Reviewerly/exHarmony huggingface dataset:
Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection

Recent saliency models extensively explore to incorporate multi-scale contextual information from Convolutional Neural Networks (CNNs). Besides direct fusion strategies, many approaches introduce message-passing to enhance CNN features or predictions. However, the messages are mainly transmitted in two ways, by feature-to-feature passing, and by prediction-to-prediction passing. In this paper, we add message-passing between features and predictions and propose a deep unified CRF saliency model . We design a novel cascade CRFs architecture with CNN to jointly refine deep features and predictions at each scale and progressively compute a final refined saliency map. We formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-prediction, from the coarse scale to the finer scale, to update the features and the corresponding predictions. Also, we formulate the mean-field updates for joint end-to-end model training with CNN through back propagation. The proposed deep unified CRF saliency model is evaluated over six datasets and shows highly competitive performance among the state of the arts.
Blockchain distributed ledger technology: An introduction and focus on smart contracts

Abstract The rapid evolution of the blockchain and distributed ledger technology and the associated applications will require many changes in the way business is transacted and how transactions are recorded. Significant R&amp;D investments in blockchain environments and the maturation of application uses of “smart contracts” and other types of self‐executing tools are establishing frameworks for expanded use, infrastructure growth, and further development. The realization of the impact of these investments is currently changing entire business processes, transforming industries, and impacting governments globally. This global disruptive impact is a major technology evolution and will be transformational to society over the next several decades much as the Internet has been over the last 30 years. As these changes will be especially transformational to fundamental accounting processes, tools, and methodologies of today, accounting and finance professionals would be wise to embrace the technological challenges associated with blockchain technology.
Bridge-GAN: Interpretable Representation Learning for Text-to-Image Synthesis

Text-to-image synthesis is to generate images with the consistent content as the given text description, which is a highly challenging task with two main issues: visual reality and content consistency. Recently, it is available to generate images with high visual reality due to the significant progress of generative adversarial networks. However, translating text description to image with high content consistency is still ambitious. For addressing the above issues, it is reasonable to establish a transitional space with interpretable representation as a bridge to associate text and image. So we propose a text-to-image synthesis approach named Bridge-like Generative Adversarial Networks (Bridge-GAN). Its main contributions are: (1) A transitional space is established as a bridge for improving content consistency, where the interpretable representation can be learned by guaranteeing the key visual information from given text descriptions. (2) A ternary mutual information objective is designed for optimizing the transitional space and enhancing both the visual reality and content consistency. It is proposed under the goal to disentangle the latent factors conditioned on text description for further interpretable representation learning. Comprehensive experiments on two widely-used datasets verify the effectiveness of our Bridge-GAN with the best performance.
Personalized itinerary recommendation: Deep and collaborative learning with textual information

Personalized itinerary recommendation is a complicated and challenging task, which aims to construct and recommend a visit sequence consists of multiple Points of Interest (POIs) with the constraints that maximizing user satisfaction while adhering time budget. User interests, therefore, becomes the most crucial element in the recommendation task, determining the POIs and their visit durations in the itinerary. In this paper, we propose a novel framework named DCC-PersIRE to infer the user interests and recommend personalized itinerary consists of POIs, visit durations and visit sequence. Specifically, we employ an unsupervised deep learning model to embed the POI textual contents, and then propose a DCC model, which seamlessly integrates the embedded POI textual contents with the traditional and widely used user-POI visits and POI categories, to predict the user interests as well as the visit durations. Then, after formulating itinerary construction as a variant of the Orienteering Problem, an Iterated Local Search based algorithm is proposed to calculate the visit sequence with maximized satisfaction consists of multiple POIs and personalized POI visit durations, i.e., the optimal itinerary. Extensive experiments on eight real-world datasets validate the effectiveness of DCC-PersIRE. The experimental results show that our algorithm delivers a more fine-grained prediction of user interests and outperforms various state-of-the-art baselines in tour itinerary recommendation.
Improving head pose estimation using two-stage ensembles with top-k regression

