Reviewerly/exHarmony huggingface dataset:
Selective Sampling-based Scalable Sparse Subspace Clustering

Sparse subspace clustering (SSC) represents each data point as a sparse linear combination of other data points in the dataset. In the representation learning step SSC finds a lower dimensional representation of data points, while in the spectral clustering step data points are clustered according to the underlying subspaces. However, both steps suffer from high computational and memory complexity, preventing the application of SSC to large-scale datasets. To overcome this limitation, we introduce Selective Sampling-based Scalable Sparse Subspace Clustering (S5C) algorithm which selects subsamples based on the approximated subgradients and linearly scales with the number of data points in terms of time and memory requirements. Along with the computational advantages, we derive theoretical guarantees for the correctness of S5C. Our theoretical result presents novel contribution for SSC in the case of limited number of subsamples. Extensive experimental results demonstrate effectiveness of our approach.
User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning

Local differential privacy is a strong notion of privacy in which the provider of the data guarantees privacy by perturbing the data with random noise. In the standard application of local differential differential privacy the distribution of the noise is constant and known by the learner. In this paper we generalize this approach by allowing the provider of the data to choose the distribution of the noise without disclosing any parameters of the distribution to the learner, under the constraint that the distribution is symmetrical. We consider this problem in the unconstrained Online Convex Optimization setting with noisy feedback. In this setting the learner receives the subgradient of a loss function, perturbed by noise, and aims to achieve sublinear regret with respect to some competitor, without constraints on the norm of the competitor. We derive the first algorithms that have adaptive regret bounds in this setting, i.e. our algorithms adapt to the unknown competitor norm, unknown noise, and unknown sum of the norms of the subgradients, matching state of the art bounds in all cases.
Atmospheric Weather Prediction Using various machine learning Techniques: A Survey

Generally, weather is comprised of numerous parameters including rainfall, precipitation, wind speed, etc. Environmental weather forecast is a testing errand for researchers and it has drawn a great deal of research enthusiasm in the recent years. Our study considers a wide variety of weather figure methods which can observe weather in the midst, or month to month or annually by considering the available meteorological information. So the precise forecast of weather parameters is emerging as a challenging task due to their dynamic environment conditions. Different machine learning systems are connected to foresee air parameters. Along with weather prediction, various applications based on Numerical Weather Prediction outputs are also analyzed.
Calibration of Fisheye Camera Using Entrance Pupil

Most conventional camera calibration algorithms assume that the imaging device has a Single Viewpoint (SVP). This is not necessarily true for special imaging device such as fisheye lenses. As a consequence, the intrinsic camera calibration result is not always reliable. In this paper, we propose a new formation model that tends to relax this assumption so that a Non-Single Viewpoint (NSVP) system is corrected to always maintain a SVP, by taking into account the variation of the Entrance Pupil (EP) using thin lens modeling. In addition, we present a calibration procedure for the image formation to estimate these EP parameters using non linear optimization procedure with bundle adjustment. From experiments, we are able to obtain slightly better re-projection error than traditional methods, and the camera parameters are better estimated. The proposed calibration procedure is simple and can easily be integrated to any other thin lens image formation model.
Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning

Search techniques, such as Monte Carlo Tree Search (MCTS) and Proof-Number Search (PNS), are effective in playing and solving games. However, the understanding of their performance in industrial applications is still limited. We investigate MCTS and Depth-First Proof-Number (DFPN) Search, a PNS variant, in the domain of Retrosynthetic Analysis (RA). We find that DFPN's strengths, that justify its success in games, have limited value in RA, and that an enhanced MCTS variant by Segler et al. significantly outperforms DFPN. We address this disadvantage of DFPN in RA with a novel approach to combine DFPN with Heuristic Edge Initialization. Our new search algorithm DFPN-E outperforms the enhanced MCTS in search time by a factor of 3 on average, with comparable success rates.
On the Calibration of Multiclass Classification with Rejection

