Reviewerly/exHarmony huggingface dataset:
Relationships Between Personality Features and the Rubber Hand Illusion: An Exploratory Study

The rubber hand illusion paradigm allows investigating human body ownership by inducing an illusion of owning a life-sized fake hand. Despite the wide consensus on the fact that integration of multisensory signals is the main interpretative framework of the rubber hand illusion, increasing amount of data show that additional factors might contribute to the emergence of the illusion and, in turn, explain the strong inter-individual differences of the illusory patterns. Here, we explored whether and how personality features contribute to the emergence of the illusion by administering to healthy participants the rubber hand illusion paradigm along with two well-known personality tests, i.e., the Personality Assessment Inventory (PAI) and the Rorschach test. Results showed that two Rorschach domains (i.e., "Perception and Thinking Problems" and "Self and Other Representation") were positively correlated with the illusory mislocalization of the own left hand toward the fake hand. Further analyses suggested that while the tendency to perceive unconventionally is related to mislocalizing the own hand toward the fake hand, the association of the RHI index and other personality features measured by the Rorschach remain uncertain. However, our findings in general suggest that personality features might have a role in the emergence of the rubber hand illusion. This, in turn, could explain the high inter-individual variability of the illusory effects.
Advanced iterative algorithm for phase extraction: performance evaluation and enhancement

Phase-shifting techniques are extremely important in modern optical metrology.The advanced iterative algorithm (AIA) is an elegant, flexible and effective phase-shifting algorithm that can extract phase from fringe patterns with arbitrary unknown phase-shifts.However, comparing it with traditional phase-shifting algorithms, AIA has not been sufficiently investigated on (i) its applicability to different types of fringe patterns; (ii) its performance with respect to different phase-shifts, frame numbers and noise levels and thus the possibility of further improvement; and (iii) the predictability of its accuracy.To solve these problems, a series of innovations are proposed in this paper.First, condition numbers are introduced to characterize the least squares matrices used in AIA, and subsequently a fringe density requirement is suggested for the success of AIA.Second, the performance of AIA regarding different phase-shifts, frame numbers and noise levels is thoroughly evaluated by simulations, based on which, an overall phase error model is established.With such understanding, three individual improvements of AIA, i.e., controlling phase-shifts, controlling frame numbers and suppressing noise, are proposed for better performance of AIA.Third, practical methods for estimating the overall phase errors are developed to make the AIA performance predictable even before AIA is executed.We then integrate all these three innovations into an enhanced AIA (eAIA), which solves all the problems we mentioned earlier.The significant contributions of eAIA include the insurability of the convergence, the controllability of the performance, and achievability of a desired accuracy.An experiment is carried out to demonstrate the effectiveness of eAIA.
Towards Building a Multilingual Sememe Knowledge Base: Predicting Sememes for BabelNet Synsets

A sememe is defined as the minimum semantic unit of human languages. Sememe knowledge bases (KBs), which contain words annotated with sememes, have been successfully applied to many NLP tasks. However, existing sememe KBs are built on only a few languages, which hinders their widespread utilization. To address the issue, we propose to build a unified sememe KB for multiple languages based on BabelNet, a multilingual encyclopedic dictionary. We first build a dataset serving as the seed of the multilingual sememe KB. It manually annotates sememes for over 15 thousand synsets (the entries of BabelNet). Then, we present a novel task of automatic sememe prediction for synsets, aiming to expand the seed dataset into a usable KB. We also propose two simple and effective models, which exploit different information of synsets. Finally, we conduct quantitative and qualitative analyses to explore important factors and difficulties in the task. All the source code and data of this work can be obtained on https://github.com/thunlp/BabelNet-Sememe-Prediction.
Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search

Vector quantization (VQ) techniques are widely used in similarity search for data compression, computation acceleration and etc. Originally designed for Euclidean distance, existing VQ techniques (e.g., PQ, AQ) explicitly or implicitly minimize the quantization error. In this paper, we present a new angle to analyze the quantization error, which decomposes the quantization error into norm error and direction error. We show that quantization errors in norm have much higher influence on inner products than quantization errors in direction, and small quantization error does not necessarily lead to good performance in maximum inner product search (MIPS). Based on this observation, we propose norm-explicit quantization (NEQ) — a general paradigm that improves existing VQ techniques for MIPS. NEQ quantizes the norms of items in a dataset explicitly to reduce errors in norm, which is crucial for MIPS. For the direction vectors, NEQ can simply reuse an existing VQ technique to quantize them without modification. We conducted extensive experiments on a variety of datasets and parameter configurations. The experimental results show that NEQ improves the performance of various VQ techniques for MIPS, including PQ, OPQ, RQ and AQ.
Vector Quantization-Based Regularization for Autoencoders