Conventional head pose estimation methods are regarded as a classification or regression paradigm, individually. The accuracy of classification-based approaches is limited to pose quantized interval and regression-based methods are fragile due to extremely large pose in non-ideal conditions. On the contrary to these methods, this paper introduces a novel head pose estimation method using two-stage ensembles with average top-k regression. The first stage is a binned classification subtask with the optimal pose partition. The second stage achieves average top-k regression based on the former prediction. Then we combine the two subtasks by considering the task-dependent weights instead of setting coefficients by grid search. We conduct several experiments to analyze the optimal pose partition for classification part and to validate the average top-k loss for regression part. Furthermore, we report the performance of proposed method on AFW, AFLW2000 and BIWI datasets and results show rather competitive performance in head pose prediction.
Semi-supervised cross-modal image generation with generative adversarial networks

Cross-modal image generation is an important aspect of the multi-modal learning. Existing methods usually use the semantic feature to reduce the modality gap. Although these methods have achieved notable progress, there are still some limitations: (1) they usually use single modality information to learn the semantic feature; (2) they require the training data to be paired. To overcome these problems, we propose a novel semi-supervised cross-modal image generation method, which consists of two semantic networks and one image generation network. Specifically, in the semantic networks, we use image modality to assist non-image modality for semantic feature learning by using a deep mutual learning strategy. In the image generation network, we introduce an additional discriminator to reduce the image reconstruction loss. By leveraging large amounts of unpaired data, our method can be trained in a semi-supervised manner. Extensive experiments demonstrate the effectiveness of the proposed method.
Improved Techniques for Adversarial Discriminative Domain Adaptation

Adversarial discriminative domain adaptation (ADDA) is an efficient framework for unsupervised domain adaptation in image classification, where the source and target domains are assumed to have the same classes, but no labels are available for the target domain. While ADDA has already achieved better training efficiency and competitive accuracy on image classification in comparison to other adversarial based methods, we investigate whether we can improve its performance with a new framework and new loss formulations. Following the framework of semi-supervised GANs, we first extend the discriminator output over the source classes, in order to model the joint distribution over domain and task. We thus leverage on the distribution over the source encoder posteriors (which is fixed during adversarial training) and propose maximum mean discrepancy (MMD) and reconstruction-based loss functions for aligning the target encoder distribution to the source domain. We compare and provide a comprehensive analysis of how our framework and loss formulations extend over simple multi-class extensions of ADDA and other discriminative variants of semi-supervised GANs. In addition, we introduce various forms of regularization for stabilizing training, including treating the discriminator as a denoising autoencoder and regularizing the target encoder with source examples to reduce overfitting under a contraction mapping (i.e., when the target per-class distributions are contracting during alignment with the source). Finally, we validate our framework on standard datasets like MNIST, USPS, SVHN, MNIST-M and Office-31. We additionally examine how the proposed framework benefits recognition problems based on sensing modalities that lack training data. This is realized by introducing and evaluating on a neuromorphic vision sensing (NVS) sign language recognition dataset, where the source domain constitutes emulated neuromorphic spike events converted from conventional pixel-based video and the target domain is experimental (real) spike events from an NVS camera. Our results on all datasets show that our proposal is both simple and efficient, as it competes or outperforms the state-of-the-art in unsupervised domain adaptation, such as DIFA and MCDDA, whilst offering lower complexity than other recent adversarial methods.
Discrete Mutation Hopfield Neural Network in Propositional Satisfiability

The dynamic behaviours of an artificial neural network (ANN) system are strongly dependent on its network structure. Thus, the output of ANNs has long suffered from a lack of interpretability and variation. This has severely limited the practical usability of the logical rule in the ANN. The work presents an integrated representation of k-satisfiability (kSAT) in a mutation hopfield neural network (MHNN). Neuron states of the hopfield neural network converge to minimum energy, but the solution produced is confined to the limited number of solution spaces. The MHNN is incorporated with the global search capability of the estimation of distribution algorithms (EDAs), which typically explore various solution spaces. The main purpose is to estimate other possible neuron states that lead to global minimum energy through available output measurements. Furthermore, it is shown that the MHNN can retrieve various neuron states with the lowest minimum energy. Subsequent simulations performed on the MHNN reveal that the approach yields a result that surpasses the conventional hybrid HNN. Furthermore, this study provides a new paradigm in the field of neural networks by overcoming the overfitting issue.
Neuroadaptive integral robust control of visual quadrotor for tracking a moving object

