Reviewerly/exHarmony huggingface dataset:
Motion-Excited Sampler: Video Adversarial Attack with Sparked Prior

Deep neural networks are known to be susceptible to adversarial noise, which is tiny and imperceptible perturbation. Most of previous works on adversarial attack mainly focus on image models, while the vulnerability of video models is less explored. In this paper, we aim to attack video models by utilizing intrinsic movement pattern and regional relative motion among video frames. We propose an effective motion-excited sampler to obtain motion-aware noise prior, which we term as sparked prior. Our sparked prior underlines frame correlations and utilizes video dynamics via relative motion. By using the sparked prior in gradient estimation, we can successfully attack a variety of video classification models with fewer number of queries. Extensive experimental results on four benchmark datasets validate the efficacy of our proposed method.
Symbiotic Adversarial Learning for Attribute-Based Person Search

Attribute-based person search is in significant demand for applications where no detected query images are available, such as identifying a criminal from witness. However, the task itself is quite challenging because there is a huge modality gap between images and physical descriptions of attributes. Often, there may also be a large number of unseen categories (attribute combinations). The current state-of-the-art methods either focus on learning better cross-modal embeddings by mining only seen data, or they explicitly use generative adversarial networks (GANs) to synthesize unseen features. The former tends to produce poor embeddings due to insufficient data, while the latter does not preserve intra-class compactness during generation. In this paper, we present a symbiotic adversarial learning framework, called SAL. Two GANs sit at the base of the framework in a symbiotic learning scheme: one synthesizes features of unseen classes/categories, while the other optimizes the embedding and performs the cross-modal alignment on the common embedding space. Specifically, two different types of generative adversarial networks learn collaboratively throughout the training process and the interactions between the two mutually benefit each other. Extensive evaluations show SAL’s superiority over nine state-of-the-art methods with two challenging pedestrian benchmarks, PETA and Market-1501. The code is publicly available at: https://github.com/ycao5602/SAL .
Deep Learning Using Genetic Algorithm Optimization for Short Term Solar Irradiance Forecasting

The increase in the use of renewable energy sources (RES) has been remarkable in recent years, especially photovoltaic energy, which is one of the most widely used renewable energy sources for electricity generation. In fact, the world has known the installation of a large number of autonomous or grid-connected photovoltaic systems. However the problem with the introduction of PV is the improvised nature of solar energy that can influence the stability of electricity grids and reliability of the grid, Accurate solar forecasting makes it easier to integrate solar generation into the grid by reducing the integration and operating costs associated with intermittent solar power.In this context, the objective of this work is to improve the development of appropriate forecasting models for the prediction of photovoltaic energy production. For that reason, this article presents new hybrid methods to optimize deep learning forecasting by using genetic algorithm based Deep Neural Network. The model is employed to forecast time series of solar irradiation output. Comparisons are made between the performances of three types of neural networks: long short-term memory (LSTM), Gate recurrent unit (GRU), and Recurrent Neural Network (RNN). GA is exploited to find the most appropriate number of window size, and number of units (neurons) in each and all three hidden layers.
Graded Modal Dependent Type Theory

Abstract Graded type theories are an emerging paradigm for augmenting the reasoning power of types with parameterizable, fine-grained analyses of program properties. There have been many such theories in recent years which equip a type theory with quantitative dataflow tracking, usually via a semiring-like structure which provides analysis on variables (often called ‘quantitative’ or ‘coeffect’ theories). We present Graded Modal Dependent Type Theory ( Grtt for short), which equips a dependent type theory with a general, parameterizable analysis of the flow of data, both in and between computational terms and types. In this theory, it is possible to study, restrict, and reason about data use in programs and types, enabling, for example, parametric quantifiers and linearity to be captured in a dependent setting. We propose Grtt , study its metatheory, and explore various case studies of its use in reasoning about programs and studying other type theories. We have implemented the theory and highlight the interesting details, including showing an application of grading to optimising the type checking procedure itself.
VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization

During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an extensive search to infer the best hyperparameter set for the given problem. The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial-and-error processes that could remarkably affect the predictive result. Moreover, each hyperparameter of an ML algorithm is potentially intertwined with the others, and changing it might result in unforeseeable impacts on the remaining hyperparameters. Evolutionary optimization is a promising method to try and address those issues. According to this method, performant models are stored, while the remainder are improved through crossover and mutation processes inspired by genetic algorithms. We present VisEvol, a visual analytics tool that supports interactive exploration of hyperparameters and intervention in this evolutionary procedure. In summary, our proposed tool helps the user to generate new models through evolution and eventually explore powerful hyperparameter combinations in diverse regions of the extensive hyperparameter space. The outcome is a voting ensemble (with equal rights) that boosts the final predictive performance. The utility and applicability of VisEvol are demonstrated with two use cases and interviews with ML experts who evaluated the effectiveness of the tool.
Meta-learning with Network Pruning

