Reviewerly/exHarmony huggingface dataset:
The Effects of Motion Artifacts on Self-Avatar Agency

One way of achieving self-agency in virtual environments is by using a motion capture system and retargeting user’s motion to the virtual avatar. In this study, we investigated whether the self-agency is affected when motion artifacts appear on top of the baseline motion capture data assigned to the self-avatar. For this experiment, we implemented four artifacts: noise, latency, motion jump, and offset rotation of joints. The data provided directly from the motion capture system formed the baseline of the study. We developed three observation tasks to assess self-agency: self-observation, observation through a virtual mirror, and observation during locomotion. A questionnaire was adopted and used to capture the self-agency of participants. We analyzed the collected responses of participants to determine whether the motion artifacts significantly altered the participants’ sense of self-agency. The obtained results indicated that participants are not always sensitive to the motion artifacts assigned to the self-avatar, but the sense of self-agency is dependent on the observation task they were asked to perform. Implications for further research are discussed.
Effective Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion and Improved Nonmaximal Suppression Algorithm

Aiming at the problem of insufficient representation ability of weak and small objects and overlapping detection boxes in airplane object detection, an effective airplane detection method in remote sensing images based on multilayer feature fusion and an improved nonmaximal suppression algorithm is proposed. Firstly, based on the common low-level visual features of natural images and airport remote sensing images, region-based convolutional neural networks are chosen to conduct transfer learning for airplane images using a limited amount of data. Then, the L2 norm normalization, feature connection, scale scaling, and feature dimension reduction are introduced to achieve effective fusion of low- and high-level features. Finally, a nonmaximal suppression method based on a soft decision function is proposed to solve the overlap problem of detection boxes. The experimental results show that the proposed method can effectively improve the representation ability of weak and small objects, as well as quickly and accurately detect airplane objects in the airport area.
Video-Based Abnormal Driving Behavior Detection via Deep Learning Fusions

Video-based abnormal driving behavior detection is becoming more and more popular for the time being, as it is highly important in ensuring safeties of drivers and passengers in the vehicle, and it is an essential step in realizing automatic driving at the current stage.Thanks to recent developments in deep learning techniques, this challenging detection task can be largely facilitated via the prominent generalization capability of sophisticated deep learning models as well as large volumes of video clips which are indispensable for thoroughly training these data-driven deep learning models.In this paper, deep learning fusion techniques are emphasized, and three novel deep learning-based fusion models inspired by the recently proposed and popular densely connected convolutional network (DenseNet) are introduced, to fulfill the video-based abnormal driving behavior detection task for the first time.These three new deep learning-based fusion models are named as the wide group densely (WGD) network, the wide group residual densely (WGRD) network, and the alternative wide group residual densely (AWGRD) network, respectively.Technically, WGD takes important issues of deep learning models, i.e., the depth, the width and the cardinality, into consideration when designing its model structure based on DenseNet.For the WGRD and AWGRD, they are more sophisticated as the important idea of residual networks with superpositions of previous layers is incorporated.The extensive experiments are conducted to verify the effectiveness of three new models.Their superiority has been suggested based on rigorous comparisons towards several popular deep learning models in this video-based abnormal driving behavior detection study.
Front-End Smart Visual Sensing and Back-End Intelligent Analysis: A Unified Infrastructure for Economizing the Visual System of City Brain

The visual data, which are acquired from the ubiquitous visual sensors deployed in metropolitans, are of great value and paramount significance to enhance the effectiveness and pursue the future development of smart cities. In this paper, the essential building blocks of the unified visual data management and analysis infrastructure that serve as the foundation for the economical visual system in the city brain, are introduced to facilitate the utilization of the visual signal in the artificial intelligence era. In particular, we start by the discussion of the front-end smart visual sensing in the context of economical communication and service with the heterogeneous network, and the functionalities and necessities of compact visual feature and deep learning model representations are detailed. Subsequently, the utilities of the infrastructure are demonstrated through two intelligent applications at the back-end, including vehicle re-identification and person re-identification. The standardizations regarding compact feature and deep neural network representations, which are regarded as the key ingredients in this infrastructure and greatly facilitate the construction of the visual system in the city brain, are also discussed. Finally, we envision how the potential issues regarding the economical visual communications for future smart cities might be pragmatically approached within this unified infrastructure.
A gaze model improves autonomous driving

