Reviewerly/exHarmony huggingface dataset:
On Private Information Retrieval Array Codes

Given a database, the private information retrieval (PIR) protocol allows a user to make queries to several servers and retrieve a certain item of the database via the feedbacks without revealing the identity of the specific item to any single server. Classic k-server PIR protocols work on replicated databases, i.e., each of the k servers stores a whole copy of the database. Recently, new PIR models were proposed with coding techniques arising from the distributed storage system. In these new models, each server only stores a fraction 1/s of the whole database, where s > 1 is the given rational number. The PIR array codes are recently proposed by Fazeli, Vardy, and Yaakobi to characterize the new models. The central problem in designing a PIR array code with m servers and the k-PIR property (which indicates that these m servers may emulate a classic k-server PIR protocol) is to maximize k/m, known as the virtual server rate. Our main contribution to this problem is twofold. First, for the case 1 <; s ≤ 2, although the PIR array codes with optimal rate have been constructed recently by Blackburn and Etzion, the number of servers in their construction is rather large. We determine the minimum number of servers admitting the existence of a PIR array code with an optimal rate for a certain range of parameters. Second, for the case s > 2, a new upper bound on the rate of a PIR array code is presented. Besides, we also have some discussions on an asymptotically optimal construction by Blackburn and Etzion.
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense

Present attack methods can make state-of-the-art classification systems based on deep neural networks mis-classify every adversarially modified test example. The design of general defense strategies against a wide range of such attacks still remains a challenging problem. In this paper, we draw inspiration from the fields of cybersecurity and multi-agent systems and propose to leverage the concept of Moving Target Defense (MTD) in designing a meta-defense for ‘boosting’ the robustness of an ensemble of deep neural networks (DNNs) for visual classification tasks against such adversarial attacks. To classify an input image at test time, a constituent network is randomly selected based on a mixed policy. To obtain this policy, we formulate the interaction between a Defender (who hosts the classification networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg Game (BSG). We empirically show that our approach MTDeep, reduces misclassification on perturbed images for various datasets such as MNIST, FashionMNIST, and ImageNet while maintaining high classification accuracy on legitimate test images. We then demonstrate that our framework, being the first meta-defense technique, can be used in conjunction with any existing defense mechanism to provide more resilience against adversarial attacks that can be afforded by these defense mechanisms alone. Lastly, to quantify the increase in robustness of an ensemble-based classification system when we use MTDeep, we analyze the properties of a set of DNNs and introduce the concept of differential immunity that formalizes the notion of attack transferability.
An exponential quantum projection filter for open quantum systems

An approximate exponential quantum projection filtering scheme is developed for a class of open quantum systems described by Hudson–Parthasarathy quantum stochastic differential equations, aiming to reduce the computational burden associated with online calculation of the quantum filter. By using a differential geometric approach, the quantum trajectory is constrained in a finite-dimensional differentiable manifold consisting of an unnormalized exponential family of quantum density operators, and an exponential quantum projection filter is then formulated as a number of stochastic differential equations satisfied by the finite-dimensional coordinate system of this manifold. A convenient design of the differentiable manifold is also presented through reduction of the local approximation errors, which yields a simplification of the quantum projection filter equations. It is shown that the computational cost can be significantly reduced by using the quantum projection filter instead of the quantum filter. It is also shown that when the quantum projection filtering approach is applied to a class of open quantum systems that asymptotically converge to a pure state, the input-to-state stability of the corresponding exponential quantum projection filter can be established. Simulation results from an atomic ensemble system example are provided to illustrate the performance of the projection filtering scheme. It is expected that the proposed approach can be used in developing more efficient quantum control methods.
Zeroing Neural Dynamics and Models for Various Time-Varying Problems Solving with ZLSF Models as Minimization-Type and Euler-Type Special Cases [Research Frontier]

Zeroing neural dynamics (ZND), a special class of neural dynamics, is a powerful methodology for time-varying problems solving. On the basis of this methodology, different continuous-time ZND models are obtained for various time-varying problems solving. Continuous-time ZND models are supposed to be discretized for the sake of prevalent digital-equipment applications, and a discretization formula is needed to transform a continuous-time ZND model into a discrete-time ZND model. In this article, continuous-time ZND models, new discretization formulas and various discrete-time ZND models are presented. The time-varying minimization problem, which is a representative time-varying issue, is also discussed as an example throughout this article. The relationship between ZND and Zhao-Lu-Swamy-Feng (ZLSF) models is identified; i.e., the ZLSF models are minimization-type and Euler-type special cases of ZND models. In addition, ZND models are compared with other models to demonstrate their differences. The article aims to introduce the ZND methodology and illustrate the manner by which it is used, provide readers with new discretization formulas and various continuous-time and discrete-time ZND models for time-varying problems solving, discuss the factors affecting the performance of the aforementioned models, exemplify the differences between ZND models and other models, and point out future research directions.
On the Computational Complexity of Learning Bithreshold Neural Units and Networks

