Reviewerly/exHarmony huggingface dataset:
Low-Light Image Enhancement by Principal Component Analysis

Under extreme low-lighting conditions, images have low contrast, low brightness, and high noise.In this paper, we propose a principal component analysis framework to enhance low-light-level images with decomposed luminance-chrominance components.A multi-scale retinex-based adaptive filter is developed for the luminance component to enhance contrast and brightness significantly.Noise is attenuated by a proposed collaborative filtering employed to both the luminance and chrominance components that reveal every finest detail by preserving the unique features in the image.To evaluate the effectiveness of the proposed algorithm, a simulation model is proposed to generate nighttime images for various levels of contrast and noise.The proposed algorithm can process a wide range of images without introducing ghosting and halo artifacts.The quantitative performance of the algorithm is measured in terms of both full-reference and blind performance metrics.It shows that the proposed method delivers state-of-the-art performance both in terms of objective criteria and visual quality compared to the existing methods.
Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks

Convolutional Neural Networks (CNN) offer state of the art performance in various computer vision tasks. Many of those tasks require different subtypes of affine invariances (scale, rotational, translational) to image transformations. Convolutional layers are translation equivariant by design, but in their basic form lack invariances. In this work we investigate how best to include rotational invariance in a CNN for image classification. Our experiments show that networks trained with data augmentation alone can classify rotated images nearly as well as in the normal unrotated case; this increase in representational power comes only at the cost of training time. We also compare data augmentation versus two modified CNN models for achieving rotational invariance or equivariance, Spatial Transformer Networks and Group Equivariant CNNs, finding no significant accuracy increase with these specialized methods. In the case of data augmented networks, we also analyze which layers help the network to encode the rotational invariance, which is important for understanding its limitations and how to best retrain a network with data augmentation to achieve invariance to rotation.
Application of the Non-Contact Video Gauge on the Mechanical Properties Test for Steel Cable at Elevated Temperature

As the limit of traditional contact measurement, it is difficult to precisely measure the steel cables twisted by a branch of wires especially at elevated temperature. In this paper the strain-stress relationships of S355 and S690 structural steel, 1860 MPa steel cable twisted by seven wires have been measured by the strain gauge, extensometer and non-contact video gauge at ambient temperature and elevated temperature, respectively. Comparison of the stress-strain curves gotten by different measuring technology, it indicates that the non-contact video gauge can provide a more efficient and reliable database than the strain gauge as well as extensometer, especially at an elevated temperature. It is worth noting that the non-contact video gauge can capture not only the full range of stress-strain curves of steel cables, but is also efficient for the specimens with a complex shape.
Positioning Your Organization to Respond to Insider Threats

Insider threats refer to actions by trusted individuals that directly or indirectly bring harm to the organization or expose the organization to external risks. Insider threat detection research and mitigation approaches have over-relied on technical solutions that tend to miss the human behavioral aspects of the problem. This paper focuses on approaches to assemble knowledge about insider threats and to apply this knowledge in support of insider threat assessment. This information can help organizations set up or improve existing insider threat mitigation programs by promoting a greater understanding of insider threat variables of concern; facilitating sharing of the knowledge base of insider threat indicators across organizations; raising awareness of the need to examine behavioral factors in addition to the more traditional focus on technical indicators; and discussing methods and challenges in acquiring and applying expert knowledge of insider threat indicators to facilitate decisions about responding to threat assessment alerts.
Mimic and Fool: A Task-Agnostic Adversarial Attack

At present, adversarial attacks are designed in a task-specific fashion. However, for downstream computer vision tasks such as image captioning, image segmentation etc., the current deep learning systems use an image classifier like VGG16, ResNet50, Inception-v3 etc. as a feature extractor. Keeping this in mind, we propose Mimic and Fool, a task agnostic adversarial attack. Given a feature extractor, the proposed attack finds an adversarial image which can mimic the image feature of the original image. This ensures that the two images give the same (or similar) output regardless of the task. We randomly select 1000 MSCOCO validation images for experimentation. We perform experiments on two image captioning models, Show and Tell, Show Attend and Tell and one VQA model, namely, end-to-end neural module network (N2NMN). The proposed attack achieves success rate of 74.0%, 81.0% and 87.1% for Show and Tell, Show Attend and Tell and N2NMN respectively. We also propose a slight modification to our attack to generate natural-looking adversarial images. In addition, we also show the applicability of the proposed attack for invertible architecture. Since Mimic and Fool only requires information about the feature extractor of the model, it can be considered as a gray-box attack.
Evaluating Blockchain Risks by Using a MCDM Methodology Based on Pythagorean Fuzzy Sets

