Reviewerly/exHarmony huggingface dataset:
CNN based localisation of forged region in object‐based forgery for HD videos

The location of the smallest object in a scene plays an essential role in the perception of a viewer. Any tampering with it, may evolve in adverse consequences especially with surveillance videos of banks, ATMs, traffic monitoring etc. Therefore, a scientific approach is required to thoroughly observe the fine details of tampering (forgery) in a video. A spatio-temporal detection method is proposed using convolutional neural network (CNN) to detect as well as localise the forged region in a forged video frame. The proposed method is employed in two stages. The first stage is detecting forged frames using proposed temporal CNN, while the second stage is localising the forged region in a novel way using proposed spatial CNN. The vital element of a video, i.e. motion residual is used to train the proposed network. Thus, making the network comprehensive in detecting the object-based forgery in HD videos. The performance of the proposed method is evaluated on SYSU-OBJFORG dataset (object-based video forgery dataset) and a derived test dataset of variable length and frame size videos. The results are compared with state-of-the-art methods to prove the efficacy of the proposed method.
Computational Hardness of Certifying Bounds on Constrained PCA Problems

Given a random n × n symmetric matrix 𝑾 drawn from the Gaussian orthogonal ensemble (GOE), we consider the problem of certifying an upper bound on the maximum value of the quadratic form 𝒙^⊤ 𝑾 𝒙 over all vectors 𝒙 in a constraint set 𝒮 ⊂ ℝⁿ. For a certain class of normalized constraint sets we show that, conditional on a certain complexity-theoretic conjecture, no polynomial-time algorithm can certify a better upper bound than the largest eigenvalue of 𝑾. A notable special case included in our results is the hypercube 𝒮 = {±1/√n}ⁿ, which corresponds to the problem of certifying bounds on the Hamiltonian of the Sherrington-Kirkpatrick spin glass model from statistical physics. Our results suggest a striking gap between optimization and certification for this problem.Our proof proceeds in two steps. First, we give a reduction from the detection problem in the negatively-spiked Wishart model to the above certification problem. We then give evidence that this Wishart detection problem is computationally hard below the classical spectral threshold, by showing that no low-degree polynomial can (in expectation) distinguish the spiked and unspiked models. This method for predicting computational thresholds was proposed in a sequence of recent works on the sum-of-squares hierarchy, and is conjectured to be correct for a large class of problems. Our proof can be seen as constructing a distribution over symmetric matrices that appears computationally indistinguishable from the GOE, yet is supported on matrices whose maximum quadratic form over 𝒙 ∈ 𝒮 is much larger than that of a GOE matrix.
Low-Light Image Enhancement Based on Nonsubsampled Shearlet Transform

To improve the observability of low-light images, a low-light image enhancement algorithm based on nonsubsampled shearlet transform (NSST) is presented (LIEST).The proposed algorithm can synchronously achieve contrast improvement, noise suppression, and the enhancement of specific directional details.An enhancement framework of low-light noisy images is first derived, and then, according to the framework, a low-light noisy image is decomposed into low-pass subband coefficients and bandpass direction subband coefficients by NSST.Then, in the NSST domain, an illumination map is estimated based on a bright channel of the low-pass subband coefficients, and noise is simultaneously suppressed by shrinking the bandpass direction subband coefficients.Finally, based on the estimated illumination map, the low-pass subband coefficients, and the shrunken bandpass direction subband coefficients, inverse NSST is implemented to achieve low-light image enhancement.Experiments demonstrate that the LIEST exhibits superior performance in improving contrast, suppressing noise, and highlighting specific details as compared to seven similar algorithms.
Digital Transformation and Governance Innovation for Public Biobanks and Free/Libre Open Source Software Using a Blockchain Technology

Digitalization and digital health are transforming research practices, while economic growth is increasingly driven by the information commons. In the case of biological sciences, information commons, such as public biobanks and free/libre open source software (FLOSS), are of paramount importance for both research and the bioeconomy. In a time of digitalization, however, information commons are vulnerable to violations, such as the free-rider problem, that render the commons unsustainable. Consequently, it has been argued that the enclosure of the informational common resources is the only means to effectively exploit them. Given the social and economic importance of the information commons, the new digital environment in biology and health requires governance innovation that will regulate the social embedding of the commons and their relationship to the free market, that is, a new political economy is needed. In this context, the need for a core common infrastructure, stretching from the physical to the logical and content layer of the information environment, that will guarantee the protection of the commons from both violations and enclosures, has been highlighted. Focusing on the interaction between two biological/bioinformatics commons, namely public biobanks and the FLOSS, we have set up an ecosystem relying on a blockchain technology. The proposed governance mechanism protects the information commons from the free-rider problem and guarantees their sustainability without hampering their operational framework. Our model demonstrates the interdependence and protection of the information commons not as an abstract theoretical exercise, but rather as a physical reality on the digital ontological matrix.
A Commentary on the Unsupervised Learning of Disentangled Representations

The goal of the unsupervised learning of disentangled representations is to separate the independent explanatory factors of variation in the data without access to supervision. In this paper, we summarize the results of (Locatello et al. 2019b) and focus on their implications for practitioners. We discuss the theoretical result showing that the unsupervised learning of disentangled representations is fundamentally impossible without inductive biases and the practical challenges it entails. Finally, we comment on our experimental findings, highlighting the limitations of state-of-the-art approaches and directions for future research.
Poster-Based Multiple Movie Genre Classification Using Inter-Channel Features

As the scale of the film industry grows, the demand for well-established movie databases is also growing.The genre of a movie supplies information on its overall content and has multiple values.Therefore, it should be well classified utilizing the characteristics of movies, without omissions in the database.In this study, we extract the optimal information and characteristics from movie posters to aid the classification of movies into genres and propose the use of a Gram layer in a convolutional neural network (CNN).The Gram layer first extracts style features by applying the Gram matrix to produce a feature map of a poster image.Using this as a style weight, the existing feature map is merged with style information to perform the genre classification task.The proposed Gram layer performed multi-genre classification tasks with higher efficiency than a residual neural network (ResNet), which is the current CNN model used for such tasks.We compared the activation map with the Squeeze-and-Excitation network, which gives weight to the image, and we confirmed that the introduction of the Gram layer actually focuses on the style of the movie poster.To classify the movie genres, we reconstructed the poster dataset into 12 multi-genres that emphasized the characteristics of each poster.
Multichannel Attention Refinement for Video Question Answering

Video Question Answering (VideoQA) is the extension of image question answering (ImageQA) in the video domain. Methods are required to give the correct answer after analyzing the provided video and question in this task. Comparing to ImageQA, the most distinctive part is the media type. Both tasks require the understanding of visual media, but VideoQA is much more challenging, mainly because of the complexity and diversity of videos. Particularly, working with the video needs to model its inherent temporal structure and analyze the diverse information it contains. In this article, we propose to tackle the task from a multichannel perspective. Appearance, motion, and audio features are extracted from the video, and question-guided attentions are refined to generate the expressive clues that support the correct answer. We also incorporate the relevant text information acquired from Wikipedia as an attempt to extend the capability of the method. Experiments on TGIF-QA and ActivityNet-QA datasets show the advantages of our method compared to existing methods. We also demonstrate the effectiveness and interpretability of our method by analyzing the refined attention weights during the question-answering procedure.
GraKeL: A Graph Kernel Library in Python

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promising approach to this problem. There are now many kernels, each focusing on different structural aspects of graphs. