Reviewerly/exHarmony huggingface dataset:
Meta-Learning for Few-Shot Named Entity Recognition

Meta-learning has recently been proposed to learn models and algorithms that can generalize from a handful of examples.However, applications to structured prediction and textual tasks pose challenges for meta-learning algorithms.In this paper, we apply two metalearning algorithms, Prototypical Networks and Reptile, to few-shot Named Entity Recognition (NER), including a method for incorporating language model pre-training and Conditional Random Fields (CRF).We propose a task generation scheme for converting classical NER datasets into the few-shot setting, for both training and evaluation.Using three public datasets, we show these meta-learning algorithms outperform a reasonable fine-tuned BERT baseline.In addition, we propose a novel combination of Prototypical Networks and Reptile.
Are we human, or are we users? The role of natural language processing in human-centric news recommenders that nudge users to diverse content

In this position paper, we present a research agenda and ideas for facilitating exposure to diverse viewpoints in news recommendation.Recommending news from diverse viewpoints is important to prevent potential filter bubble effects in news consumption, and stimulate a healthy democratic debate.To account for the complexity that is inherent to humans as citizens in a democracy, we anticipate (among others) individual-level differences in acceptance of diversity.We connect this idea to techniques in Natural Language Processing, where distributional language models would allow us to place different users and news articles in a multidimensional space based on semantic content, where diversity is operationalized as distance and variance.In this way, we can model individual "latitudes of diversity" for different users, and thus personalize viewpoint diversity in support of a healthy public debate.In addition, we identify technical, ethical and conceptual issues related to our presented ideas.Our investigation describes how NLP can play a central role in diversifying news recommendations.
Searching for Activity Results and Experts in a Given Subject Area, Taking Results Significance into Account

The paper describes the mathematical models, algorithms and software tools developed by the author for searching for objects in information analysis systems. A search query is a subject area, which is specified by keywords or rubrics of various classifiers. The search takes into account the relevance of the objects to the query, which is calculated by constructing thematic portraits of the objects and comparing them to the query, as well as the significance of the objects, the rules for calculating which can be set dynamically. Thematic portraits of objects are built by analyzing their relations with other objects in the system, for which a topic is specified. When applied to current research information systems, such an approach can be used to search for scientific results in a given subject area and for experts in this area.
Federated Control: Toward Information Security and Rights Protection

Welcome to the fourth issue of IEEE Transactions on Computational Social Systems (TCSS) this year. I am excited to share some great news.
The University Students’ Perspectives on the Advantages and Disadvantages of Online Learning Due to COVID-19

The purpose of this study was to analyze the advantages and disadvantages of online learning due to the COVID-19 pandemic from the university students' perspectives.This phenomenon is a stage for humans to adapt to the current situation and conditions so that they will find a new pattern of life or what is known as New Normal Life.During this pandemic, tertiary institutions have transformed the system through full online learning.At this moment, an analysis of online learning advantages and disadvantages based on the university students' perspectives as actors who directly feel the impact of these changes is needed.The method in this research employs a narrative approach.Data collection was done through interviews set online.This research was conducted on students at Universitas Negeri Malang.The informants were selected based on the representation of each department in the university.The research data were analyzed based on the results of studies and focus group discussions (FGD) related to the implementation of online learning during the COVID-19 Pandemic.The results showed that the flexibility of time and material filing, as well as documentation became more organized.Based on the perspective of the students, on the other hand, online learning has some shortcomings, including less interactive lecturers, excessive assignments given, frequently changing schedules, inadequate facilities, limited internet access or unstable internet networks, and constraints on practicum course.This article could contribute to the evaluation of online teaching during the Covid-19 pandemic
A Clustering-based framework for Classifying Data Streams

The non-stationary nature of data streams strongly challenges traditional machine learning techniques. Although some solutions have been proposed to extend traditional machine learning techniques for handling data streams, these approaches either require an initial label set or rely on specialized design parameters. The overlap among classes and the labeling of data streams constitute other major challenges for classifying data streams. In this paper, we proposed a clustering-based data stream classification framework to handle non-stationary data streams without utilizing an initial label set. A density-based stream clustering procedure is used to capture novel concepts with a dynamic threshold and an effective active label querying strategy is introduced to continuously learn the new concepts from the data streams. The sub-cluster structure of each cluster is explored to handle the overlap among classes. Experimental results and quantitative comparison studies reveal that the proposed method provides statistically better or comparable performance than the existing methods.
What If We Could Not See? Counterfactual Analysis for Egocentric Action Anticipation

