Reviewerly/exHarmony huggingface dataset:
Mother to mother: Mothers’ social comparison-based emotions on social networking sites

<p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">Social networking sites (SNSs) are platforms where people make social comparisons very frequently, and because of those comparisons, they have the potential for evoking a wide range of emotions. According to typology of social comparison-based emotions, the emotions felt after social comparisons may vary according to the direction of comparison (upward vs. downward) as well as the internal process that triggered by those comparisons (assimilation vs. contrast). The current study aims to examine the mothers' emotions they felt after social comparisons they make with other mothers on the SNSs, and search out the usefulness of using the typology of social comparison-based emotions in examining those emotions. For this purpose, an online survey was conducted on 42 mothers between the ages of 20 and 48, who have been a member of SNSs for at least six months. Mothers responses to two open-ended questions; one is about the emotions they feel after upward comparisons, and the other is about the ones that they felt after downward comparison they made with other mothers on SNSs, were examined through thematic analyses. The results pointed out that the emotion classification offered in Smith’s theory might be useful in examining the social comparisons on SNSs made by mothers, with the addition of some new categories. Specifically, it was found that some mothers feel doubts about the credibility of information in other mothers’ posts, and some others denied they are emotionally influenced by social comparisons. Another interesting finding was that mothers reported to feel assimilative and contrastive emotions simultaneously.
Validity of Born-Markov master equations for single- and two-qubit systems

The urgent need for reliable simulation tools to match the extreme accuracy needed to control tailored quantum devices highlights the importance of understanding open quantum systems and their modeling. To this end, we compare here the commonly used Redfield and Lindblad master equations against numerically exact results in the case of one and two resonant qubits transversely coupled at a single point to a Drude-cut ohmic bath. All the relevant parameters are varied over a broad range, which allows us to give detailed predictions about the validity and physically meaningful applicability of the weak-coupling approaches. We characterize the accuracy of the approximate approaches by comparing the maximum difference of their system evolution superoperators with numerically exact results. After optimizing the parameters of the approximate models to minimize the difference, we also explore if and to what extent the weak-coupling equations can be applied at least as phenomenological models. Optimization may lead to an accurate reproduction of experimental data, but yet our results are important to estimate the reliability of the extracted parameter values such as the bath temperature. Our findings set general guidelines for the range of validity of the usual Born-Markov master equations and indicate that they fail to accurately describe the physics in a surprisingly broad range of parameters, in particular, at low temperatures. Since quantum-technological devices operate there, their accurate modeling calls for a careful choice of methods.
A Modified Whale Optimization Algorithm with Single-Dimensional Swimming for Global Optimization Problems

As a novel meta-heuristic algorithm, the Whale Optimization Algorithm (WOA) has well performance in solving optimization problems. However, WOA usually tends to trap in local optimal and it suffers slow convergence speed for large-scale and high-dimension optimization problems. A modified whale optimization algorithm with single-dimensional swimming (abbreviated as SWWOA) is proposed in order to overcome the shortcoming. First, tent map is applied to generate the initialize population for maximize search ability. Second, quasi-opposition learning is adopted after every iteration for further improving the search ability. Third, a novel nonlinearly control parameter factor that is based on logarithm function is presented in order to balance exploration and exploitation. Additionally, the last, single-dimensional swimming is proposed in order to replace the prey behaviour in standard WOA for tuning. The simulation experiments were conducted on 20 well-known benchmark functions. The results show that the proposed SWWOA has better performance in solution precision and higher convergence speed than the comparison methods.
RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder

Existing object detection frameworks are usually built on a single format of object/part representation, i.e., anchor/proposal rectangle boxes in RetinaNet and Faster R-CNN, center points in FCOS and RepPoints, and corner points in CornerNet. While these different representations usually drive the frameworks to perform well in different aspects, e.g., better classification or finer localization, it is in general difficult to combine these representations in a single framework to make good use of each strength, due to the heterogeneous or non-grid feature extraction by different representations. This paper presents an attention-based decoder module similar as that in Transformer~\cite{vaswani2017attention} to bridge other representations into a typical object detector built on a single representation format, in an end-to-end fashion. The other representations act as a set of \emph{key} instances to strengthen the main \emph{query} representation features in the vanilla detectors. Novel techniques are proposed towards efficient computation of the decoder module, including a \emph{key sampling} approach and a \emph{shared location embedding} approach. The proposed module is named \emph{bridging visual representations} (BVR). It can perform in-place and we demonstrate its broad effectiveness in bridging other representations into prevalent object detection frameworks, including RetinaNet, Faster R-CNN, FCOS and ATSS, where about $1.5\sim3.0$ AP improvements are achieved. In particular, we improve a state-of-the-art framework with a strong backbone by about $2.0$ AP, reaching $52.7$ AP on COCO test-dev. The resulting network is named RelationNet++. The code will be available at this https URL.
Incremental Processing in the Age of Non-Incremental Encoders: An Empirical Assessment of Bidirectional Models for Incremental NLU

