Reviewerly/exHarmony huggingface dataset:
Shoe-Print Image Retrieval With Multi-Part Weighted CNN

Identifying shoe-print impressions in the scene of crime (SoC) from database images is a challenging problem in forensic science due to the complicated impressing surface, the partial absence of on-site impressions, and the huge domain gap between the query and the gallery images. The existing approaches pay much attention to feature extraction while ignoring its distinctive characteristics. In this paper, we propose a novel multi-part weighted convolutional neural network (MP-CNN) for shoe-print image retrieval. Specifically, the proposed CNN model processes images in three steps: 1) dividing the input images vertically into two parts and extracting sub-features by a parameter-shared network individually; 2) calculating the importance weight matrix of the sub-features based on the informative pixels they contained and concatenating them as the final feature, and; 3) using the triplet loss function to measure the similarity between the query and the gallery images. In addition to the proposed network, we adopt an effective strategy to enhance the quality of the images and to reduce the domain gap using the U-Net structure. The experimental evaluations demonstrate that our proposed method significantly outperforms other fine-grained cross-domain methods on SPID dataset and obtains comparative results with the state-of-the-art shoe-print retrieval methods on FID300 dataset.
Multi-Channel Convolutional Neural Network for Twitter Emotion and Sentiment Recognition

Jumayel Islam, Robert E. Mercer, Lu Xiao. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Calling for user-centric VR design research in hospitality and tourism

Virtual reality (VR) has enjoyed a steep growth in awareness in society in recent years and is considered a promising tool for the design and enhancement of experiences. However, as research and use cases in the hospitality context are expanding rapidly, it is crucial to define a clearer research direction that aligns the number of scattered studies across various fields. It is time to overcome the boundaries of the technological dimension and explore methods for purposeful VR design. This research note calls for more user-centric VR studies and developments to define the future direction of VR implementations in the hospitality and tourism industry. To achieve this, the authors recommend the use of design methodology with a focus on the first steps in the design process to clearly identify and understand customers’ needs and desires independent of VR technology.
Classification and comparison via neural networks

We consider learning from comparison labels generated as follows: given two samples in a dataset, a labeler produces a label indicating their relative order. Such comparison labels scale quadratically with the dataset size; most importantly, in practice, they often exhibit lower variance compared to class labels. We propose a new neural network architecture based on siamese networks to incorporate both class and comparison labels in the same training pipeline, using Bradley-Terry and Thurstone loss functions. Our architecture leads to a significant improvement in predicting both class and comparison labels, increasing classification AUC by as much as 35% and comparison AUC by as much as 6% on several real-life datasets. We further show that, by incorporating comparisons, training from few samples becomes possible: a deep neural network of 5.9 million parameters trained on 80 images attains a 0.92 AUC when incorporating comparisons.
Multi-grained Attention with Object-level Grounding for Visual Question Answering

Attention mechanisms are widely used in Visual Question Answering (VQA) to search for visual clues related to the question.Most approaches train attention models from a coarsegrained association between sentences and images, which tends to fail on small objects or uncommon concepts.To address this problem, this paper proposes a multi-grained attention method.It learns explicit wordobject correspondence by two types of wordlevel attention complementary to the sentenceimage association.Evaluated on the VQA benchmark, the multi-grained attention model achieves competitive performance with stateof-the-art models.And the visualized attention maps demonstrate that addition of objectlevel groundings leads to a better understanding of the images and locates the attended objects more precisely.
The Impact of Using the Interactive E-Book on Students’ Learning Outcomes

This study aimed to determine the impact of using the interactive e-book media on the students' learning outcomes in the historical topic of the social subject of the seventh class of junior high schools.This study employed the experimental research method involving 59 students in the control group and 59 students in the experimental group.The results showed that the mean score of the experimental group before using the e-book was 54.78 and the mean score after the treatment of using e-books was 81.69.This means that there has been an increase in the mean score by 26.91 after using the interactive e-book in the social subject.In addition, the results of the comparative test of the knowledge between the control and experimental groups showed that the experimental group had a mean score of 81.69 higher than the control group, which was 64.75 with the Sig.value of (2tailed) 0.000 < 0.05 which means that there are significant differences in the scores between the control group and the experimental group.Hence, the interactive ebook media is effective to be used to improve the students' knowledge in the social subject of the seventh class.
Optimal Filtering and a Smoothing Algorithm for a Singular System With a Complex Stochastic Uncertain Parameter Matrix

