from Passive Human Videos

Can we learn robot manipulation for everyday tasks, only by watching videos of humans doing arbitrary tasks in different unstructured settings? Unlike widely adopted strategies of learning task-specific behaviors or direct imitation of a human video, we develop a a framework for extracting agent-agnostic action representations from human videos, and then map it to the agent's embodiment during deployment. Our framework is based on predicting plausible human hand trajectories given an initial image of a scene. After training this prediction model on a diverse set of human videos from the internet, we deploy the trained model zero-shot for physical robot manipulation tasks, after appropriate transformations to the robot's embodiment. This simple strategy lets us solve coarse manipulation tasks like opening and closing drawers, pushing, and tool use, without access to any in-domain robot manipulation trajectories. Our real-world deployment results establish a strong baseline for action prediction information that can be acquired from diverse arbitrary videos of human activities, and be useful for zero-shot robotic manipulation in unseen scenes.
A Simple Approach for Local and Global Variable Importance in Nonlinear Regression Models

The ability to interpret machine learning models has become increasingly important as their usage in data science continues to rise. Most current interpretability methods are optimized to work on either (\textit{i}) a global scale, where the goal is to rank features based on their contributions to overall variation in an observed population, or (\textit{ii}) the local level, which aims to detail on how important a feature is to a particular individual in the data set. In this work, a new operator is proposed called the "GlObal And Local Score" (GOALS): a simple \textit{post hoc} approach to simultaneously assess local and global feature variable importance in nonlinear models. Motivated by problems in biomedicine, the approach is demonstrated using Gaussian process regression where the task of understanding how genetic markers are associated with disease progression both within individuals and across populations is of high interest. Detailed simulations and real data analyses illustrate the flexible and efficient utility of GOALS over state-of-the-art variable importance strategies.
Waiting for Q: An Exploration of QAnon Users' Online Migration to Poal in the Wake of Voat's Demise

Controversial and hateful online communities often face bans on mainstream social networks as part of moderation efforts. Reddit has implemented such measures by banning various communities and users in recent years. Although banning these communities eliminates the problem on one platform, the dispersal of users to other social networks, often with more lenient policies and moderation, remains a challenge. Voat, a Reddit-like platform, provided a haven for sharing controversial and hateful discourse that experienced significant growth and popularity with every Reddit ban until its eventual shutdown in December 2020. This study examines the migration patterns of QAnon adherents across Reddit, Voat, and Poal following the sudden Reddit bans and the announced Voat shutdown. We perform a large-scale measurement of the migrations, including Poal's influx following the Voat shutdown, which has not been studied before under this lens. We observe, among other things, that Reddit users suspected the imminent bans, so they suggested Voat as an alternative. However, after their most used subreddit, r/CBTS_Stream, was banned, users moved to r/greatawakening instead. It was only after the banning of both subreddits that users migrated to Voat. Similarly, Voat users proposed Poal as an alternative after Voat announced its shutdown, but their migration primarily occurred on the last day of Voat, resulting in approximately half of Voat's QAnon user base moving to Poal. This represents a significantly higher percentage compared to the migration from Reddit to Voat (9.7%), indicating that a warning with little time left, can aid towards an effective coordination among the community users. Lastly, our research uncovers evidence suggesting that discussions and planning related to the January 6th attack on the US Capitol emerged predominantly among Poal users, many of whom were Voat migrants.
Reducing Nearest Neighbor Training Sets Optimally and Exactly

In nearest-neighbor classification, a training set $P$ of points in $\mathbb{R}^d$ with given classification is used to classify every point in $\mathbb{R}^d$: Every point gets the same classification as its nearest neighbor in $P$. Recently, Eppstein [SOSA'22] developed an algorithm to detect the relevant training points, those points $p\in P$, such that $P$ and $P\setminus\{p\}$ induce different classifications. We investigate the problem of finding the minimum cardinality reduced training set $P'\subseteq P$ such that $P$ and $P'$ induce the same classification. We show that the set of relevant points is such a minimum cardinality reduced training set if $P$ is in general position. Furthermore, we show that finding a minimum cardinality reduced training set for possibly degenerate $P$ is in P for $d=1$, and NP-complete for $d\geq 2$.
Entangling gates for trapped-ion quantum computation and quantum simulation