While humans process language incrementally, the best language encoders currently used in NLP do not.Both bidirectional LSTMs and Transformers assume that the sequence that is to be encoded is available in full, to be processed either forwards and backwards (BiL-STMs) or as a whole (Transformers).We investigate how they behave under incremental interfaces, when partial output must be provided based on partial input seen up to a certain time step, which may happen in interactive systems.We test five models on various NLU datasets and compare their performance using three incremental evaluation metrics.The results support the possibility of using bidirectional encoders in incremental mode while retaining most of their non-incremental quality.The "omni-directional" BERT model, which achieves better non-incremental performance, is impacted more by the incremental access.This can be alleviated by adapting the training regime (truncated training), or the testing procedure, by delaying the output until some right context is available or by incorporating hypothetical right contexts generated by a language model like GPT-2.
On Cross-Dataset Generalization in Automatic Detection of Online Abuse

NLP research has attained high performances in abusive language detection as a supervised classification task.While in research settings, training and test datasets are usually obtained from similar data samples, in practice systems are often applied on data that are different from the training set in topic and class distributions.Also, the ambiguity in class definitions inherited in this task aggravates the discrepancies between source and target datasets.We explore the topic bias and the task formulation bias in cross-dataset generalization.We show that the benign examples in the Wikipedia Detox dataset are biased towards platformspecific topics.We identify these examples using unsupervised topic modeling and manual inspection of topics' keywords.Removing these topics increases cross-dataset generalization, without reducing in-domain classification performance.For a robust dataset design, we suggest applying inexpensive unsupervised methods to inspect the collected data and downsize the non-generalizable content before manually annotating for class labels.
A hybrid algorithm framework for small quantum computers with application to finding Hamiltonian cycles

Recent works have shown that quantum computers can polynomially speed up certain SAT-solving algorithms even when the number of available qubits is significantly smaller than the number of variables. Here, we generalize this approach. We present a framework for hybrid quantum-classical algorithms which utilize quantum computers significantly smaller than the problem size. Given an arbitrarily small ratio of the quantum computer to the instance size, we achieve polynomial speedups for classical divide-and-conquer algorithms, provided that certain criteria on the time- and space-efficiency are met. We demonstrate how this approach can be used to enhance Eppstein’s algorithm for the cubic Hamiltonian cycle problem and achieve a polynomial speedup for any ratio of the number of qubits to the size of the graph.
RELATION EXTRACTION DATASET FOR THE RUSSIAN

There are few existing relation extraction datasets for the Russian language and they contain a rather small number of examples. Thus, we decided to create a new Ontonotes-based named entities and relation extraction sentence-level dataset called RURED. The dataset contains more than 500 annotated texts and more than 5,000 labelled relations. We also publish baseline models for relation extraction and named entity recognition trained on the dataset. Our models achieve 0.85 for named entity recognition and 0.78 for relation extraction in F1-score.
A class of time-energy uncertainty relations for time-dependent Hamiltonians

A new class of time-energy uncertainty relations is directly derived from the Schrödinger equations for time-dependent Hamiltonians. Only the initial states and the Hamiltonians, but neither the instantaneous eigenstates nor the full time-dependent wave funct- ions, which would demand a full solution for a time-dependent Hamiltonian, are required for our time-energy relations. Explicit results are then presented for particular subcases of interest for time-independent Hamiltonians and also for time-varying Hamiltonians employed in adiabatic quantum computation. Some estimates of the lower bounds on computational time are given for general adiabatic quantum algorithms, with Grover's search as an illustration. We particularly emphasize the role of required energy resources, besides the space and time complexity, for the physical process of (quantum) computation, in general.
Phonology-Augmented Statistical Framework for Machine Transliteration Using Limited Linguistic Resources

