Reviewerly/exHarmony huggingface dataset:
Semantic Scene Understanding in Unstructured Environment with Deep Convolutional Neural Network

Number of road fatalities have been continuously increasing since last few decades all over the world. Nowadays advanced driver assistance systems are being developed to help the driver in driving process and semantic scene understanding is an essential task for it. Convolutional Neural Networks (CNN) have shown impressive progress in various computer vision tasks including the semantic segmentation. Various architectures have been proposed in literature but loss of spatial acuity in semantic segmentation prevents them from achieving better results as details of small objects are lost in downsampling. To overcome this drawback, we propose to use dilated residual network as backbone in DeepLabV3+ which enables to preserve the details of smaller objects in the scene without reducing the receptive field. We focus our work on India Driving dataset (IDD) containing data from unstructured traffic scenario. Proposed architecture proves to be effective compared to earlier approaches in literature with 0.618 mIoU.
Multi-Scale Dilated Convolution Network Based Depth Estimation in Intelligent Transportation Systems

Vision based depth estimation plays a significant role in Intelligent Transportation Systems (ITS) because of its low cost and high efficiency, which can be used to analyze driving environment, improve driving safety, etc. Although recently proposed approaches abandon time consuming pre-processing or post-processing steps and achieve an end-to-end prediction manner, fine details may be lost through max-pooling based encode modules. To tackle this problem, we propose Multi-Scale Dilated Convolution Network (MSDC-Net), a dilated convolution based deep network. For the feature encoding and decoding part, dilated layers maintain the scale of original image and reduce lost details. After that, a pyramid dilated feature extraction module is added to integrate the knowledge learned through forward steps with different receptive fields. The proposed approach is evaluated on KITTI dataset, and achieves a state-of-the-art result on the dataset.
A Novel Path Planning Method for Articulated Road Roller Using Support Vector Machine and Longest Accessible Path With Course Correction

Aiming at Articulated Road Roller (ARR) path planning near the construction area, a novel path planning method using Support Vector Machine (SVM
Representation Learning for Heterogeneous Information Networks via Embedding Events

Network Representation Learning (NRL) has been widely used to analyze networks by mapping original networks into a low-dimensional vector space. However, existing NRL methods ignore the impact of properties of relations on the object relevance in heterogeneous information networks (HINs). To tackle this issue, this paper proposes a new NRL framework, called Event2vec, for HINs to consider both quantities and properties of relations during the representation learning process. Specifically, an event (i.e., a complete semantic unit) is used to represent the relation among multiple objects, and both event-driven first-order and second-order proximities are defined to measure the object relevance according to the quantities and properties of relations. We theoretically prove how event-driven proximities can be preserved in the embedding space by Event2vec, which utilizes event embeddings to facilitate learning the object embeddings. Experimental studies demonstrate its advantages over state-of-the-art algorithms on four real-world datasets and two network analysis tasks (i.e., link prediction and node classification).
Leveraging Side-Channel Information for Disassembly and Security

With the rise of Internet of Things (IoT), devices such as smartphones, embedded medical devices, smart home appliances, as well as traditional computing platforms such as personal computers and servers have been increasingly targeted with a variety of cyber attacks. Due to limited hardware resources for embedded devices and difficulty in wide-coverage and on-time software updates, software-only cyber defense techniques, such as traditional anti-virus and malware detectors, do not offer a silver-bullet solution. Hardware-based security monitoring and protection techniques, therefore, have gained significant attention. Monitoring devices using side-channel leakage information, e.g., power supply variation and electromagnetic (EM) radiation, is a promising avenue that promotes multiple directions in security and trust applications. In this article, we provide a taxonomy of hardware-based monitoring techniques against different cyber and hardware attacks, highlight the potentials and unique challenges, and display how power-based side-channel instruction-level monitoring can offer suitable solutions to prevailing embedded device security issues. Further, we delineate approaches for future research directions.
High-Level Synthesis of Number-Theoretic Transform: A Case Study for Future Cryptosystems

Compared to traditional hardware development methodologies, high-level synthesis (HLS) offers a faster time-to-market and lower design-cost at the expense of implementation efficiency. Although HLS tools are becoming popular in some applications, such as digital signal processing and neural network classification, their usability on cryptographic applications is largely unexplored. This feasibility is critical especially for cryptosystems that are under development, such as the next-generation public-key cryptosystems needed for quantum-resistance. This letter provides a thorough investigation of HLS on number theoretic transform (NTT)-the core arithmetic function of lattice-based quantum-resistant cryptosystems. We demonstrate a fast yet extensive design space exploration of NTT through the Vivado HLS tool, analyze the shortcomings/challenges of optimized configurations, and quantitatively compare the results to software-based and hand-coded hardware designs.
Advancing non-convex and constrained learning

