Reviewerly/exHarmony huggingface dataset:
Efficient Algorithms and Lower Bounds for Robust Linear Regression

We study the prototypical problem of high-dimensional linear regression in a robust model where an ε-fraction of the samples can be adversarially corrupted. We focus on the fundamental setting where the covariates of the uncorrupted samples are drawn from a Gaussian distribution N(0, ∑) on ℝd. We give nearly tight upper bounds and computational lower bounds for this problem. Specifically, our main contributions are as follows:•For the case that the covariance matrix is known to be the identity, we give a sample near-optimal and computationally efficient algorithm that draws Õ(d/ε2) labeled examples and outputs a candidate hypothesis vector that approximates the unknown regression vector β within ℓ2-norm O(ε log(1/ε)σ), where σ is the standard deviation of the random observation noise. An error of Ω(εσ) is information-theoretically necessary, even with infinite sample size. Hence, the error guarantee of our algorithm is optimal, up to a logarithmic factor in 1/ε. Prior work gave an algorithm for this problem with sample complexity whose error guarantee scales with the ℓ2-norm of β.•For the case of unknown covariance ∑, we show that we can efficiently achieve the same error guarantee of O(ε log(1/ε)σ), as in the known covariance case, using an additional Õ(d2/ε2) unlabeled examples. On the other hand, an error of O(εσ) can be information-theoretically attained with O(d/ε2) samples. We prove a Statistical Query (SQ) lower bound providing evidence that this quadratic tradeoff in the sample size is inherent. More specifically, we show that any polynomial time SQ learning algorithm for robust linear regression (in Huber's contamination model) with estimation complexity O(d2–c), where c > 0 is an arbitrarily small constant, must incur an error of .
Secure Range Search Over Encrypted Uncertain IoT Outsourced Data

Internet of Things (IoT) is an increasingly popular technological trend. The operation of IoT needs a strong data-handling capacity, where most of the data are sensor data. Limitations associated with measurement, delays in data updating, and/or the need to preserve the privacy of data can result in the sensor data being uncertain. Thus, one key challenge is “how do we ensure the privacy of data collected from IoT devices, particularly uncertain data, that are being outsourced to the cloud for analysis, storage and archival?”. Searchable encryption scheme is a promising technique that allows the searching over encrypted (uncertain) data stored offshore. In this paper, we propose a secure range search for encrypted data from IoT devices. Specifically, we use homomorphic and order-preserving encryption to encrypt data published by the data owners. We then use the k-dimensional tree to build the data index. Our scheme is designed to ensure the privacy of the dataset, without affecting the efficiency of keyword search on the (encrypted) dataset. We also demonstrate that our scheme can preserve both data and query privacy, as well as evaluating its performance to demonstrate efficiency.
A Unified Framework for Cross-Domain and Cross-System Recommendations

Cross-Domain Recommendation (CDR) and Cross-System Recommendation (CSR) have been proposed to improve the recommendation accuracy in a target dataset (domain/system) with the help of a source one with relatively richer information. However, most existing CDR and CSR approaches are single-target, namely, there is a single target dataset, which can only help the target dataset and thus cannot benefit the source dataset. In this paper, we focus on three new scenarios, i.e., Dual-Target CDR (DTCDR), Multi-Target CDR (MTCDR), and CDR+CSR, and aim to improve the recommendation accuracy in all datasets simultaneously for all scenarios. To do this, we propose a unified framework, called GA (based on <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</i> raph embedding and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</i> ttention techniques), for all three scenarios. In GA, we first construct separate heterogeneous graphs to generate more representative user and item embeddings. Then, we propose an element-wise attention mechanism to effectively combine the embeddings of common entities (users/items) learned from different datasets. Moreover, to avoid negative transfer, we further propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> ersonalized training strategy to minimize the embedding difference of common entities between a richer dataset and a sparser dataset, deriving three new models, i.e., GA-DTCDR-P, GA-MTCDR-P, and GA-CDR+CSR-P, for the three scenarios respectively. Extensive experiments conducted on four real-world datasets demonstrate that our proposed GA models significantly outperform the state-of-the-art approaches.
Adaptive recommendation model using meta-learning for population-based algorithms