We investigate the problem of multiclass classification with rejection, where a classifier can choose not to make a prediction to avoid critical misclassification. First, we consider an approach based on simultaneous training of a classifier and a rejector, which achieves the state-of-the-art performance in the binary case. We analyze this approach for the multiclass case and derive a general condition for calibration to the Bayes-optimal solution, which suggests that calibration is hard to achieve by general loss functions unlike the binary case. Next, we consider another traditional approach based on confidence scores, in which the existing work focuses on a specific class of losses. We propose rejection criteria for more general losses for this approach and guarantee calibration to the Bayes-optimal solution. Finally, we conduct experiments to validate the relevance of our theoretical findings.
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions

We present a family of algorithms, called descent algorithms, for optimizing convex and non-convex functions. We also introduce a new first-order algorithm, called rescaled gradient descent (RGD), and show that RGD achieves a faster convergence rate than gradient descent provided the function is strongly smooth - a natural generalization of the standard smoothness assumption on the objective function. When the objective function is convex, we present two frameworks for “accelerating” descent methods, one in the style of Nesterov and the other in the style of Monteiro and Svaiter. Rescaled gradient descent can be accelerated under the same strong smoothness assumption using both frameworks. We provide several examples of strongly smooth loss functions in machine learning and numerical experiments that verify our theoretical findings.
Improved File Security System Using Multiple Image Steganography

Steganography is the process of hiding a secret message within an ordinary message & extracting it at its destination. Image steganography is one of the most common and secure forms of steganography available today. Traditional steganography techniques use a single cover image to embed the secret data which has few security shortcomings. Therefore, batch steganography has been adopted which stores data on multiple images. In this paper, a novel approach is proposed for slicing the secret data and storing it on multiple cover images. In addition, retrieval of this secret data from the cover images on the destination side has also been discussed. The data slicing ensures secure transmission of the vital data making it merely impossible for the intruder to decrypt the data without the encrypting details.
Deep Active Learning with a Neural Architecture Search

We consider active learning of deep neural networks. Most active learning works in this context have focused on studying effective querying mechanisms and assumed that an appropriate network architecture is a priori known for the problem at hand. We challenge this assumption and propose a novel active strategy whereby the learning algorithm searches for effective architectures on the fly, while actively learning. We apply our strategy using three known querying techniques (softmax response, MC-dropout, and coresets) and show that the proposed approach overwhelmingly outperforms active learning using fixed architectures.
Gaussian-Based Pooling for Convolutional Neural Networks

Convolutional neural networks (CNNs) contain local pooling to effectively downsize feature maps for increasing computation efficiency as well as robustness to input variations. The local pooling methods are generally formulated in a form of convex combination of local neuron activations for retaining the characteristics of an input feature map in a manner similar to image downscaling. In this paper, to improve performance of CNNs, we propose a novel local pooling method based on the Gaussian-based probabilistic model over local neuron activations for flexibly pooling (extracting) features, in contrast to the previous model restricting the output within the convex hull of local neurons. In the proposed method, the local neuron activations are aggregated into the statistics of mean and standard deviation in a Gaussian distribution, and then on the basis of those statistics, we construct the probabilistic model suitable for the pooling in accordance with the knowledge about local pooling in CNNs. Through the probabilistic model equipped with trainable parameters, the proposed method naturally integrates two schemes of adaptively training the pooling form based on input feature maps and stochastically performing the pooling throughout the end-to-end learning. The experimental results on image classification demonstrate that the proposed method favorably improves performance of various CNNs in comparison with the other pooling methods.
On Differentially Private Graph Sparsification and Applications

In this paper, we study private sparsification of graphs. In particular, we give an algorithm that given an input graph, returns a sparse graph which approximates the spectrum of the input graph while ensuring differential privacy. This allows one to solve many graph problems privately yet efficiently and accurately. This is exemplified with application of the proposed meta-algorithm to graph algorithms for privately answering cut-queries, as well as practical algorithms for computing {\scshape MAX-CUT} and {\scshape SPARSEST-CUT} with better accuracy than previously known. We also give the first efficient private algorithm to learn Laplacian eigenmap on a graph.
Statistical Model Aggregation via Parameter Matching