As data gets more complex and applications of machine learning (ML) algorithms for decision-making broaden and diversify, traditional ML methods by minimizing an unconstrained or simply constrained convex objective are becoming increasingly unsatisfactory. To address this new challenge, recent ML research has sparked a paradigm shift in learning predictive models into non-convex learning and heavily constrained learning. Non-Convex Learning (NCL) refers to a family of learning methods that involve optimizing non-convex objectives. Heavily Constrained Learning (HCL) refers to a family of learning methods that involve constraints that are much more complicated than a simple norm constraint (e.g., data-dependent functional constraints, non-convex constraints), as in conventional learning. This paradigm shift has already created many promising outcomes: (i) non-convex deep learning has brought breakthroughs for learning representations from large-scale structured data (e.g., images, speech) (LeCun, Bengio, &amp; Hinton, 2015; Krizhevsky, Sutskever, &amp; Hinton, 2012; Amodei et al., 2016; Deng &amp; Liu, 2018); (ii) non-convex regularizers (e.g., for enforcing sparsity or low-rank) could be more effective than their convex counterparts for learning high-dimensional structured models (C.-H. Zhang &amp; Zhang, 2012; J. Fan &amp; Li, 2001; C.-H. Zhang, 2010; T. Zhang, 2010); (iii) constrained learning is being used to learn predictive models that satisfy various constraints to respect social norms (e.g., fairness) (B. E. Woodworth, Gunasekar, Ohannessian, &amp; Srebro, 2017; Hardt, Price, Srebro, et al., 2016; Zafar, Valera, Gomez Rodriguez, &amp; Gummadi, 2017; A. Agarwal, Beygelzimer, Dudík, Langford, &amp; Wallach, 2018), to improve the interpretability (Gupta et al., 2016; Canini, Cotter, Gupta, Fard, &amp; Pfeifer, 2016; You, Ding, Canini, Pfeifer, &amp; Gupta, 2017), to enhance the robustness (Globerson &amp; Roweis, 2006a; Sra, Nowozin, &amp; Wright, 2011; T. Yang, Mahdavi, Jin, Zhang, &amp; Zhou, 2012), etc. In spite of great promises brought by these new learning paradigms, they also bring emerging challenges to the design of computationally efficient algorithms for big data and the analysis of their statistical properties.
End-to-end Deep Reinforcement Learning for Multi-agent Collaborative Exploration

Exploring an unknown environment by multiple autonomous robots is a major challenge in domains such as autonomous surveillance, search and rescue, and domestic cleaning.Conventional methods usually employ a fixed strategy to allocate the robots or agents to explore selected locations like frontier points to construct a map of the environment.Although these methods can be effective in a single agent case, assigning multiple robots to explore different locations is more challenging as different agents may interfere each other making the overall tasks less efficient.In this paper, we present an approach to multi-agent exploration wherein the agents learn the effective strategy to allocate and explore the environment using a new deep reinforcement learning architecture.We propose CNN-based Multi-agent Proximal Policy Optimization (CMAPPO) algorithm for allocating multiple agents to explore different environments while, over time, improving their strategies to allocate the tasks more efficiently.This algorithm combines convolutional neural network to process multi-channel visual inputs from the observed environment, curriculum-based learning for improving learning efficiency, and PPO algorithm for motivation based reinforcement learning.Based on our evaluation, the proposed method can learn more efficient strategy for multiple agents to explore the environment than the conventional frontier-based method.
Creating Humanlike Chatbots: What Chatbot Developers Could Learn from Webcare Employees in Adopting a Conversational Human Voice

Currently, conversations with chatbots are perceived as unnatural and impersonal. One way to enhance the feeling of humanlike responses is by implementing an engaging communication style (i.e., Conversational Human Voice (CHV); Kelleher 2009) which positively affects people's perceptions of the organization. This communication style contributes to the effectiveness of online communication between organizations and customers (i.e., webcare), and is of high relevance to chatbot design and development. This project aimed to investigate how insights on the use of CHV in organizations' messages and the perceptions of CHV can be implemented in customer service automation. A corpus study was conducted to investigate which linguistic elements are used in organizations' messages. Subsequently, an experiment was conducted to assess to what extent linguistic elements contribute to the perception of CHV. Based on these two studies, we investigated whether the amount of CHV can be identified automatically. These findings could be used to design humanlike chatbots that use a natural and personal communication style like their human conversation partner.
Hybrid CNNs-LSTM Deep Analyzer for Arabic Opinion Mining