Meta-learning is a powerful paradigm for few-shot learning. Although with remarkable success witnessed in many applications, the existing optimization based meta-learning models with over-parameterized neural networks have been evidenced to ovetfit on training tasks. To remedy this deficiency, we propose a network pruning based meta-learning approach for overfitting reduction via explicitly controlling the capacity of network. A uniform concentration analysis reveals the benefit of network capacity constraint for reducing generalization gap of the proposed meta-learner. We have implemented our approach on top of Reptile assembled with two network pruning routines: Dense-Sparse-Dense (DSD) and Iterative Hard Thresholding (IHT). Extensive experimental results on benchmark datasets with different over-parameterized deep networks demonstrate that our method not only effectively alleviates meta-overfitting but also in many cases improves the overall generalization performance when applied to few-shot classification tasks.
DisQ

Superconducting quantum computing technology has ushered in a new era of computational possibilities. While a considerable research effort has been geared toward improving the quantum technology and building the software stack to efficiently execute quantum algorithms with reduced error rate, effort toward optimizing how quantum output states are defined and classified for the purpose of reducing the error rate is still limited. To this end, this paper proposes DisQ, a quantum output state classification approach which reduces error rates of quantum programs on NISQ devices.
ElaMorph Projection: Deformation of 3D Shape by Dynamic Projection Mapping

We propose a projector-based method that provides an illusion of geometry change, similar to that caused by a change in physical properties such as elasticity, in response to inertia caused by physical motion. The proposed method is named “ElaMorph projection.” Although several projection mapping methods capable of deforming targets have been previously proposed, these methods require the preparation of animations in advance. Moreover, these methods are unable to deform the shape in real-time according to actual movements of the object. To address these issues, we perform real-time geometry deformation and rendering based on the spatial motion of the object. To render the projection image, we extend the conventional method of deformation for 2D pictures or static 3D objects to include dynamic 3D objects. This study involves projection onto a dynamic 3D object; however, the projection quality decreases if a part of the rendered image extends beyond the projection target. To address this issue, the proposed algorithm ensures that the vertices after deformation always remain within the projection target. In addition, we develop a robust algorithm to generate projection images under dynamic illuminative conditions, through real-time estimation of the environmental lighting required for rendering. Moreover, using an elasticity map that can be easily constructed using a UV map, our method enables users to specify the vertices to be deformed, using an elasticity map. We present projections under several different sets of elasticity maps, environmental lighting, and elasticities. Finally, we evaluate the latency and throughput of our system.
Continued Functions and Perturbation Series: Simple Tools for Convergence of Diverging Series in O(n)-Symmetric $$\phi ^4$$ Field Theory at Weak Coupling Limit

We determine universal critical exponents that describe the continuous phase transitions in different dimensions of space. We use continued functions without any external unknown parameters to obtain analytic continuation for the recently derived 7- loop $\epsilon$ expansion from $O(n)$-symmetric $\phi^4$ field theory. Employing a new blended continued function, we obtain critical exponent $\alpha=-0.0121(22)$ for the phase transition of superfluid helium which matches closely with the most accurate experimental value. This result addresses the long-standing discrepancy between the theoretical predictions and precise experimental result of $O(2)$ $\phi^4$ model known as "$\lambda$-point specific heat experimental anomaly". Further we have also examined the applicability of such continued functions in other examples of field theories.
Image registration: Maximum likelihood, minimum entropy and deep learning

In this work, we propose a theoretical framework based on maximum profile likelihood for pairwise and groupwise registration. By an asymptotic analysis, we demonstrate that maximum profile likelihood registration minimizes an upper bound on the joint entropy of the distribution that generates the joint image data. Further, we derive the congealing method for groupwise registration by optimizing the profile likelihood in closed form, and using coordinate ascent, or iterative model refinement. We also describe a method for feature based registration in the same framework and demonstrate it on groupwise tractographic registration. In the second part of the article, we propose an approach to deep metric registration that implements maximum likelihood registration using deep discriminative classifiers. We show further that this approach can be used for maximum profile likelihood registration to discharge the need for well-registered training data, using iterative model refinement. We demonstrate that the method succeeds on a challenging registration problem where the standard mutual information approach does not perform well.
A systematic mapping study on architectural smells detection

