Reviewerly/exHarmony huggingface dataset:
Semisupervised Affinity Matrix Learning via Dual-Channel Information Recovery

This article explores the problem of semisupervised affinity matrix learning, that is, learning an affinity matrix of data samples under the supervision of a small number of pairwise constraints (PCs). By observing that both the matrix encoding PCs, called pairwise constraint matrix (PCM) and the empirically constructed affinity matrix (EAM), express the similarity between samples, we assume that both of them are generated from a latent affinity matrix (LAM) that can depict the ideal pairwise relation between samples. Specifically, the PCM can be thought of as a partial observation of the LAM, while the EAM is a fully observed one but corrupted with noise/outliers. To this end, we innovatively cast the semisupervised affinity matrix learning as the recovery of the LAM guided by the PCM and EAM, which is technically formulated as a convex optimization problem. We also provide an efficient algorithm for solving the resulting model numerically. Extensive experiments on benchmark datasets demonstrate the significant superiority of our method over state-of-the-art ones when used for constrained clustering and dimensionality reduction. The code is publicly available at https://github.com/jyh-learning/LAM.
Advanced Active Learning Strategies for Object Detection

Future self-driving cars must be able to perceive and understand their surroundings. Deep learning based approaches promise to solve the perception problem but require a large amount of manually labeled training data. Active learning is a training procedure in which the model itself selects interesting samples for labeling based on their uncertainty, with substantially less data required for training. Recent research in active learning has mostly focused on the simple image classification task. In this paper, we propose novel methods to estimate sample uncertainties for 2D and 3D object detection using Ensembles. We moreover evaluate different training strategies including Continuous Training to alleviate increasing training times introduced by the active learning cycle. Finally, we investigate the effects of active learning on imbalanced datasets and possible interactions with class weighting. Experiment results show both increased time saving around 55% and data saving rates of around 30%. For the 3D object detection task, we show that our proposed uncertainty estimation method is valid, saving 35% of labeling efforts and thus is ready for application for automotive object detection use cases.
Iterative deformation calibration of a transmission flat via the ring-point support on a 300-mm-aperture vertical Fizeau interferometer

In the development of a high-precision vertical Fizeau interferometer with a 300-mm aperture, the deformation of the transmission flat because of clamping and gravity must be considered. In this paper, we proposed a ring-point support scheme for the deformation calibration of a large-diameter transmission flat. The calibration theory of the ring-point support system with elastic deformation was derived. The changes in the surface and stress field of the transmission flat were analyzed quantitatively by finite element method modeling, leading to the optimization of the support structure. To validate the proposed calibration approach, we performed an absolute test of the transmission flat using a liquid reference. The test result was compared to a measurement of the Zygo interferometer demonstrating the effectiveness of the proposed ring-point support design. Finally, with the iterative deformation calibration, the accuracy of the transmission flat reached λ/25 (Peak Valley, PV) for a 300-mm aperture.
Addressing the multi-label imbalance for neural networks: An approach based on stratified mini-batches

Imbalanced learning in the multi-label scenario is a challenging issue, and it also exists in the training of deep neural network. Previous studies have demonstrated that the resampling methods are capable of reducing bias towards the majority group. Nonetheless, when being extended to neural network, these methods display some obvious drawbacks, such as the introduction of extra hyperparameters, the fixed training mode, etc. In order to eliminate the disadvantages, in this paper, an efficient training technique named Mini-Batch Gradient Descent with Stratified sampling (MBGD-Ss) is proposed to alliviate the issue with imbalanced data by dynamically sampling. In view of the specialty of multi-label domain, we put forward two specific strategies as Label Powerset based (SsLP) and Label-based (SsL), respectively. Particularly, SsLP takes the label combination (labelset) that appears in the dataset as a stratum, and SsL directly sets the label as a stratum. Extensive experiments validate the effectiveness of the proposed approach in decreasing the imbalance of sampled data. Moreover, the empirical analysis also shows that the proposed method can mitigate the classifier's bias against labels, especially improve the prediction accuracy of minority labels.
Outsourced attribute‐based signatures with perfect privacy for circuits in cloud computing