We study the questions concerning the properties and capabilities of computational bithreshold real-weighted neural-like units. We give and justify the two sufficient conditions ensuring the possibility of separation of two sets in n-dimensional vector space by means of one bithreshold neuron. Our approach is based on application of convex and affine hulls of sets and is feasible in the case when one of the two sets is a compact and the second one is finite. We also correct and refine some previous results concerning bithreshold separability. Then the hardness of the learning bithreshold neurons is considered. We examine the complexity of the problem of checking whether the given Boolean function of n variables can be realizable by single bithreshold unit. Our main result is that the problem of verifying the bithreshold separability is NP-complete. The same is true for neural networks consisting of such computational units. We propose some continuous modifications of the bithreshold activation function to smooth away these difficulties and to make possible the application of modern paradigms and learning techniques for such networks.
Adaptive Streaming in Interactive Multiview Video Systems

Multiview applications endow final users with the possibility to freely navigate within 3D scenes with minimum-delay. A real feeling of scene navigation is enabled by transmitting multiple high-quality camera views, which can be used to synthesize additional virtual views to offer a smooth navigation. However, when network resources are limited, not all camera views can be sent at high quality. It is therefore important, yet challenging, to find the right tradeoff between coding artifacts (reducing the quality of camera views) and virtual synthesis artifacts (reducing the number of camera views sent to users). To this aim, we propose an optimal transmission strategy for interactive multiview HTTP adaptive streaming. We propose a problem formulation to select the optimal set of camera views that the client requests for downloading, such that the navigation quality experienced by the user is optimized while the bandwidth constraints are satisfied. We show that our optimization problem is NP-hard, and we therefore develop an optimal solution based on the dynamic programming algorithm with polynomial time complexity. To further simplify the deployment, we present a suboptimal greedy algorithm with effective performance and lower complexity. The proposed controller is evaluated in theoretical and realistic settings characterized by realistic network statistics estimation, buffer management, and server-side representation optimization. Simulation results show significant improvement in terms of navigation quality compared with alternative baseline multiview adaptation logic solutions.
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations

The linear and non-flexible nature of deep convolutional models makes them vulnerable to carefully crafted adversarial perturbations. To tackle this problem, we propose a non-linear radial basis convolutional feature mapping by learning a Mahalanobis-like distance function. Our method then maps the convolutional features onto a linearly well-separated manifold, which prevents small adversarial perturbations from forcing a sample to cross the decision boundary. We test the proposed method on three publicly available image classification and segmentation datasets namely, MNIST, ISBI ISIC 2017 skin lesion segmentation, and NIH Chest X-Ray-14. We evaluate the robustness of our method to different gradient (targeted and untargeted) and non-gradient based attacks and compare it to several non-gradient masking defense strategies. Our results demonstrate that the proposed method can increase the resilience of deep convolutional neural networks to adversarial perturbations without accuracy drop on clean data.
Bayes, Oracle Bayes and Empirical Bayes

This article concerns the Bayes and frequentist aspects of empirical Bayes inference. Some of the ideas explored go back to Robbins in the 1950s, while others are current. Several examples are discussed, real and artificial, illustrating the two faces of empirical Bayes methodology: “oracle Bayes” shows empirical Bayes in its most frequentist mode, while “finite Bayes inference” is a fundamentally Bayesian application. In either case, modern theory and computation allow us to present a sharp finite-sample picture of what is at stake in an empirical Bayes analysis.
Attentive Spatio-Temporal Representation Learning for Diving Classification

Competitive diving is a well recognized aquatic sport in which a person dives from a platform or a springboard into the water. Based on the acrobatics performed during the dive, diving is classified into a finite set of action classes which are standardized by FINA. In this work, we propose an attention guided LSTM-based neural network architecture for the task of diving classification. The network takes the frames of a diving video as input and determines its class. We evaluate the performance of the proposed model on a recently introduced competitive diving dataset, Diving48. It contains over 18000 video clips which covers 48 classes of diving. The proposed model outperforms the classification accuracy of the state-of-the-art models in both 2D and 3D frameworks by 11.54% and 4.24%, respectively. We show that the network is able to localize the diver in the video frames during the dive without being trained with such a supervision.
On visual BMI analysis from facial images