Using blockchain technology (BT) in companies has become more common nowadays. But implementing a new BT contains many risk factors from different perspectives. Some of these risks can be critical for companies’ processes. These risks should be carefully identified and analyzed to minimize the negative effects of them. Evaluation of the risks can be considered as a multi criteria decision making (MCDM) problem. In this study, the risks that can occur when implementing BT are evaluated by using a MCDM methodology based on Pythagorean fuzzy analytic hierarchy process (PF-AHP). By using Delphi method, the risks are determined and the problem is constructed as a hierarchical form. After that, the risks are prioritized by using PF-AHP and the most important risks are obtained. The main aim of this paper is to help companies to determine which risks are more critical and to which of them should be given more priority during BT implementation.
Holistic Multi-Modal Memory Network for Movie Question Answering

Answering questions using multi-modal context is a challenging problem, as it requires a deep integration of diverse data sources. Existing approaches only consider a subset of all possible interactions among data sources during one attention hop. In this paper, we present a holistic multi-modal memory network (HMMN) framework that fully considers interactions between different input sources (multi-modal context and question) at each hop. In addition, to hone in on relevant information, our framework takes answer choices into consideration during the context retrieval stage. Our HMMN framework effectively integrates information from the multi-modal context, question, and answer choices, enabling more informative context to be retrieved for question answering. Experimental results on the Movie QA and TVQA datasets validate the effectiveness of our HMMN framework. Extensive ablation studies show the importance of holistic reasoning and reveal the contributions of different attention strategies to model performance.
Summit Navigator: A Novel Approach for Local Maxima Extraction

This paper presents a novel method, called the Summit Navigator, to effectively extract local maxima of an image histogram for multi-object segmentation of images. After smoothing with a moving average filter, the obtained histogram is analyzed, based on the data density and distribution to find the best observing location. An observability index for each initial peak is proposed to evaluate if it can be considered as dominant by using the calculated observing location. Recursive algorithms are then developed for peak searching and merging to remove any false detection of peaks that are located on one side of each mode. Experimental results demonstrated the advantages of the proposed approach in terms of accuracy and consistency in different reputable datasets.
Latent Elastic-Net Transfer Learning

Subspace learning based transfer learning methods commonly find a common subspace where the discrepancy of the source and target domains is reduced. The final classification is also performed in such subspace. However, the minimum discrepancy does not guarantee the best classification performance and thus the common subspace may be not the best discriminative. In this paper, we propose a latent elastic-net transfer learning (LET) method by simultaneously learning a latent subspace and a discriminative subspace. Specifically, the data from different domains can be well interlaced in the latent subspace by minimizing Maximum Mean Discrepancy (MMD). Since the latent subspace decouples inputs and outputs and, thus a more compact data representation is obtained for discriminative subspace learning. Based on the latent subspace, we further propose a low-rank constraint based matrix elastic-net regression to learn another subspace in which the intrinsic intra-class structure correlations of data from different domains is well captured. In doing so, a better discriminative alignment is guaranteed and thus LET finally learns another discriminative subspace for classification. Experiments on visual domains adaptation tasks show the superiority of the proposed LET method.
qkdSim, a Simulation Toolkit for Quantum Key Distribution Including Imperfections: Performance Analysis and Demonstration of the B92 Protocol Using Heralded Photons

Quantum key distribution (QKD) is one of the most important aspects of quantum cryptography. Using laws of quantum mechanics as the basis for security, the key-distribution process makes information theoretically secure in QKD. With the advancement and commercialization of QKD, an end-to-end QKD simulation software is required that can include experimental imperfections. Software of this kind will ensure that resources are invested only after prior performance analysis, and is faithful to experimental capacities and limitations. In this work, we introduce our QKD simulation toolkit qkdSim, which is ultimately aimed at being developed into such a software package that can precisely model and analyze any generic QKD protocol. We present the design, implementation, and testing of a prototype of qkdSim that can accurately simulate our own experimental demonstration of the B92 protocol. The simulation results match well with experiment; a representative key rate and the quantum bit error rate from experiment is $51\ifmmode\pm\else\textpm\fi{}0.5$ kbit/sec and $4.79\mathrm{%}\ifmmode\pm\else\textpm\fi{}0.01\mathrm{%}$ respectively, wherein the simulation yields $52.83\ifmmode\pm\else\textpm\fi{}0.36$ kbit/sec and $4.79\mathrm{%}\ifmmode\pm\else\textpm\fi{}0.01\mathrm{%}$, respectively.
Global Optimal Structured Embedding Learning for Remote Sensing Image Retrieval