Summary Attribute‐based signature (ABS) is a widely used cryptographic primitive, that provides both data integrity and fine‐grained privacy protection. However, the signing overhead of ABS is usually too large, making it unsuitable for resource‐constrained devices. Using cloud computing technology, the notion of outsourced attribute‐based signatures (OABS) was introduced to overcome this drawback. In OABS, the user can outsource him/her signing tasks to the signing‐cloud service provider (S‐CSP). The previous notion of privacy of OABS does not take the S‐CSP's attack into account, so it is not strong enough. To conform to the reality of cloud computing, we present a more accurate notion of privacy, called perfect privacy , which ensures that any adversary, even the S‐CSP, cannot identify which policy is used to generate the signature. Then, we propose a key‐policy OABS scheme for circuits based on multilinear mapping with perfect privacy. By using the outsourcing technique, in our scheme, the computation cost of the signer is only three or four pairing operations, which is independent of the complexity of the policy and the size of the attribute set. Our scheme achieves three advantages: perfect privacy, expressive policy, and constant signing cost, simultaneously.
Constructing ANFIS With Sparse Data Through Group-Based Rule Interpolation: An Evolutionary Approach

An adaptive-network-based fuzzy inference system (ANFIS) offers a popular and powerful fuzzy inference mechanism. As with many other advanced data-driven techniques, developing an effective ANFIS typically requires sufficient training data. However, in many real-world applications, it is not always straightforward to obtain a large amount of representative data that cover the entire problem space to accomplish the required training, seriously restricting the performance of a learned ANFIS. This article introduces a new ANFIS learning approach through an evolutionary process, which is able to generate an ANFIS with only a small amount of training data in a certain problem region, by interpolating well-trained ANFISs in the neighboring regions. Such a process works by first producing an initial population of candidate fuzzy rules in the region of data shortage, through interpolating a rule dictionary constructed from trained ANFISs in the neighborhood regions. The crossover and mutation operations over these candidate rules are then executed in an effort to attain candidates of improved performance. When this genetic learning process terminates, the chromosomes in the final population either collectively form or each individually represents a learned ANFIS, depending on whether a single fuzzy rule or a set of fuzzy rules representing an entire ANFIS is implemented with a chromosome within the evolving population. Comparative experimental evaluations on both synthetic and real-world datasets are carried out, demonstrating that in spite of data shortage, the proposed interpolation approach is able to produce ANFIS models that significantly outperform those trained using existing learning mechanisms.
Steplength and Mini-batch Size Selection in Stochastic Gradient Methods

The steplength selection is a crucial issue for the effectiveness of the stochastic gradient methods for large-scale optimization problems arising in machine learning. In a recent paper, Bollapragada et al. [1] propose to include an adaptive subsampling strategy into a stochastic gradient scheme. We propose to combine this approach with a selection rule for the steplength, borrowed from the full-gradient scheme known as Limited Memory Steepest Descent (LMSD) method [4] and suitably tailored to the stochastic framework. This strategy, based on the Ritz-like values of a suitable matrix, enables to give a local estimate of the local Lipschitz constant of the gradient of the objective function, without introducing line-search techniques, while the possible increase of the subsample size used to compute the stochastic gradient enables to control the variance of this direction. An extensive numerical experimentation for convex and non-convex loss functions highlights that the new rule makes the tuning of the parameters less expensive than the selection of a suitable constant steplength in standard and mini-batch stochastic gradient methods. The proposed procedure has also been compared with the Momentum and ADAM methods.
Complete type functionals for homogeneous time delay systems

For linear time-invariant time delay systems, the so-called Lyapunov–Krasovskii functionals of complete type (Kharitonov and Zhabko, 2003) are known to be effective in the stability analysis and a number of applications. More precisely, there exist the necessary and sufficient asymptotic stability and instability conditions expressed in terms of these functionals. The case excluded from consideration in the theory (since the functionals either do not exist or are not uniquely defined) is violation of the Lyapunov condition, i.e. the case of systems with the eigenvalues placed symmetrically with respect to the origin of the complex plane. In this paper, an analogue of this theory for a class of nonlinear time delay systems with homogeneous right-hand sides of degree greater than one and a constant delay is developed. An explicit expression for the Lyapunov–Krasovskii functionals as well as necessary and sufficient conditions for the asymptotic stability and instability of the trivial solution based on these functionals are given. An important assumption, which constitutes an analogue of the Lyapunov condition for linear systems, is existence of the Lyapunov function for a delay free system, obtained from the original one setting the delay equal to zero. Furthermore, this Lyapunov function is a key element in the construction of the functional. The functionals are applied to estimating the region of attraction.
Accelerating Continuous Integration by Caching Environments and Inferring Dependencies