Egocentric action anticipation aims at predicting the near future based on past observation in first-person vision. While future actions may be wrongly predicted due to the dataset bias, we present a counterfactual analysis framework for egocentric action anticipation (CA-EAA) to enhance the capacity. In the factual case, we can predict the upcoming action based on visual features and semantic labels from past observation. Imagining one counterfactual situation where no visual representation had been observed, we would obtain a counterfactual predicted action only using past semantic labels. In this way, we can reduce the side-effect caused by semantic labels via a comparison between factual and counterfactual outcomes, which moves a step towards unbiased prediction for egocentric action anticipation. We conduct experiments on two large-scale egocentric video datasets. Qualitative and quantitative results validate the effectiveness of our proposed CA-EAA.
Does Every Data Instance Matter? Enhancing Sequential Recommendation by Eliminating Unreliable Data

Most sequential recommender systems (SRSs) predict next-item as target for each user given its preceding items as input, assuming that each input is related to its target. However, users may unintentionally click on items that are inconsistent with their preference. We empirically verify that SRSs can be misguided with such unreliable instances (i.e. targets mismatch inputs). This inspires us to design a novel SRS By Eliminating unReliable Data (BERD) guided with two observations: (1) unreliable instances generally have high training loss; and (2) high-loss instances are not necessarily unreliable but uncertain ones caused by blurry sequential pattern. Accordingly, BERD models both loss and uncertainty of each instance via a Gaussian distribution to better distinguish unreliable instances; meanwhile an uncertainty-aware graph convolution network is exploited to assist in mining unreliable instances by lowering uncertainty. Extensive experiments on four real-world datasets demonstrate the superiority of our proposed BERD.
Fast Depth Reconstruction Using Deep Convolutional Neural Networks

In this paper, we study depth reconstruction via RGB-based, Sparse-Depth, and RGBd approaches. We showed that combination of RGB and Sparse Depth approach in RGBd scenario provides the best results. We also proved that the models performance can be further tuned via proper selection of architecture blocks and number of depth points guiding RGB-to-depth reconstruction. We also provide real-time architecture for depth estimation that is on par with state-of-the-art real-time depth reconstruction methods.
Which abbreviations should be expanded?

Abbreviations are common in source code. Properly designed abbreviations may significantly facilitate typing, typesetting, and reading of lengthy source code. However, abbreviations, if used improperly, may also significantly reduce the readability and maintainability of source code. Although a few automated approaches have been proposed to suggest full terms for given abbreviations, to the best of our knowledge, there is no automated approaches to suggest whether abbreviations are used properly, i.e., whether they should be replaced with corresponding full terms. Notably, it is often challenging for inexperienced developers and maintainers to make such decisions. To this end, in this paper, we propose an automated approach to assisting developers and maintainers in making the decisions. The rationale of the approach is that abbreviations should not be expanded if the expansion would result in unacceptably lengthy identifiers or if developers/maintainers can easily figure out the meaning (full terms) of the abbreviations based on their domain knowledge or contexts of the abbreviations. From a corpus of programs, we leverage data mining techniques to discover common abbreviations that are frequently employed by various developers in similar contexts. The key of the data mining is to turn the problem of mining common abbreviations into the maximal clique problem that has been extensively studied. We suggest to not expand given abbreviation if it matches at least one of the discovered common abbreviations. From the same corpus, we also calculate the probability distribution for the length of different types of identifier, e.g., variable names and method names. The probability distribution specifies how likely an identifier of type T is composed of exactly n characters. Our heuristic is to not expand the abbreviation if the probability of its enclosing identifier would be reduced by the expansion. Finally, we also suggest to not expand the abbreviation if its full terms are contained in surrounding contexts of the abbreviation, i.e., tokens on the same source code line. Other abbreviations that do not receive suggestions from the proposed approach are expected to be replaced with their full terms. Our evaluation results on 1,818 abbreviations from five open-source applications suggest that the proposed approach is accurate with a high accuracy of 95%.
Designing Reenacted Chatbots to Enhance Museum Experience

