Reviewerly/exHarmony huggingface dataset:
Lattice Kalman Filters

In this paper, a new filter in the nonlinear Kalman filtering framework is proposed. The new filter is referred to as the lattice Kalman filter (LKF) and is based on a class of quasi-Monte Carlo (QMC) methods known as lattice rules. The proposed LKF method uses the Korobov type lattice rule to deterministically generate sample points that are randomly shifted based on the Cranley-Patterson shift method in order to approximate multi-dimensional integrals in the Gaussian filtering context. The mathematical formulation of the proposed LKF method as well as its error bound propagation are discussed. To evaluate the efficiency of the LKF, it is applied on a nonlinear aerospace system and compared with four other well-known methods presented in the literature. Simulation results demonstrate LKF uses significantly fewer sampling points yielding a significantly lower computational burden than another variant of QMC filter while maintaining the estimation accuracy. Furthermore, it provides asymptotically similar results to the unscented Kalman filter (UKF) but with less computational complexity, which is an important consideration in real applications.
Pure Random Orthogonal Search (PROS): A Plain and Elegant Parameterless Algorithm for Global Optimization

A new, fast, elegant, and simple stochastic optimization search method is proposed, which exhibits surprisingly good performance and robustness considering its simplicity. We name the algorithm pure random orthogonal search (PROS). The method does not use any assumptions, does not have any parameters to adjust, and uses basic calculations to evolve a single candidate solution. The idea is that a single decision variable is randomly changed at every iteration and the candidate solution is updated only when an improvement is observed; therefore, moving orthogonally towards the optimal solution. Due to its simplicity, PROS can be easily implemented with basic programming skills and any non-expert in optimization can use it to solve problems and start exploring the fascinating optimization world. In the present work, PROS is explained in detail and is used to optimize 12 multi-dimensional test functions with various levels of complexity. The performance is compared with the pure random search strategy and other three well-established algorithms: genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). The results indicate that, despite its simplicity, the proposed PROS method exhibits very good performance with fast convergence rates and quick execution time. The method can serve as a simple alternative to established and more complex optimizers. Additionally, it could also be used as a benchmark for other metaheuristic optimization algorithms as one of the simplest, yet powerful, optimizers. The algorithm is provided with its full source code in MATLAB for anybody interested to use, test or explore.
A Deployment Scheme of YOLOv5 with Inference Optimizations Based on the Triton Inference Server

Object detection constitutes a large part of computer vision applications. You Only Look Once (YOLO) v5 is a salient object detection algorithm that provides high accuracy and real-time performance. This paper illustrates a deployment scheme of YOLOv5 with inference optimizations on Nvidia graphics cards using an open-source deep-learning deployment framework named Triton Inference Server. Moreover, we developed a non-maximum suppression (NMS) operator with dynamic-batch-size support in TensorRT to accelerate inference. The experimental results show that both throughput and latency are improved significantly through our deployment scheme.
Extraction of Complex DNN Models: Real Threat or Boogeyman?

Recently, machine learning (ML) has introduced advanced solutions to many domains. Since ML models provide business advantage to model owners, protecting intellectual property of ML models has emerged as an important consideration. Confidentiality of ML models can be protected by exposing them to clients only via prediction APIs. However, model extraction attacks can steal the functionality of ML models using the information leaked to clients through the results returned via the API. In this work, we question whether model extraction is a serious threat to complex, real-life ML models. We evaluate the current state-of-the-art model extraction attack (Knockoff nets) against complex models. We reproduce and confirm the results in the original paper. But we also show that the performance of this attack can be limited by several factors, including ML model architecture and the granularity of API response. Furthermore, we introduce a defense based on distinguishing queries used for Knockoff nets from benign queries. Despite the limitations of the Knockoff nets, we show that a more realistic adversary can effectively steal complex ML models and evade known defenses.
KeMRE: Knowledge-enhanced medical relation extraction for Chinese medicine instructions

Medicine instructions usually contain rich medical relations, and extracting them is very helpful for many downstream tasks such as medicine knowledge graph construction and medicine side-effect prediction. Existing relation extraction (RE) methods usually predict relations between entities from their contexts and do not consider medical knowledge. However, understanding a part of medical relations may need some expert knowledge in the medical field, making it challenging for existing methods to achieve satisfying performances of medical RE. In this paper, we propose a knowledge-enhanced framework for medical RE, which can exploit medical knowledge of medicines to better conduct medical RE on Chinese medicine instructions. We first propose a BERT-CNN-LSTM based framework for text modeling and learn representations of characters from their contexts. Then we learn representations of each entity by aggregating representations of their characters. Besides, we propose a CNN-LSTM based framework for entity modeling and learn entity representations from their relatedness. In addition, there are usually many different instructions for the same medicine, which usually share general knowledge on this medicine. Thus, to obtain medical knowledge of medicines, we annotate relations on a randomly-sampled instruction of each medicine. Then we build knowledge embeddings to represent potential relations between entities from knowledge of medicines. Finally, we use an MLP network to predict relations between entities from their representations and knowledge embeddings. Extensive experiments on a real-world dataset show that our method can significantly outperform existing methods.
Analysis and Applications of Class-wise Robustness in Adversarial Training