Automatically assessing body mass index (BMI) from facial images is an interesting and challenging problem in computer vision. Facial feature extraction is an important step for visual BMI estimation. This work studies the visual BMI estimation problem based on the characteristics and performance of different facial representations, which has not been well studied yet. Various facial representations, including geometry based representations and deep learning based, are comprehensively evaluated and analyzed from three perspectives: the overall performance on visual BMI prediction, the redundancy in facial representations and the sensitivity to head pose changes. The experiments are conducted on two databases: a new dataset we collected, called the FIW-BMI and an existing large dataset Morph II. Our studies provide some deep insights into the facial representations for visual BMI analysis: 1) The deep model based methods perform better than geometry based methods. Among them, the VGG-Face and Arcface show more robustness than others in most cases; 2) Removing the redundancy in VGG-Face representation can increase the accuracy and efficiency in BMI estimation; 3) Large head poses lead to low performance for BMI estimation. The Arcface, VGG-Face and PIGF are more robust than the others to head pose variations.
Examining characteristics of predictive models with imbalanced big data

High class imbalance between majority and minority classes in datasets can skew the performance of Machine Learning algorithms and bias predictions in favor of the majority (negative) class. This bias, for cases where the minority (positive) class is of greater interest and the occurrence of false negatives is costlier than false positives, may result in adverse consequences. Our paper presents two case studies, each utilizing a unique, combined approach of Random Undersampling and Feature Selection to investigate the effect of class imbalance on big data analytics. Random Undersampling is used to generate six class distributions ranging from balanced to moderately imbalanced, and Feature Importance is used as our Feature Selection method. Classification performance was reported for the Random Forest, Gradient-Boosted Trees, and Logistic Regression learners, as implemented within the Apache Spark framework. The first case study utilized a training dataset and a test dataset from the ECBDL'14 bioinformatics competition. The training and test datasets contain about 32 million instances and 2.9 million instances, respectively. For the first case study, Gradient-Boosted Trees obtained the best results, with either a features-set of 60 or the full set, and a negative-to-positive ratio of either 45:55 or 40:60. The second case study, unlike the first, included training data from one source (POST dataset) and test data from a separate source (Slowloris dataset), where POST and Slowloris are two types of Denial of Service attacks. The POST dataset contains about 1.7 million instances, while the Slowloris dataset contains about 0.2 million instances. For the second case study, Logistic Regression obtained the best results, with a features-set of 5 and any of the following negative-to-positive ratios: 40:60, 45:55, 50:50, 65:35, and 75:25. We conclude that combining Feature Selection with Random Undersampling improves the classification performance of learners with imbalanced big data from different application domains.
Visual Image Caption Generation for Service Robotics and Industrial Applications

Image caption generation is a task that generates a sentence from a raw image, which is mimicking the intelligence of human that can acquire knowledge from the view. The difficulty of this task is the combination of multimodal knowledge learning, i.e. recognition of objects, actions, scenes, human, etc. In order to perform semantic understanding for service robotics or other industrial applications, the caption must be enhanced for recognition of the objects in the confined environment. We propose a template-based augmentation method for improving the capability of object recognition while retaining the other capability of the image caption model. This work opens a new era of image caption generation training procedure that the caption dataset and the classification dataset can be combined to train the deep captioning model. We show in our experiments that our improved model outperforms the original model in SPICE metrics by 4 times.
Running on Fumes

Gas is a measurement unit of the computational effort that it will take to execute every single operation that takes part in the Ethereum blockchain platform. Each instruction executed by the Ethereum Virtual Machine (EVM) has an associated gas consumption specified by Ethereum. If a transaction exceeds the amount of gas allotted by the user (known as gas limit), an out-of-gas exception is raised. There is a wide family of contract vulnerabilities due to out-of-gas behaviors. We report on the design and implementation of Gastap, a Gas-Aware Smart contracT Analysis Platform, which takes as input a smart contract (either in EVM, disassembled EVM, or in Solidity source code) and automatically infers gas upper bounds for all its public functions. Our bounds ensure that if the gas limit paid by the user is higher than our inferred gas bounds, the contract is free of out-of-gas vulnerabilities.
Width optimization of RBF kernels for binary classification of support vector machines: A density estimation-based approach

Kernels are often used for modelling non-linear data, developing a main role in models like the SVM. The optimization of its parameters to better fit each dataset is a frequently faced challenge: A bad choice of kernel parameters often implies a poor model. This problem is usually worked out using exhaustive search approaches, such as cross-validation. These methods, however, do not take into account existent information on data arrangement. This paper proposes an alternative approach, based on density estimation. By making use of density estimation methods to analyze the dataset structure, it is proposed a function over the kernel parameters. This function can be used to choose the parameters that best suit the data.
A Reinforced Blind Color Image Watermarking Scheme Based on Schur Decomposition