Autoencoders and their variations provide unsupervised models for learning low-dimensional representations for downstream tasks. Without proper regularization, autoencoder models are susceptible to the overfitting problem and the so-called posterior collapse phenomenon. In this paper, we introduce a quantization-based regularizer in the bottleneck stage of autoencoder models to learn meaningful latent representations. We combine both perspectives of Vector Quantized-Variational AutoEncoders (VQ-VAE) and classical denoising regularization methods of neural networks. We interpret quantizers as regularizers that constrain latent representations while fostering a similarity-preserving mapping at the encoder. Before quantization, we impose noise on the latent codes and use a Bayesian estimator to optimize the quantizer-based representation. The introduced bottleneck Bayesian estimator outputs the posterior mean of the centroids to the decoder, and thus, is performing soft quantization of the noisy latent codes. We show that our proposed regularization method results in improved latent representations for both supervised learning and clustering downstream tasks when compared to autoencoders using other bottleneck structures.
V-PROM: A Benchmark for Visual Reasoning Using Visual Progressive Matrices

Advances in machine learning have generated increasing enthusiasm for tasks that require high-level reasoning on top of perceptual capabilities, particularly over visual data. Such tasks include, for example, image captioning, visual question answering, and visual navigation. Their evaluation is however hindered by task-specific confounding factors and dataset biases. In parallel, the existing benchmarks for abstract reasoning are limited to synthetic stimuli (e.g. images of simple shapes) and do not capture the challenges of real-world data. We propose a new large-scale benchmark to evaluates abstract reasoning over real visual data. The test involves visual questions that require operations fundamental to many high-level vision tasks, such as comparisons of counts and logical operations on complex visual properties. The benchmark measures a method's ability to infer high-level relationships and to generalise them over image-based concepts. We provide multiple training/test splits that require controlled levels of generalization. We evaluate a range of deep learning architectures, and find that existing models, including those popular for vision-and-language tasks, are unable to solve seemingly-simple instances. Models using relational networks fare better but leave substantial room for improvement.
Relation Extraction from Arabic Wikipedia

Objectives/Methods: This study aims to extract relations between entities from Arabic text.RelationExtraction is one of the most important tasks in text mining.Relation extraction is considered as a main step for many applications such as extracting triples from the text, Question Answering and Ontology building.However, extracting relations from the Arabic text is a difficult task compared to English due to lack of annotated Arabic corpora.This paper proposes a method for extracting relations from Arabic text based on ArabicWikipedia articles characteristics.The propose system extracts sentences that contain principle entity, secondary entity and relation from Wikipedia article, then we use WordNet and DBpedia to build the training set.Finally Naive Bayes Classifier is used to train and test the datasets.Finding: There are few works to extract relations from Arabic text.These works depend on classification, clustering and rule based.Application/ improvement: The experiments show the effectiveness of the proposed approach which achieves high precision with 89% for classifying 19 type of semantic relations.• Existence of implicit relations between two entities.• The absence of the actor in the Arabic sentences.This causes some problems such asdetermining the distance between the two entities.• There is no positive relation in view of negative words in a sentence.
Learning Support System using Chatbot in "Kejar C Package" Homeschooling Program

The research objective was to develop a learning media support system using chatbot technology that was integrated with the LINE messaging mobile application and content management system (CMS) with homeschooling students and teachers as target users. The renewal of this research is to add access to subject matter features and discussion forum features. These features do not yet exist in the chatbot application system in other studies. Authentication on the chatbot is needed to find out who the user is accessing the chatbot for the material, discussion forum, and work on the problem training and National Examination exercises. E-learning innovation from chatbots is integrated with the LINE and CMS messaging mobile application that contains subject matter, exam practice, discussion forums, and evaluation of student evaluations. This system supports students who have limited time to attend traditional face-to-face classes and have limited access to laptops. The results of this study prove that Chatbot on smartphones can maximize student learning functions, increase student interest and achievement (value).
Open Domain Event Text Generation