Deep learning models have showed great capabilities in data modelling on natural language processing various applications, including sentiment analysis, part-of-speech tagging, machine translation, and many others. In particular, convolutional neural network (CNNs) and long-term short memory (LSTM) have proved to be effective in capturing longterm dependencies in sequential data that result in state-of-the-art performance in comparison to traditional machine learning algorithms. This research paper, therefore, structures an enhanced model of both CNNs and LSTM for the feature resourcefulness of Arabic text data on freely available benchmark datasets, with word2vec representation model for each corpus. The model is projected for Arabic sentiment analysis (ASA) in highlight. The proposed architecture has achieved better performance on three datasets out of five in comparison to previous studies. In research conduct, the model achieved a total accuracy of 0.881 for Main-AHS, 0.968 for Sub-AHS, 0.842 for Ar-Twitter, 0.7918 for ASTD, 0.903 for OCLAR.
GPU acceleration of liver enhancement for tumor segmentation

Background and objective: Medical image segmentation plays a vital role in medical image analysis. There are many algorithms developed for medical image segmentation which are based on edge or region characteristics. These are dependent on the quality of the image. The contrast of a CT or MRI image plays an important role in identifying region of interest i.e. lesion(s). In order to enhance the contrast of image, clinicians generally use manual histogram adjustment technique which is based on 1D histogram specification. This is time consuming and results in poor distribution of pixels over the image. Cross modality based contrast enhancement is 2D histogram specification technique. This is robust and provides a more uniform distribution of pixels over CT image by exploiting the inner structure information from MRI image. This helps in increasing the sensitivity and accuracy of lesion segmentation from enhanced CT image. The sequential implementation of cross modality based contrast enhancement is slow. Hence we propose GPU acceleration of cross modality based contrast enhancement for tumor segmentation. Methods: The aim of this study is fast parallel cross modality based contrast enhancement for CT liver images. This includes pairwise 2D histogram, histogram equalization and histogram matching. The sequential implementation of the cross modality based contrast enhancement is computationally expensive and hence time consuming. We propose persistence and grid-stride loop based fast parallel contrast enhancement for CT liver images. We use enhanced CT liver image for the lesion or tumor segmentation. We implement the fast parallel gradient based dynamic seeded region growing for lesion segmentation. Results: The proposed parallel approach is 104.416 ( ± 5.166) times faster compared to the sequential implementation and increases the sensitivity and specificity of tumor segmentation. Conclusion: The cross modality approach is inspired by 2D histogram specification which incorporates spatial information existing in both guidance and input images for remapping the input image intensity values. The cross modality based liver contrast enhancement improves the quality of tumor segmentation.
Generating robotic emotional body language with variational autoencoders

Humanoid robots in social environments can become more engaging by using their embodiment to display emotional body language. For such expressions to be effective in long term interaction, they need to be characterized by variation and complexity, so that the robot can sustain the user's interest beyond the novelty effect period. Hand-coded, pose-to-pose robotic animations can be of high quality and interpretability, but the demanding process of creating them results in limited sets; therefore, after a while, the user will realize that the behavior is repetitive. This work proposes the application of deep learning methods, and more specifically the variational autoencoder framework, for generating numerous emotional body language animations for the Pepper robot, after being trained with a few examples of hand-coded animations. Interestingly, the latent space of the model exhibits topological features that can be used to modulate the amplitude of the motion; we propose that this can be potentially useful for generating animations of specific arousal according to the dimensional theory of emotion.
Fundamental Limits of Approximate Gradient Coding

In the distributed graident coding problem, it has been established that, to exactly recover the gradient under s slow machines, the mmimum computation load (number of stored data partitions) of each worker is at least linear ($s+1$), which incurs a large overhead when s is large[13]. In this paper, we focus on approximate gradient coding that aims to recover the gradient with bounded error ε. Theoretically, our main contributions are three-fold: (i) we analyze the structure of optimal gradient codes, and derive the information-theoretical lower bound of minimum computation load: O(log(n)/log(n/s)) for ε = 0 and d≥ O(log(1/ε)/log(n/s)) for ε>0, where d is the computation load, and ε is the error in the gradient computation; (ii) we design two approximate gradient coding schemes that exactly match such lower bounds based on random edge removal process; (iii) we implement our schemes and demonstrate the advantage of the approaches over the current fastest gradient coding strategies. The proposed schemes provide order-wise improvement over the state of the art in terms of computation load, and are also optimal in terms of both computation load and latency.
User-Defined Gestures for Dual-Screen Mobile Interaction

Existing mobile phones use the front screen for gesture interactions, but they often suffer from usability problems such as content occlusion and spurious triggering caused by fat finger errors. Recent advances in new sensor technologies have made it possible for modern mobile phones to recognize gesture inputs on the rear screen. Although inputs from the backside of a mobile phone have demonstrated their usefulness for simple interaction tasks such as target acquisition, little is known regarding the best practices in gesture design for complex concurrent tasks on dual-screen mobile devices. Therefore, we conducted a three-stage study to explore user-defined multi-finger gestures for concurrent game tasks on a dual-screen mobile device. Experimental results indicate the existence of a consensus between participants regarding gesture choices for specified game tasks. Based on this consensus, we developed a gesture taxonomy and generated a set of user-defined gestures for dual-screen mobile devices. A subsequent benchmark test validated the popularity of the user-defined gestures with both ordinary and professional game players.
Robust Filtering of Discrete-Time Linear Systems With Correlated Process and Measurement Noises