End-to-end behavioral cloning trained by human demonstration is now a popular approach for vision-based autonomous driving. A deep neural network maps drive-view images directly to steering commands. However, the images contain much task-irrelevant data. Humans attend to behaviorally relevant information using saccades that direct gaze towards important areas. We demonstrate that behavioral cloning also benefits from active control of gaze. We trained a conditional generative adversarial network (GAN) that accurately predicts human gaze maps while driving in both familiar and unseen environments. We incorporated the predicted gaze maps into end-to-end networks for two behaviors: following and overtaking. Incorporating gaze information significantly improves generalization to unseen environments. We hypothesize that incorporating gaze information enables the network to focus on task critical objects, which vary little between environments, and ignore irrelevant elements in the background, which vary greatly.
Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table

Clinical letters are infamously impenetrable for the lay patient.This work uses neural text simplification methods to automatically improve the understandability of clinical letters for patients.We take existing neural text simplification software and augment it with a new phrase table that links complex medical terminology to simpler vocabulary by mining SNOMED-CT.In an evaluation task using crowdsourcing, we show that the results of our new system are ranked easier to understand (average rank 1.93) than using the original system (2.34) without our phrase table.We also show improvement against baselines including the original text (2.79) and using the phrase table without the neural text simplification software (2.94).Our methods can easily be transferred outside of the clinical domain by using domain-appropriate resources to provide effective neural text simplification for any domain without the need for costly annotation.
Privacy-preserving authentication for general directed graphs in industrial IoT

Previous solutions on security and privacy in Industrial Internet of Things (IIoT) focus primarily on the data or the deployment and communication problems of devices. However, the management and authentication of IoT devices for IIoT systems has received less attention. To facilitate the research on this area, we take the IIoT control system as an example. It is crucial to authenticate the relationships among devices to ensure that data can only be accessed by authenticated parties. In this paper, we provide a feasible solution to address the aforementioned issue. Considering that a directed graph can be used to intuitively represent such relationships, we present a dynamically updatable privacy-preserving authentication scheme for general directed graphs. Specifically, our scheme is based on the cryptographic accumulator and an underlying standard digital signature scheme. We formalize its security notions and prove its security under the adaptive chosen-message attacks. The efficiency of our scheme is evaluated in order to illustrate its practically for real-world applications.
Neural Machine Translation with Reordering Embeddings

The reordering model plays an important role in phrase-based statistical machine translation.However, there are few works that exploit the reordering information in neural machine translation.In this paper, we propose a reordering mechanism to learn the reordering embedding of a word based on its contextual information.These reordering embeddings are stacked together with self-attention networks to learn sentence representation for machine translation.The reordering mechanism can be easily integrated into both the encoder and the decoder in the Transformer translation system.Experimental results on WMT'14 English-to-German, NIST Chinese-to-English, and WAT ASPEC Japanese-to-English translation tasks demonstrate that the proposed methods can significantly improve the performance of the Transformer translation system.
Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings

It has been shown that implicit connectives can be exploited to improve the performance of the models for implicit discourse relation recognition (IDRR).An important property of the implicit connectives is that they can be accurately mapped into the discourse relations conveying their functions.In this work, we explore this property in a multi-task learning framework for IDRR in which the relations and the connectives are simultaneously predicted, and the mapping is leveraged to transfer knowledge between the two prediction tasks via the embeddings of relations and connectives.We propose several techniques to enable such knowledge transfer that yield the state-of-the-art performance for IDRR on several settings of the benchmark dataset (i.e., the Penn Discourse Treebank dataset).
Semi-supervised Domain Adaptation for Dependency Parsing

During the past decades, due to the lack of sufficient labeled data, most studies on crossdomain parsing focus on unsupervised domain adaptation, assuming there is no targetdomain training data.However, unsupervised approaches make limited progress so far due to the intrinsic difficulty of both domain adaptation and parsing.This paper tackles the semi-supervised domain adaptation problem for Chinese dependency parsing, based on two newly-annotated large-scale domain-specific datasets. 1 We propose a simple domain embedding approach to merge the sourceand target-domain training data, which is shown to be more effective than both direct corpus concatenation and multi-task learning.In order to utilize unlabeled target-domain data, we employ the recent contextualized word representations and show that a simple fine-tuning procedure can further boost cross-domain parsing accuracy by large margins.
Functional Mock-up Interface: An empirical survey identifies research challenges and current barriers

Co-simulation is a promising approach for the analysis of complex, multi-domain systems, that leverages mature simulation tools of the respective domains.It has been applied in many different disciplines in academia and industry, with limited sharing of findings.With the increasing adoption of the FMI standard, researchers have set to work on surveying the scattered knowledge on co-simulation in academia.This paper complements the existing surveys by taking on the social and empirical aspect, corroborating, and prioritizing, previous findings.We focus on understanding the perceived research challenges, and the current barriers, based on expert assessment.One of the main barriers pointed out is the limited support for discrete event and hybrid co-simulation.
Adversarial Attention Modeling for Multi-dimensional Emotion Regression