Text generation tasks aim at generating human-readable text from different kinds of data. Normally, the generated text only contains the information included in the data and its application is thus restricted to some limited scenarios. In this paper, we extend the task to an open domain event text generation scenario with an entity chain as its skeleton. Specifically, given an entity chain containing several related event entities, the model should retrieve from a trustworthy repository (e.g. Wikipedia) the detailed information of these entities and generate a description text based on the retrieved sentences. We build a new dataset called WikiEvent1 that provides 34K pairs of entity chain and its corresponding description sentences. To solve the problem, we propose a wiki augmented generator framework that contains an encoder, a retriever, and a decoder. The encoder encodes the entity chain into a hidden space while the decoder decodes from the hidden space and generates description text. The retriever retrieves relevant text from a trustworthy repository which provides more information for generation. To alleviate the overfitting problem, we propose a novel random drop component that randomly deletes words from the retrieved sentences making our model more robust for handling long input sentences. We apply the proposed model on the WikiEvent dataset and compare it with a few baselines. The experimental results show that our carefully-designed architecture does help generate better event text, and extensive analysis further uncovers the characteristics of the proposed task.
Structure-Aware Feature Fusion for Unsupervised Domain Adaptation

Unsupervised domain Adaptation (UDA) aims to learn and transfer generalized features from a labelled source domain to a target domain without any annotations. Existing methods only aligning high-level representation but without exploiting the complex multi-class structure and local spatial structure. This is problematic as 1) the model is prone to negative transfer when the features from different classes are misaligned; 2) missing the local spatial structure poses a major obstacle in performing the fine-grained feature alignment. In this paper, we integrate the valuable information conveyed in classifier prediction and local feature maps into global feature representation and then perform a single mini-max game to make it domain invariant. In this way, the domain-invariant feature not only describes the holistic representation of the original image but also preserves mode-structure and fine-grained spatial structural information. The feature integration is achieved by estimating and maximizing the mutual information (MI) among the global feature, local feature and classifier prediction simultaneously. As the MI is hard to measure directly in high-dimension spaces, we adopt a new objective function that implicitly maximizes the MI via an effective sampling strategy and a discriminator design. Our STructure-Aware Feature Fusion (STAFF) network achieves the state-of-the-art performances in various UDA datasets.
Synthesizing Action Sequences for Modifying Model Decisions

When a model makes a consequential decision, e.g., denying someone a loan, it needs to additionally generate actionable, realistic feedback on what the person can do to favorably change the decision. We cast this problem through the lens of program synthesis, in which our goal is to synthesize an optimal (realistically cheapest or simplest) sequence of actions that if a person executes successfully can change their classification. We present a novel and general approach that combines search-based program synthesis and test-time adversarial attacks to construct action sequences over a domain-specific set of actions. We demonstrate the effectiveness of our approach on a number of deep neural networks.
A preliminary study on the treatment of restaurant wastewater using electrocoagulation technique

Restaurant Wastewater (RW) is characterized by high volume and strength, with minimal treatment before its release into the environment. Electrocoagulation (EC) could be a better alternative due to its environmental friendliness. The study investigates the use of EC in RW treatment. The samples were collected from the University of Ibadan student Union Building restaurant, between 7-9 am. The EC consisted of an aerator, carbon electrodes, power source, a beaker, and stirrer. Electrodes were connected in mono-polar system at inter-distance of 10 mm, with retention times of 0, 30, 60 and 90 minutes. The RW was aerated before passing direct currents of 0.25, 0.5, 0.75, and 1.0 Amperes. The Colour, Particulate Phosphorus (PP), Soluble Reactive Polyphosphate (PO 4 3- ), Phosphorus Pentoxide (P 2 O 5 ), Chemical Oxygen Demand (COD) and Dissolved Oxygen (DO) were determined. The study was three replicates. Mean percentage removal efficiencies for PP, PO 4 3- , P 2 O 5 , COD, DO and colour at 30 minutes (16.85, 19.33, 17.15, 12.58, -32.88 and 20.12%), 60 minutes (30.88, 27.1, 30.57, 21.13, -57.18 and 34.30%), and 90 minutes (40.75, 33.00, 32.83, 25.83, -87.88 and 40.7%) respectively. Better treatment efficiencies of treated wastewater using the electrolytic reactor were obtained with an increase in direct current passing through.
A split target detection and tracking algorithm for ballistic missile tracking during the re-entry phase

In the re-entry phase of a ballistic missile, decoys can be deployed as a mean to overburden enemy defenses. This results in a single track being split into multiple track-lets. Tracking of these track-lets is a critical task as any miss in the tracking procedure can become a cause of a major threat. The tracking process becomes more complicated in the presence of clutter. The low detection rate is one of the factors that may contribute to increasing the difficulty level in terms of tracking in the cluttered environment. This work introduces a new algorithm for the split event detection and target tracking under the framework of the joint integrated probabilistic data association (JIPDA) algorithm. The proposed algorithm is termed as split event-JIPDA (SE-JIPDA). This work establishes the mathematical foundation for the split target detection and tracking mechanism. The performance analysis is made under different simulation conditions to provide a clear insight into the merits of the proposed algorithm. The performance parameters in these simulations are the root mean square error (RMSE), confirmed true track rate (CTTR) and confirmed split true track rate (CSTTR).
HAMNER: Headword Amplified Multi-Span Distantly Supervised Method for Domain Specific Named Entity Recognition

