Reviewerly/exHarmony huggingface dataset:
Lightning search algorithm: a comprehensive survey

The lightning search algorithm (LSA) is a novel meta-heuristic optimization method, which is proposed in 2015 to solve constraint optimization problems. This paper presents a comprehensive survey of the applications, variants, and results of the so-called LSA. In LSA, the best-obtained solution is defined to improve the effectiveness of the fitness function through the optimization process by finding the minimum or maximum costs to solve a specific problem. Meta-heuristics have grown the focus of researches in the optimization domain, because of the foundation of decision-making and assessment in addressing various optimization problems. A review of LSA variants is displayed in this paper, such as the basic, binary, modification, hybridization, improved, and others. Moreover, the classes of the LSA's applications include the benchmark functions, machine learning applications, network applications, engineering applications, and others. Finally, the results of the LSA is compared with other optimization algorithms published in the literature. Presenting a survey and reviewing the LSA applications is the chief aim of this survey paper.
InferLine

Serving ML prediction pipelines spanning multiple models and hardware accelerators is a key challenge in production machine learning. Optimally configuring these pipelines to meet tight end-to-end latency goals is complicated by the interaction between model batch size, the choice of hardware accelerator, and variation in the query arrival process.
Relaxed multi-view clustering in latent embedding space

Although many multi-view clustering approaches have been developed recently, one common shortcoming of most of them is that they generally rely on the original feature space or consider the two components of the similarity-based clustering separately (i.e., similarity matrix construction and cluster indicator matrix calculation), which may negatively affect the clustering performance. To tackle this shortcoming, in this paper, we propose a new method termed Multi-view Clustering in Latent Embedding Space (MCLES), which jointly recovers a comprehensive latent embedding space, a robust global similarity matrix and an accurate cluster indicator matrix in a unified optimization framework. In this framework, each variable boosts each other in an interplay manner to achieve the optimal solution. To avoid the optimization problem of quadratic programming, we further propose to relax the constraint of the global similarity matrix, based on which an improved version termed Relaxed Multi-view Clustering in Latent Embedding Space (R-MCLES) is proposed. Compared with MCLES, R-MCLES achieves lower computational complexity with more correlations between pairs of data points. Extensive experiments conducted on both image and document datasets have demonstrated the superiority of the proposed methods when compared with the state-of-the-art.
A Recurrent Neural Network based deep learning model for offline signature verification and recognition system

With the recent advancement in information technology field, the demand to develop a person authentication system through verifying their offline signatures is gradually increasing. This type of system may be used to verify various official documents through verifying the signatures of the concerned persons present in the documents. This article proposes a Recurrent Neural Network (RNN), a deep learning network, based method to verify and recognize offline signatures of different persons. Various structural and directional features have been extracted locally from each signature sample and the generated feature vectors have been studied using two different models of RNN—long-short term memory (LSTM) and bidirectional long–short term memory (BLSTM). The performance of the proposed system has been tested on six widely used public signature databases—GPDS synthetic, GPDS-300, MCYT-75, CEDAR, BHSig260 Hindi, and BHSig260 Bengali. Experiment has also been performed using Convolutional Neural Network (CNN) to have a comparison with RNN based results. Experimental results demonstrate that the proposed RNN based signature verification and recognition system is superior over CNN and also outperforms the existing state-of-the-art results in this regard.
Binarized Neural Network for Single Image Super Resolution

Lighter model and faster inference are the focus of current single image super-resolution (SISR) research. However, existing methods are still hard to be applied in real-world applications due to the heavy computation requirement. Model quantization is an effective way to significantly reduce model size and computation time. In this work, we investigate the binary neural network-based SISR problem and propose a novel model binarization method. Specially, we design a bit-accumulation mechanism (BAM) to approximate the full-precision convolution with a value accumulation scheme, which can gradually refine the precision of quantization along the direction of model inference. In addition, we further construct an efficient model structure based on the BAM for lower computational complexity and parameters. Extensive experiments show the proposed model outperforms the state-of-the-art binarization methods by large margins on 4 benchmark datasets, specially by average more than 0.7 dB in terms of Peak Signal-to-Noise Ratio on Set5 dataset.
A Gamified Augmented Reality Application for Digital Heritage and Tourism