To facilitate the rapid release cadence of modern software (on the order of weeks, days, or even hours), software development organizations invest in practices like Continuous Integration (CI), where each change submitted by developers is built (e.g., compiled, tested, linted) to detect problematic changes early. A fast and efficient build process is crucial to provide timely CI feedback to developers. If CI feedback is too slow, developers may switch contexts to other tasks, which is known to be a costly operation for knowledge workers. Thus, minimizing the build execution time for CI services is an important task. While recent work has made several important advances in the acceleration of CI builds, optimizations often depend upon explicitly defined build dependency graphs (e.g., make, Gradle, CloudBuild, Bazel). These hand-maintained graphs may be (a) underspecified, leading to incorrect build behaviour; or (b) overspecified, leading to missed acceleration opportunities. In this paper, we propose <small>Kotinos</small> —a language-agnostic approach to infer data from which build acceleration decisions can be made without relying upon build specifications. After inferring this data, our approach accelerates CI builds by caching the build environment and skipping unaffected build steps. <small>Kotinos</small> is at the core of a commercial CI service with a growing customer base. To evaluate <small>Kotinos</small> , we mine 14,364 historical CI build records spanning three proprietary and seven open-source software projects. We find that: (1) at least 87.9 percent of the builds activate at least one <small>Kotinos</small> acceleration; and (2) 74 percent of accelerated builds achieve a speed-up of two-fold with respect to their non-accelerated counterparts. Moreover, (3) the benefits of <small>Kotinos</small> can also be replicated in open source software systems; and (4) <small>Kotinos</small> imposes minimal resource overhead (i.e., <inline-formula><tex-math notation="LaTeX">$&lt;$</tex-math></inline-formula> 1 percent median CPU usage, 2 MB – 2.2 GB median memory usage, and 0.4 GB – 5.2 GB median storage overhead) and does not compromise build outcomes. Our results suggest that migration to <small>Kotinos</small> yields substantial benefits with minimal investment of effort (e.g., no migration of build systems is necessary).
Learning to focus: cascaded feature matching network for few-shot image recognition

Deep networks can learn to accurately recognize objects of a category by training on a large number of annotated images. However, a meta-learning challenge known as a low-shot image recognition task comes when only a few images with annotations are available for learning a recognition model for one category. The objects in testing/query and training/support images are likely to be different in size, location, style, and so on. Our method, called Cascaded Feature Matching Network (CFMN), is proposed to solve this problem. We train the meta-learner to learn a more fine-grained and adaptive deep distance metric by focusing more on the features that have high correlations between compared images by the feature matching block which can align associated features together and naturally ignore those non-discriminative features. By applying the proposed feature matching block in different layers of the few-shot recognition network, multi-scale information among the compared images can be incorporated into the final cascaded matching feature, which boosts the recognition performance further and generalizes better by learning on relationships. The experiments for few-shot learning on two standard datasets, \emph{mini}ImageNet and Omniglot, have confirmed the effectiveness of our method. Besides, the multi-label few-shot task is first studied on a new data split of COCO which further shows the superiority of the proposed feature matching network when performing few-shot learning in complex images. The code will be made publicly available.
Optimizing the performance of MLP and SVR predictors based on logical oring and experimental ranking equation

Improving conventional prediction systems is widely used to optimize the learning process, achieve higher performance, and avoid overfitting. This paper’s purpose is to propose a new predictor for solar tracking systems applications based on oring operator and ranking equation with a conventional predictor including Multi-Layer Perceptron (MLP) and Support Vector Machine Regression (SVR). The point of using oring and ranking equation is to create a new variable that stores the information of combined attributes. This process aims to increase the accuracy of predictors and increase the efficiency of intelligent solar tracking systems. The experiments used 6 different datasets for solar tracking systems. The results revealed that the proposed predictors performed better than conventional predictors. Using the proposed predictors has improved both Root Mean Square Error (RMSE) and Coefficient of Determination (R2). The developed MLP models showed lower RMSE and higher R2 compared to conventional MLP models. The improvement ranges for using MLP are from 1.0013 to 1.4614 degrees for RMSE, and from 1.0019 to 1.4984 times for R2, while the improvement ranges using SVM are from 1.001 to 1.988 degrees for RMSE and from 1.000 to 2.385 times for R2.
A Graphical Social Topology Model for RGB-D Multi-Person Tracking

Tracking multiple persons is a challenging task especially when persons move in groups and occlude one another. Existing research have investigated the problems of group division and segmentation; however, lacking overall person-group topology modeling limits the ability to handle complex person and group dynamics. We propose a Graphical Social Topology (GST) model in the RGB-D data domain, and estimate object group dynamics by jointly modeling the group structure and states of persons using RGB-D topological representation. With our topology representation, moving persons are not only assigned to groups, but also dynamically connected with each other, which enables in-group individuals to be correctively associated and the cohesion of each group to be precisely modeled. Using the learned typical topology pattern and group online update modules, we infer the birth/death and merging/splitting of dynamic groups. With the GST model, the proposed multi-person tracker can naturally facilitate the occlusion problem by treating the occluded object and other in-group members as a whole, while leveraging overall state transition. Experiments on different RGB-D and RGB datasets confirm that the proposed multi-person tracker improves the state-of-the-arts.
Tensor analysis with n-mode generalized difference subspace

