Reviewerly/exHarmony huggingface dataset:
DIViS: Domain Invariant Visual Servoing for Collision-Free Goal Reaching

Train in SimulationFig. 1: Domain Invariant Visual Servoing (DIVIS) learns collision-free goal reaching entirely in simulation using dense multi-step rollouts and a recurrent fully convolutional neural network (bottom).DIViS can be directly deployed on real physical robots with RGB cameras for servoing to visually indicated goals as well as servoing to semantic object categories (top).Abstract-Robots should understand both semantics and physics to be functional in the real world.While robot platforms provide means for interacting with the physical world they cannot autonomously acquire object-level semantics without needing human.In this paper, we investigate how to minimize human effort and intervention to teach robots perform real world tasks that incorporate semantics.We study this question in the context of visual servoing of mobile robots and propose DIViS, a Domain Invariant policy learning approach for collision free Visual Servoing.DIViS incorporates high level semantics from previously collected static human-labeled datasets and learns collision free servoing entirely in simulation and without any real robot data.However, DIViS can directly be deployed on a real robot and is capable of servoing to the user-specified object categories while avoiding collisions in the real world.DIViS is not constrained to be queried by the final view of goal but rather is robust to servo to image goals taken from initial robot view with high occlusions without this impairing its ability to maintain a collision free path.We show the generalization capability of DIViS on real mobile robots in more than 90 real world test scenarios with various unseen object goals in unstructured environments.DIViS is compared to prior approaches via real world experiments and rigorous tests in simulation.For supplementary videos, see: https://fsadeghi.github.io/DIViS
Towards Practical Unsupervised Anomaly Detection on Retinal Images

Supervised deep learning approaches provide state-of-the-art performance on medical image classification tasks for disease screening. However, these methods require large labeled datasets that involve resource-intensive expert annotation. Further, disease screening applications have low prevalence of abnormal samples; this class imbalance makes the task more akin to anomaly detection. While the machine learning community has proposed unsupervised deep learning methods for anomaly detection, they have yet to be characterized on medical images where normal vs. anomaly distinctions may be more subtle and variable. In this work, we characterize existing unsupervised anomaly detection methods on retinal fundus images, and find that they require significant fine tuning and offer unsatisfactory performance. We thus propose an efficient and effective transfer-learning based approach for unsupervised anomaly detection. Our method employs a deep convolutional neural network trained on ImageNet as a feature extractor, and subsequently feeds the learned feature representations into an existing unsupervised anomaly detection method. We show that our approach significantly outperforms baselines on two natural image datasets and two retinal fundus image datasets, all with minimal fine-tuning. We further show the ability to leverage very small numbers of labelled anomalies to improve performance. Our work establishes a strong unsupervised baseline for image-based anomaly detection, alongside a flexible and scalable approach for screening applications.
Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text

Automatic question generation (QG) is a useful yet challenging task in NLP.Recent neural network-based approaches represent the stateof-the-art in this task.In this work, we attempt to strengthen them significantly by adopting a holistic and novel generator-evaluator framework that directly optimizes objectives that reward semantics and structure.The generator is a sequence-to-sequence model that incorporates the structure and semantics of the question being generated.The generator predicts an answer in the passage that the question can pivot on.Employing the copy and coverage mechanisms, it also acknowledges other contextually important (and possibly rare) keywords in the passage that the question needs to conform to, while not redundantly repeating words.The evaluator model evaluates and assigns a reward to each predicted question based on its conformity to the structure of ground-truth questions.We propose two novel QG-specific reward functions for text conformity and answer conformity of the generated question.The evaluator also employs structure-sensitive rewards based on evaluation measures such as BLEU, GLEU, and ROUGE-L, which are suitable for QG.In contrast, most of the previous works only optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation) measures.Our evaluation shows that our approach significantly outperforms state-of-the-art systems on the widelyused SQuAD benchmark as per both automatic and human evaluation.
Image steganography using least significant bit and secret map techniques