This paper aims to achieve the visual quadrotor tracking for ground moving target in the presence of various uncertainties. In order to provide a stable quadrotor motion, the perspective image moments extracted from the defined virtual image plane are selected as the visual features to deduce a decoupled visual quadrotor model. Then a neuroadaptive integral robust controller is designed for the outer and inner loops of visual quadrotor within the image based visual servo (IBVS) framework, which enables error stabilization and improves the anti-disturbance capability. Then, the unknown lumped uncertainties consisting of parametric uncertainties and external disturbances are estimated via constructing a minimal learning parameter-neural network (MLP-NN) and compensated in the feedforward loop, subsequently the residual estimation errors are further suppressed by the structured robust integral of the sign of the error (RISE) feedback control law, hence a high-precise servo tracking performance can be expected. The innovations of the algorithm lie in, (1) with the NN approximation compensated in feedforward loop, the residual estimation errors in feedback loop are decreased, hence the conservatism that using high gains when facing large uncertainties is relaxed, (2) by means of the peculiarity of RISE control, asymptotic stability can be guaranteed with continuous and bounded control inputs, (3) using MLP technology, the number of NN learning parameters are decreased to only two for the closed-loop system, and the online computational load is greatly reduced. Finally, the validity and superiority of the proposed method are validated through affluent simulations and comparisons.
Post-quantum zero knowledge in constant rounds

We construct a constant-round zero-knowledge classical argument for NP secure against quantum attacks. We assume the existence of Quantum Fully-Homomorphic Encryption and other standard primitives, known based on the Learning with Errors Assumption for quantum algorithms. As a corollary, we also obtain a constant-round zero-knowledge quantum argument for QMA.
Invariance-Preserving Localized Activation Functions for Graph Neural Networks

Graph signals are signals with an irregular structure that can be described by a graph. Graph neural networks (GNNs) are information processing architectures tailored to these graph signals and made of stacked layers that compose graph convolutional filters with nonlinear activation functions. Graph convolutions endow GNNs with invariance to permutations of the graph nodes' labels. In this paper, we consider the design of trainable nonlinear activation functions that take into consideration the structure of the graph. This is accomplished by using graph median filters and graph max filters, which mimic linear graph convolutions and are shown to retain the permutation invariance of GNNs. We also discuss modifications to the backpropagation algorithm necessary to train local activation functions. The advantages of localized activation function architectures are demonstrated in four numerical experiments: source localization on synthetic graphs, authorship attribution of 19th century novels, movie recommender systems and scientific article classification. In all cases, localized activation functions are shown to improve model capacity.
Constrained Deep Q-Learning Gradually Approaching Ordinary Q-Learning

A deep Q network (DQN) (Mnih et al., 2013) is an extension of Q learning, which is a typical deep reinforcement learning method. In DQN, a Q function expresses all action values under all states, and it is approximated using a convolutional neural network. Using the approximated Q function, an optimal policy can be derived. In DQN, a target network, which calculates a target value and is updated by the Q function at regular intervals, is introduced to stabilize the learning process. A less frequent updates of the target network would result in a more stable learning process. However, because the target value is not propagated unless the target network is updated, DQN usually requires a large number of samples. In this study, we proposed Constrained DQN that uses the difference between the outputs of the Q function and the target network as a constraint on the target value. Constrained DQN updates parameters conservatively when the difference between the outputs of the Q function and the target network is large, and it updates them aggressively when this difference is small. In the proposed method, as learning progresses, the number of times that the constraints are activated decreases. Consequently, the update method gradually approaches conventional Q learning. We found that Constrained DQN converges with a smaller training dataset than in the case of DQN and that it is robust against changes in the update frequency of the target network and settings of a certain parameter of the optimizer. Although Constrained DQN alone does not show better performance in comparison to integrated approaches nor distributed methods, experimental results show that Constrained DQN can be used as an additional components to those methods.
Length of stay prediction for ICU patients using individualized single classification algorithm

