Reviewerly/exHarmony huggingface dataset:
Enforcing Consistency in Weakly Supervised Semantic Parsing

The predominant challenge in weakly supervised semantic parsing is that of spurious programs that evaluate to correct answers for the wrong reasons. Prior work uses elaborate search strategies to mitigate the prevalence of spurious programs; however, they typically consider only one input at a time. In this work we explore the use of consistency between the output programs for related inputs to reduce the impact of spurious programs. We bias the program search (and thus the model's training signal) towards programs that map the same phrase in related inputs to the same sub-parts in their respective programs. Additionally, we study the importance of designing logical formalisms that facilitate this kind of consAistency-based training. We find that a more consistent formalism leads to improved model performance even without consistency-based training. When combined together, these two insights lead to a 10% absolute improvement over the best prior result on the Natural Language Visual Reasoning dataset.
Subject Guides: Open Access Resources in German Studies (OARGS): News Media

A partial and selective inventory of open access materials for German Studies. A resource may appear in more than one category. Open access to historical German-language newspapers, news radio, streaming video or other news outlets via browsable lists or search engines.
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach

Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems. In a real-world enterprise setting where dialogue systems are developed rapidly and are expected to work robustly for an ever-growing variety of domains, products, and scenarios, efficient learning from a limited number of examples becomes indispensable. In this paper, we introduce a technique to achieve state-of-the-art dialogue generation performance in a few-shot setup, without using any annotated data. We do this by leveraging background knowledge from a larger, more highly represented dialogue source — namely, the MetaLWOz dataset. We evaluate our model on the Stanford Multi-Domain Dialogue Dataset, consisting of human-human goal-oriented dialogues in in-car navigation, appointment scheduling, and weather information domains. We show that our few-shot approach achieves state-of-the art results on that dataset by consistently outperforming the previous best model in terms of BLEU and Entity F1 scores, while being more data-efficient than it by not requiring any data annotation.
SSPNet: Scale Selection Pyramid Network for Tiny Person Detection from UAV Images

With the increasing demand for search and rescue, it is highly demanded to detect objects of interest in large-scale images captured by Unmanned Aerial Vehicles (UAVs), which is quite challenging due to extremely small scales of objects. Most existing methods employed Feature Pyramid Network (FPN) to enrich shallow layers' features by combing deep layers' contextual features. However, under the limitation of the inconsistency in gradient computation across different layers, the shallow layers in FPN are not fully exploited to detect tiny objects. In this paper, we propose a Scale Selection Pyramid network (SSPNet) for tiny person detection, which consists of three components: Context Attention Module (CAM), Scale Enhancement Module (SEM), and Scale Selection Module (SSM). CAM takes account of context information to produce hierarchical attention heatmaps. SEM highlights features of specific scales at different layers, leading the detector to focus on objects of specific scales instead of vast backgrounds. SSM exploits adjacent layers' relationships to fulfill suitable feature sharing between deep layers and shallow layers, thereby avoiding the inconsistency in gradient computation across different layers. Besides, we propose a Weighted Negative Sampling (WNS) strategy to guide the detector to select more representative samples. Experiments on the TinyPerson benchmark show that our method outperforms other state-of-the-art (SOTA) detectors.
ANALYSIS OF MORPHOLOGICAL AWARENESS IN READING COMPREHENSION

This research is analyzes morphological awareness in reading comprehension. The purpose of this research was to analyze the morphology of students' reading comprehension, to identify the types of morphological processes in reading comprehension and to describe the morphological processes in reading comprehension.&#x0D; The method is used in this research is qualitative method. During the current Pandemic, researchers decided to conduct research online through the Zoom application, this research was conducted by examining Jayabaya University students. There were 6 correspondents from the first semester students from several study programs to be given learning material and interviews. In this study, there are two types of instruments used, namely morphological tests and interviews.&#x0D; The way to obtain the results of this research is to analyze morphological awareness in reading comprehension using morphological test instruments and structured interviews. After being given an understanding of morphology, students can take a morphological awareness test in reading comprehension. The results of each student showed a fairly good percentage with the average score they got, namely 92.34.&#x0D;
Image restoration quality assessment based on regional differential information entropy

