Reviewerly/exHarmony huggingface dataset:
Mobile Robot Exploration Based on Rapidly-exploring Random Trees and Dynamic Window Approach

Exploration is a critical function for autonomous mobile robots. Traditionally, the entire map has to be processed to extract frontiers and perform path planning. However, as the robot explores the environment, the map grows over time, and increasing computational resources are required, especially for large-scale environments. Moreover, only a few methods focus on the exploration on point cloud maps. Here, I present a new practical method to autonomous mobile robot exploration based on a sparse, relatively small-size point cloud local map, which combines Rapidly-exploring Random Tree (RRT) and dynamic window approach (DWA) algorithm together. The local map is built from the consecutive inputs of raw point clouds using an inexpensive 3D sensor, i.e. Kinect V2. Frontiers are effectively detected and local path planning is performed by RRT algorithm directly on unordered point cloud local maps. Motion planning is performed online by DWA to avoid obstacles and direct a nonholonomic mobile robot towards frontiers separating known environments from unknown environments. Embedded with simultaneous localization and mapping (SLAM) system of my previous research, the performance of the proposed method is evaluated in a large-scale customized virtual environment with a size of 33 ×29 ×6m using Gazebo as the robotic simulator. The results suggest that the proposed algorithm can accurately direct the nonholonomic mobile robot to unexplored environments in real time. Also, it successfully helps build a coherent semi-metric topological map. The proposed algorithm shows great efficient performance and is suitable for both static and dynamic environments. Combination of RRT and DWA algorithm on point clouds, as a general approach, can be extended to generic 3D nonplanar physical environments. Videos of the experiments can be found at https://youtu.be/0i766fhs9Ds.
Building a Private Currency Service Using Exonum

Zero-knowledge proofs are an emerging cryptographic technology that have many potential applications for blockchains. Exonum is an extensible open-source framework for creating blockchain applications. In this article, we describe how zero-knowledge proofs, specifically bulletproofs, can be applied to build a privacy-focused service using Exonum. The token logic is implemented as a platform service and is a proof of concept.
Llrnet: A Multiscale Subband Learning Approach for Low Light Image Restoration

We consider the problem of low light image restoration through joint contrast enhancement and denoising. Deep convolutional neural networks (CNNs) based on residual learning have been successful in achieving state of the art performance in image denoising. However, their application to joint contrast enhancement and denoising poses challenges owing to the nature of the distortion process involving both loss of details and noise. Thus, we propose a multiscale learning approach by learning the subbands obtained in a Laplacian pyramid decomposition through a subband CNN (SCNN). The enhanced subbands at multiple scales are then combined to obtain the final restored image using a recomposition CNN (ReCNN). We refer to the overall network involving SCNN and ReCNN as low light restoration network (LLRNet). We show through extensive experiments based on the `See in the Dark' Dataset that our approach produces better quality restored images when compared to other contrast enhancement techniques and CNN based approaches.
An Adaptive Path Tracking Controller Based on Reinforcement Learning with Urban Driving Application

Urban driving requires the autonomous vehicles to drive with smooth control and track the planned path accurately. However, most of the existing path-tracking controllers pay more attention to the tracking errors than the smoothness because of the difficulties to balance them. This paper proposes a learning-based method to achieve the trade-off between the smooth control and the tracking-error control. An Reinforcement Learning algorithm, which is called Proximal Policy Optimization, is used to train a neural model to tune the weights of a designed controller PP_PID (Pure-Pursuit_Proportional Integral Derivative). The successfully trained model will adaptively select the optimal weights for the Pure-Pursuit and PID to guarantee the control smoothness and accuracy. Finally, the proposed controller will be tested in two path tracking scenarios. The results show that the proposed controller can change the weights adaptively to maintain a balance in the tracking error and lateral acceleration under the 35km/h.
Physiological Fusion Net: Quantifying Individual VR Sickness with Content Stimulus and Physiological Response

Quantifying Virtual Reality (VR) sickness is demanded in industry to address viewing safety issue. In this paper, we develop a new method to quantify VR sickness. We propose a novel physiological fusion deep network which estimates individual VR sickness with content stimulus and physiological response. In the proposed framework, content stimulus guider and physiological response guider are devised to effectively represent feature related with VR sickness. Deep stimulus feature from the content stimulus guiders reflects the content sickness tendency while deep physiology feature from the physiological response guider reflects the individual sickness characteristics. By combining those features, VR sickness predictor quantifies individual Simulation Sickness Questionnaires (SSQ) scores. To evaluate the performance of the proposed method, we built a new dataset that consists of 360-degree videos with physiological signals and SSQ scores. Experimental results show that the proposed method achieved meaningful correlation with human subjective scores.
Learning Optimal Phase-Coded Aperture for Depth of Field Extension