A rich line of works focus on designing elegant loss functions under the deep metric learning (DML) paradigm to learn a discriminative embedding space for remote sensing image retrieval (RSIR). Essentially, such embedding space could efficiently distinguish deep feature descriptors. So far, most existing losses used in RSIR are based on triplets, which have disadvantages of local optimization, slow convergence and insufficient use of similarity structure in a mini-batch. In this paper, we present a novel DML method named as global optimal structured loss to deal with the limitation of triplet loss. To be specific, we use a softmax function rather than a hinge function in our novel loss to realize global optimization. In addition, we present a novel optimal structured loss, which globally learn an efficient deep embedding space with mined informative sample pairs to force the positive pairs within a limitation and push the negative ones far away from a given boundary. We have conducted extensive experiments on four public remote sensing datasets and the results show that the proposed global optimal structured loss with pairs mining scheme achieves the state-of-the-art performance compared with the baselines.
Scalable Probabilistic Matrix Factorization with Graph-Based Priors

In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations learnt from the incomplete data matrix. We show that removing these $\textit{contested}$ edges improves prediction accuracy and scalability. We identify the contested edges through a highly-efficient graphical lasso approximation. The identification and removal of contested edges adds no computational complexity to state-of-the-art graph-regularized matrix factorization, remaining linear with respect to the number of non-zeros. Computational load even decreases proportional to the number of edges removed. Formulating a probabilistic generative model and using expectation maximization to extend graph-regularised alternating least squares (GRALS) guarantees convergence. Rich simulated experiments illustrate the desired properties of the resulting algorithm. On real data experiments we demonstrate improved prediction accuracy with fewer graph edges (empirical evidence that graph side-information is often inaccurate). A 300 thousand dimensional graph with three million edges (Yahoo music side-information) can be analyzed in under ten minutes on a standard laptop computer demonstrating the efficiency of our graph update.
Signatures of Associative Memory Behavior in a Multimode Dicke Model

Dicke-like models can describe a variety of physical systems, such as atoms in a cavity or vibrating ion chains. In equilibrium these systems often feature a radical change in their behavior when switching from weak to strong spin-boson interaction. This usually manifests in a transition from a ``dark'' to a ``superradiant'' phase. However, understanding the out-of-equilibrium physics of these models is extremely challenging, and even more so for strong spin-boson coupling. Here we show that the nonequilibrium strongly interacting multimode Dicke model can mimic some fundamental properties of an associative memory---a system which permits the recognition of patterns, such as letters of an alphabet. Patterns are encoded in the couplings between spins and bosons, and we discuss the dynamics of the spins from the perspective of pattern retrieval in associative memory models. We identify two phases, a ``paramagnetic'' and a ``ferromagnetic'' one, and a crossover behavior between these regimes. The ``ferromagnetic'' phase is reminiscent of pattern retrieval. We highlight similarities and differences with the thermal dynamics of a Hopfield associative memory and show that indeed elements of ``machine learning behavior'' emerge in the strongly coupled multimode Dicke model.
Impact of Information Placement and User Representations in VR on Performance and Embodiment

Human sensory processing is sensitive to the proximity of stimuli to the body. It is therefore plausible that these perceptual mechanisms also modulate the detectability of content in VR, depending on its location. We evaluate this in a user study and further explore the impact of the user's representation during interaction. We also analyze how embodiment and motor performance are influenced by these factors. In a dual-task paradigm, participants executed a motor task, either through virtual hands, virtual controllers, or a keyboard. Simultaneously, they detected visual stimuli appearing in different locations. We found that, while actively performing a motor task in the virtual environment, performance in detecting additional visual stimuli is higher when presented near the user's body. This effect is independent of how the user is represented and only occurs when the user is also engaged in a secondary task. We further found improved motor performance and increased embodiment when interacting through virtual tools and hands in VR, compared to interacting with a keyboard. This article contributes to better understanding the detectability of visual content in VR, depending on its location in the virtual environment, as well as the impact of different user representations on information processing, embodiment, and motor performance.
Scheme for automatic differentiation of complex loss functions with applications in quantum physics

For a real function, automatic differentiation is such a standard algorithm used to efficiently compute its gradient, that it is integrated in various neural network frameworks. However, despite the recent advances in using complex functions in machine learning and the well-established usefulness of automatic differentiation, the support of automatic differentiation for complex functions is not as well-established and widespread as for real functions. In this work we propose an efficient and seamless scheme to implement automatic differentiation for complex functions, which is a compatible generalization of the current scheme for real functions. This scheme can significantly simplify the implementation of neural networks which use complex numbers.
Direct reconstruction of the quantum-master-equation dynamics of a trapped-ion qubit