We consider the problem of aggregating models learned from sequestered, possibly heterogeneous datasets. Exploiting tools from Bayesian nonparametrics, we develop a general meta-modeling framework that learns shared global latent structures by identifying correspondences among local model parameterizations. Our proposed framework is model-independent and is applicable to a wide range of model types. After verifying our approach on simulated data, we demonstrate its utility in aggregating Gaussian topic models, hierarchical Dirichlet process based hidden Markov models, and sparse Gaussian processes with applications spanning text summarization, motion capture analysis, and temperature forecasting.
Private Testing of Distributions via Sample Permutations

Statistical tests are at the heart of many scientific tasks. To validate their hypothesis, researchers in medical and social sciences use individuals' data. The sensitivity of participants' data requires the design of statistical tests that ensure the privacy of the individuals in the most efficient way. In this paper, we use the framework of property testing to design algorithms to test the properties of the distribution that the data is drawn from with respect to differential privacy. In particular, we investigate testing two fundamental properties of distributions: (1) testing the equivalence of two distributions when we have unequal numbers of samples from the two distributions. (2) Testing independence of two random variables. In both cases, we show that our testers achieve near optimal sample complexity (up to logarithmic factors). Moreover, our dependence on the privacy parameter is an additive term, which indicates that differential privacy can be obtained in most regimes of parameters for free.
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning

We propose and study a general framework for regularized Markov decision processes (MDPs) where the goal is to find an optimal policy that maximizes the expected discounted total reward plus a policy regularization term. The extant entropy-regularized MDPs can be cast into our framework. Moreover, under our framework, many regularization terms can bring multi-modality and sparsity, which are potentially useful in reinforcement learning. In particular, we present sufficient and necessary conditions that induce a sparse optimal policy. We also conduct a full mathematical analysis of the proposed regularized MDPs, including the optimality condition, performance error, and sparseness control. We provide a generic method to devise regularization forms and propose off-policy actor critic algorithms in complex environment settings. We empirically analyze the numerical properties of optimal policies and compare the performance of different sparse regularization forms in discrete and continuous environments.
Perception-guided multi-channel visual feature fusion for image retargeting

Image retargeting aims to arbitrarily change the aspect ratio of images with minimal visual artifacts, that is, preserving salient regions within the image. Conventional approaches have achieved impressive performance, however, only low-level features are exploited. Besides, these approaches do not take human visual system into account. To solve these problems, we propose a perception-guided multi-channel visual feature fusion method for image retargeting, where human visual perception is well encoded. Specifically, we first construct a series of graphlets which represent salient regions that might attract human attention, where both low-level and high-level features are utilized to describe graphlets. Considering that only a few regions can attract human attention when observing the image, we engineer a sparsity-constraint algorithm to select these regions, which will be further concatenated to form human gaze shifting paths (GSPs). Subsequently, we design a statistic-based CNN architecture to extract the deep representation of GSPs. Afterward, a probabilistic model is built to learn the priors of GSPs and the learned probabilistic model will guide image retargeting. A test image whose distribution is similar to the learned probabilistic model is deemed as the aesthetic image and thus should be shrunk slightly. Comprehensive experiments demonstrate the effectiveness and robustness of the proposed method.
A novel robust feature extraction with GSO-optimized extreme learning for age-invariant face recognition

This paper presents a novel age function modelling technique on the basis of the fusion of local features obtained by local texture descriptors. Initially, image normalization is performed and a feature extraction process is carried out. The age estimation performances of new texture descriptors Local Phase Quantization, Weber Local Descriptor and the familiar texture descriptor Local Binary Patterns, which are not examined thoroughly for age estimation modelling, are analysed in this paper. Then the feature fusion process is taken place for investigating the age estimation precisions of various concatenation of the local texture descriptors. By using PCA, dimensionality reduction is implemented for reducing the dimensions of the images. Extreme Learning Machine (ELM) classifier is applied to evaluate the output images for the corresponding input images. Because of the mild optimization restrictions, ELM can be simple for execution and normally attains the finer generalization performance. The outcomes display that, when compared with the earlier techniques, the age function modelling accuracy of the developed system is better.
Overton: A Data System for Monitoring and Improving Machine-Learned Products

We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machine learning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems.
Specifying Object Attributes and Relations in Interactive Scene Generation