The trapped-ion system has been a leading platform for practical quantum computation and quantum simulation since the first scheme of a quantum gate was proposed by Cirac and Zoller in 1995. Quantum gates with trapped ions have shown the highest fidelity among all physical platforms. Recently, sophisticated schemes of quantum gates such as amplitude, phase, frequency modulation, or multi-frequency application, have been developed to make the gates fast, robust to many types of imperfections, and applicable to multiple qubits. Here, we review the basic principle and recent development of quantum gates with trapped ions.
TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Data

This paper addresses a cross-modal learning framework, where the objective is to enhance the performance of supervised learning in the primary modality using an unlabeled, unpaired secondary modality. Taking a probabilistic approach for missing information estimation, we show that the extra information contained in the secondary modality can be estimated via Nadaraya-Watson (NW) kernel regression, which can further be expressed as a kernelized cross-attention module (under linear transformation). Our results lay the foundations for introducing The Attention Patch (TAP), a simple neural network add-on that allows data-level knowledge transfer from the unlabeled modality. We provide extensive numerical simulations using four real-world datasets to show that TAP can provide statistically significant improvement in generalization across different domains and different neural network architectures, making use of seemingly unusable unlabeled cross-modal data.
Diversity Induced Environment Design via Self-Play

Recent work on designing an appropriate distribution of environments has shown promise for training effective generally capable agents. Its success is partly because of a form of adaptive curriculum learning that generates environment instances (or levels) at the frontier of the agent's capabilities. However, such an environment design framework often struggles to find effective levels in challenging design spaces and requires costly interactions with the environment. In this paper, we aim to introduce diversity in the Unsupervised Environment Design (UED) framework. Specifically, we propose a task-agnostic method to identify observed/hidden states that are representative of a given level. The outcome of this method is then utilized to characterize the diversity between two levels, which as we show can be crucial to effective performance. In addition, to improve sampling efficiency, we incorporate the self-play technique that allows the environment generator to automatically generate environments that are of great benefit to the training agent. Quantitatively, our approach, Diversity-induced Environment Design via Self-Play (DivSP), shows compelling performance over existing methods.
A Disparity Refinement Framework for Learning-based Stereo Matching Methods in Cross-domain Setting for Laparoscopic Images

Purpose: Stereo matching methods that enable depth estimation are crucial for visualization enhancement applications in computer-assisted surgery (CAS). Learning-based stereo matching methods are promising to predict accurate results on laparoscopic images. However, they require a large amount of training data, and their performance may be degraded due to domain shifts. Methods: Maintaining robustness and improving the accuracy of learning-based methods are still open problems. To overcome the limitations of learning-based methods, we propose a disparity refinement framework consisting of a local disparity refinement method and a global disparity refinement method to improve the results of learning-based stereo matching methods in a cross-domain setting. Those learning-based stereo matching methods are pre-trained on a large public dataset of natural images and are tested on two datasets of laparoscopic images. Results: Qualitative and quantitative results suggest that our proposed disparity framework can effectively refine disparity maps when they are noise-corrupted on an unseen dataset, without compromising prediction accuracy when the network can generalize well on an unseen dataset. Conclusion: Our proposed disparity refinement framework could work with learning-based methods to achieve robust and accurate disparity prediction. Yet, as a large laparoscopic dataset for training learning-based methods does not exist and the generalization ability of networks remains to be improved, the incorporation of the proposed disparity refinement framework into existing networks will contribute to improving their overall accuracy and robustness associated with depth estimation.
Improving Fair Training under Correlation Shifts