In this paper, we propose an Adversarial Attention Network for the task of multidimensional emotion regression, which automatically rates multiple emotion dimension scores for an input text.Especially, to determine which words are valuable for a particular emotion dimension, an attention layer is learnt to weight the words in an input sequence.Furthermore, adversarial training is employed between two attention layers to learn better word weights via a discriminator.In particular, a shared attention layer is incorporated to learn public word weights between two emotion dimensions.Empirical evaluation on the EMOBANK corpus shows that our approach achieves notable improvements in r-values on both EMOBANK Reader's and Writer's multi-dimensional emotion regression tasks in all domains over the state-of-the-art baselines.
PENGGUNAAN MEDIA VIDEO SCRIBE DALAM PEMBELAJARAN LITERASI SAINS UNTUK MAHASISWA PGPAUD

Abstract:. This research is motivated by the importance of Early childhood education preservice teacher to understand the concepts and content of scientific literacy so that they can develop this ability in children comprehensively. Therefore learning media innovation is needed to develop scientific literacy of Early Childhood Teacher Education students. One of them is through the use of video scribe. The design of this study is Classroom Action Research, which is carried out in cycles with each action cycle covering planning, implementing Acting, Observation, Reflexing. The data collection technique used in this study is to use observation, interview, documentation, and learning outcomes test techniques. The results of the study show that learning using scribe video learning media makes students more enthusiastic about learning and focus on the material presented by the researcher. Students are more active in learning. An increase in understanding the material evidences this; this can be seen from the average value of the evaluation results obtained.Keyword: student learning outcomes, video scribe media, evolutionary learning.Abstrak: Penelitian ini dilatarbelakangi oleh pentingnya calon guru PAUD untuk memahami konsep dan konten literasi sains sehingga dapat mengembangkan kemampuan ini pada anak secara komprehensif. Karena itu diperlukan inovasi media pembelajaran yang untuk mengembangkan literasi sains para mahasiswa PGPAUD. Salah satunya melalui penggunaan video scribe. Desain penelitian ini adalah penelitian tindakan kelas (Classroom Action Research) yang dilaksanakan dalam siklus-siklus dengan setiap siklus tindakan meliputi perencanaan tindakan (Planning), pelaksanaan tindakan (Acting), pengamatan (Observation), relfeksi (Refelecting). Teknik pengumpulan data yang digunakan dalam penelitian ini adalah dengan menggunakan teknik observasi, wawancara, dokumentasi dan tes hasil belajar. Hasil penelitian menunjukkan bahwa pembelajaran dengan menggunakan media pembelajaran video scribe membuat siswa lebih antusias untuk belajar dan fokus terhadap materi yang disampaikan oleh peneliti. Mahasiswa lebih aktif dalam pembelajaran hal ini terbukti dengan adanya peningkatan dalam memahami materi, hal ini terlihat dari nilai rata-rata hasil evaluasi yang diperoleh. Kata Kunci: hasil belajar siswa, media video scribe, pembelajaran evolusi.
Mining Fraudsters and Fraudulent Strategies in Large-Scale Mobile Social Networks

The rapid development of modern communication technologies-in particular, (mobile) phone communications-has largely facilitated human social interactions and information exchange. However, the emergence of telemarketing frauds can significantly dissipate individual fortune and social wealth, resulting in a potential slow down or damage to economics. In this work, we propose to spot telemarketing frauds, with an emphasis on unveiling the “precise fraud” phenomenon and the strategies that are used by fraudsters to precisely select targets. To study this problem, we employ a one-month complete dataset of telecommunication metadata in Shanghai with 54 million users and 698 million call logs. Through our study, we find that user's information might have been seriously leaked, and fraudsters have a preference over the target user's age and activity in mobile network. We further propose a novel semi-supervised learning framework to distinguish fraudsters from non-fraudsters. Experimental results on a real-world data show that our approach outperforms several state-of-the-art algorithms in accuracy of detecting fraudsters (e.g., +0.278 in terms of F1 on average). We believe that our study can potentially inform policymaking for government and mobile service providers.
Adaptive Multi-Attention Network Incorporating Answer Information for Duplicate Question Detection