The Gaussian noise assumption is only an approximation to reality and practical engineering systems are often subject to non-Gaussian disturbances, instrument failures and human errors, which all induce outliers. This paper deals with the problem of robust filter design for discrete-time linear systems with correlated process and measurement noises. In the presence of correlated noises, the impact of outliers may spread through correlations and even cause the breakdown of a robust estimator. To address this issue, the robust estimation problem is formulated as a regularized linear regression problem in which the outliers are explicitly modeled and regularized with ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm to promote sparse solutions. Because of the explicit modeling of outliers, their negative influences propagated through correlations can be estimated and reduced. Hence more accurate estimation results can be expected. By selecting an appropriate regularization parameter, the proposed filter reduces to the well-known Kalman filter and hence is capable of providing a good balance between robustness and optimality. Besides, the extra flexibility of assigning different weights to state and measurement outliers makes it possible to put more focus on outliers from a specific source. Examples using both simulated and experimental data demonstrate the effectiveness of the proposed robust filtering approach.
Automated monitoring of the crack propagation in mode I testing of thermoplastic composites by means of digital image correlation

One of the main challenges of the mode I double cantilever beam (DCB) test is the simultaneous determination of the applied load and displacement with the developing delamination length. The present work addresses this issue by side-view tracking the crack propagation by means of digital image correlation (DIC). Two different reduction methods were developed to determine the crack length from the DIC data. On the one hand, the crack tip position was defined by the high strain concentration in the immediate vicinity of the crack tip, and on the other hand, by crack tip opening displacement (CTOD). The data obtained enabled the calculation of the energy release rate of carbon fibre reinforced thermoplastic specimens with either run-arrest or stable crack extension. For reasons of comparability, top surface analysis (TSA), as recently reported, was also carried out. Following this approach, the crack propagation was tracked applying DIC to the top specimen surface. The methods developed showed a good correlation with both the standardised procedure and TSA. It was shown that DIC can be used as an alternative to the conventional optical measuring tools to follow the crack propagation in the mode I DCB test.
Visual Subterranean Junction Recognition for MAVs based on Convolutional Neural Networks

This article proposes a novel visual framework for detecting tunnel crossings/junctions in underground mine areas towards the autonomous navigation of Micro Aerial Vehicles (MAVs). Usually mine environments have complex geometries, including multiple crossings with different tunnels that challenge the autonomous planning of aerial robots. Towards the envisioned scenario of autonomous or semi-autonomous deployment of MAVs with limited Line-of-Sight in subterranean environments, the proposed module acknowledges the existence of junctions by providing crucial information to the autonomy and planning layers of the aerial vehicle. The capability for a junction detection is necessary in the majority of mission scenarios, including unknown area exploration, known area inspection and robot homing missions. The proposed novel method has the ability to feed the image stream from the vehicles on-board forward facing camera in a Convolutional Neural Network (CNN) classification architecture, expressed in four categories: 1) left junction, 2) right junction, 3) left & right junction, and 4) no junction in the local vicinity of the vehicle. The core contribution stems for the incorporation of AlexNet in a transfer learning scheme for detecting multiple branches in a subterranean environment. The validity of the proposed method has been validated through multiple data-sets collected from real underground environments, demonstrating the performance and merits of the proposed module.
A robust hybrid SVD-based image watermarking scheme for color images

The rapid progress of digital multimedia technologies and communications led to the different challenges such as intellectual property or copyright protection. Digital watermarking is a well-known solution for such issues which does not increase overhead, so it is superior to Digital Signature. However, a major problem with digital image watermarking is its weakness against geometric distortions. This paper presents a robust hybrid SVD-based watermarking scheme for color images. By implementing particle swarm optimization (PSO), the scaling factors are selected automatically and the embedding regions are chosen based on human visual system (HVS) in the proposed scheme; moreover, the capacity is improved through embedding two watermark bits into the each selected embedding block regions, while its transparency is kept high. The experimental and analysis results proved the high robustness and imperceptibility of the proposed scheme.
Deep Learning-Based Massive MIMO CSI Feedback

Massive multi-input and multi-output technology is a key technology for future 5G wireless communication. The channel feedback problem of massive mimo becomes more and more challenging as the size of the mimo channel matrix becomes larger. A supervised deep learning-based encoder-decoder scheme was proposed to improve reconstruction quality recovery channel state information.Compared with the traditional compression-based sensing algorithm, Residual Attention-Net can still maintain good performance when compression is low.