Reviewerly/exHarmony huggingface dataset:
Rotated cascade R-CNN: A shape robust detector with coordinate regression

General object detection task mainly takes axis-aligned bounding-boxes as the detection outputs. To address more challenging scenarios, such as curved text detection and multi-oriented object detection in aerial images, we propose a novel two-stage approach for shape robust object detection. In the first stage, a locally sliding line-based point regression (LocSLPR) approach is presented to estimate the outline of the object, which is denoted as the intersections of the sliding lines and the bounding-box of the object. To make full use of information, we only regress partial coordinates and calculate the remaining coordinates by the sliding rule. We find that regression can achieve higher precision with fewer parameters than the segmentation method. In the second stage, a rotated cascade region-based convolutional neural network (RCR-CNN) is used to gradually regress the target object, which can further improve the performance of our system. Experiments demonstrate that our method achieves state-of-the-art performance in several quadrangular object detection tasks. For example, our method yielded a score of 0.796 in the ICPR 2018 Contest on Robust Reading for Multi-Type Web Images, where we won first place for text detection tasks. The method also achieved 69.2% mAP on Task 1 of the ICPR 2018 Contest on Object Detection in Aerial Images, which was our best single model, where we also won first place. In addition, the method outperforms the previously published best record on the curved text dataset (CTW1500).
Exploiting Interaction Links for Node Classification with Deep Graph Neural Networks

Node classification is an important problem in relational machine learning. However, in scenarios where graph edges represent interactions among the entities (e.g., over time), the majority of current methods either summarize the interaction information into link weights or aggregate the links to produce a static graph. In this paper, we propose a neural network architecture that jointly captures both temporal and static interaction patterns, which we call Temporal-Static-Graph-Net (TSGNet). Our key insight is that leveraging both a static neighbor encoder, which can learn aggregate neighbor patterns, and a graph neural network-based recurrent unit, which can capture complex interaction patterns, improve the performance of node classification. In our experiments on node classification tasks, TSGNet produces significant gains compared to state-of-the-art methods—reducing classification error up to 24% and an average of 10% compared to the best competitor on four real-world networks and one synthetic dataset.
IRC-GAN: Introspective Recurrent Convolutional GAN for Text-to-video Generation

Automatically generating videos according to the given text is a highly challenging task, where visual quality and semantic consistency with captions are two critical issues. In existing methods, when generating a specific frame, the information in those frames generated before is not fully exploited. And an effective way to measure the semantic accordance between videos and captions remains to be established. To address these issues, we present a novel Introspective Recurrent Convolutional GAN (IRC-GAN) approach. First, we propose a recurrent transconvolutional generator, where LSTM cells are integrated with 2D transconvolutional layers. As 2D transconvolutional layers put more emphasis on the details of each frame than 3D ones, our generator takes both the definition of each video frame and temporal coherence across the whole video into consideration, and thus can generate videos with better visual quality. Second, we propose mutual information introspection to semantically align the generated videos to text. Unlike other methods simply judging whether the video and the text match or not, we further take mutual information to concretely measure the semantic consistency. In this way, our model is able to introspect the semantic distance between the generated video and the corresponding text, and try to minimize it to boost the semantic consistency.We conduct experiments on 3 datasets and compare with state-of-the-art methods. Experimental results demonstrate the effectiveness of our IRC-GAN to generate plausible videos from given text.
Nurturing Environmental Education at the Tertiary Education Level in China: Can Mobile Augmented Reality and Gamification Help?

In the educational context, there currently emerges a growing research interest in using mobile augmented reality (AR) and the gamification concept to promote environmental education (EE). However, to date, scant attention has been paid to practically linking this approach to formal curricula at the tertiary level in China. Given the situation, we designed a geolocation-based mobile AR scavenger hunt to explore students’ perception of embedding technology-enhanced and gamified EE in their language learning process. Ninety-eight first-year students, majoring in English at a Chinese university, were invited to participate in this study. In this game, students need to find the answers to 24 environment-themed questions phrased in English. Guided by prior technology acceptance research, we employed a mixed methods approach to capture participants’ experience and perception of the process. The results show that this approach was positively perceived among the participants, for it could not only enrich their language learning experience but also promote their awareness of the environment. The findings offer insights into how EE can be purposefully integrated with tertiary education by leveraging current technological and pedagogical innovations.
Dynamic VM allocation and traffic control to manage QoS and energy consumption in cloud computing environment