In this paper, the deficiency of Schur decomposition (SD) for image watermarking has been comprehensively investigated.A remedial scheme is developed not only to fix the existing problems but also to reinforce the performance in robustness and imperceptibility.This scheme starts with partitioning the host image into non-overlapping blocks of 4 × 4 pixels and then applying SD to each block individually.Level shifting serves as a controlling gauge for embedding strength.The use of intentional perturbation prevents nil orthonormal vectors derived from zero eigenvalues.The dominant vector is identified by analyzing the entire Schur matrix instead of just diagonal elements.To achieve effective watermark embedding and extraction, the orthonormality of the acquired unitary matrix ought to be preserved while the resulting distortion can be compensated via the systematic modification of the Schur matrix.Finally, a recursive regulation guarantees the retrieval of watermark bits.Experiment results indicate that the proposed scheme is free of errors in the absence of attacks and can withstand a variety of image processing attacks as well.Moreover, the inclusion of distortion compensation contributes an improvement of 2.47 dB in terms of peak signal-tonoise ratio.In comparison with previous SD-based schemes, the proposed one exhibits superior robustness and imperceptibility while operating at the same payload capacity.
A Maximally Split and Relaxed ADMM for Regularized Extreme Learning Machines

One of the salient features of the extreme learning machine (ELM) is its fast learning speed. However, in a big data environment, the ELM still suffers from an overly heavy computational load due to the high dimensionality and the large amount of data. Using the alternating direction method of multipliers (ADMM), a convex model fitting problem can be split into a set of concurrently executable subproblems, each with just a subset of model coefficients. By maximally splitting across the coefficients and incorporating a novel relaxation technique, a maximally split and relaxed ADMM (MS-RADMM), along with a scalarwise implementation, is developed for the regularized ELM (RELM). The convergence conditions and the convergence rate of the MS-RADMM are established, which exhibits linear convergence with a smaller convergence ratio than the unrelaxed maximally split ADMM. The optimal parameter values of the MS-RADMM are obtained and a fast parameter selection scheme is provided. Experiments on ten benchmark classification data sets are conducted, the results of which demonstrate the fast convergence and parallelism of the MS-RADMM. Complexity comparisons with the matrix-inversion-based method in terms of the numbers of multiplication and addition operations, the computation time and the number of memory cells are provided for performance evaluation of the MS-RADMM.
Evaluating Fault Resiliency of Compressed Deep Neural Networks

Model compression is considered to be an effective way to reduce the implementation cost of deep neural networks (DNNs) while maintaining the inference accuracy. Many recent studies have developed efficient model compression algorithms and implementations in accelerators on various devices. Protecting integrity of DNN inference against fault attacks is important for diverse deep learning enabled applications. However, there has been little research investigating the fault resilience of DNNs and the impact of model compression on fault tolerance. In this work, we consider faults on different data types and develop a simulation framework for understanding the fault resiliency of compressed DNN models as compared to uncompressed models. We perform our experiments on two common DNNs, LeNet-5 and VGG16, and evaluate their fault resiliency with different types of compression. The results show that binary quantization can effectively increase the fault resilience of DNN models by 10000x for both LeNet5 and VGG16. Finally, we propose software and hardware mitigation techniques to increase the fault resiliency of DNN models.
Sampler Design for Bayesian Personalized Ranking by Leveraging View Data

Bayesian Personalized Ranking (BPR) is a representative pairwise learning method for optimizing recommendation models. It is widely known that the performance of BPR depends largely on the quality of negative sampler. In this paper, we make two contributions with respect to BPR. First, we find that sampling negative items from the whole space is unnecessary and may even degrade the performance. Second, focusing on the purchase feedback of E-commerce, we propose a negative sampler for BPR by leveraging the additional view data. In our proposed sampler, users' viewed interactions are considered as an intermediate feedback between the purchased and unobserved interactions. We jointly learn the pairwise rankings of user preference among these three types of interactions and design a user-oriented weighting strategy during learning process, which is more effective and flexible. Compared to the vanilla BPR that applies a uniform sampler on all candidates, our view-enhanced sampler enhances BPR with a relative improvement over 36.64 and 16.40 percent on Beibei and Tmall datasets, respectively. Empirical studies demonstrate the importance of considering users' additional feedback when modeling their preference on different items, which can effectively improve the quality of sampled negative items towards learning a better personalized ranking function. Our implementation is available at https://github.com/dingjingtao/NegativeSamplerBPR.