Model fairness is an essential element for Trustworthy AI. While many techniques for model fairness have been proposed, most of them assume that the training and deployment data distributions are identical, which is often not true in practice. In particular, when the bias between labels and sensitive groups changes, the fairness of the trained model is directly influenced and can worsen. We make two contributions for solving this problem. First, we analytically show that existing in-processing fair algorithms have fundamental limits in accuracy and group fairness. We introduce the notion of correlation shifts, which can explicitly capture the change of the above bias. Second, we propose a novel pre-processing step that samples the input data to reduce correlation shifts and thus enables the in-processing approaches to overcome their limitations. We formulate an optimization problem for adjusting the data ratio among labels and sensitive groups to reflect the shifted correlation. A key benefit of our approach lies in decoupling the roles of pre- and in-processing approaches: correlation adjustment via pre-processing and unfairness mitigation on the processed data via in-processing. Experiments show that our framework effectively improves existing in-processing fair algorithms w.r.t. accuracy and fairness, both on synthetic and real datasets.
Drastic changes before the 2011 Tohoku earthquake, revealed by exploratory data analysis

Predicting earthquakes is of the utmost importance, especially to those countries of high risk, and although much effort has been made, it has yet to be realised. Nevertheless, there is a paucity of statistical approaches in seismic studies to the extent that an old theory is believed without verification. Seismic records of time and magnitude in Japan were analysed by exploratory data analysis (EDA). EDA is a parametric statistical approach based on the characteristics of data and is suitable for data-driven investigations. The distribution style of each dataset was determined, and the important parameters were found. This enabled us to identify and evaluate the anomalies in the data. Before the huge 2011 Tohoku earthquake, swarm earthquakes occurred before the main earthquake at improbable frequencies. The frequency and magnitude of all earthquakes increased. Both changes made larger earthquakes more likely to occur: even an M9 earthquake was expected every two years. From these simple measurements, the EDA succeeded in extracting useful information. Detecting and evaluating anomalies using this approach for every set of data would lead to a more accurate prediction of earthquakes.
Semi-Supervised Domain Adaptation with Source Label Adaptation

Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen target data with a few labeled and lots of unlabeled target data, along with many labeled source data from a related domain. Current SSDA approaches usually aim at aligning the target data to the labeled source data with feature space mapping and pseudo-label assignments. Nevertheless, such a source-oriented model can sometimes align the target data to source data of the wrong classes, degrading the classification performance. This paper presents a novel source-adaptive paradigm that adapts the source data to match the target data. Our key idea is to view the source data as a noisily-labeled version of the ideal target data. Then, we propose an SSDA model that cleans up the label noise dynamically with the help of a robust cleaner component designed from the target perspective. Since the paradigm is very different from the core ideas behind existing SSDA approaches, our proposed model can be easily coupled with them to improve their performance. Empirical results on two state-of-the-art SSDA approaches demonstrate that the proposed model effectively cleans up the noise within the source labels and exhibits superior performance over those approaches across benchmark datasets. Our code is available at https://github.com/chu0802/SLA .
Explainable Machine Learning: The importance of a system-centric perspective [Lecture Notes]

The landscape in the context of several signal processing applications and even education <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> appears to be significantly affected by the emergence of machine learning (ML) and particularly deep learning (DL). The main reason for this is the ability of DL to model complex and unknown relationships between signals and the tasks of interest. In particular, supervised DL algorithms have been fairly successful at recognizing perceptually or semantically useful signal information in different applications (e.g., identifying objects or regions of interest from image/video signals or recognizing spoken words from speech signals, such as in speech recognition). In all of these tasks, the training process uses labeled data to learn a mapping function (typically implicitly) from signals to the desired information (class label or target label). The trained DL model is then expected to correctly recognize/classify relevant information in a given test signal. Therefore, a DL-based framework is, in general, very appealing since the features and characteristics of the required mapping are learned almost exclusively from the data without resorting to explicit model/system development.