In the last few years, cloud computing technology has revolutionised the IT industry. However, the guarantee of the desired Quality of Service (QoS) and the power consumption control are two of the major challenges that this paradigm poses. In this paper, we propose a mechanism combining a scheme for virtual machine utilisation with a mechanism to control the access for incoming requests to the virtual machine monitor. The number of activated virtual machines is defined according to the workload, and the control access is based on the number of requests. The studied mechanism and its performance parameters are described by a mathematical model, and a power consumption model is built and evaluated. Numerical examples evaluating QoS parameters are given. Moreover, the impact of the proposed mechanism on the behaviour of energy consumption is evaluated. Analysis of the obtained results shows the positive impact of the proposed mechanism.
PhantomLegs: Reducing Virtual Reality Sickness Using Head-Worn Haptic Devices

Virtual Reality (VR) sickness occurs when exposure to a virtual environment causes symptoms that are similar to motion sickness, and has been one of the major user experience barriers of VR. To reduce VR sickness, prior work has explored dynamic field-of-view modification and galvanic vestibular stimulation (GVS) that recou-ples the visual and vestibular systems. We propose a new approach to reduce VR sickness, called PhantomLegs, that applies alternating haptic cues that are synchronized to users' footsteps in VR. Our prototype consists of two servos with padded swing arms, one set on each side of the head, that lightly taps the head as users walk in VR. We conducted a three-session, multi-day user study with 30 participants to evaluate its effects as users navigate through a VR environment while physically being seated. Results show that our approach significantly reduces VR sickness during the initial exposure while remaining comfortable to users.
Modelling deception using theory of mind in multi-agent systems

Agreement, cooperation and trust would be straightforward if deception did not ever occur in communicative interactions. Humans have deceived one another since the species began. Do machines deceive one another or indeed humans? If they do, how may we detect this? To detect machine deception, argua bly requires a model of how machines may deceive, and how such deception may be identified. Theory of Mind (ToM) provides the opportunity to create intelligent machines that are able to model the minds of other agents. The future implications of a machine that has the capability to understand other minds (human or artificial) and that also has the reasons and intentions to deceive others are dark from an ethical perspective. Being able to understand the dishonest and unethical behaviour of such machines is crucial to current research in AI. In this paper, we present a high-level approach for modelling machine deception using ToM under factors of uncertainty and we propose an implementation of this model in an Agent-Oriented Programming Language (AOPL). We show that the Multi-Agent Systems (MAS) paradigm can be used to integrate concepts from two major theories of deception, namely Information Manipulation Theory 2 (IMT2) and Interpersonal Deception Theory (IDT), and how to apply these concepts in order to build a model of computational deception that takes into account ToM. To show how agents use ToM in order to deceive, we define an epistemic agent mechanism using BDI-like architectures to analyse deceptive interactions between deceivers and their potential targets and we also explain the steps in which the model can be implemented in an AOPL. To the best of our knowledge, this work is one of the first attempts in AI that (i) uses ToM along with components of IMT2 and IDT in order to analyse deceptive interactions and (ii) implements such a model.
Enhanced state estimation and bad data identification in active power distribution networks using photovoltaic power forecasting

In view of the problems of insufficient real-time measurements in active distribution networks, a state estimation method for active distribution networks is proposed based on the forecasting of photovoltaic (PV) power generation. First, the extreme learning machine (ELM) enhanced by the genetic algorithm (GA) is used to forecast the PV power generation. Second, the Gaussian mixture model (GMM) is used to model the forecasting error. The weighted mean of the forecasting error is used to correct the forecasting value of the PV power generation, and the weighted variance of the forecasting error is used as the basis for setting the pseudo measurement weight. Finally, the real-time measurements collected by the supervisory control and data acquisition (SCADA) system, the forecasted pseudo measurements, and the virtual measurements are used to estimate the state of the active distribution network using the weighted least square (WLS) algorithm. Through simulations in the IEEE 33-bus system, it is shown that the proposed model provides accurate and reliable pseudo measurements for the active distribution network, improves the redundancy of the system, and thus further improves the accuracy of the state estimation and the capability of detecting and identifying bad data in active distribution systems without adding measurement devices.
Topics to Avoid: Demoting Latent Confounds in Text Classification

Despite impressive performance on many text classification tasks, deep neural networks tend to learn frequent superficial patterns that are specific to the training data and do not always generalize well.In this work, we observe this limitation with respect to the task of native language identification.We find that standard text classifiers which perform well on the test set end up learning topical features which are confounds of the prediction task (e.g., if the input text mentions Sweden, the classifier predicts that the author's native language is Swedish).We propose a method that represents the latent topical confounds and a model which "unlearns" confounding features by predicting both the label of the input text and the confound; but we train the two predictors adversarially in an alternating fashion to learn a text representation that predicts the correct label but is less prone to using information about the confound.We show that this model generalizes better and learns features that are indicative of the writing style rather than the content. 1
Bayesian Batch Active Learning as Sparse Subset Approximation

Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. When the cost of acquiring labels is high, probabilistic active learning methods can be used to greedily select the most informative data points to be labeled. However, for many large-scale problems standard greedy procedures become computationally infeasible and suffer from negligible model change. In this paper, we introduce a novel Bayesian batch active learning approach that mitigates these issues. Our approach is motivated by approximating the complete data posterior of the model parameters. While naive batch construction methods result in correlated queries, our algorithm produces diverse batches that enable efficient active learning at scale. We derive interpretable closed-form solutions akin to existing active learning procedures for linear models, and generalize to arbitrary models using random projections. We demonstrate the benefits of our approach on several large-scale regression and classification tasks.
Computer Vision with a Single (Robust) Classifier.

We show that the basic classification framework alone can be used to tacklesome of the most challenging computer vision tasks. In contrast to otherstate-of-the-art approaches, the toolkit we develop is rather minimal: it usesa single, off-the-shelf classifier for all these tasks. The crux of ourapproach is that we train this classifier to be adversarially robust. It turnsout that adversarial robustness is precisely what we need to directlymanipulate salient features of the input. Overall, our findings demonstrate theutility of robustness in the broader machine learning context. Code and modelsfor our experiments can be found at https://git.io/robust-apps.
Unsupervised Linear Discriminant Analysis for Jointly Clustering and Subspace Learning

Linear discriminant analysis (LDA) is one of commonly used supervised subspace learning methods. However, LDA will be powerless faced with the no-label situation. In this paper, the unsupervised LDA (Un-LDA) is proposed and first formulated as a seamlessly unified objective optimization which guarantees convergence during the iteratively alternative solving process. The objective optimization is in both the ratio trace and the trace ratio forms, forming a complete framework of a new approach to jointly clustering and unsupervised subspace learning. The extension of LDA into Un-LDA enables to not only complete unsupervised subspace learning via the explicitly presented subspace projection matrix but also simultaneously finish clustering and even clustering out-of-sample data via the explicitly presented transformation matrix. To overcome the difficulty in solving the non-convex objective optimization, we mathematically prove that the Un-LDA optimization in both forms can be transformed into the simple K-means clustering optimization when the subspace is determined. The Un-LDA optimization is eventually completed by alternatively optimizing the clusters using K-means and the subspace using the supervised LDA methods and iterating this whole process until convergence or stopping criterion. The experiments demonstrate that our proposed Un-LDA algorithms are comparable or even much superior to the counterparts.
Recognizing Hand Gestures With Pressure-Sensor-Based Motion Sensing

This paper proposes a novel framework to process pressure signals for real-time and robust gesture recognition, which includes an innovative segmentation scheme, a gesture recognition scheme and a pressure-parameter adaptive updating strategy. A prototype system, including a wearable gesture sensing device with four pressure sensors and the corresponding algorithmic framework, is developed to realize real-time gesture-based interaction. With the device worn on the wrist, the user can interact with the computer using 8 predefined gestures. Experimental results show that the delay of gesture recognition is about 100 ms, with the average accuracy of 95.28% in the experienced-user test and 86.20% in the inexperienced-user test. Finally, the system is evaluated by a mouse-controlling interaction task and performs well. Both experienced and inexperienced people can easily and quickly complete interactive tasks. These results demonstrate that a pressure-sensor based wristband can be used to classify hand gestures well and to control the mouse interaction. This approach provides an interactive way to replace the mouse for decreasing the risk of the carpal tunnel syndrome (CTS).
A Feature Selection and Classification Method for Activity Recognition Based on an Inertial Sensing Unit

The purpose of activity recognition is to identify activities through a series of observations of the experimenter’s behavior and the environmental conditions. In this study, through feature selection algorithms, we researched the effects of a large number of features on human activity recognition (HAR) assisted by an inertial measurement unit (IMU), and applied them to smartphones of the future. In the research process, we considered 585 features (calculated from tri-axial accelerometer and tri-axial gyroscope data). We comprehensively analyzed the features of signals and classification methods. Three feature selection algorithms were considered, and the combination effect between the features was used to select a feature set with a significant effect on the classification of the activity, which reduced the complexity of the classifier and improved the classification accuracy. We used five classification methods (support vector machine [SVM], decision tree, linear regression, Gaussian process, and threshold selection) to verify the classification accuracy. The activity recognition method we proposed could recognize six basic activities (BAs) (standing, going upstairs, going downstairs, walking, lying, and sitting) and postural transitions (PTs) (stand-to-sit, sit-to-stand, stand-to-lie, lie-to-stand, sit-to-lie, and lie-to-sit), with an average accuracy of 96.4%.
State-independent quantum state tomography by photon-number-resolving measurements