To efficiently solve complex optimization problems, numerous population-based meta-heuristics and extensions have been developed. However, the performances of the algorithms vary depending on the problems. In this research, we propose an Adaptive Recommendation Model (ARM) using meta-learning to identify appropriate problem-dependent population-based algorithm. In ARM, the algorithms are adaptively selected by mapping the problem characteristics to the algorithm performance. Since the meta-features extracted and meta-learner adopted would significantly affect the system performance, 18 meta-features including statistical, geometrical and landscape features are extracted to characterize optimization problem spaces. Both instance-based and model-based learners are investigated. Two performance metrics, Spearman's rank correlation coefficient and success rate are used to evaluate the accuracy of optimizer's ranking prediction and the precision of the best optimizer recommendation. The proposed ARM is compared against population-based algorithms with distinct search capabilities such as PSO variants, non-PSO population-based optimizers, hyper-heuristics and ensemble methods. Benchmark functions and real-world problems with various properties are adopted in the experiments. Experimental results reveal the extendibility and effectiveness of ARM on the diverse tested problems in terms of solution accuracy, ranking and success rate.
Offline signature verification using a region based deep metric learning network

Handwritten signature verification is a widely used biometric for person identity authentication in document forensics. Despite the tremendous efforts in past research, offline signature verification still remains a challenge, particularly in discriminating between genuine signatures and skilled forgeries, because the difference of appearance between genuine and skilled forgery may be smaller than that between genuine ones. This challenge is even more critical in writer-independent scenario, where each writer has very few samples for training. This paper proposes a region based Deep Convolutional Siamese Network using metric learning method, which is applicable to both writer-dependent (WD) and writer-independent (WI) scenario. For representing minute but discriminative details, a Mutual Signature DenseNet (MSDN) is designed to extract features and learn the similarity measure from local regions instead of whole signature images. Based on local regions comparison, the similarity scores of multiple regions are fused for final decision of verification. In experiments on public datasets CEDAR and GPDS, the proposed method achieved state-of-the-art performance of 6.74% EER and 8.24% EER in WI scenario, respectively, and 1.67% EER and 1.65% EER in WD scenario, respectively.
Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks

Astronomy of the 21st century increasingly finds itself with extreme quantities of data. This growth in data is ripe for modern technologies such as deep image processing, which has the potential to allow astronomers to automatically identify, classify, segment, and deblend various astronomical objects. In this paper, we explore the use of chained generative adversarial networks (GANs), a class of generative models that learn mappings from latent spaces to data distributions by modelling the joint distribution of the data, to produce physically realistic galaxy images as one use case of such models. In cosmology, such data sets can aid in the calibration of shape measurements for weak lensing by augmenting data with synthetic images. By measuring the distributions of multiple physical properties, we show that images generated with our approach closely follow the distributions of real galaxies, further establishing state-of-the-art GAN architectures as a valuable tool for modern-day astronomy.
A novel hierarchical clustering analysis method based on Kullback–Leibler divergence and application on dalaimiao geochemical exploration data

In this paper, we propose a new hierarchical clustering analysis method (HCA) that uses Kullback–Leibler divergence (DKLS) of pairwise geochemical datasets of geo-objects (e.g., lithological units) as a measure of proximity. The method can reveal relationships among geo-objects based on geochemistry. This capability is verified through an application with geochemical exploration data from regolith that overlies the Dalaimiao region in China. DKLSM and DKLSC, two parts of DKLS, respectively describe the differences on the mean and the differences on covariance and are also used as measures of proximity. DKLSM characterizes rock type and DKLSC describes spatial relationships and component similarities between geo-objects. This contribution not only provides a tool that can reveal relationships between geo-objects based on geochemical data, but also reveals that DKLS and its two parts can characterize geochemical differences from different perspectives. These measures hold promise in the enhancement of methods for recognizing geochemical patterns.
Least squares support vector machine with self-organizing multiple kernel learning and sparsity

In recent years, least squares support vector machines (LSSVMs) with various kernel functions have been widely used in the field of machine learning. However, the selection of kernel functions is often ignored in practice. In this paper, an improved LSSVM method based on self-organizing multiple kernel learning is proposed for black-box problems. To strengthen the generalization ability of the LSSVM, some appropriate kernel functions are selected and the corresponding model parameters are optimized using a differential evolution algorithm based on an improved mutation strategy. Due to the large computation cost, a sparse selection strategy is developed to extract useful data and remove redundant data without loss of accuracy. To demonstrate the effectiveness of the proposed method, some benchmark problems from the UCI machine learning repository are tested. The results show that the proposed method performs better than other state-of-the-art methods. In addition, to verify the practicability of the proposed method, it is applied to a real-world converter steelmaking process. The results illustrate that the proposed model can precisely predict the molten steel quality and satisfy the actual production demand.
Non-Rigid Image Registration With Dynamic Gaussian Component Density and Space Curvature Preservation