To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming. On the contrary, distantly supervised methods acquire automatically annotated data using dictionaries to alleviate this requirement. Unfortunately, dictionaries hinder the effectiveness of distantly supervised methods for NER due to its limited coverage, especially in specific domains. In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. We generalize the distant supervision by extending the dictionary with headword based non-exact matching. We apply a function to better weight the matched entity mentions. We propose a span-level model, which classifies all the possible spans then infers the selected spans with a proposed dynamic programming algorithm. Experiments on all three benchmark datasets demonstrate that our method outperforms previous state-of-the-art distantly supervised methods.
Enhancing Unsupervised Requirements Traceability with Sequential Semantics

Requirements traceability provides important support throughout all software life cycle; however, creating such links manually is time-consuming and error-prone. Supervised automated solutions use machine learning or deep learning techniques to generate trace links, but require large labeled dataset to train an effective model. Unsupervised solutions as word embedding approaches can generate links by capturing the semantic meaning of artifacts and are gaining more attention. Despite that, our observation revealed that, besides the semantic information, the sequential information of terms in the artifacts would provide additional assistance for building the accurate links. This paper proposes an unsupervised requirements traceability approach (named S2Trace) which learns the Sequential Semantics of software artifacts to generate the trace links. Its core idea is to mine the sequential patterns and use them to learn the document embedding representation. Evaluation is conducted on five public datasets, and results show that our approach outperforms three typical baselines. The modeling of sequential information in this paper provides new insights into the unsupervised traceability solutions, and the improvement in the traceability accuracy further proves the usefulness of the sequential information.
Forgetting to Learn Logic Programs

Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other programs. To improve learning performance, we explore the idea of forgetting, where a learner can additionally remove programs from its BK. We consider forgetting in an inductive logic programming (ILP) setting. We show that forgetting can significantly reduce both the size of the hypothesis space and the sample complexity of an ILP learner. We introduce Forgetgol, a multi-task ILP learner which supports forgetting. We experimentally compare Forgetgol against approaches that either remember or forget everything. Our experimental results show that Forgetgol outperforms the alternative approaches when learning from over 10,000 tasks.
Parsing as Pretraining

Recent analyses suggest that encoders pretrained for language modeling capture certain morpho-syntactic structure. However, probing frameworks for word vectors still do not report results on standard setups such as constituent and dependency parsing. This paper addresses this problem and does full parsing (on English) relying only on pretraining architectures – and no decoding. We first cast constituent and dependency parsing as sequence tagging. We then use a single feed-forward layer to directly map word vectors to labels that encode a linearized tree. This is used to: (i) see how far we can reach on syntax modelling with just pretrained encoders, and (ii) shed some light about the syntax-sensitivity of different word vectors (by freezing the weights of the pretraining network during training). For evaluation, we use bracketing F1-score and las, and analyze in-depth differences across representations for span lengths and dependency displacements. The overall results surpass existing sequence tagging parsers on the ptb (93.5%) and end-to-end en-ewt ud (78.8%).
An Iterative Polishing Framework Based on Quality Aware Masked Language Model for Chinese Poetry Generation

Owing to its unique literal and aesthetical characteristics, automatic generation of Chinese poetry is still challenging in Artificial Intelligence, which can hardly be straightforwardly realized by end-to-end methods. In this paper, we propose a novel iterative polishing framework for highly qualified Chinese poetry generation. In the first stage, an encoder-decoder structure is utilized to generate a poem draft. Afterwards, our proposed Quality-Aware Masked Language Model (QA-MLM) is employed to polish the draft towards higher quality in terms of linguistics and literalness. Based on a multi-task learning scheme, QA-MLM is able to determine whether polishing is needed based on the poem draft. Furthermore, QA-MLM is able to localize improper characters of the poem draft and substitute with newly predicted ones accordingly. Benefited from the masked language model structure, QA-MLM incorporates global context information into the polishing process, which can obtain more appropriate polishing results than the unidirectional sequential decoding. Moreover, the iterative polishing process will be terminated automatically when QA-MLM regards the processed poem as a qualified one. Both human and automatic evaluation have been conducted, and the results demonstrate that our approach is effective to improve the performance of encoder-decoder structure.