Transliteration converts words in a source language (e.g., English) into words in a target language (e.g., Vietnamese). This conversion considers the phonological structure of the target language, as the transliterated output needs to be pronounceable in the target language. For example, a word in Vietnamese that begins with a consonant cluster is phonologically invalid and thus would be an incorrect output of a transliteration system. Most statistical transliteration approaches, albeit being widely adopted, do not explicitly model the target language's phonology, which often results in invalid outputs. The problem is compounded by the limited linguistic resources available when converting foreign words to transliterated words in the target language. In this paper, we present a phonology-augmented statistical framework suitable for transliteration, especially when only limited linguistic resources are available. We propose the concept of pseudo-syllables as structures representing how segments of a foreign word are organized according to the syllables of the target language's phonology. We performed transliteration experiments on Vietnamese and Cantonese. We show that the proposed framework outperforms the statistical baseline by up to 44.68% relative, when there are limited training examples (587 entries).
Robust Backed-off Estimation of Out-of-Vocabulary Embeddings

Out-of-vocabulary (OOV) words cause serious troubles in solving natural language tasks with a neural network.Existing approaches to this problem resort to using subwords, which are shorter and more ambiguous units than words, in order to represent OOV words with a bag of subwords.In this study, inspired by the processes for creating words from known words, we propose a robust method of estimating OOV word embeddings by referring to pre-trained word embeddings for known words with similar surfaces to target OOV words.We collect known words by segmenting OOV words and by approximate string matching, and we then aggregate their pre-trained embeddings.Experimental results show that the obtained OOV word embeddings improve not only word similarity tasks but also downstream tasks in Twitter and biomedical domains where OOV words often appear, even when the computed OOV embeddings are integrated into a BERT-based strong baseline.
Rethinking feature aggregation for deep RGB-D salient object detection

Two-stream UNet based architectures are widely used in deep RGB-D salient object detection (SOD) models. However, UNet only adopts a top-down decoder network to progressively aggregate high-level features with low-level ones. In this paper, we propose to enrich feature aggregation via holistic aggregation paths and an extra bottom-up decoder network. The former aggregates multi-level features holistically to learn abundant feature interactions while the latter aggregates improved low-level features with high-level features, thus promoting their representation ability. Aiming at the two-stream architecture, we propose another early aggregation scheme to aggregate and propagate multi-modal encoder features at each level, thereby improving the encoder capability. We also propose a factorized attention module to efficiently modulate the feature aggregation action for each feature node with multiple learned attention factors. Experimental results demonstrate that all of the proposed components can gradually improve RGB-D SOD results. Consequently, our final SOD model performs favorably against other state-of-the-art methods.
Provably Consistent Partial-Label Learning

Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods-none of the PLL methods hitherto possesses a generation process of candidate label sets, and then it is still unclear why such a method works on a specific dataset and when it may fail given a different dataset. In this paper, we propose the first generation model of candidate label sets, and develop two novel PLL methods that are guaranteed to be provably consistent, i.e., one is risk-consistent and the other is classifier-consistent. Our methods are advantageous, since they are compatible with any deep network or stochastic optimizer. Furthermore, thanks to the generation model, we would be able to answer the two questions above by testing if the generation model matches given candidate label sets. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed generation model and two PLL methods.
Diverse and Relevant Visual Storytelling with Scene Graph Embeddings

A problem in automatically generated stories for image sequences is that they use overly generic vocabulary and phrase structure and fail to match the distributional characteristics of human-generated text.We address this problem by introducing explicit representations for objects and their relations by extracting scene graphs from the images.Utilizing an embedding of this scene graph enables our model to more explicitly reason over objects and their relations during story generation, compared to the global features from an object classifier used in previous work.We apply metrics that account for the diversity of words and phrases of generated stories as well as for reference to narratively-salient image features and show that our approach outperforms previous systems.Our experiments also indicate that our models obtain competitive results on reference-based metrics.
A Shared-Private Representation Model with Coarse-to-Fine Extraction for Target Sentiment Analysis