The Wigner quasiprobability distribution of a narrowband single-photon state was reconstructed by quantum state tomography using photon-number-resolving measurements with transition-edge sensors (TES) at system efficiency 58(2)%. This method makes no assumptions on the nature of the measured state, save for the limitation on photon flux imposed by the TES. Negativity of the Wigner function was observed in the raw data without any inference or correction for decoherence.
QoE Evaluation Methods for 360-Degree VR Video Transmission

Virtual reality (VR) videos, especially 360-degree VR videos, have attracted substantial research interests with the explosion of mobile VR devices in recent years. In this work, we propose a VR quality of experience (QoE) evaluation framework including online, offline and mixed scenarios that can meet the requirement of real applications. Under this framework, we further develop a subjective evaluation method and an objective QoE evaluation model based on network transmission parameters. A VR panoramic video database is established with nine different contents. This database contains 468 different samples coming from nine original contents. Then, in the subjective evaluation part, we propose to adopt four dimensions, i.e., quality score, immersion score, non-spinning sensation score and global score for the subjective evaluation purpose. These dimensions can meet the interaction and immersion features of the VR video service. In the objective evaluation part, an improved two-step neural network (INN) model is developed by using the features of the physiological psychology and cognitive neurology. This model captures the intrinsic link between the initial input network parameters and the final perception in VR transmission. Our results suggest that the proposed INN can achieve a higher pearson correlation coefficients than the other methods. Especially, it increases by 8.9% than the traditional NN method. Our work can make up for the shortage of traditional evaluation methods in term of the VR QoE subjective and objective evaluation in transmission scopes.
On Robustness of Neural Ordinary Differential Equations

Neural ordinary differential equations (ODEs) have been attracting increasing attention in various research domains recently. There have been some works studying optimization issues and approximation capabilities of neural ODEs, but their robustness is still yet unclear. In this work, we fill this important gap by exploring robustness properties of neural ODEs both empirically and theoretically. We first present an empirical study on the robustness of the neural ODE-based networks (ODENets) by exposing them to inputs with various types of perturbations and subsequently investigating the changes of the corresponding outputs. In contrast to conventional convolutional neural networks (CNNs), we find that the ODENets are more robust against both random Gaussian perturbations and adversarial attack examples. We then provide an insightful understanding of this phenomenon by exploiting a certain desirable property of the flow of a continuous-time ODE, namely that integral curves are non-intersecting. Our work suggests that, due to their intrinsic robustness, it is promising to use neural ODEs as a basic block for building robust deep network models. To further enhance the robustness of vanilla neural ODEs, we propose the time-invariant steady neural ODE (TisODE), which regularizes the flow on perturbed data via the time-invariant property and the imposition of a steady-state constraint. We show that the TisODE method outperforms vanilla neural ODEs and also can work in conjunction with other state-of-the-art architectural methods to build more robust deep networks.
Sparse deep nonnegative matrix factorization

Nonnegative Matrix Factorization (NMF) is a powerful technique to perform dimension reduction and pattern recognition through single-layer data representation learning. However, deep learning networks, with their carefully designed hierarchical structure, can combine hidden features to form more representative features for pattern recognition. In this paper, we proposed sparse deep NMF models to analyze complex data for more accurate classification and better feature interpretation. Such models are designed to learn localized features or generate more discriminative representations for samples in distinct classes by imposing L1-norm penalty on the columns of certain factors. By extending a one-layer model into a multilayer model with sparsity, we provided a hierarchical way to analyze big data and intuitively extract hidden features due to nonnegativity. We adopted the Nesterov's accelerated gradient algorithm to accelerate the computing process. We also analyzed the computing complexity of our frameworks to demonstrate their efficiency. To improve the performance of dealing with linearly inseparable data, we also considered to incorporate popular nonlinear functions into these frameworks and explored their performance. We applied our models using two benchmarking image datasets, and the results showed that our models can achieve competitive or better classification performance and produce intuitive interpretations compared with the typical NMF and competing multilayer models.