The increasing use of multiple sensors, which produce a large amount of multi-dimensional data, requires efficient representation and classification methods. In this paper, we present a new method for multi-dimensional data classification that relies on two premises: (1) multi-dimensional data are usually represented by tensors, since this brings benefits from multilinear algebra and established tensor factorization methods; and (2) multilinear data can be described by a subspace of a vector space. The subspace representation has been employed for pattern-set recognition, and its tensor representation counterpart is also available in the literature. However, traditional methods do not use discriminative information of the tensors, degrading the classification accuracy. In this case, generalized difference subspace (GDS) provides an enhanced subspace representation by reducing data redundancy and revealing discriminative structures. Since GDS does not handle tensor data, we propose a new projection called n-mode GDS, which efficiently handles tensor data. We also introduce the n-mode Fisher score as a class separability index and an improved metric based on the geodesic distance for tensor data similarity. The experimental results on gesture and action recognition show that the proposed method outperforms methods commonly used in the literature without relying on pre-trained models or transfer learning.
Preliminary Evaluation of TinyYOLO on a New Dataset for Search-and-Rescue with Drones

Unmanned aerial vehicles (UAVs), more commonly known as drones, are increasingly used as a technological support tool for search-and-rescue (SAR) operations and also for post-disaster area explorations. UAVs can be equipped with high-resolution cameras and embed GPUs powerful enough to provide effective and efficient aid to emergency rescue operations. Victims can be unconscious or injured, and any means of locating them as quickly as possible is critical to increasing their chances of survival. In particular, using drones that can automatically detect people in scenes can increase detection rate, while reducing rescue time. In this work, we present a new dataset specifically designed for SAR operations from drones using computer vision. Being small in size, the dataset is currently intended for testing and evaluation purposes only. We also provide baseline results on this dataset, obtained with the state-of-the-art TinyYOLOv3 object detector. The purpose of this preliminary work is to stimulate interest and encourage contributions to this research topic.
3DPoseLite: A Compact 3D Pose Estimation Using Node Embeddings

Efficient pose estimation finds utility in Augmented Reality (AR) and other computer vision applications such as autonomous navigation and robotics, to name a few. A compact and accurate pose estimation methodology is of paramount importance for on-device inference in such applications. Our proposed solution 3DPoseLite, estimates pose of generic objects by utilizing a compact node embedding representation, unlike computationally expensive multi-view and point-cloud representations. The neural network outputs a 3D pose, taking RGB image and its corresponding graph (obtained by skeletonizing the 3D meshes [31]) as inputs. Our approach utilizes node2vec framework to learn low-dimensional representations for nodes in a graph by optimizing a neighborhood preserving objective. We achieve a space and time reduction by a factor of 11 × and 3 × respectively, with respect to the state-of-the-art approach, Pose-FromShape [50], on benchmark Pascal3D dataset [48]. We also test the performance of our model on unseen data using Pix3D dataset.
Fairness in machine learning with tractable models

Machine Learning techniques have become pervasive across a range of different applications, and are now widely used in areas as disparate as recidivism prediction, consumer credit–risk analysis and insurance pricing. The prevalence of machine learning techniques has raised concerns about the potential for learned algorithms to become biased against certain groups. Many definitions have been proposed in the literature, but the fundamental task of reasoning about probabilistic events is a challenging one, owing to the intractability of inference. The focus of this paper is taking steps towards the application of tractable probabilistic models to fairness in machine learning. Tractable probabilistic models have recently emerged that guarantee that conditional marginal can be computed in time linear in the size of the model. In particular, we show that sum product networks (SPNs) enable an effective technique for determining the statistical relationships between protected attributes and other training variables. We will also motivate the concept of “fairness through percentile equivalence”, a new definition predicated on the notion that individuals at the same percentile of their respective distributions should be treated equivalently, and this prevents unfair penalisation of those individuals who lie at the extremities of their respective distributions. We compare the efficacy of this pre-processing technique with an alternative approach that assumes an additive contribution. It was found that when these two approaches were compared on a data set containing the results of law school applicants, the percentile equivalence method reduced the average underestimation in the exam score of ethnic minority applicants black applicants at the bottom end of their conditional distribution by about a fifth. We conclude by outlining potential improvements to our existing methodology and suggest opportunities for further work in this field.