The increased availability of chatbots has drawn attention and interest to the study of what answers they provide and how they provide them. Chatbots have become a common sight in museums but are limited to answering only simple and basic questions. Based on the observed potential of chatbots for history education in museums, we investigate how chatbots impact history education and improve the overall experience according to their appearance and language style. For this, we built three models, designed by factors on embodiment and reflection, and 60 sets of answer–questions, designed for the National Museum of Korea. We conducted a study with a total of 34 participants and carried out a variety of analyses covering individual learning styles, museum experience scales, gaze data, in-depth interviews and observations from researchers. We present various results and lessons regarding the effect of embodiment and reflection on the museum experience. Our findings show how people with different learning styles connect with chatbot models and how visitors’ behavior in the museum changes depending on the chatbot model. Specifically, the chatbot model equipped with embodiment and reflection shows its superiority in enhancing the museum experience, in general.
Lyapunov-control-inspired strategies for quantum combinatorial optimization

The prospect of using quantum computers to solve combinatorial optimization problems via the quantum approximate optimization algorithm (QAOA) has attracted considerable interest in recent years. However, a key limitation associated with QAOA is the need to classically optimize over a set of quantum circuit parameters. This classical optimization can have significant associated costs and challenges. Here we provide an expanded description of Lyapunov control-inspired strategies for quantum optimization, as presented in [Magann et al., Phys. Rev. Lett. 129, 250502 (2022)], that do not require any classical optimization effort. Instead, these strategies utilize feedback from qubit measurements to assign values to the quantum circuit parameters in a deterministic manner, such that the combinatorial optimization problem solution improves monotonically with the quantum circuit depth. Numerical analyses are presented that investigate the utility of these strategies towards MaxCut on weighted and unweighted 3-regular graphs, both in ideal implementations and in the presence of measurement noise. We also discuss how how these strategies compare with QAOA and how they may be used to seed QAOA optimizations in order to improve performance for near-term applications, and we explore connections to quantum annealing.
Argumentation schemes for clinical decision support

This paper demonstrates how argumentation schemes can be used in decision support systems that help clinicians in making treatment decisions. The work builds on the use of computational argumentation, a rigorous approach to reasoning with complex data that places strong emphasis on being able to justify and explain the decisions that are recommended. The main contribution of the paper is to present a novel set of specialised argumentation schemes that can be used in the context of a clinical decision support system to assist in reasoning about what treatments to offer. These schemes provide a mechanism for capturing clinical reasoning in such a way that it can be handled by the formal reasoning mechanisms of formal argumentation. The paper describes how the integration between argumentation schemes and formal argumentation may be carried out, sketches how this is achieved by an implementation that we have created and illustrates the overall process on a small set of case studies.
Federated Learning Meets Fairness and Differential Privacy

Deep learning’s unprecedented success raises several ethical concerns ranging from biased predictions to data privacy. Researchers tackle these issues by introducing fairness metrics, or federated learning, or differential privacy. A first, this work presents an ethical federated learning model, incorporating all three measures simultaneously. Experiments on the Adult, Bank and Dutch datasets highlight the resulting “empirical interplay" between accuracy, fairness, and privacy.
Real-Time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model

3-D action recognition is referred to as the classification of action sequences which consist of 3-D skeleton joints. While many research works are devoted to 3-D action recognition, it mainly suffers from three problems: 1) highly complicated articulation; 2) a great amount of noise; and 3) low implementation efficiency. To tackle all these problems, we propose a real-time 3-D action-recognition framework by integrating the locally aggregated kinematic-guided skeletonlet (LAKS) with a supervised hashing-by-analysis (SHA) model. We first define the skeletonlet as a few combinations of joint offsets grouped in terms of the kinematic principle and then represent an action sequence using LAKS, which consists of a denoising phase and a locally aggregating phase. The denoising phase detects the noisy action data and adjusts it by replacing all the features within it with the features of the corresponding previous frame, while the locally aggregating phase sums the difference between an offset feature of the skeletonlet and its cluster center together over all the offset features of the sequence. Finally, the SHA model combines sparse representation with a hashing model, aiming at promoting the recognition accuracy while maintaining high efficiency. Experimental results on <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MSRAction3D</monospace> , <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UTKinectAction3D</monospace> , and <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Florence3DAction</monospace> datasets demonstrate that the proposed method outperforms state-of-the-art methods in both recognition accuracy and implementation efficiency.
Deep Reinforcement Learning in VizDoom via DQN and Actor-Critic Agents