The physics of Markovian open quantum systems can be described by quantum master equations. These are dynamical equations, that incorporate the Hamiltonian and jump operators, and generate the system's time evolution. Reconstructing the system's Hamiltonian and and its coupling to the environment from measurements is important both for fundamental research as well as for performance-evaluation of quantum machines. In this paper we introduce a method that reconstructs the dynamical equation of open quantum systems, directly from a set of expectation values of selected observables. We benchmark our technique both by a simulation and experimentally, by measuring the dynamics of a trapped $^{88}\text{Sr}^+$ ion under spontaneous photon scattering.
Super-Resolution of Optical Coherence Tomography Images by Scale Mixture Models

In this paper, a new statistical model is proposed for the single image super-resolution of retinal Optical Coherence Tomography (OCT) images. OCT imaging relies on interfero-metry, which explains why OCT images suffer from a high level of noise. Moreover, data subsampling is carried out during the acquisition of OCT A-scans and B-scans. So, it is necessary to utilize effective super-resolution algorithms to reconstruct high-resolution clean OCT images. In this paper, a nonlocal sparse model-based Bayesian framework is proposed for OCT restoration. For this reason, by characterizing nonlocal patches with similar structures, known as a group, the sparse coefficients of each group of OCT images are modeled by the scale mixture models. In this base, the coefficient vector is decomposed into the point-wise product of a random vector and a positive scaling variable. Estimation of the sparse coefficients depends on the proposed distribution for the random vector and scaling variable where the Laplacian random vector and Generalized Extreme-Value (GEV) scale parameter (Laplacian+GEV model) show the best goodness of fit for each group of OCT images. Finally, a new OCT super-resolution method based on this new scale mixture model is introduced, where the maximum a posterior estimation of both sparse coefficients and scaling variables are calculated efficiently by applying an alternating minimization method. Our experimental results prove that the proposed OCT super-resolution method based on the Laplacian+GEV model outperforms other competing methods in terms of both subjective and objective visual qualities.
Parametric Gaussian Process Regressors

The combination of inducing point methods with stochastic variational inference has enabled approximate Gaussian Process (GP) inference on large datasets. Unfortunately, the resulting predictive distributions often exhibit substantially underestimated uncertainties. Notably, in the regression case the predictive variance is typically dominated by observation noise, yielding uncertainty estimates that make little use of the input-dependent function uncertainty that makes GP priors attractive. In this work we propose two simple methods for scalable GP regression that address this issue and thus yield substantially improved predictive uncertainties. The first applies variational inference to FITC (Fully Independent Training Conditional; Snelson et.~al.~2006). The second bypasses posterior approximations and instead directly targets the posterior predictive distribution. In an extensive empirical comparison with a number of alternative methods for scalable GP regression, we find that the resulting predictive distributions exhibit significantly better calibrated uncertainties and higher log likelihoods--often by as much as half a nat per datapoint.
Fine-Grained Crowd Counting

Current crowd counting algorithms are only concerned about the number of people in an image, which lacks low-level fine-grained information of the crowd. For many practical applications, the total number of people in an image is not as useful as the number of people in each sub-category. E.g., knowing the number of people waiting inline or browsing can help retail stores; knowing the number of people standing/sitting can help restaurants/cafeterias; knowing the number of violent/non-violent people can help police in crowd management. In this paper, we propose fine-grained crowd counting, which differentiates a crowd into categories based on the low-level behavior attributes of the individuals (e.g. standing/sitting or violent behavior) and then counts the number of people in each category. To enable research in this area, we construct a new dataset of four real-world fine-grained counting tasks: traveling direction on a sidewalk, standing or sitting, waiting in line or not, and exhibiting violent behavior or not. Since the appearance features of different crowd categories are similar, the challenge of fine-grained crowd counting is to effectively utilize contextual information to distinguish between categories. We propose a two branch architecture, consisting of a density map estimation branch and a semantic segmentation branch. We propose two refinement strategies for improving the predictions of the two branches. First, to encode contextual information, we propose feature propagation guided by the density map prediction, which eliminates the effect of background features during propagation. Second, we propose a complementary attention model to share information between the two branches. Experiment results confirm the effectiveness of our method.
Quantum communication using code division multiple access network

For combining different single photon channels into single path, we use an effective and reliable technique which is known as quantum multiple access. We take advantage of an add-drop multiplexer capable of pushing and withdrawing a single photon into an optical fiber cable which carries quantum bits from multiusers. In addition to this, spreading spreads the channel noise at receiver side and use of filters stop the overlapping of adjacent channels, which helps in reducing the noise level and improved signal-to-noise ratio. In this way, we obtain enhanced performance of code division multiple access-based QKD links with a single photon without necessity of amplifiers and modulators.