Adversarial training is one of the most effective approaches to improve model robustness against adversarial examples. However, previous works mainly focus on the overall robustness of the model, and the in-depth analysis on the role of each class involved in adversarial training is still missing. In this paper, we propose to analyze the class-wise robustness in adversarial training. First, we provide a detailed diagnosis of adversarial training on six benchmark datasets, i.e., MNIST, CIFAR-10, CIFAR-100, SVHN, STL-10 and ImageNet. Surprisingly, we find that there are remarkable robustness discrepancies among classes, leading to unbalance/unfair class-wise robustness in the robust models. Furthermore, we keep investigating the relations between classes and find that the unbalanced class-wise robustness is pretty consistent among different attack and defense methods. Moreover, we observe that the stronger attack methods in adversarial learning achieve performance improvement mainly from a more successful attack on the vulnerable classes (i.e., classes with less robustness). Inspired by these interesting findings, we design a simple but effective attack method based on the traditional PGD attack, named Temperature-PGD attack, which proposes to enlarge the robustness disparity among classes with a temperature factor on the confidence distribution of each image. Experiments demonstrate our method can achieve a higher attack rate than the PGD attack. Furthermore, from the defense perspective, we also make some modifications in the training and inference phase to improve the robustness of the most vulnerable class, so as to mitigate the large difference in class-wise robustness. We believe our work can contribute to a more comprehensive understanding of adversarial training as well as rethinking the class-wise properties in robust models.
Unpaired Image-to-Image Translation via Latent Energy Transport

Image-to-image translation aims to preserve source contents while translating to discriminative target styles between two visual domains. Most works apply adversarial learning in the ambient image space, which could be computationally expensive and challenging to train. In this paper, we propose to deploy an energy-based model (EBM) in the latent space of a pretrained autoencoder for this task. The pretrained autoencoder serves as both a latent code extractor and an image reconstruction worker. Our model, LETIT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , is based on the assumption that two domains share the same latent space, where latent representation is implicitly decomposed as a content code and a domain-specific style code. Instead of explicitly extracting the two codes and applying adaptive instance normalization to combine them, our latent EBM can implicitly learn to transport the source style code to the target style code while preserving the content code, an advantage over existing image translation methods. This simplified solution is also more efficient in the one-sided unpaired image translation setting. Qualitative and quantitative comparisons demonstrate superior translation quality and faithfulness for content preservation. Our model is the first to be applicable to 1024×1024-resolution unpaired image translation to the best of our knowledge. Code is available at https://github.com/YangNaruto/latent-energy-transport.
Low-resolution face recognition in resource-constrained environments

• A LR face recognition model for resource-constrained environments is proposed. • Our model is lightweight and capable of being effectively trained on small data. • Our model adopts PixelHop++ which is designed based on the SSL principle. • Active learning is incorporated to minimize the required labeled training data. • We show the competitive performance of our model compared with state-of-the-art. Although Deep Neural Networks (DNNs) have achieved tremendous success in the face recognition task, utilizing them in resource-constrained environments with limited networking and computing is challenging. Such environments often demand a small model capable of being effectively trained on a small number of labeled training data, with low training complexity, and low-resolution input images. To address these challenges, we adopt an emerging machine learning methodology called Successive Subspace Learning (SSL) to propose LRFRHop, a high-performance data-efficient low-resolution face recognition model for resource-constrained environments. SSL offers an explainable non-parametric feature extraction submodel that flexibly trades the model size for the verification performance. Its training complexity is significantly lower than DNN-based models since it is trained in a one-pass feedforward manner without backpropagation. Furthermore, active learning can be conveniently incorporated to reduce the labeling cost. We demonstrate the effectiveness of LRFRHop by conducting experiments on the LFW and the CMU Multi-PIE datasets.
A Scalable Implementation of Anonymous Voting over Ethereum Blockchain