We present a learning-based optimization framework for depth of field extension, combining rigorous modeling of coded aperture imaging system and convolutional neural network based deblurring. The coded mask discretization is defined for desired depth range using wave optics based imaging model. Such approach significantly decreases the number of parameters to be optimized and increases the convergence speed of the network. We verify the proposed algorithm in different scenarios achieving superior or comparable performance with respect to existing methods.
Takagi-sugeno-kang fuzzy systems with dynamic rule weights

Fuzzy rules are very important in Takagi-Sugeno-Kang (TSK) fuzzy systems as they not only provide a mapping mechanism for input patterns but also make fuzzy systems interpretable. Current works further introduce rule weights to restrict/strengthen fuzzy rules for more situations. However, most of t he embedded rule weights in fuzzy rules are static. In other words, the rule weights keep unchanged once they have been determined by the learning algorithms. In practical applications, it is often expected that each fuzzy rule should deduce different confidence degrees with respect to different input patterns. In this paper, a new TSK fuzzy system is proposed, in which each fuzzy rule is empowered by an individual dynamic rule weight (DRW). DRW is basically a nonlinear function of the input pattern to reflect the confidence degree (acceptability) the fuzzy rule acting on the input pattern. Furthermore, for an input pattern, its “isolation” level can be measured by the aggregated DRW values of the fuzzy rules in the proposed fuzzy system. Specifically, the proposed fuzzy system can be used to identify outliers whose aggregated DRW values of all fuzzy rules are very small. In order to effectively embed DRW to each fuzzy rule, an analogous stacked structure consisting of a basic input-output unit and an augmenting unit is proposed. The stacked architecture is characterized by three features: (i) the augmented information from the augmenting unit can provide indirect pattern information for DRW learning; (ii) the predictive information from the augmenting unit can be differentiated from the interpretability of fuzzy rules; and (iii) the modeling performance can be improved by the stacked generalization principle which leverages the predictive information in the manifold of the input pattern space in the system approximation process. Experimental results on 16 real-life datasets demonstrate the approximation accuracy, interpretability and outlier detection ability of the proposed fuzzy system.
Deep Objective Assessment Model Based on Spatio-Temporal Perception of 360-Degree Video for VR Sickness Prediction

In virtual reality (VR) environment, viewing safety is one of increasing concerns because of physical symptoms induced by VR sickness. Distortion of VR video is one of main causes. In this paper, we investigate the degradation of spatial resolution as distortion causing VR sickness. We propose a novel deep learning-based VR sickness assessment framework for predicting VR sickness caused by degradation of spatial resolution. The proposed method takes into account visual perception of 360-degree videos in spatio-temporal domain for assessing VR sickness. In cooperating visual quality and the temporal flickering with deep latent feature in training stage, the proposed network could effectively learn the spatio-temporal characteristics causing VR sickness. To evaluate the performance of the proposed method, we built a new dataset consisting of 360-degree videos and ground truths (physiological signals and SSQ scores). The dataset will be open publicly. Experimental results demonstrated that the proposed VR sickness assessment had a high correlation with human subjective scores.
In-air handwritten Chinese text recognition with temporal convolutional recurrent network

As a new human-computer interaction way, in-air handwriting allows users to perform gesture-based writing in the midair. However, most existing in-air handwriting systems mainly focus on recognizing either isolated characters/words or only a small number of texts, making those systems far from practical applications. Instead, here we present a 3D in-air handwritten Chinese text recognition (IAHCTR) system for the first time, and construct the first public large-scale IAHCT dataset. Moreover, a novel architecture, named the temporal convolutional recurrent network (TCRN), is proposed for online HCTR. Specifically, the TCRN first applies the 1-dimensional convolution to extract local contextual features from low-level trajectories, and then it utilizes the recurrent network to capture long-term dependencies of high-level outputs. Compared with the state-of-the-art architecture, the TCRN not only avoids the domain-specific knowledge for feature image extraction, but also attains higher training efficiency with a more compact model. Empirically, this TCRN also outperforms the single recurrent network with faster prediction and higher accuracy. Experiments on CASIA-OLHWDB2 & ICDAR-2013 demonstrate that the TCRN yields the best result in comparison to the state-of-the-art methods for online HCTR.
Optimal Continuous State POMDP Planning With Semantic Observations: A Variational Approach