Although Augmented Reality (AR) technology has entered many market and knowledge domains such as games and leisure activities, it remains rather limited in digital heritage. After studying the potentiality of using modern AR elements in a museum context, this paper proposes the use of additional game and educational elements in the core AR application in order to enhance the overall on-the-spot museum visitor’s experience. An agile AR application design methodology was followed by taking into account the needs of small-to-medium sized real-world museums. Moreover, a heuristic evaluation protocol was applied by a group of experts in order to test the proof-of-concept AR application, in which some novel elements were proposed such as the AR quiz game. The main findings indicate that enhanced AR experiences in museum settings can make a nice fit with the user environment, physical and perceptual abilities, known metaphors, and user position and motion in 3D space. Moreover, AR services can be provided under a minimum distraction and physical effort. As a conclusion, AR technologies are mature enough to be standardized for museum usage, while the audience seems to be ready to take advantage of the related enhanced museum experiences to maximize both user satisfaction and learning outcomes.
Cover-Lossless Robust Image Watermarking Against Geometric Deformations

Cover-lossless robust watermarking is a new research issue in the information hiding community, which can restore the cover image completely in case of no attacks. Most countermeasures proposed in the literature usually focus on additive noise-like manipulations such as JPEG compression, low-pass filtering and Gaussian additive noise, but few are resistant to challenging geometric deformations such as rotation and scaling. The main reason is that in the existing cover-lossless robust watermarking algorithms, those exploited robust features are related to the pixel position. In this article, we present a new cover-lossless robust image watermarking method by efficiently embedding a watermark into low-order Zernike moments and reversibly hiding the distortion due to the robust watermark as the compensation information for restoration of the cover image. The amplitude of the exploited low-order Zernike moments are: 1) mathematically invariant to scaling the size of an image and rotation with any angle; and 2) robust to interpolation errors during geometric transformations, and those common image processing operations. To reduce the compensation information, the robust watermarking process is elaborately and luminously designed by using the quantized error, the watermarked error and the rounded error to represent the difference between the original and the robust watermarked image. As a result, a cover-lossless robust watermarking system against geometric deformations is achieved with good performance. Experimental results show that the proposed robust watermarking method can effectively reduce the compensation information, and the new cover-lossless robust watermarking system provides strong robustness to those content-preserving manipulations including scaling, rotation, JPEG compression and other noise-like manipulations. In case of no attacks, the cover image can be recovered without any loss.
Encoding Visual Sensitivity by MaxPol Convolution Filters for Image Sharpness Assessment

In this paper, we propose a novel design of Human Visual System (HVS) response in a convolution filter form to decompose meaningful features that are closely tied with image sharpness level. No-reference (NR) Image sharpness assessment (ISA) techniques have emerged as the standard of image quality assessment in diverse imaging applications. Despite their high correlation with subjective scoring, they are challenging for practical considerations due to high computational cost and lack of scalability across different image blurs. We bridge this gap by synthesizing the HVS response as a linear combination of Finite Impulse Response (FIR) derivative filters to boost the falloff of high band frequency magnitudes in natural imaging paradigm. The numerical implementation of the HVS filter is carried out with MaxPol filter library that can be arbitrarily set for any differential orders and cutoff frequencies to balance out the estimation of informative features and noise sensitivities. We then design an innovative NR-ISA metric called `HVS-MaxPol' that (a) requires minimal computational cost, (b) produce high correlation accuracy with image blurriness, and (c) scales to assess synthetic and natural image blur. Specifically, the synthetic blur images are constructed by blurring the raw images using Gaussian filter, while natural blur is observed from real-life application such as motion, out-of-focus, etc. Furthermore, we create a natural benchmark database in digital pathology for validation of image focus quality in whole slide imaging systems called `FocusPath' consisting of 864 blurred images. Thorough experiments are designed to test and validate the efficiency of HVS-MaxPol across different blur databases and state-of-the-art NR-ISA metrics. The experiment result indicates that our metric has the best overall performance with respect to speed, accuracy and scalability.
Learning Effective RGB-D Representations for Scene Recognition

Deep convolutional networks (CNN) can achieve impressive results on RGB scene recognition thanks to large datasets such as Places.In contrast, RGB-D scene recognition is still underdeveloped in comparison, due to two limitations of RGB-D data we address in this paper.The first limitation is the lack of depth data for training deep learning models.Rather than fine tuning or transferring RGB-specific features, we address this limitation by proposing an architecture and a twostep training approach that directly learns effective depth-specific features using weak supervision via patches.The resulting RGB-D model also benefits from more complementary multimodal features.Another limitation is the short range of depth sensors (typically 0.5m to 5.5m), resulting in depth images not capturing distant objects in the scenes that RGB images can.We show that this limitation can be addressed by using RGB-D videos, where more comprehensive depth information is accumulated as the camera travels across the scenes.Focusing on this scenario, we introduce the ISIA RGB-D video dataset to evaluate RGB-D scene recognition with videos.Our video recognition architecture combines convolutional and recurrent neural networks (RNNs) that are trained in three steps with increasingly complex data to learn effective features (i.e.patches, frames and sequences).Our approach obtains state-of-the-art performances on RGB-D image (NYUD2 and SUN RGB-D) and video (ISIA RGB-D) scene recognition.
Shortcuts to Adiabaticity in Driven Open Quantum Systems: Balanced Gain and Loss and Non-Markovian Evolution