In this work, we study the problem of learning reinforcement learning-based agents in a first-person shooter environment VizDoom. We compare several well-known architectures, such as DQN, DDQN, A3C, and Curiosity-driven model, while highlighting the main differences in learned policies of agents trained via these models.
Construction and Analysis of Green Investment Risk Evaluation Index System Based on Information Entropy Fuzzy Hierarchical Analysis Model

This paper adopts the information entropy fuzzy hierarchical analysis model to conduct an in-depth study and analysis of the green investment risk evaluation system and to process and analyze its indicators. The entropy method-fuzzy hierarchical analysis is used to evaluate the green financial capital operation risk. Firstly, I point out the research trends and shortcomings, define the concepts of fund operation, operation risk, risk evaluation, and green finance, and elaborate the relevant basic theories to lay the theoretical foundation for the later research; secondly, I identify the external and internal fund operation risks by describing the development plan, overall operation, and specific operation plan of green finance; then, according to the existing fund operation risks, I integrate commercial banks’ risk. Then, based on the existing fund operation risks, I integrate the risk types of commercial banks, combine the characteristics of green finance business, design the green finance fund operation risk evaluation index system, initially determine the index weights through hierarchical analysis method, introduce the entropy value method to improve the index weights, and use the fuzzy comprehensive evaluation method to obtain the final evaluation results: in the process of green finance fund operation, internal risk is the most important risk, among which credit risk and financial innovation risk have the most significant impact; external risk is the second most important risk, among which economic risk and political risk are the most important. External risks are the second most important risks, among which economic risks and political risks are more obvious. The risks are judged from single-factor aspects, and the scoring values are used to determine the higher possibility of schedule risks caused by human factors, and finally, the schedule risk level of the reservoir project is determined to be medium through a comprehensive evaluation. The reasonableness of the calculation results shows that the entropy theory and the fuzzy comprehensive evaluation method are still applicable to the assessment of green investment risks.
Frequency and predictability effects in first and second language of different script bilinguals.

Reading efficiently in a second language (L2) is a crucial skill, but it is not universally achieved. Here we ask whether L2 reading efficiency is better captured as a language specific skill or whether it is mostly shared across L1 and L2, relying on general language abilities. To this end, we examined word frequency and predictability effects in sentence reading, and tested the same readers in L1 and L2, recording participants' eye-movements. Participants were 57 undergraduate bilingual speakers of Hebrew and English, languages that use different scripts, allowing for a clearer distinction between L1 and L2 processing. Both word frequency and word predictability effects were more pronounced in participants' L2 than in the L1, suggesting that both lower level and higher-order processes in reading are sensitive to language proficiency. Further, frequency effects in the L2 were linked with L2 proficiency but not general language abilities, and L2 predictability effects were not associated with either variable. Finally, readers' frequency and predictability effects in L1 and L2 were not associated with each other. Taken together, these results suggest that for different-script bilinguals, efficient reading in the L2 is a highly specific skill, dependent upon proficiency in that language, and drawing less on L1 and general language ability. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
A Contrast Augmentation Approach to Improve Multi-Scanner Generalization in MRI

Most data-driven methods are very susceptible to data variability. This problem is particularly apparent when applying Deep Learning (DL) to brain Magnetic Resonance Imaging (MRI), where intensities and contrasts vary due to acquisition protocol, scanner- and center-specific factors. Most publicly available brain MRI datasets originate from the same center and are homogeneous in terms of scanner and used protocol. As such, devising robust methods that generalize to multi-scanner and multi-center data is crucial for transferring these techniques into clinical practice. We propose a novel data augmentation approach based on Gaussian Mixture Models (GMM-DA) with the goal of increasing the variability of a given dataset in terms of intensities and contrasts. The approach allows to augment the training dataset such that the variability in the training set compares to what is seen in real world clinical data, while preserving anatomical information. We compare the performance of a state-of-the-art U-Net model trained for segmenting brain structures with and without the addition of GMM-DA. The models are trained and evaluated on single- and multi-scanner datasets. Additionally, we verify the consistency of test-retest results on same-patient images (same and different scanners). Finally, we investigate how the presence of bias field influences the performance of a model trained with GMM-DA. We found that the addition of the GMM-DA improves the generalization capability of the DL model to other scanners not present in the training data, even when the train set is already multi-scanner. Besides, the consistency between same-patient segmentation predictions is improved, both for same-scanner and different-scanner repetitions. We conclude that GMM-DA could increase the transferability of DL models into clinical scenarios.