Background and Objective: In intensive care units (ICUs), length of stay (LOS) prediction is critical to help doctors and nurses select appropriate treatment options and predict patients’ condition. Considering that most hospitals use universal models to predict patients’ condition, which cannot meet the individual needs of special ICU patients. Our goal is to create a personalized model for patients to determine the number of hospital stays. Methods: In this study, a new combination of just-in-time learning (JITL) and one-class extreme learning machine (one-class ELM) is proposed to predict the number of days a patient stays in hospital. This combination is shortened as one-class JITL-ELM, where JITL is used to search for personalized cases for a new patient and one-class ELM is used to determine whether the patient can be discharged within 10 days. Results: The experimental results show that the one-class JITL-ELM model has an area under the curve (AUC) index of 0.8510, lift value of 2.1390, precision of 1, and G-mean is 0.7842. Its accuracy, specificity, and sensitivity were found as 0.82, 1, and 0.6150, respectively. Moreover, a novel simple mortality risk level estimation system that can determine the mortality rate of a patient by combining LOS and age is proposed. It has an accuracy rate of 66% and the miss rate of only 6.25%. Conclusions: Overall, the one-class JITL-ELM can accurately predict hospitalization days and mortality using early physiological parameters. Moreover, a simple mortality risk level estimation system based on a combination of LOS and age is proposed; the system is simple, highly interpretable, and has strong application value.
Secure Computation with Preprocessing via Function Secret Sharing

We propose a simple and powerful new approach for secure computation with input-independent preprocessing, building on the general tool of function secret sharing (FSS) and its efficient instantiations. Using this approach, we can make efficient use of correlated randomness to compute any type of gate, as long as a function class naturally corresponding to this gate admits an efficient FSS scheme. Our approach can be viewed as a generalization of the "TinyTable" protocol of Damgård et al. (Crypto 2017), where our generalized variant uses FSS to achieve exponential efficiency improvement for useful types of gates. By instantiating this general approach with efficient PRG-based FSS schemes of Boyle et al. (Eurocrypt 2015, CCS 2016), we can implement useful nonlinear gates for equality tests, integer comparison, bit-decomposition and more with optimal online communication and with a relatively small amount of correlated randomness. We also provide a unified and simplified view of several existing protocols in the preprocessing model via the FSS framework. Our positive results provide a useful tool for secure computation tasks that involve secure integer comparisons or conversions between arithmetic and binary representations. These arise in the contexts of approximating real-valued functions, machine-learning classification, and more. Finally, we study the necessity of the FSS machinery that we employ, in the simple context of secure string equality testing. First, we show that any "online-optimal" secure equality protocol implies an FSS scheme for point functions, which in turn implies one-way functions. Then, we show that information-theoretic secure equality protocols with relaxed optimality requirements would follow from the existence of big families of "matching vectors." This suggests that proving strong lower bounds on the efficiency of such protocols would be difficult.
Docschain: Blockchain-Based IoT Solution for Verification of Degree Documents

Degree verification is the process of verifying the academic credentials of successfully graduated students. It is a time-consuming and costly process as universities annually spend millions of dollars on handling the degree verification requests. Hence, there is a dire need to improve the degree verification process, and the Massachusetts Institute of Technology, Cambridge, MA, USA, has introduced the blockcerts, a blockchain-based solution for freely handling the degree verification requests. Although blockcerts eliminates the cost of the degree verification process, it also alters the existing workflow of degree issuance. This is because blockcerts are primarily focused on facilitating the students, and there is room for improvement from the perspective of educational institutes. In this article, we have introduced the docschain to tackle the three mentioned limitations of the blockcerts. Docschain seamlessly incorporates within the existing workflow of degree issuance by operating over the hard copies of the degree documents. This is achieved through optical character recognition (OCR), and the record of each degree document is stored along with the details of the corresponding OCR template to understand the semantics of the data stored at different sections of the degree document. In contrast to blockcerts, docschain also supports the bulk submission of degree details for both the previously and newly graduated students.
Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information

We propose a 3D object detection and pose estimation method for automated driving using stereo images. In contrast to existing stereo-based approaches, we focus not only on cars, but on all types of road users and can ensure real-time capability through GPU implementation of the entire processing chain. These are essential conditions to exploit an algorithm for highly automated driving. Semantic information is provided by a deep convolutional neural network and used together with disparity and geometric constraints to recover accurate 3D bounding boxes. Experiments on the challenging KITTI 3D object detection benchmark show results that are within the range of the best image-based algorithms, while the runtime is only about a fifth. This makes our algorithm the first real-time image-based approach on KITTI.