This article develops novel strategies for optimal planning with semantic observations using continuous state partially observable Markov decision processes (CPOMDPs). Two major innovations are presented in relation to Gaussian mixture (GM) CPOMDP policy approximation methods. While existing methods have many desirable theoretical properties, they are unable to efficiently represent and reason over hybrid continuous-discrete probabilistic models. The first major innovation is the derivation of closed-form variational Bayes GM approximations of point-based value iteration Bellman policy backups, using softmax models of continuous-discrete semantic observation probabilities. A key benefit of this approach is that dynamic decision making tasks can be performed with complex non-Gaussian uncertainties, while also exploiting continuous dynamic state-space models (thus avoiding cumbersome and costly discretization). The second major innovation is a new clustering-based technique for mixture condensation that scales well to very large GM policy functions and belief functions. Simulation results for a target search and interception task with semantic observations show that the GM policies resulting from these innovations are more effective than those produced by other state-of-the-art policy approximations, but require significantly less modeling overhead and online runtime cost. Additional results show the robustness of this approach to model errors and scaling to higher dimensions.
Hepatic Lesion Segmentation by Combining Plain and Contrast-Enhanced CT Images with Modality Weighted U-Net

We propose the Modality Weighted U-Net (MW-UNet) to combine plain Computed Tomography (CT) and Contrast-Enhanced Computed Tomography (CECT) images for hepatic lesion segmentation. Observing that CT and CECT images provide complimentary but different amount of information for the segmentation task, we propose to fuse their features at specific layers of the U-Net by the weighted sum rule. The weight parameters are updated through backpropagation during training. Compared with most combination methods which concatenate feature maps at the last or intermediate layers, the proposed method obtains feature level fusion with very simple combination rules. Thus, great amount of parameters and computation can be saved. We evaluate our model on the MCGHD database and demonstrate the superiority of the proposed method over other state-of-the-arts both in accuracy and computation.
An efficient non-separable architecture for Haar wavelet transform with lifting structure

In this paper, a memory efficient, fully integer to integer with parallel architecture for 2-D Haar wavelet transform with lifting scheme has been proposed. The main problem in most 2-D wavelet architecture is the intermediate or internal memory (on-chip), which is mostly proportional to the data size, the increase of the internal memory lead to increases of the die area and control complexity. The proposed algorithm is a non-separable Haar wavelet architecture which is derived by rearranging and combining the lifting steps which are carried in both vertical and horizontal directions and performing it in a simple and single step. In addition to the elimination of internal memory, the proposed algorithm is outperforming the existing architecture in term of hardware utilization, latency, number of arithmetic operation, power consumption, and used area. The proposed algorithm, is fully parallel and that make it suitable to be implanted using FPGA devices. Finally, An FPGA Development board with Zynq series “XC7Z020-1CLG484” has been used as the design platform, and all the results and tables are estimated by the Vivado® Design Suite.
Disentangled Feature Based Adversarial Learning for Facial Expression Recognition

A facial expression image can be considered as an addition of expressive component to a neutral expression face. With this in mind, in this paper, we propose a novel end-to-end adversarial disentangled feature learning (ADFL) framework for facial expression recognition. The ADFL framework is mainly composed of three branches: expression disentangling branch ADFL-d, neutral expression branch ADFL-n and residual expression branch ADFL-r. The ADFL-d and ADFL-n aim to extract the expressive component and neutral component, respectively. The ADFL-r extracts the residual expression by calculating the difference between feature maps of ADFL-d and ADFL-n, and uses the residual expression feature for expression classification. Experimental results on several benchmark databases (CK+, MMI and Oulu-CASIA) show that the proposed method has remarkable performance compared to state-of-the-art methods.
A Big Data Storage Scheme Based on Distributed Storage Locations and Multiple Authorizations

This paper introduces a secured and distributed Big Data storage scheme with multiple authorizations. It divides the Big Data into small chunks and distributes them through multiple Cloud locations. The Shamir's Secret Sharing and Secure Hash Algorithm are employed to provide the security and authenticity of this work. The proposed methodology consists of two phases: the distribution and retrieving phases. The distribution phase comprises three operations of dividing, encrypting, and distribution. The retrieving phase performs collecting and verifying operations. To increase the security level, the encryption key is divided into secret shares using Shamir's Algorithm. Moreover, the Secure Hash Algorithm is used to verify the Big Data after retrieving from the Cloud. The experimental results show that the proposed design can reconstruct a distributed Big Data with good speed while conserving the security and authenticity properties.
Tracking and Behavior Augmented Activity Recognition for Multiple Inhabitants

We develop CACE (Constraints And Correlations mining Engine), a framework that significantly improves the recognition accuracy of complex daily activities in multi-inhabitant smarthomes. CACE views the implicit relationships between the activities of multiple people as an asset, and exploits such constraints and correlations in a hierarchical fashion, taking advantage of both personspecific sensor data (generated by wearable devices) and person-independent ambient sensor data (generated by ambient sensors). To effectively utilize such couplings, CACE first uses a multi-target particle filtering approach over ambient sensors captured movement data, to identify the number of distinct users and infer individual-specific movement trajectories. We then utilize a Hierarchical Dynamic Bayesian Network (HDBN)-based model for activity recognition. This model utilizes the inter-and-intra individual correlations and constraints, at both micro-activity and macro-activity levels, to recognize individual activities accurately. These constraints are learnt automatically using data-mining techniques, and help to dramatically reduce the computational complexity of HDBN-based inferencing. Empirical studies using a real-world testbed of five multi-inhabitant smarthomes shows that CACE is able to achieve an activity recognition accuracy of 95%, with a 16-fold reduction in computational overhead compared to traditional hybrid classification approaches.
Through the Realities of Augmented Reality