This brief considers estimation algorithms for linear singular systems with random uncertain parameter matrices. We propose an optimal filtering algorithm and an optimal fixed-interval smoothing algorithm to filter out artifacts, such as noise, from the parameter matrix by using the projection theorem and an equivalent transformation. A performance analysis and simulation results verify the proposed algorithms.
Challenging Machine Learning Algorithms in Predicting Vulnerable JavaScript Functions

The rapid rise of cyber-crime activities and the growing number of devices threatened by them place software security issues in the spotlight. As around 90% of all attacks exploit known types of security issues, finding vulnerable components and applying existing mitigation techniques is a viable practical approach for fighting against cyber-crime. In this paper, we investigate how the state-of-the-art machine learning techniques, including a popular deep learning algorithm, perform in predicting functions with possible security vulnerabilities in JavaScript programs. We applied 8 machine learning algorithms to build prediction models using a new dataset constructed for this research from the vulnerability information in public databases of the Node Security Project and the Snyk platform, and code fixing patches from GitHub. We used static source code metrics as predictors and an extensive grid-search algorithm to find the best performing models. We also examined the effect of various re-sampling strategies to handle the imbalanced nature of the dataset. The best performing algorithm was KNN, which created a model for the prediction of vulnerable functions with an F-measure of 0.76 (0.91 precision and 0.66 recall). Moreover, deep learning, tree and forest based classifiers, and SVM were competitive with F-measures over 0.70. Although the F-measures did not vary significantly with the re-sampling strategies, the distribution of precision and recall did change. No re-sampling seemed to produce models preferring high precision, while re-sampling strategies balanced the IR measures.
Digital Image Correlation Applications in Composite Automated Manufacturing, Inspection, and Testing

Since its advent in the 1970s, digital image correlation (DIC) applications have been rapidly growing in different engineering fields including composite material testing and analysis. DIC combined with a stereo camera system offers full-field measurements of three-dimensional shapes, deformations (i.e., in-plane and out-of-plane deformations), and surface strains, which are of most interest in many structural testing applications. DIC systems have been used in many conventional structural testing applications in composite structures. However, DIC applications in automated composite manufacturing and inspection are scarce. There are challenges in inspection of a composite ply during automated manufacturing of composites and in measuring transient strain during in-situ manufacturing of thermoplastic composites. This article presents methodologies using DIC techniques to address these challenges. First, a few case studies where DIC was used in composite structural testing are presented, followed by development of new applications for DIC in composite manufacturing and inspection.
Extended Kalman Filtering With Nonlinear Equality Constraints: A Geometric Approach

In this paper, we focus on extended Kalman filtering (EKF), in the difficult case where a function of the state has been perfectly observed, and is thus known with certainty, while the full state still has unobserved degrees of freedom. In the linear case, the Kalman filter seamlessly handles such constraints, which result in the state being in an affine subspace. Yet, in the nonlinear case, the EKF poorly handles such type of constraints. As a remedy, we propose a novel general methodology of EKF based on an (arbitrary) nonlinear error e. And we prove that under compatibility of the error e with the constraints, the EKF based on e seamlessly handles the constraints. Furthermore, when the state space is a Lie group, we prove the EKF based on invariant errors is exactly the invariant EKF (IEKF), and we prove further properties. The theory is applied to the problem of simultaneous localization and mapping, where the IEKF is shown to perfectly handle some partial deterministic information about the map. As a byproduct, the theory is also shown to readily allow devising EKFs on state spaces defined by a class of equality constraints.
Gender Differences in Perceptions of Trolling in Livestream Video Broadcasting

Troll is a commonly used term for disinhibited individuals who create havoc as they lurk and lash out with the purpose of disrupting the environment in online communities. This research focuses on the phenomenon of trolling in a livestream video broadcast scenario. This study investigates the extent that trolling occurs in a livestream, whether there is a difference in the degree of trolling in broadcasts based on gender, and is there a difference in the extent of trolling behavior based on the gender of the viewer. Data were collected from 998 viewers that participate in the live broadcast platform called Twitch through an online survey. Participants answered questions regarding their experiences with overly aggressive, derogatory, and negative behavior, generally trolling, in the channel of their favorite broadcaster. Questions addressed the degree of acceptability of trolling and likelihood of intervention. Findings indicate that female viewers were more sensitive than male viewers in picking up trolling cues. Both males and females agreed that female broadcasters are more likely to be targets of sexual harassment trolling. Male viewers felt that it was more acceptable when male broadcasters were harassed than female broadcasters. Finally, gender is a factor determining intervention behavior.
Design and Optimization for Storage Mechanism of the Public Blockchain Based on Redundant Residual Number System