Target sentiment analysis aims to detect opinion targets along with recognizing their sentiment polarities from a sentence.Some models with span-based labeling have achieved promising results in this task.However, the relation between the target extraction task and the target classification task has not been well exploited.Besides, the span-based target extraction algorithm has a poor performance on target phrases due to the maximum target length setting or length penalty factor.To address these problems, we propose a novel framework of Shared-Private Representation Model (SPRM) with a coarse-to-fine extraction algorithm.For jointly learning target extraction and classification, we design a Shared-Private Network, which encodes not only shared information for both tasks but also private information for each task.To avoid missing correct target phrases, we also propose a heuristic coarse-to-fine extraction algorithm that first gets the approximate interval of the targets by matching the nearest predicted start and end indexes and then extracts the targets by adopting an extending strategy.Experimental results show that our model achieves stateof-the-art performance.
EFEKTIVITAS FORUM DISKUSI PADA E-LEARNING BERBASIS MOODLE UNTUK MENINGKATKAN PARTISIPASI BELAJAR

The study aims to find out the effectiveness of the discussion forum on Moodle based elearning to improve students learning participation. The study employs pre-experiment method with one group pretest-posttest design and analysis method uses non-parametric statistics with T-test. The study is conducted at Information Technology Department of Faculty of Education at University of Technology Yogyakarta. The research sample is taken from the fourth semester student of Information Technology Education Department who enroll theory course. Instrument used in this study is guided observation to observe students learning participation with 2 answer alternatives. The study result shows that discussion forum on Moodle based e-learning can improve students learning participation compared to discussion occurred in the conventional classroom or classical discussion with the significant result of improvement for about 37%.
SPARQL QUERY GENERATION FOR COMPLEX QUESTION ANSWERING WITH BERT AND BILSTM-BASED MODEL

In this paper we describe question answering system for answering of complex questions over Wikidata knowledge base. Unlike simple questions, which require extraction of single fact from the knowledge base, complex questions are based on more than one triplet and need logical or comparative reasoning. The proposed question answering system translates a natural language question into a query in SPARQL language, execution of which gives an answer. The system includes the models which define the SPARQL query template corresponding to the question and then fill the slots in the template with entities, relations and numerical values. For entity detection we use BERTbased sequence labelling model. Ranking of candidate relations is performed in two steps with BiLSTM and BERT-based models. The proposed models are the first solution for LC-QUAD2.0 dataset. The system is capable of answering complex questions which involve comparative or boolean reasoning.
Mean-Field Solution of the Weak-Strong Cluster Problem for Quantum Annealing with Stoquastic and Non-Stoquastic Catalysts

We study the weak-strong cluster problem for quantum annealing in its mean-field version as proposed by Albash [Phys. Rev. A 99, 042334 (2019)] who showed by numerical diagonalization that non-stoquastic XX interactions (non-stoquastic catalysts) remove the problematic first-order phase transition. We solve the problem exactly in the thermodynamic limit by analytical methods and show that the removal of the first-order transition is successfully achieved either by stoquastic or non-stoquastic XX interactions depending on whether the XX interactions are introduced within the weak cluster, within the strong cluster, or between them. We also investigate the case where the interactions between the two clusters are sparse, i.e., not of the mean-field all-to-all type. The results again depend on where to introduce the XX interactions. We further analyze how inhomogeneous driving of the transverse field affects the performance of the system without XX interactions and find that inhomogeneity in the transverse field removes the first-order transition if appropriately implemented.
Analog nature of quantum adiabatic unstructured search

Abstract The quantum adiabatic unstructured search algorithm is one of only a handful of quantum adiabatic optimization algorithms to exhibit provable speedups over their classical counterparts. With no fault tolerance theorems to guarantee the resilience of such algorithms against errors, understanding the impact of imperfections on their performance is of both scientific and practical significance. We study the robustness of the algorithm against various types of imperfections: limited control over the interpolating schedule, Hamiltonian misspecification, and interactions with a thermal environment. We find that the unstructured search algorithm’s quadratic speedup is generally not robust to the presence of any one of the above non-idealities, and in some cases we find that it imposes unrealistic conditions on how the strength of these noise sources must scale to maintain the quadratic speedup.
Logo Recognition with the Use of Deep Convolutional Neural Networks

Automatic logo recognition is gaining importance due to the increasing number of its applications. Unlike other object recognition tasks, logo recognition is more challenging because of the limited amount of the available original data. In this paper, the transfer leaning technique was applied to a Deep Convolutional Neural Network model to guarantee logo recognition using a small computational overhead. The proposed method was based on the Densely Connected Convolutional Networks (DenseNet). The experimental results show that for the FlickrLogos-32 logo recognition dataset, our proposed method performs comparably with state-of-the-art methods while using fewer parameters.