The recognition of the need for high-quality software architecture is evident from the increasing trend in investigating architectural smells. Detection of architectural smells is paramount because they can seep through to design and implementation stages if left unidentified. Many architectural smells detection techniques and tools are proposed in the literature. The diversity in the detection techniques and tools suggests the need for their collective analysis to identify interesting aspects for practice and open research areas. To fulfill this, in this paper, we unify the knowledge about the detection of architectural smells through a systematic mapping study. We report on the existing detection techniques and tools for architectural smells to identify their limitations. We find there has been limited investigation of some architectural smells (e.g., micro-service smells); many architectural smells are not detected by tools yet; and there are limited empirical validations of techniques and tools. Based on our findings, we suggest several open research problems, including the need to (1) investigate undetected architectural smells (e.g., Java package smells), (2) improve the coverage of architectural smell detection across architecture styles (e.g., service-oriented and cloud), and (3) perform empirical validations of techniques and tools in industry across different languages and project domains.
Multi-Scale Image Super-Resolution Via a Single Extendable Deep Network

Deep neural networks have achieved remarkable success in single image super-resolution (SISR). However, in most cases, image SR with different scale factors is considered as different tasks and solved by training specific models. It makes the image SR applications inefficient and tedious. Hence, to tackle these problems, we propose a lightweight and fast network (MSWSR) to implement multi-scale SR simultaneously by learning multi-level wavelet coefficients of the target image. The proposed network is composed of one CNN part and one RNN part. The CNN part is used for predicting the highest-level low-frequency wavelet coefficients, while the RNN part is used for predicting the rest frequency bands of wavelet coefficients. Moreover, the RNN part is extendable to more scales. For further lightweight, a non-square (side window) convolution kernel is proposed to reduce the network parameters. Experiments on commonly-used datasets demonstrate that the proposed method achieves favorable reconstruction performance with a fast speed and lightweight network. The code is available at https://github.com/FVL2020/MSWSR.
Kernel-attended residual network for single image super-resolution

Single image super-resolution is very important as a low-level computer vision task. With the development of deep convolution neural networks (CNNs), recent approaches with CNNs have outperformed existing traditional methods in the single image super-resolution (SISR) field. However, these methods may suffer from weaker representational power and overly-smoothing textures. To handle these problems, we propose a Kernel-Attended Residual Network (KARN). Our KARN possesses the optimal performance for feature fusion and feature representation. Specifically, we present a multi-channel fusion block (MCFB) to restore plentiful textual feature information, and a kernel-attended block (KAB) to improve the representation power of our network with multiple kernels. Besides, we present a space-feature re-calibration block (SFRB) to integrate the calibration into features in the spatial aspect. Owing to the advanced information that we extract, KARN achieves a more notable performance than state-of-the-art methods by evaluating the performance of results based on benchmark datasets.
The Multi-Base Discrete Logarithm Problem: Tight Reductions and Non-rewinding Proofs for Schnorr Identification and Signatures

We introduce the Multi-Base Discrete Logarithm (MBDL) problem. We use this to give reductions, for Schnorr and Okamoto identification and signatures, that are non-rewinding and, by avoiding the notorious square-root loss, tighter than the classical ones from the Discrete Logarithm (DL) problem. This fills a well-known theoretical and practical gap regarding the security of these schemes. We show that not only is the MBDL problem hard in the generic group model, but with a bound that matches that for DL, so that our new reductions justify the security of these primitives for group sizes in actual use.
Image guidance based 3D vehicle detection in traffic scene

3D vehicle detection is a significant step for traffic scene understanding. Several recent works have achieved state-of-the-art performance with only LiDAR point clouds. However, there are still existing challenges due to the intrinsic limitations of lidar data. In this paper, we propose a novel 3D vehicle detection method, in which visual information is introduced to remedy the deficiency of sparse point clouds. Our method is composed of two stages. In first stage, a novel proposal generator with the guidance of visual information is proposed. In the proposal generator, 2D detected bounding boxes are registered with 3D candidates from LiDAR by calibrating and then weighted non-maximum suppression (WNMS) is applied to increase the confidence of proposals to remove redundant proposals. In second stage, to construct a box predictor, instead of sampling in a sphere space, a novel sampling with cylinder space for pointnet++ is leveraged to learn local features of the point clouds. Furthermore, to achieve a better balance between confidence and localization accuracy of boxes, an Intersection-over-Union (IoU) prediction branch is modified and attached to the network. We conduct multiple experiments on the KITTI 3D object detection dataset and compare our method with state-of-the-art methods. The comparison results show that our algorithm is effective and can improve the performance of 3D vehicle detection.
Deep Unsupervised Embedding for Remote Sensing Image Retrieval Using Textual Cues