With the development of image recovery models,especially those based on adversarial and perceptual losses,the detailed texture portions of images are being recovered more naturally.However,these restored images are similar but not identical in detail texture to their reference images.With traditional image quality assessment methods,results with better subjective perceived quality often score lower in objective scoring.Assessment methods suffer from subjective and objective inconsistencies.This paper proposes a regional differential information entropy (RDIE) method for image quality assessment to address this problem.This approach allows better assessment of similar but not identical textural details and achieves good agreement with perceived quality.Neural networks are used to reshape the process of calculating information entropy,improving the speed and efficiency of the operation. Experiments conducted with this study image quality assessment dataset and the PIPAL dataset show that the proposed RDIE method yields a high degree of agreement with people average opinion scores compared to other image quality assessment metrics,proving that RDIE can better quantify the perceived quality of images.
LibGuides: Research Data Support Services for Researchers: RDM Explained

This guide supports the University’s Policy on Research Data Management contained within the Research Integrity Policy Framework. The guide provides guidance for Swansea University researchers on expectations, obligations, advice and support for research
Teacher Agency in Adapting to Online Teaching during COVID-19: A Case Study on Teachers of Chinese as an Additional Language in Macau

The COVID-19 university closure forced a rapid transition and adaptation to online teaching. This paper reports on a case study that examined teacher agency in response to online teaching from February to September 2020. In the study we collected multiple data from three teachers of Chinese as an additional language, including semi-structured interviews, institutional documents, and field notes, to investigate their exercise of agency in adapting to online teaching. The analysis revealed that the participants displayed strong agency to build digital competence and develop student-centered pedagogy at different stages. At the same time, the shift to online from classroom-based teaching allowed them an opportunity to transform existing practices and seek innovative pedagogy, such as a hybrid model blending asynchronous and synchronous online teaching. This study also suggests the influence of flexible and collaborative institutional culture and teacher professional digital competence in shaping the participants' agency in addressing the diverse challenges of online teaching. These findings offer insights into the value of an agency-oriented approach to professional learning and development in educational change. Educational stakeholders should pay more attention to the dynamic interaction between educational institutional systems and teacher agentic practice.
THE PROBLEM OF METHODOLOGICAL READINESS OF A FOREIGN LANGUAGE TEACHER FOR TEACHING PUPILS IN CONDITIONS OF INCLUSIVE PRACTICES

The article presents one of the most important problems of modern professional and methodo-logical preparation namely training of a future teacher to teach the language and culture to schoolchildren with special educational needs. The structure of the teacher’s methodological readiness to interact with atypical schoolchildren is revealed. The article points out the place of inclusive education in the system of foreign language education. The emphasis is placed on the fact that the methodological readiness of the teacher contains specialized skills, which are a set of practical educational actions based on the awareness of the goal of communicative approach in teaching language and culture, patterns, particular methodological principles of organizing a lesson, conditions, means, forms and methods of organizing the stages of familiarization, training and introduction of language material into speech activity, taking into account the characteristics of an atypical student. The article highlights the methodological tasks that the teacher solves during a foreign language lesson, the essential characteristic of the personality of a foreign language teacher as a key subject in an inclusive educational field. The authors make a conclusion that the specialized skills are based on a system of certain linguistic and linguodidactic knowledge that a teacher needs to master in order to carry out their professional activities. Based on the established methodological readiness, a foreign language teacher is able to design an educational process for students with impaired and normal development in conditions of inclusion, its didactic content; use various methods of pedagogical interaction between all subjects of the correctional and educational process; organize joint and individual activities with children with various types of impaired development; the ability to determine the needs and capabilities of each child in the educational program and strategies for accompanying him.
Quality that Matters: QoE Monitoring in Education Service Provider (ESP) Networks

Education Service Providers (ESPs) play a crucial role in the digitization of education as they equip students and teachers with reliable devices and provide high quality Internet access at schools. This paper investigates four months worth of continuous measurements conducted by an ESP using a large-scale, passive, in-device Quality of Experience (QoE) monitoring system deployed into a nationwide network of education-purpose devices. These measurements cover more than 800 education centers and about 4000 devices, used both in schools and at home. Using this rich dataset, we present an exhaustive characterization of the browsing behavior, and a quantification of the web and video QoE in this educational context. Web QoE results showed a better performance for school Wi-Fi networks compared to home connections, suggesting that several issues may arise for ESPs due to the increasing relevance of home-schooling caused by the COVID-19 pandemic.
Universal Approximation for Log-concave Distributions using Well-conditioned Normalizing Flows