We considered scalable anonymous voting on the Ethereum blockchain. We identified three major bottlenecks in implementation: (1) division overflow in encryption of voting values for anonymity; (2) large time complexity in tallying, which limited scalability in the number of candidates and voters; and (3) tallying failure due to “no votes” from registered voters. Previous schemes failed at tallying if one (or more) registered voters did not send encrypted voting values. Algorithmic solutions and implementation details are provided. An experiment using Truffle and Remix running on a desktop PC was performed for evaluation. Our scheme shows great reduction in gas, which measures the computational burden of smart contracts to be executed on Ethereum. For instance, our scheme consumed 1/53 of the gas compared to a state-of-the-art solution for 60 voters. Time complexity analysis shows that our scheme is asymptotically superior to known solutions. In addition, we propose a solution to the tallying failure due to the “no vote” from registered voters.
Attribute-Based Pseudonymity for Privacy-Preserving Authentication in Cloud Services

Attribute-based authentication is considered a cornerstone component to achieve scalable fine-grained access control in the fast growing market of cloud-based services. Unfortunately, it also poses a privacy concern. User’s attributes should not be linked to the users’ identity and spread across different organizations. To tackle this issue, several solutions have been proposed such as Privacy Attribute-Based Credentials (Privacy-ABCs), which support pseudonym-based authentication with embedded attributes. Privacy-ABCs allow users to establish anonymous accounts with service providers while hiding the identity of the user under a pseudonym. However, Privacy-ABCs require the selective disclosure of the attribute values towards service providers. Other schemes such as Attribute-Based Signatures (ABS) and mesh signatures do not require the disclosure of attributes; unfortunately, these schemes do not cater for pseudonym generation in their construction, and hence cannot be used to establish anonymous accounts. In this article, we propose a pseudonym-based signature scheme that enables unlinkable pseudonym self-generation with embedded attributes, similarly to Privacy-ABCs, and integrates a secret sharing scheme in a similar fashion to ABS and mesh signature schemes for attribute verification. Our proposed scheme also provides verifiable delegation, enabling users to share attributes according to the service providers’ policies.
NL-EDIT: Correcting Semantic Parse Errors through Natural Language Interaction

We study semantic parsing in an interactive setting in which users correct errors with natural language feedback.We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors.We show that NL-EDIT can boost the accuracy of existing text-to-SQL parsers by up to 20% with only one round of correction.We analyze the limitations of the model and discuss directions for improvement and evaluation.The code and datasets used in this paper are publicly available at http://aka.ms/NLEdit.
GraphZero

Subgraph matching is a fundamental task in many applications which identifies all the embeddings of a query pattern in an input graph. Compilation-based subgraph matching systems generate specialized implementations for the provided patterns and often substantially outperform other systems. However, the generated code causes significant computation redundancy and the compilation process incurs too much overhead to be used online, both due to the inherent symmetry in the structure of the query pattern. In this paper, we propose an optimizing query compiler, named GraphZero, to completely address these limitations through symmetry breaking based on group theory. GraphZero implements three novel techniques. First, its schedule explorer efficiently prunes the schedule space without missing any high-performance schedule. Second, it automatically generates and enforces a set of restrictions to eliminate computation redundancy. Third, it generalizes orientation, a surprisingly effective optimization that was only used for clique patterns, to apply to arbitrary patterns. Evaluation on multiple query patterns shows that GraphZero outperforms two state-of-the-art compilation and non-compilation based systems by up to 40X and 2654X, respectively.
Unsupervised moiré pattern removal for recaptured screen images

Moiré patterns, mainly due to the aliasing between the grids of display devices and camera sensors, often occur in recaptured screen images (videos). The spatial variety in densities and scales makes it difficult to remove moiré patterns with hand-crafted priors. Prevalent supervised-learning based methods require huge amount of paired images, which are difficult to capture and align. This paper proposes an unsupervised Generative Adversarial Network for Moiré Removal (MR-GAN), which is the first attempt at unsupervised learning based moiré removal. The recaptured images are corrected against vignetting artifacts to make MR-GAN focus on correcting brightness and contrast distortions while removing moiré patterns. We propose two complementary discriminator groups to effectively distinguish moiré patterns and image features at both large scales and small scales. A group of self-supervised loss functions, including cycle consistent loss, identity loss, cosine similarity loss, and content leakage loss, are designed to train effective generators. Experimental results demonstrate that our method outperforms state-of-the-art demoiréing methods on a large set of test images. Our code and dataset are released in the link: https://github.com/JerryLeolfl/pytorch-MRGAN-master.
A High-Availability and Integrity Layer for Cloud Storage, Cloud Computing Security: From Single to Multi-Clouds