We introduce a method for the generation of images from an input scene graph. The method separates between a layout embedding and an appearance embedding. The dual embedding leads to generated images that better match the scene graph, have higher visual quality, and support more complex scene graphs. In addition, the embedding scheme supports multiple and diverse output images per scene graph, which can be further controlled by the user. We demonstrate two modes of per-object control: (i) importing elements from other images, and (ii) navigation in the object space, by selecting an appearance archetype. Our code is publicly available at this https URL
Analyzing Temporal Graphs with Gradoop

The temporal analysis of evolving graphs is an important requirement in many domains but hardly supported in current graph database and graph processing systems. We therefore have started with extending the distributed graph analysis framework Gradoop for temporal graph analysis by adding time properties to vertices, edges and graphs and using them within graph operators. We outline these extensions and illustrate their use within analysis workflows. We further describe the implementation of the snapshot and diff operators and evaluated them.
Protection of trade secrets in a data-driven, networked environment – Is the update already out-dated?

Directive (EU) 2016/943 (Trade Secret Directive) was adopted to level the disparities in the protection of trade secrets throughout the European Union. The Directive solely grants protection when the information is secret, which requires measures to prevent access. In this regard, the Directive retains the basic structure of the protection of trade secrets. In the future industry, however, it will become inevitable to provide a broad access to data for third parties in order to exploit the full potential of digitalization. These developments are not taken into account as neither the applicability to data nor the demands of the Industry 4.0 are addressed in the Directive. This leads to the question if the update of trade secret protection is already out-dated or if it is possible to protect trade secrets even in highly networked environments as they will emerge in the future. The aim of this paper is therefore to assess whether and how protection of trade secrets can be realized in such environments. To answer this question, the paper will first examine the conflict mentioned above in detail (I.). Then, it will identify the shortcomings of the Trade Secret Directive when its requirements are applied to data-driven, networked environments (II.). However, with organizational, contractual and technical measures, these shortcomings can be resolved to a certain extent (III.). Especially new encryption technologies that are not yet wide-spread will be a fundamental asset to trade secret protection in the future (III. 3.).***
Deep Variational Autoencoders for NPC Behaviour Classification

Procedural content generation (PCG) can create novel, player-specific content in video games, including behaviours of AI-controlled non-playable characters (NPC). Here we present our first results on comparing unsupervised and supervised machine learning for procedurally generated NPC behaviours. Using an artificial life environment as a stand-in for a video game, we run artificial evolution and generate AI agents with various behaviours. We then train deep variational autoencoders on commonly evolved behaviour and measure its efficacy in detecting behaviours unseen during training. As a reference, we use an off-the-shelf deep network trained in a supervised manner to detect behaviours both seen and unseen during its training. Preliminary results demonstrate promising performance that holds even when the training set contains a mixture of several types of behaviours without proper labels.
A Framework for Data-Driven Robotics

We present a framework for data-driven robotics that makes use of a largedataset of recorded robot experience and scales to several tasks using learnedreward functions. We show how to apply this framework to accomplish threedifferent object manipulation tasks on a real robot platform. Givendemonstrations of a task together with task-agnostic recorded experience, weuse a special form of human annotation as supervision to learn a rewardfunction, which enables us to deal with real-world tasks where the rewardsignal cannot be acquired directly. Learned rewards are used in combinationwith a large dataset of experience from different tasks to learn a robot policyoffline using batch RL. We show that using our approach it is possible to trainagents to perform a variety of challenging manipulation tasks includingstacking rigid objects and handling cloth.
Google AutoML: Cloud Natural Language Processing

This chapter will build a language toxicity classification model to classify and recognize toxic and non-toxic or clean phrases using Google Cloud AutoML for natural language processing (NLP). The data used in this project is from the Toxic Comment Classification Challenge on Kaggle by Jigsaw and Google. The data is modified to have a sample of 16,000 toxic and 16,000 non-toxic words as inputs to build the model on AutoML NLP.
Feature-Less End-to-End Nested Term Extraction

In this paper, we proposed a deep learning-based end-to-end method on domain specified automatic term extraction (ATE), it considers possible term spans within a fixed length in the sentence and predicts them whether they can be conceptual terms. In comparison with current ATE methods, the model supports nested term extraction and does not crucially need extra (extracted) features. Results show that it can achieve a high recall and a comparable precision on term extraction task with inputting segmented raw text.