Normalizing flows are a widely used class of latent-variable generative models with a tractable likelihood. Affine-coupling (Dinh et al, 2014-16) models are a particularly common type of normalizing flows, for which the Jacobian of the latent-to-observable-variable transformation is triangular, allowing the likelihood to be computed in linear time. Despite the widespread usage of affine couplings, the special structure of the architecture makes understanding their representational power challenging. The question of universal approximation was only recently resolved by three parallel papers (Huang et al.,2020;Zhang et al.,2020;Koehler et al.,2020) -- who showed reasonably regular distributions can be approximated arbitrarily well using affine couplings -- albeit with networks with a nearly-singular Jacobian. As ill-conditioned Jacobians are an obstacle for likelihood-based training, the fundamental question remains: which distributions can be approximated using well-conditioned affine coupling flows? In this paper, we show that any log-concave distribution can be approximated using well-conditioned affine-coupling flows. In terms of proof techniques, we uncover and leverage deep connections between affine coupling architectures, underdamped Langevin dynamics (a stochastic differential equation often used to sample from Gibbs measures) and Hénon maps (a structured dynamical system that appears in the study of symplectic diffeomorphisms). Our results also inform the practice of training affine couplings: we approximate a padded version of the input distribution with iid Gaussians -- a strategy which Koehler et al.(2020) empirically observed to result in better-conditioned flows, but had hitherto no theoretical grounding. Our proof can thus be seen as providing theoretical evidence for the benefits of Gaussian padding when training normalizing flows.
A Simple Reward-free Approach to Constrained Reinforcement Learning

In constrained reinforcement learning (RL), a learning agent seeks to not only optimize the overall reward but also satisfy the additional safety, diversity, or budget constraints. Consequently, existing constrained RL solutions require several new algorithmic ingredients that are notably different from standard RL. On the other hand, reward-free RL is independently developed in the unconstrained literature, which learns the transition dynamics without using the reward information, and thus naturally capable of addressing RL with multiple objectives under the common dynamics. This paper bridges reward-free RL and constrained RL. Particularly, we propose a simple meta-algorithm such that given any reward-free RL oracle, the approachability and constrained RL problems can be directly solved with negligible overheads in sample complexity. Utilizing the existing reward-free RL solvers, our framework provides sharp sample complexity results for constrained RL in the tabular MDP setting, matching the best existing results up to a factor of horizon dependence; our framework directly extends to a setting of tabular two-player Markov games, and gives a new result for constrained RL with linear function approximation.
Communication in Banking Sector: A Systematic Review

Background: Effective banking communication strengthens the relationship between customer, suppliers, stakeholders, manager, client, employees and board of directors. Banker’s experience on banking communication enhances banking system, employee’s behavior and core banking services facilities management. Banking communication improves organizational effectiveness through training, knowledge management, risk management, internal control system and data security management. In modern era, communication channel is transformed into electronic channel promoting e-banking which includes internet banking, mobile banking and e-payment system. Moreover, clerical work shifted into electronic form, which cut costs and satisfy customer.Objectives: This paper explores how banker’s experience effects on banking communication in commercial banks, which enhances their understanding level and determines effective communication in workplace.Methods: Extensive desk reviews followed by related literature werecarried out to gain better insight regarding the field of study.Findings: With the passage of time, computers and technologies have changed the traditional method of communication system. Banks are now using windows, world processing system, excel, computer operating system, DOS, database management system, data planning and database design, data security, internet, intranet, extranet service and email system which increase working performance.Conclusions: Bank managers need to understand importance of communication skills in order to increase effectiveness of internal communication between manager and employees.Implications: Commercial banks of Nepal requireto update in their communication practices and strategies in order to build proper channel which would help in communication between employees and management.
Intrinsic uncertainties and where to find them

We introduce a framework for uncertainty estimation that both describes and extends many existing methods. We consider typical hyperparameters involved in classical training as random variables and marginalise them out to capture various sources of uncertainty in the parameter space. We investigate which forms and combinations of marginalisation are most useful from a practical point of view on standard benchmarking data sets. Moreover, we discuss how some marginalisations may produce reliable estimates of uncertainty without the need for extensive hyperparameter tuning and/or large-scale ensembling.
Contrastive Learning for Cold-Start Recommendation

Recommending cold-start items is a long-standing and fundamental challenge in recommender systems. Without any historical interaction on cold-start items, CF scheme fails to use collaborative signals to infer user preference on these items. To solve this problem, extensive studies have been conducted to incorporate side information into the CF scheme. Specifically, they employ modern neural network techniques (e.g., dropout, consistency constraint) to discover and exploit the coalition effect of content features and collaborative representations. However, we argue that these works less explore the mutual dependencies between content features and collaborative representations and lack sufficient theoretical supports, thus resulting in unsatisfactory performance. In this work, we reformulate the cold-start item representation learning from an information-theoretic standpoint. It aims to maximize the mutual dependencies between item content and collaborative signals. Specifically, the representation learning is theoretically lower-bounded by the integration of two terms: mutual information between collaborative embeddings of users and items, and mutual information between collaborative embeddings and feature representations of items. To model such a learning process, we devise a new objective function founded upon contrastive learning and develop a simple yet effective Contrastive Learning-based Cold-start Recommendation framework(CLCRec). In particular, CLCRec consists of three components: contrastive pair organization, contrastive embedding, and contrastive optimization modules. It allows us to preserve collaborative signals in the content representations for both warm and cold-start items. Through extensive experiments on four publicly accessible datasets, we observe that CLCRec achieves significant improvements over state-of-the-art approaches in both warm- and cold-start scenarios.
On Training Instance Selection for Few-Shot Neural Text Generation