Abstract The utilization of distributed computing has expanded quickly in numerous associations. Distributed computing gives numerous advantages regarding the ease and availability of information. Guaranteeing the security of distributed computing is a the central point in the distributed computing condition, as clients regularly store delicate data with cloud capacity suppliers, however, these suppliers might be untrusted. Managing ”single cloud” suppliers is anticipated to turn out to be less famous with clients due to dangers of administration accessibility disappointment and the probability of vindictive insiders in the single cloud. A the development towards ”multi-mists”, or at the end of the day, ”inter clouds” has developed as of late. High-Availability and Integrity Layer (HAIL), a conveyed cryptographic framework that allows a set of workers to demonstrate to a customer that a put-away record is flawless what’s more, retrievable. HAIL fortifies, officially brings together, what’s more, smoothes out unmistakable methodologies from the cryptographic furthermore, dispersed frameworks networks. Evidence in HAIL are proficiently process able by workers and profoundly reduced regularly tens or many bytes, regardless of record size. This paper reviews the ongoing examination identified with single what’s more, multi-cloud security and addresses conceivable arrangements. It is discovered that the examination into the utilization of multi-cloud suppliers to keep up security has gotten less consideration from the examination network than has the utilization of single mists. This work means to advance the utilization of multi-mists because of its capacity to decrease security dangers that influence the distributed computing client.
Object Detection in Complex Road Scenarios: Improved YOLOv4-Tiny Algorithm

This paper proposes an object detection algorithm based on YOLOV4-Tiny, which can improves the recognition of small objects in street scenario, as well as the robustness in the extreme climate, e.g., the rainy or foggy days. Specifically, as for the small objects in the street scenario, we first conduct the up-sampling in the feature map obtained by 8x down-sampling, and then fused it with the feature map obtained by 4x down-sampling. Meanwhile, as for the images in the extreme climate, we use the sharpness algorithm based on secondary blur (ReBlur) to characterize the image blurriness, where the blurred images will be restored a dark channel prior algorithm. The simulation results indicate that the proposed algorithm can improve the recognition of the objects in the complex street scenario, where the mean average precision (mAP) is increased by 4.13%.
Detection of Leukemia and Its Types Using Combination of Support Vector Machine and K-Nearest Neighbors Algorithm

White blood cell cancer is additionally mentioned as leukemia could also be a really perilous disease. To this day, the tactic of recognizing white blood cell cancer or leukemia remains done conventionally, which if done by different doctors, can cause a difference within the diagnosis. To retort these problems, a computer-assisted method is proposed during which leukemia is detected from microscopic images employing a mixture of SVM and KNN. Initially, preprocessing is performed to arrange the image for processing. Later support vector machine (SVM) and K-nearest neighbor are used for classification. The proposed algorithm classifies healthy and cancerous cells into one of the four types such as acute lymphocytic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myeloid leukemia (CML). Additionally, the counting of the infected cells is also performed.
Fastsvc: Fast Cross-Domain Singing Voice Conversion With Feature-Wise Linear Modulation

This paper presents FastSVC, a light-weight cross-domain singing voice conversion (SVC) system, which can achieve high conversion performance, with inference speed 4x faster than real-time on CPUs. FastSVC uses Conformer-based phoneme recognizer to extract singer-agnostic linguistic features from singing signals. A feature-wise linear modulation based generator is used to synthesize waveform directly from linguistic features, leveraging information from sine-excitation signals and loudness features. The waveform generator can be trained conveniently using a multi-resolution spectral loss and an adversarial loss. Experimental results show that the proposed FastSVC system, compared with a computationally heavy baseline system, can achieve comparable conversion performance in some scenarios and significantly better conversion performance in other scenarios. Moreover, the proposed FastSVC system achieves desirable cross-lingual singing conversion performance. The inference speed of the FastSVC system is 3x and 70x faster than the baseline system on GPUs and CPUs, respectively.
Application of hybrid metaheuristic with perturbation-based K-nearest neighbors algorithm and densest imputation to collaborative filtering in recommender systems

Since the rise of E-commerce companies and many other web services, the applications of recommender systems have been adopted more broadly than ever before. Although collaborative filtering is the most well-known approach which utilizes customer’s preference to discover their interest, the problems of data sparsity and similarities selection still exist in it. Thus, this study intends to propose a hybrid metaheuristic with perturbation-based K-nearest neighbors and densest imputation for collaborative filtering (KDI-KNN) algorithm to reduce the effects of data sparsity. A similarities union function is proposed to determine the fittest similarity and enhance the prediction performance. Eventually, the experimental results indicate that hybrid metaheuristics with perturbation-based KDI-KNN algorithms are superior to basic KNN, original KDI-KNN, and most single metaheuristic-based KDI-KNN. In addition, a real-world dataset, fund transaction dataset is adopted in the case study. The analysis reveals that the similarity is seriously affected by the different content of the dataset.