Compared to image-image retrieval, text-image retrieval has been less investigated in the remote sensing community, possibly because of the complexity of appropriately tying textual data to respective visual representations. Moreover, a single image may be described via multiple sentences according to the perception of the human labeler and the structure/body of the language they use, which magnifies the complexity even further. In this paper, we propose an unsupervised method for text-image retrieval in remote sensing imagery. In the method, image representation is obtained via visual Big Transfer (BiT) Models, while textual descriptions are encoded via a bidirectional Long Short-Term Memory (Bi-LSTM) network. The training of the proposed retrieval architecture is optimized using an unsupervised embedding loss, which aims to make the features of an image closest to its corresponding textual description and different from other image features and vise-versa. To demonstrate the performance of the proposed architecture, experiments are performed on two datasets, obtaining plausible text/image retrieval outcomes.
Artificial bee colony algorithm with a pure crossover operation for binary optimization

Artificial bee colony (ABC) algorithm is one of famous swarm intelligence approaches for continuous optimization. With the help of a solution transformation technique, it can evolve in continuous space but consequences belong to binary space. On the basis of binary ABC, a novel artificial bee colony algorithm (nABC for short) is first proposed for better solving the uncapacitated facility location problem (UFLP). In nABC, a pure crossover operation is proposed to improve information sharing quality and remove random perturbation of original search strategy in employed bees phase. Next, a new frequency of perturbation is presented for enhancing the scale of information sharing between different individuals. Then, a new search strategy without probability mechanism of basic ABC is introduced in the onlooker bees phase. To further balance the enhanced exploitation ability, the original strategy of randomly producing an individual is substituted with an opposition-based learning technique with multiple scout bees and the frequency of perturbation mechanism. To testify the effectiveness and the convergence performance of nABC, it is compared with basic ABC and other famous methods for solving fifteen UFLPs from OR-library. Experimental results demonstrate that the proposed nABC is superior to other state-of-the-art approaches in terms of solution accuracy, convergence speed and robustness.
Countering variations and thermal effects for accurate optical neural networks

Optical neural networks (ONNs) have emerged as a promising high-performance computing platform to accelerate deep neural networks. In ONNs, phases of light are modulated through Mach-Zehnder Interferometers (MZIs), and MZIs are connected in a gridlike layout to implement multiply-accumulate operations. However, ONNs are very sensitive to process variations and thermal effects. This sensitivity leads to a significant degradation of inference accuracy of ONNs and thus renders them unusable in practice. In this paper, we propose a framework to calibrate process variations and counter thermal effects by power compensation. Experimental results demonstrate that the proposed framework can recover the inference accuracy under variations and thermal effects, e.g., from as low as 11.05% back to 74.11% for LeNet-5 on Cifar10, so that ONNs can achieve an inference accuracy similar to the accuracy after software training while providing their high bandwidth in neuromorphic computing.
Few-Shot Object Detection via Knowledge Transfer

Conventional methods for object detection usually require substantial amounts of training data and annotated bounding boxes. If there are only a few training data and annotations, the object detectors easily overfit and fail to generalize. It exposes the practical weakness of the object detectors. On the other hand, human can easily master new reasoning rules with only a few demonstrations using previously learned knowledge. In this paper, we introduce a few-shot object detection via knowledge transfer, which aims to detect objects from a few training examples. Central to our method is prototypical knowledge transfer with an attached meta-learner. The meta-learner takes support set images that include the few examples of the novel categories and base categories, and predicts prototypes that represent each category as a vector. Then, the prototypes reweight each RoI (Region-of-Interest) feature vector from a query image to remodels R-CNN predictor heads. To facilitate the remodeling process, we predict the prototypes under a graph structure, which propagates information of the correlated base categories to the novel categories with explicit guidance of prior knowledge that represents correlations among categories. Extensive experiments on the PASCAL VOC dataset verifies the effectiveness of the proposed method.