Community-based question answering (CQA), which provides a platform for people with diverse backgrounds to share information and knowledge, has become increasingly popular. With the accumulation of site data, methods to detect duplicate questions in CQA sites have attracted considerable attention. Existing methods typically use only questions to complete the task. However, the paired answers may also provide valuable information. In this paper, we propose an answer information- enhanced adaptive multi-attention network (AMAN) to perform this task. AMAN takes full advantage of the semantic information in the paired answers while alleviating the noise problem caused by adding the answers. To evaluate the proposed method, we use a CQADupStack set and the Quora question-pair dataset expanded with paired answers. Experimental results demonstrate that the proposed model can achieve state-of-the-art performance on the above two data sets.
Reducing the template ageing effect in on‐line signature biometrics

On-line signature recognition is an area of growing interest in recent years due to the massive deployment of high-quality digitising tablets, smartphones, and tablets in many commercial sectors such as banking. In addition, handwritten signature is one of the most socially accepted biometric traits as it has been used in financial and legal agreements for over a century. In this current environment for signature biometrics, the number of stored samples or templates per user can grow very fast, making it possible to train more robust statistical user models, improving the performance of the biometric systems and in particular reducing the template ageing effect. This study carries out an exhaustive experimental analysis of template update strategies for three well-known on-line signature verification approaches, extracts various practical findings related to the template ageing effect in signature biometrics, and configures time-adaptive improved versions of the considered baseline approaches overcoming to some extent the template ageing. The proposed improved approach achieves system performances of 2.1 and 0.2% equal error rate for skilled and random forgery cases, respectively. These results show the efficacy of the proposed methodology.
Kyrix: Interactive Pan/Zoom Visualizations at Scale

Abstract Pan and zoom are basic yet powerful interaction techniques for exploring large datasets. However, existing zoomable UI toolkits such as Pad++ and ZVTM do not provide the backend database support and data‐driven primitives that are necessary for creating large‐scale visualizations. This limitation in existing general‐purpose toolkits has led to many purpose‐built solutions (e.g. Google Maps and ForeCache) that address the issue of scalability but cannot be easily extended to support visualizations beyond their intended data types and usage scenarios. In this paper, we introduce Kyrix to ease the process of creating general and large‐scale web‐based pan/zoom visualizations. Kyrix is an integrated system that provides the developer with a concise and expressive declarative language along with a backend support for performance optimization of large‐scale data. To evaluate the scalability of Kyrix, we conducted a set of benchmarked experiments and show that Kyrix can support high interactivity (with an average latency of 100 ms or below) on pan/zoom visualizations of 100 million data points. We further demonstrate the accessibility of Kyrix through an observational study with 8 developers. Results indicate that developers can quickly learn Kyrix's underlying declarative model to create scalable pan/zoom visualizations. Finally, we provide a gallery of visualizations and show that Kyrix is expressive and flexible in that it can support the developer in creating a wide range of customized visualizations across different application domains and data types.
A Novel Reject Inference Model Using Outlier Detection and Gradient Boosting Technique in Peer-to-Peer Lending

Credit scoring is an efficient tool in handling the information asymmetry of peer-to-peer (P2P) lending.Credit scoring models are typically built only with the accepted applicants, which may cause sample bias and further hinder the predictive performances.Reject inference methods utilize the information contained in the rejected samples by inferring their potential status and incorporate them with the accepted samples.In this study, we propose a novel reject inference model (i.e., OD-LightGBM) that combines an outlier detection technique (i.e., isolation forest) and a state-of-the-art gradient boosting decision tree algorithm.The model is evaluated on two real-world P2P lending datasets, and the results of predictive performances demonstrate that our proposed model significantly outperforms the benchmarks in terms of discriminative capability.The analysis of computational cost shows the great potential of our proposed model in handling large-sized problems.The proposed framework remains robust under different parameter settings and provides stable results given various combinations of outlier detection algorithms and classifiers.
Lifted Proximal Operator Machines

We propose a new optimization method for training feedforward neural networks. By rewriting the activation function as an equivalent proximal operator, we approximate a feedforward neural network by adding the proximal operators to the objective function as penalties, hence we call the lifted proximal operator machine (LPOM). LPOM is block multiconvex in all layer-wise weights and activations. This allows us to use block coordinate descent to update the layer-wise weights and activations. Most notably, we only use the mapping of the activation function itself, rather than its derivative, thus avoiding the gradient vanishing or blow-up issues in gradient based training methods. So our method is applicable to various non-decreasing Lipschitz continuous activation functions, which can be saturating and non-differentiable. LPOM does not require more auxiliary variables than the layer-wise activations, thus using roughly the same amount of memory as stochastic gradient descent (SGD) does. Its parameter tuning is also much simpler. We further prove the convergence of updating the layer-wise weights and activations and point out that the optimization could be made parallel by asynchronous update. Experiments on MNIST and CIFAR-10 datasets testify to the advantages of LPOM.