A universal scheme is introduced to speed up the dynamics of a driven open quantum system along a prescribed trajectory of interest. This framework generalizes counterdiabatic driving to open quantum processes. Shortcuts to adiabaticity designed in this fashion can be implemented in two alternative physical scenarios: one characterized by the presence of balanced gain and loss, the other involves non-Markovian dynamics with time-dependent Lindblad operators. As an illustration, we engineer superadiabatic cooling, heating, and isothermal strokes for a two-level system, and provide a protocol for the fast thermalization of a quantum oscillator.
Increased Use of Bayesian Network Models Has Improved Environmental Risk Assessments

Environmental and ecological risk assessments are defined as the process for evaluating the likelihood that the environment may be impacted as a result of exposure to stressors. Although this definition implies the calculation of probabilities, risk assessments traditionally rely on nonprobabilistic methods such as calculation of a risk quotient. Bayesian network (BN) models are a tool for probabilistic and causal modeling, increasingly used in many fields of environmental science. Bayesian networks are defined as directed acyclic graphs where the causal relationships and the associated uncertainty are quantified in conditional probability tables. Bayesian networks inherently incorporate uncertainty and can integrate a variety of information types, including expert elicitation. During the last 2 decades, there has been a steady increase in reports on BN applications in environmental risk assessment and management. At recent annual meetings of the Society of Environmental Toxicology and Chemistry (SETAC) North America and SETAC Europe, a number of applications of BN models were presented along with new theoretical developments. Likewise, recent meetings of the European Geosciences Union (EGU) have dedicated sessions to Bayesian modeling in relation to water quality. This special series contains 10 articles based on presentations in these sessions, reflecting a range of BN applications to systems, ranging from cells and populations to watersheds and national scale. The articles report on recent progress in many topics, including climate and management scenarios, ecological and socioeconomic endpoints, machine learning, diagnostic inference, and model evaluation. They demonstrate that BNs can be adapted to established conceptual frameworks used to support environmental risk assessments, such as adverse outcome pathways and the relative risk model. The contributions from EGU demonstrate recent advancements in areas such as spatial (geographic information system [GIS]-based) and temporal (dynamic) BN modeling. In conclusion, this special series supports the prediction that increased use of Bayesian network models will improve environmental risk assessments. Integr Environ Assess Manag 2021;17:53-61. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
Bayesian deep learning on a quantum computer

Bayesian methods in machine learning, such as Gaussian processes, have great advantages com-pared to other techniques. In particular, they provide estimates of the uncertainty associated with a prediction. Extending the Bayesian approach to deep architectures has remained a major challenge. Recent results connected deep feedforward neural networks with Gaussian processes, allowing training without backpropagation. This connection enables us to leverage a quantum algorithm designed for Gaussian processes and develop a new algorithm for Bayesian deep learning on quantum computers. The properties of the kernel matrix in the Gaussian process ensure the efficient execution of the core component of the protocol, quantum matrix inversion, providing an at least polynomial speedup over classical algorithms. Furthermore, we demonstrate the execution of the algorithm on contemporary quantum computers and analyze its robustness with respect to realistic noise models.
A Qualitative Study of Dependency Management and Its Security Implications

Several large scale studies on the Maven, NPM, and Android ecosystems point out that many developers do not often update their vulnerable software libraries thus exposing the user of their code to security risks. The purpose of this study is to qualitatively investigate the choices and the interplay of functional and security concerns on the developers' overall decision-making strategies for selecting, managing, and updating software dependencies.
Fast Adaptation to Super-Resolution Networks via Meta-learning

Conventional supervised super-resolution (SR) approaches are trained with massive external SR datasets but fail to exploit desirable properties of the given test image. On the other hand, self-supervised SR approaches utilize the internal information within a test image but suffer from computational complexity in run-time. In this work, we observe the opportunity for further improvement of the performance of single-image super-resolution (SISR) without changing the architecture of conventional SR networks by practically exploiting additional information given from the input image. In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time. Then, in the test stage, parameters of this meta-learned network are rapidly fine-tuned with only a few iterations by only using the given low-resolution image. The adaptation at the test time takes full advantage of patch-recurrence property observed in natural images. Our method effectively handles unknown SR kernels and can be applied to any existing model. We demonstrate that the proposed model-agnostic approach consistently improves the performance of conventional SR networks on various benchmark SR datasets.
Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks With Adversarial Traces

Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity, even when the traffic is protected by a VPN or an anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain over 98% accuracy on Tor traffic. In this paper, we explore a novel defense, Mockingbird, based on the idea of adversarial examples that have been shown to undermine machine-learning classifiers in other domains. Since the attacker gets to design and train his attack classifier based on the defense, we first demonstrate that at a straightforward technique for generating adversarial-example based traces fails to protect against an attacker using adversarial training for robust classification. We then propose Mockingbird, a technique for generating traces that resists adversarial training by moving randomly in the space of viable traces and not following more predictable gradients. The technique drops the accuracy of the state-of-the-art attack hardened with adversarial training from 98% to 42-58% while incurring only 58% bandwidth overhead. The attack accuracy is generally lower than state-of-the-art defenses, and much lower when considering Top-2 accuracy, while incurring lower bandwidth overheads.
Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition Under Occlusion

Computer vision systems in real-world applications need to be robust to partial occlusion while also being explainable. In this work, we show that black-box deep convolutional neural networks (DCNNs) have only limited robustness to partial occlusion. We overcome these limitations by unifying DCNNs with part-based models into Compositional Convolutional Neural Networks (CompositionalNets)—an interpretable deep architecture with innate robustness to partial occlusion. Specifically, we propose to replace the fully connected classification head of DCNNs with a differentiable compositional model that can be trained end-to-end. The structure of the compositional model enables CompositionalNets to decompose images into objects and context, as well as to further decompose object representations in terms of individual parts and the objects’ pose. The generative nature of our compositional model enables it to localize occluders and to recognize objects based on their non-occluded parts. We conduct extensive experiments in terms of image classification and object detection on images of artificially occluded objects from the PASCAL3D+ and ImageNet dataset, and real images of partially occluded vehicles from the MS-COCO dataset. Our experiments show that CompositionalNets made from several popular DCNN backbones (VGG-16, ResNet50, ResNext) improve by a large margin over their non-compositional counterparts at classifying and detecting partially occluded objects. Furthermore, they can localize occluders accurately despite being trained with class-level supervision only. Finally, we demonstrate that CompositionalNets provide human interpretable predictions as their individual components can be understood as detecting parts and estimating an objects’ viewpoint.
Classification of malignant tumors in breast ultrasound using a pretrained deep residual network model and support vector machine

In this study, a transfer learning method was utilized to recognize and classify benign and malignant breast tumors, using two-dimensional breast ultrasound (US) images, to decrease the effort expended by physicians and improve the quality of clinical diagnosis. The pretrained deep residual network model was utilized for image feature extraction from the convolutional layer of the trained network; whereas, the linear support vector machine (SVM), with a sequential minimal optimization solver, was used to classify the extracted feature. We used an image dataset with 2099 unlabeled two-dimensional breast US images, collected from 543 patients (benign: 302, malignant: 241). The classification performance yielded a sensitivity of 94.34 % and a specificity of 93.22 % for malignant images (Area under curve = 0.938). The positive and negative predictive values were 92.6 and 94.8, respectively. A comparison between the diagnosis made by the physician and the automated classification by a trained classifier, showed that the latter had significantly better outcomes. This indicates the potential applicability of the proposed approach that incorporates both the pretrained deep learning network and a well-trained classifier, to improve the quality and efficacy of clinical diagnosis.
Patch-Wise Attack for Fooling Deep Neural Network

By adding human-imperceptible noise to clean images, the resultant adversarial examples can fool other unknown models. Features of a pixel extracted by deep neural networks (DNNs) are influenced by its surrounding regions, and different DNNs generally focus on different discriminative regions in recognition. Motivated by this, we propose a patch-wise iterative algorithm – a black-box attack towards mainstream normally trained and defense models, which differs from the existing attack methods manipulating pixel-wise noise. In this way, without sacrificing the performance of white-box attack, our adversarial examples can have strong transferability. Specifically, we introduce an amplification factor to the step size in each iteration, and one pixel’s overall gradient overflowing the $$\epsilon $$ -constraint is properly assigned to its surrounding regions by a project kernel. Our method can be generally integrated to any gradient-based attack methods. Compared with the current state-of-the-art attacks, we significantly improve the success rate by 9.2% for defense models and 3.7% for normally trained models on average. Our code is available at https://github.com/qilong-zhang/Patch-wise-iterative-attack