In steganography, secret data are invisible in cover media, such as text, audio, video and image. Hence, attackers have no knowledge of the original message contained in the media or which algorithm is used to embed or extract such message. Image steganography is a branch of steganography in which secret data are hidden in host images. In this study, image steganography using least significant bit and secret map techniques is performed by applying 3D chaotic maps, namely, 3D Chebyshev and 3D logistic maps, to obtain high security. This technique is based on the concept of performing random insertion and selecting a pixel from a host image. The proposed algorithm is comprehensively evaluated on the basis of different criteria, such as correlation coefficient, information entropy, homogeneity, contrast, image, histogram, key sensitivity, hiding capacity, quality index, mean square error (MSE), peak signal-to-noise ratio (PSNR) and image fidelity. Results show that the proposed algorithm satisfies all the aforementioned criteria and is superior to other previous methods. Hence, it is efficient in hiding secret data and preserving the good visual quality of stego images. The proposed algorithm is resistant to different attacks, such as differential and statistical attacks, and yields good results in terms of key sensitivity, hiding capacity, quality index, MSE, PSNR and image fidelity.
Robust face alignment by cascaded regression and de-occlusion

Face alignment is a typical facial behavior analysis task in computer vision. However, the performance of face alignment is degraded greatly when the face image is partially occluded. In order to achieve better mapping between facial appearance features and shape increments, we propose a robust and occlusion-free face alignment algorithm in which a face de-occlusion module and a deep regression module are integrated into a cascaded deep generative regression model. The face de-occlusion module is a disentangled representation learning Generative Adversarial Networks (GANs) which aims to locate occlusions and recover the genuine appearance from partially occluded face image. The deep regression module can enhance facial appearance representation by utilizing the recovered faces to obtain more accurate regressors. Then, by the cascaded deep generative regression model, we recover the partially occluded face image and achieve accurate locating of landmarks gradually. It is interesting to show that the cascaded deep generative regression model can effectively locate occlusions and recover more genuine faces, which can be further used to improve the performance of face alignment. Experimental results conducted on four challenging occluded face datasets demonstrate that our method outperforms state-of-the-art methods.
A Survey of Application and Classification on Teaching-Learning-Based Optimization Algorithm

Teaching-Learning-based Optimization is an optimization technique which does not require any algorithm-specific parameters and is popular for its less computational cost and high consistency.Therefore, it has achieved great success application by the researchers in various disciplines of engineering.It works on the philosophy of teaching and learning which is used to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency.Recently the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities and the performance.However, there is less surveys on TLBO algorithm recent advances and its application.In this paper, the successful researches of TLBO algorithm of the past decade are surveyed.Firstly, the available intelligent optimization algorithms were reviewed.Then the application fields of TLBO and the improved TLBO were discussed and analyzed.Furthermore, some representative TLBO methods were classified into three main groups: 1) Improvement of teaching process; and 2) Fusion with Other Optimization Methods; and 3) Weight Methods and Others.Finally, our viewpoints were shared on the open issues and challenges in TLBO as well as research trends in the future.
AIM 2019 Challenge on Bokeh Effect Synthesis: Methods and Results

This paper reviews the first AIM challenge on bokeh effect synthesis with the focus on proposed solutions and results. The participating teams were solving a real-world image-to-image mapping problem, where the goal was to map standard narrow-aperture photos to the same photos captured with a shallow depth-of-field by the Canon 70D DSLR camera. In this task, the participants had to restore bokeh effect based on only one single frame without any additional data from other cameras or sensors. The target metric used in this challenge combined fidelity scores (PSNR and SSIM) with solutions' perceptual results measured in a user study. The proposed solutions significantly improved baseline results, defining the state-of-the-art for practical bokeh effect simulation.
High-Order Residual Network for Light Field Super-Resolution

Plenoptic cameras usually sacrifice the spatial resolution of their SAIs to acquire geometry information from different viewpoints. Several methods have been proposed to mitigate such spatio-angular trade-off, but seldom make use of the structural properties of the light field (LF) data efficiently. In this paper, we propose a novel high-order residual network to learn the geometric features hierarchically from the LF for reconstruction. An important component in the proposed network is the high-order residual block (HRB), which learns the local geometric features by considering the information from all input views. After fully obtaining the local features learned from each HRB, our model extracts the representative geometric features for spatio-angular upsampling through the global residual learning. Additionally, a refinement network is followed to further enhance the spatial details by minimizing a perceptual loss. Compared with previous work, our model is tailored to the rich structure inherent in the LF, and therefore can reduce the artifacts near non-Lambertian and occlusion regions. Experimental results show that our approach enables high-quality reconstruction even in challenging regions and outperforms state-of-the-art single image or LF reconstruction methods with both quantitative measurements and visual evaluation.
Analysis of the Cryptographic Tools for Blockchain and Bitcoin