Blockchain has a broad development potential and application prospects. At present, huge storage volume on each node has become one of the primary bottlenecks that restrict the expansibility of blockchain. Optimization for storage mechanism has become a necessary way to accelerate the improvement of blockchain. There are a variety of solutions to relieve the storage burden by modifying the architecture of blockchain, but most of them weaken the decentralization property of the system, which is the core advantages of the blockchain. In this paper, an optimization scheme based on the redundant residual number system is proposed to dramatically reduce the storage volume of nodes in the blockchain system, and fault tolerance mechanism is designed based on the new storage scheme. The simulation experiments are performed to prove the effectiveness of the proposed mechanism.
A Generic-Driven Wrapper Embedded With Feature-Type-Aware Hybrid Bayesian Classifier for Breast Cancer Classification

Breast cancer is one of the most common cancers diagnosed in women. For preventive diagnosis, feature selection is an essential step to construct the breast cancer classifier. The features of a real breast cancer dataset are usually composed of discrete and continuous ones. Also, the Area Under the Curve (AUC) of the receiver operating characteristic receives more attention in such a medical field. The existing research work is insufficient to take into account both the hybrid trait of the features and the specific classification objective. We have proposed a wrapper method, i.e., a integrated framework in which Bayesian classifiers are embedded for the feature selection of breast cancer datasets. To deal with both the discrete features and the continuous features, we adopt the naive approach for the discrete features but the kernel probability density estimation for the continuous ones, respectively, which leads to feature-type-aware hybrid Bayesian classifiers. All the classifiers are fed with different feature subsets and evaluated by their AUC metrics as the fitness indexes. Thus, with the genetic algorithm, we can obtain a near optimal feature subset, which yields a good AUC metric with its corresponding classifiers. Moreover, the one-class F-score is used to help enhance the convergence of the algorithm. Experiments are done both with the continuous Wisconsin diagnostic breast cancer dataset and the real breast cancer dataset for Chinese women. The results prove that the proposed wrapper is feasible, accurate and efficient, compared with the related genetic algorithm based approaches.
A Deep Generative Model for Code Switched Text

Code-switching, the interleaving of two or more languages within a sentence or discourse is pervasive in multilingual societies. Accurate language models for code-switched text are critical for NLP tasks. State-of-the-art data-intensive neural language models are difficult to train well from scarce language-labeled code-switched text. A potential solution is to use deep generative models to synthesize large volumes of realistic code-switched text. Although generative adversarial networks and variational autoencoders can synthesize plausible monolingual text from continuous latent space, they cannot adequately address code-switched text, owing to their informal style and complex interplay between the constituent languages. We introduce VACS, a novel variational autoencoder architecture specifically tailored to code-switching phenomena. VACS encodes to and decodes from a two-level hierarchical representation, which models syntactic contextual signals in the lower level, and language switching signals in the upper layer. Sampling representations from the prior and decoding them produced well-formed, diverse code-switched sentences. Extensive experiments show that using synthetic code-switched text with natural monolingual data results in significant (33.06\%) drop in perplexity.
A Hybrid Classification Framework Based on Clustering

The traditional supervised classification algorithms tend to focus on uncovering the relationship between sample attributes and the class labels; they seldom consider the potential structural characteristics of the sample space, often leading to unsatisfactory classification results. To improve the performance of classification models, many scholars have sought to construct hybrid models by combining both supervised and unsupervised learning. Although the existing hybrid models have shown significant potential in industrial applications, our experiments indicate that some shortcomings remain. With the aim of overcoming such shortcomings of the existing hybrid models, this article proposes a hybrid classification framework based on clustering (HCFC). First, it applies a clustering algorithm to partition the training samples into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> clusters. It then constructs a clustering-based attribute selection measure—namely, the hybrid information gain ratio, based upon which it then trains a C4.5 decision tree. Depending on the differences in the clustering algorithms used, this article constructs two different versions of the HCFC (HCFC-K and HCFC-D) and tests them on eight benchmark datasets in the healthcare and disease diagnosis industries and on 15 datasets from other fields. The results indicate that both versions of the HCFC achieve a comparable or even better classification performance than the other three hybrid and six single models considered. In addition, the HCFC-D has a stronger ability to resist class noise compared with the HCFC-K.
Grand Challenge of 106-Point Facial Landmark Localization