Speaking of Augmented Reality (AR), it is about augmenting an actual world with some virtually generated digital information in order to make the combination of two worlds as seamless as possible. Creating seamless AR effects in real time is non-trivial, requiring interdisciplinary knowledge integration from many fields such as computer vision, signal processing, sensor network, internet of things (IoT), three-dimensional computer graphics, human-computer interaction, and hardware designs. Nevertheless, for the past two decades, it is computer vision that has dominated the field of AR. Hence, common forms of AR that most people are familiar with are about utilizing a hardware device with embedded camera(s) together with a software program powering by computer vision algorithms. Based on our first-hand experiences in AR researches and communities, this paper presents a new summary regarding the world of modern AR from the beginning of the 21st century until now. Our summary divides the modern AR into five major waves based on important trends happening both inside and outside research communities.
BLAST-NET: Semantic Segmentation of Human Blastocyst Components via Cascaded Atrous Pyramid and Dense Progressive Upsampling

Components of a human blastocyst (day-5 embryo) and their morphological attributes highly correlate with the embryo's potentials for a viable pregnancy. Automatic semantic segmentation of human blastocyst components is a crucial step toward achieving objective quality assessment of such blastocyst. In this paper, a semantic segmentation system is proposed for human blastocyst components in microscopic images. The proposed Blast-Net features two novel components: a Cascaded Atrous Pyramid Pooling (CAPP) module to incorporate multi-scale global contextual priors, and a Dense Progressive Sub-pixel Upsampling (DPSU) module to recover the high-resolution prediction map. Experimental results confirm that the proposed method achieves the best-reported segmentation performance to date with a mean Jaccard Index of 82.85 % for microscopic images of the human blastocyst.
Artificial Intelligence Methods for Surgical Site Infection: Impacts on Detection, Monitoring, and Decision Making

Background: There has been tremendous growth in the amount of new surgical site infection (SSI) data generated. Key challenges exist in understanding the data for robust clinical decision-support. Limitations of traditional methodologies to handle these data led to the emergence of artificial intelligence (AI). This article emphasizes the capabilities of AI to identify patterns of SSI data. Method: Artificial intelligence comprises various subfields that present potential solutions to identify patterns of SSI data. Discussions on opportunities, challenges, and limitations of applying these methods to derive accurate SSI prediction are provided. Results: Four main challenges in dealing with SSI data were defined: (1) complexities in using SSI data, (2) disease knowledge, (3) decision support, and (4) heterogeneity. The implications of some of the recent advances in AI methods to optimize clinical effectiveness were discussed. Conclusions: Artificial intelligence has the potential to provide insight in detecting and decision-support of SSI. As we turn SSI data into intelligence about the disease, we increase the possibility of improving surgical practice with the promise of a future optimized for the highest quality patient care.
Hierarchical LSTM network for text classification

Text classification has always been an important and practical issue so that we need to use the computer to classify and discover the information in the text. If we want to recognize the offending words in a text without human intervention, we should use this. In this article we will compare recurrent neural networks, convolutional neural networks and hierarchical attention networks with detailed information about each of which. We will represent a HAN model using Theano framework, which indicates more accurate validation for large datasets. For text classification problem in large datasets, we will use hierarchical attention networks to get a better result.
An Adaptive Framework of Learner Model Using Learner Characteristics for Intelligent Tutoring Systems

Learner Model is the base for providing the adaptivity in the Intelligent Tutoring Systems (ITS) as learner performance data and information are stored in the learner model. Recently, there has been a rapid progress in education delivery through web due to the advancement in internet technology. With the limitations such as lack of adaptive support and presentations reasoned that research has been expanded in the domain of ITS. The aim of the paper is to present an adaptive framework of the learner model using learner characteristics that helps to provide the adaptive presentation and feedback to the prospective learner. Adaptive learner model has three component such as Learner Characteristics Model, Learner Classification Model, and Learner Adaptation Model. Learner Characteristics Model includes the characteristics of learner such as learning style, knowledge levels, and cognitive and meta-cognitive skills. Learner Classification Model classifies the learner into groups based on his/her learning style, levels of knowledge, and performance data and implemented through artificial intelligence technique. The Learner Adaptation Model recommends the tutoring strategy which best suits the learner to provide the adaptation.