Image registration plays an important role in military and civilian applications, such as natural disaster damage assessment, environmental monitoring, ground change detection and military damage assessment, etc. This work presents a new feature-based non-rigid image registration method. The main contributions of this work are: (i) a dynamic Gaussian component density is designed to better exploit available potential image information and provide sufficient inlier pairs for image transformation; (ii) a spatial structure preservation, which consists of an image transformation space curvature preservation and a local spatial structure constrain, is proposed to constrain the image transforming cost as well as the local structure of feature points during feature point set registration. The performances of the proposed method in multi-spectral natural images, lowaltitude aerial images and medical images against four types of nine state-of-the-art methods are tested where our method shows the best performances in most scenarios.
Interpreting Deep Models for Text Analysis via Optimization and Regularization Methods

Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose an approach to investigate the meaning of hidden neurons of the convolutional neural network (CNN) models. We first employ saliency map and optimization techniques to approximate the detected information of hidden neurons from input sentences. Then we develop regularization terms and explore words in vocabulary to interpret such detected information. Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for hidden spatial locations. Additionally, we show that our approach can describe the decision procedure of deep NLP models.
Verifiable outsourced computation over encrypted data

In recent years, cloud computing has become the most popular and promising service platform. A cloud user can outsource its heavy computation overhead to a cloud service provider (CSP) and let the CSP make the computation instead. In order to guarantee the correctness of the outsourced processing (e.g., machine learning and data mining), a proof should be provided by the CSP in order to make sure that the processing is carried out properly. On the other hand, from the security and privacy points of view, users will always encrypt their sensitive data first before they are outsourced to the CSP rather than sending the raw data directly. However, processing and verifying of encrypted data computation has always been a challenging problem. Homomorphic Encryption (HE) has been proposed to tackle this task on computations over encrypted data and ensure the confidentiality of the data. However, original HE cannot provide an efficient approach to verify the correctness of computation over encrypted data that is processed by CSP. In this paper, we propose a verifiable outsourced computation scheme over encrypted data with the help of fully homomorphic encryption and polynomial factorization algorithm. Our scheme protects user data security in outsourced processing and allows public verification on the computation result processed by CSP with zero knowledge. We then prove the security of our scheme and analyze its performance by comparing it with some latest related works. Performances analysis shows that our scheme reduces the overload of both the cloud users and the verifier.
Hierarchical Encoder with Auxiliary Supervision for Neural Table-to-Text Generation: Learning Better Representation for Tables

Generating natural language descriptions for the structured tables which consist of multiple attribute-value tuples is a convenient way to help people to understand the tables. Most neural table-to-text models are based on the encoder-decoder framework. However, it is hard for a vanilla encoder to learn the accurate semantic representation of a complex table. The challenges are two-fold: firstly, the table-to-text datasets often contain large number of attributes across different domains, thus it is hard for the encoder to incorporate these heterogeneous resources. Secondly, the single encoder also has difficulties in modeling the complex attribute-value structure of the tables. To this end, we first propose a two-level hierarchical encoder with coarse-to-fine attention to handle the attribute-value structure of the tables. Furthermore, to capture the accurate semantic representations of the tables, we propose 3 joint tasks apart from the prime encoder-decoder learning, namely auxiliary sequence labeling task, text autoencoder and multi-labeling classification, as the auxiliary supervisions for the table encoder. We test our models on the widely used dataset WIKIBIO which contains Wikipedia infoboxes and related descriptions. The dataset contains complex tables as well as large number of attributes across different domains. We achieve the state-of-the-art performance on both automatic and human evaluation metrics.
Sensitivity Analysis of Deep Neural Networks

Deep neural networks (DNNs) have achieved superior performance in various prediction tasks, but can be very vulnerable to adversarial examples or perturbations. Therefore, it is crucial to measure the sensitivity of DNNs to various forms of perturbations in real applications. We introduce a novel perturbation manifold and its associated influence measure to quantify the effects of various perturbations on DNN classifiers. Such perturbations include various external and internal perturbations to input samples and network parameters. The proposed measure is motivated by information geometry and provides desirable invariance properties. We demonstrate that our influence measure is useful for four model building tasks: detecting potential ‘outliers’, analyzing the sensitivity of model architectures, comparing network sensitivity between training and test sets, and locating vulnerable areas. Experiments show reasonably good performance of the proposed measure for the popular DNN models ResNet50 and DenseNet121 on CIFAR10 and MNIST datasets.
Recognizing Unseen Attribute-Object Pair with Generative Model