Facial landmark localization is a very crucial step in numerous face related applications, such as face recognition, facial pose estimation, face image synthesis, etc. However, previous competitions on facial landmark localization (i.e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components. In order to overcome this problem, we construct a challenging dataset, named JD-landmark. Each image is manually annotated with 106-point landmarks. This dataset covers large variations on pose and expression, which brings a lot of difficulties to predict accurate landmarks. We hold a 106-point facial landmark localization competition <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> on this dataset in conjunction with IEEE International Conference on Multimedia and Expo (ICME) 2019. The purpose of this competition is to discover effective and robust facial landmark localization approaches.
Single-Photon Sources

Exploring and extending the promise of quantum technologies requires the ability to work with single quantum objects&#8212;especially photons. Here&#8217;s a look at the range of single-photon-source technologies available, and how and where they work.
Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis

In this work, we investigate the presence of occupational gender stereotypes in sentiment analysis models.Such a task has implications for reducing implicit biases in these models, which are being applied to an increasingly wide variety of downstream tasks.We release a new gender-balanced dataset 1 of 800 sentences pertaining to specific professions and propose a methodology for using it as a test bench to evaluate sentiment analysis models.We evaluate the presence of occupational gender stereotypes in 3 different models using our approach, and explore their relationship with societal perceptions of occupations.
High-level mission specification for multiple robots

Mobile robots are increasingly used in our everyday life to autonomously realize missions. A variety of languages has been proposed to support roboticists in the systematic development of robotic applications, ranging from logical languages with well-defined semantics to domain-specific languages with user-friendly syntax. The characteristics of both of them have distinct advantages, however, developing a language that combines those advantages remains an elusive task. We present PROMISE, a novel language that enables domain experts to specify missions on a high level of abstraction for teams of autonomous robots in a user-friendly way, while having well-defined semantics. Our ambition is to permit users to specify high-level goals instead of a series of specific actions the robots should perform. The language contains a set of atomic tasks that can be executed by robots and a set of operators that allow the composition of these tasks in complex missions. The language is supported by a standalone tool that permits mission specification through a textual and a graphical interface and that can be integrated within a variety of frameworks. We integrated PROMISE with a software platform providing functionalities such as motion control and planning. We conducted experiments to evaluate the correctness of the specification and execution of complex robotic missions with both simulators and real robots. We also conducted two user studies to assess the simplicity of PROMISE. The results show that PROMISE effectively supports users to specify missions for robots in a user-friendly manner.
Causality-Based Attribute Weighting via Information Flow and Genetic Algorithm for Naive Bayes Classifier

Naive Bayes classifier (NBC) is an effective classification technique in data mining and machine learning, which is based on the attribute conditional independence assumption. However, this assumption rarely holds true in real-world applications, so numerous researches have been made to alleviate the assumption by attribute weighting. To the best of our knowledge, almost all studies have calculated attribute weights according to correlation measure or classification accuracy. In this paper, we propose a novel causality-based attribute weighting method to establish the weighted NBC called IFG-WNBC, where causal information flow (IF) theory and genetic algorithm (GA) are adopted to search for optimal weights. The introduction of IF produces a bran-new weight measure criterion from the angle of causality other than correlation. The population initialization in GA is also improved with IF-based weights for efficient optimization. Multi-set of comparison experiments on UCI data sets demonstrate that IFG-WNBC achieves superiority over classic NBC and other common weighted NBC algorithms in classification accuracy and running time.
Auto-Sizing the Transformer Network: Improving Speed, Efficiency, and Performance for Low-Resource Machine Translation

Neural sequence-to-sequence models, particularly the Transformer, are the state of the art in machine translation.Yet these neural networks are very sensitive to architecture and hyperparameter settings.Optimizing these settings by grid or random search is computationally expensive because it requires many training runs.In this paper, we incorporate architecture search into a single training run through autosizing, which uses regularization to delete neurons in a network over the course of training.On very low-resource language pairs, we show that auto-sizing can improve BLEU scores by up to 3.9 points while removing one-third of the parameters from the model.
Findings of the Fourth Workshop on Neural Generation and Translation

We describe the finding of the Fourth Workshop on Neural Generation and Translation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2020).First, we summarize the research trends of papers presented in the proceedings.Second, we describe the results of the three shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document-level generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language and 3) STAPLE task: creation of as many possible translations of a given input text.This last shared task was organised by Duolingo.