Blockchain is one of the most interesting emerging technologies nowadays, with applications ranging from cryptocurrencies to smart contracts. This paper presents a review of the cryptographic tools necessary to understand the fundamentals of this technology and the foundations of its security. Among other elements, hash functions, digital signatures, elliptic curves, and Merkle trees are reviewed in the scope of their usage as building blocks of this technology.
Evaluation of Eye-Movement Metrics ina Software DebbugingTask using GP3 Eye Tracker

Teaching different programming subjects is an increasing challenge nowadays as because of growing user demands, the latest paradigms and technologies must be taught.Students in higher education now are of the age who were born into the digital world; however, the success of fulfilling programming courses is lagging behind.Humancomputer interface-based research has emerged in numerous fields of science recently, which could lead to the revolutionising of education.These interfaces could also help professors as a support system in transferring knowledge in a more efficient way besides supporting students acquiring adequate home learning methods.In this study, the applicability of eye movement tracking systems in respect of a programming task is examined, in which during the exploration and correction of the errors of an incorrectly functioning algorithm, the eye movement parameters are observed, recorded and evaluated.The test subjects participating in the research were divided into two groups according to some of their characteristic parameters, where the first group during debugging rather applied minor modifications and the more common technique of compile and run, which otherwise is also the most characteristic feature of students studying programming, while the members of the other group increased emphasis on analysing.In the statistic evaluation of the research, the parameters characteristic of the eye movement tracking of the two groups, as well as the efficiency of these groups were analysed.Based on the results, regarding the efficiency and the number of fixations, a significant difference could be shown between the two groups (U=22.5,Z=-2.236, p=0.025 (2-tailed), r=0.48) and (t(20)=2.507,p=0.021 (2-tailed), d=1.106), while concerning the duration of fixation and the saccade length, the difference shown was infinitesimal (t(20)=0.544,p=0.592 (2tailed), d=0.26) and (t(19.992)=1.965,p=0.063 (2-tailed), d=0.79).
Blockchain Applications and Institutional Trust

In the current discussions around Blockchain and distributed ledger technologies, we find a lack of theory to conceptualize and understand application scenarios. In this paper we propose to conceptualize distributed ledger technologies as trust mechanisms. Whereas previously one had to rely on a trusted third party (e.g. notary), now one must trust a complex software system – the Blockchain and distributed ledger application– as well as the parties that host the software system and ensure its effectiveness. Based on theories of e-commerce, business networks, and trust, we explore relations between trust and Blockchain design. We analyze three case studies of Blockchain applications in the diamond industry. In each case we study two complementary research questions: (1) how does the blockchain application influence trust, and (2) how do trust based requirements affect the design of a blockchain application? We formulate two propositions and find dynamic interactions between trust requirements, blockchain application design, and transaction trust.
A Journey in Applying Blockchain for Cyberphysical Systems

Cyberphysical Systems (CPS) are transforming the way we interact with the physical world around us.However, centralised approaches for CPS systems are not capable of addressing the unique challenges of CPS due to the complexity, constraints, and dynamic nature of the interactions.To realize the true potential of CPS, a decentralized approach that takes into account these unique features is required.Recently, blockchainbased solutions have been proposed to address CPS challenges.Yet, applying blockchain for diverse CPS domains is not straightforward and has its own challenges.In this paper, we share our experiences in applying blockchain technology for CPS to provide insights and highlight the challenges and future opportunities.
Formal verification of input-output mappings of tree ensembles

Recent advances in machine learning and artificial intelligence are now being considered in safety-critical autonomous systems where software defects may cause severe harm to humans and the environment. Design organizations in these domains are currently unable to provide convincing arguments that their systems are safe to operate when machine learning algorithms are used to implement their software. In this paper, we present an efficient method to extract equivalence classes from decision trees and tree ensembles, and to formally verify that their input-output mappings comply with requirements. The idea is that, given that safety requirements can be traced to desirable properties on system input-output patterns, we can use positive verification outcomes in safety arguments. This paper presents the implementation of the method in the tool VoTE (Verifier of Tree Ensembles), and evaluates its scalability on two case studies presented in current literature. We demonstrate that our method is practical for tree ensembles trained on low-dimensional data with up to 25 decision trees and tree depths of up to 20. Our work also studies the limitations of the method with high-dimensional data and preliminarily investigates the trade-off between large number of trees and time taken for verification.
Pose-Based View Synthesis for Vehicles: A Perspective Aware Method