In this paper, we are studying the problem of recognizing attribute-object pairs that do not appear in the training dataset, which is called unseen attribute-object pair recognition. Existing methods mainly learn a discriminative classifier or compose multiple classifiers to tackle this problem, which exhibit poor performance for unseen pairs. The key reasons for this failure are 1) they have not learned an intrinsic attributeobject representation, and 2) the attribute and object are processed either separately or equally so that the inner relation between the attribute and object has not been explored. To explore the inner relation of attribute and object as well as the intrinsic attribute-object representation, we propose a generative model with the encoder-decoder mechanism that bridges visual and linguistic information in a unified end-to-end network. The encoder-decoder mechanism presents the impressive potential to find an intrinsic attribute-object feature representation. In addition, combining visual and linguistic features in a unified model allows to mine the relation of attribute and object. We conducted extensive experiments to compare our method with several state-of-the-art methods on two challenging datasets. The results show that our method outperforms all other methods.
Mammography Images Segmentation via Fuzzy C-mean and K-mean

Breast Cancer is one of the common and dangerous among women at the age of forty, so it is better for woman to have mammography testing as a significant step for the early detection of breast cancer and is diagnosis for treatment; There is an important need to an algorithm is used to determine the boundaries of the tumor in a finite accuracy.In this work, two algorithms were built depending on clustering approach as segmentation method.In the first algorithm has employed (K-mean) method, whilst in the second algorithm has employed fuzzy c-mean method (FCM).In both, the lazy snapping algorithm was used as an additional step to improve the segmentation performance of the detection of abnormal area.The proposed methods have been tested using mini-MIAS database, after assessment the results obtained.it indicates the accuracy of segmentation first algorithm, are 91.18% and accuracy of second algorithm is 94.12%.from results, it concludes that the proposed second algorithm is capable of estimate breast abnormal region boundary at high accuracy because it used fuzzy logic technique.
Federated Online Learning to Rank with Evolution Strategies

Online Learning to Rank is a powerful paradigm that allows to train ranking models using only online feedback from its users.In this work, we consider Federated Online Learning to Rank setup (FOLtR) where on-mobile ranking models are trained in a way that respects the users' privacy. We require that the user data, such as queries, results, and their feature representations are never communicated for the purpose of the ranker's training. We believe this setup is interesting, as it combines unique requirements for the learning algorithm: (a) preserving the user privacy, (b) low communication and computation costs, (c) learning from noisy bandit feedback, and (d) learning with non-continuous ranking quality measures. We propose a learning algorithm FOLtR-ES that satisfies these requirements. A part of FOLtR-ES is a privatization procedure that allows it to provide ε-local differential privacy guarantees, i.e. protecting the clients from an adversary who has access to the communicated messages. This procedure can be applied to any absolute online metric that takes finitely many values or can be discretized to a finite domain. Our experimental study is based on a widely used click simulation approach and publicly available learning to rank datasets MQ2007 and MQ2008. We evaluate FOLtR-ES against offline baselines that are trained using relevance labels, linear regression model and RankingSVM. From our experiments, we observe that FOLtR-ES can optimize a ranking model to perform similarly to the baselines in terms of the optimized online metric, Max Reciprocal Rank.
Protection Schemes for Key Service in Optical Networks Secured by Quantum Key Distribution (QKD)

Quantum key distribution (QKD) is considered a secure solution for key distribution and applied in optical networks to overcome security issues by providing key services in different lightpaths. In optical networks, lightpaths in fibers can be used for the delivery of QKD due to their good transmission performance. Accordingly, optical networks secured by QKD have been regarded as an important paradigm, and survivability in this paradigm is an issue worthy of study. In this paper, we focus on the protection schemes for key services in optical networks secured by QKD. Specifically, in consistency with two characteristics of key services (i.e., the key-updating process and key-volume adaptive routing), two key-volume adaptive protection schemes (i.e., dedicated protection and shared protection) are proposed for the networks against link failures. In such scenarios, the designed key-protection threshold can alleviate the tight network resources caused by the key-updating process. Simulation work has been conducted to evaluate the performance of the proposed schemes in terms of blocking probability, resource utilization, and proportion of secret-key consumption with different key-protection thresholds and update periods. Numerical results show that the two proposed schemes are effective in providing protection. Furthermore, evaluating the impact of protection on network security verifies a trade-off that exists between how much protection can be provided for key services versus the overall security of the network, given a certain resource budget.