Large-scale pretrained language models have led to dramatic improvements in text generation. Impressive performance can be achieved by finetuning only on a small number of instances (few-shot setting). Nonetheless, almost all previous work simply applies random sampling to select the few-shot training instances. Little to no attention has been paid to the selection strategies and how they would affect model performance. In this work, we present a study on training instance selection in few-shot neural text generation. The selection decision is made based only on the unlabeled data so as to identify the most worthwhile data points that should be annotated under some budget of labeling cost. Based on the intuition that the few-shot training instances should be diverse and representative of the entire data distribution, we propose a simple selection strategy with K-means clustering. We show that even with the naive clustering-based approach, the generation models consistently outperform random sampling on three text generation tasks: data-to-text generation, document summarization and question generation. We hope that this work will call for more attention on this largely unexplored area.
HUBUNGAN PENGUASAAN ISTILAH ASING DENGAN HASIL BELAJAR MAKANAN KONTINENTAL SISWA KELAS XI SMK PUTRA ANDA BINJAI

ABST RACT This study aims to: (1) To determine the mastery of foreign terms in students' continental food, (2) to find out the learning outcomes of students' continental food, (3) to find out whether there is a relationship between the mastery of foreign terms and the students' learning outcomes of continental food. The research location was conducted at SMK Putra Anda Binjai, when the research was conducted in August 2019. The population in this study were students of class XI SMK Putra Anda Binjai totaling 51 students. From the results of the product moment correlation analysis, it was obtained that the r_xy correlation was 0.429 and the r table value at the 5% significant level with n = 30 was 0.361. Thus, the price 〖 r_count> r 〗 _table, namely (0.429> 0.361). So it can be concluded that there is a relationship between the Mastery of Foreign Terms and the Continental Food Learning Outcomes in class XI students of SMK Putra Anda Binjai. Keywords: Mastery of Foreign Terms, Learning Outcomes ABSTRAK Penelitian ini bertujuan untuk : (1) Untuk mengetahui penguasaan istilah asing pada makanan kontinental siswa, (2) Untuk mengetahui hasil belajar makanan kontinental siswa, (3) Untuk mengetahui adakah hubungan penguasaan istilah asing dengan hasil belajar makanan kontinental siswa. Lokasi penelitian dilaksanakan di SMK Putra Anda Binjai, waktu penelitian dilaksanakan Agustus 2019. Populasi dalam penelitian ini adalah siswa kelas XI SMK Putra Anda Binjai berjumlah 51 siswa. Dari hasil analisis korelasi product moment diperoleh korelasi  sebesar 0,429 dan nilai  pada taraf signifikan 5% dengan n=30 adalah sebesar 0,361. Dengan demikian harga  yaitu (0,429 > 0,361). Maka dapat disimpulkan bahwa tedapat hubungan antara Penguasaan Istilah Asing dengan Hasil Belajar Makanan Kontinental pada siswa kelas XI SMK Putra Anda Binjai. Kata K unci : Penguasaan Istilah Asing, Hasil Belajar
한국어 학습자 오류 사전 개발 모형 연구 -사례 분석과 사전의 미시구조 설계를 중심으로-

This study is based on the premise that the microstructure of the Korean learner’s common errors dictionary. For this purpose, theoretically, it was reviewed the importance of learner’s errors and were analyzed the dictionaries of learner’s common errors published in both domestic and abroad. The results showed that there is a fundamental difference in the contents of the dictionary and the description method according to the user of the dictionary and the purpose of use. In this study, Korean language teachers and researchers were included as main users, and upper-intermediate and advanced learners as second users. This work proposes a microstructured design model of the learner''s dictionary, which consists of language knowledge, error information about the items and reference information for teaching and learning. In conclusion, although the research so far has been the result of error studies by Korean learners which have accumulated only theoretical results, this study extends to practical discussions for the development of common error dictionaries for Korean learners. (Yonsei University)