In this paper, we focus on the problem of novel view synthesis for vehicles. Some previous works solve the problem of novel view synthesis in a controlled 3D environment by exploiting additional 3D details ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , camera viewpoints and underlying 3D models). However, in real scenarios, the 3D details are difficult to obtain. In this case, we find that introducing vehicle pose to represent the views of vehicles is an alternative paradigm to solve the lack of 3D details. In novel view synthesis, preserving local details is one of the most challenging problems. To address this problem, we propose a perspective-aware generative model (PAGM). We are motivated by the prior that vehicles are made of quadrilateral planes. Preserving these rigid planes during image generation ensures that image details are kept. To this end, a classic image transformation method is leveraged, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , perspective transformation. In our GAN-based system, the perspective transformation is applied to the encoder feature maps, and the resulting maps are regarded as new conditions for the decoder. This strategy preserves the quadrilateral planes all the way through the network, thus shuttling the texture details from the input image to the generated image. In the experiments, we show that PAGM can generate high-quality vehicle images with fine details. Quantitatively, our method is superior to several competing approaches employing either GAN or the perspective transformation. Code is available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/ilvkai/view-synthesis-for-vehicles</uri>
A Hybrid Swarm Algorithm for Collective Construction of 3D Structures

In order to decrease the time of construction of self-assembly algorithm based on the construction of wasp nests, with respect to the number of iterations, this work proposes a novel hybrid swarm algorithm combining strengths of self-assembly and the particle swarm optimization. The paper also considers integration of adaptive values of inertia to further balance exploration and exploitation for improving the construction process. According to the obtained results, it is shown experimentally with two types of benchmark structures that the convergence speed of our hybrid swarm algorithm is considerably improved because each structure is built complete on a lower number of iterations compared with the classical algorithm.
BiTipText: Bimanual Eyes-Free Text Entry on a Fingertip Keyboard

We present a bimanual text input method on a miniature fingertip keyboard, that invisibly resides on the first segment of a user's index finger on both hands. Text entry can be carried out using the thumb-tip to tap the tip of the index finger. The design of our keyboard layout followed an iterative process, where we first conducted a study to understand the natural expectation of the handedness of the keys in a QWERTY layout for users. Among a choice of 67,108,864 design variations, we identified 1295 candidates offering a good satisfaction for user expectations. Based on these results, we computed an optimized bimanual keyboard layout, while considering the joint optimization problems of word ambiguity and movement time. Our user evaluation revealed that participants achieved an average text entry speed of 23.4 WPM.
Bitcoin transactions, information asymmetry and trading volume

The underlying transparency of the Bitcoin blockchain allows transactions in the network to be tracked in near real-time. When someone transfers a large number of Bitcoins, the market receives this information and traders can adjust their expectations based on the new information. This paper investigates trading volume and its relation to asymmetric information around transfers on the Bitcoin blockchain. We collect data on 2132 large transactions on the Bitcoin blockchain between September 2018 and November 2019, where 500 or more Bitcoins were transferred. Using event study methodology, we identify significant positive abnormal trading volume for the 15-minute window before a large Bitcoin transaction as well as during and after the event. Using public information about Bitcoin addresses of cryptocurrency exchanges as proxies for information asymmetry, we find that transactions with high levels of information asymmetry negatively affect abnormal trading volume once the event becomes public knowledge, while some effects are even opposite for transactions with lower information asymmetry. The results show that blockchain transaction activity is a relevant aspect of Bitcoinns microstructure, as informed traders make use of the information in general and adjust their expectations based on the degree of information asymmetry.
Visual and Textual Deep Feature Fusion for Document Image Classification

The topic of text document image classification has been explored extensively over the past few years. Most recent approaches handled this task by jointly learning the visual features of document images and their corresponding textual contents. Due to the various structures of document images, the extraction of semantic information from its textual content is beneficial for document image processing tasks such as document retrieval, information extraction, and text classification. In this work, a two-stream neural architecture is proposed to perform the document image classification task. We conduct an exhaustive investigation of nowadays widely used neural networks as well as word embedding procedures used as backbones, in order to extract both visual and textual features from document images. Moreover, a joint feature learning approach that combines image features and text embeddings is introduced as a late fusion methodology. Both the theoretical analysis and the experimental results demonstrate the superiority of our proposed joint feature learning method comparatively to the single modalities. This joint learning approach outperforms the state-of-the-art results with a classification accuracy of 97.05% on the large-scale RVL-CDIP dataset.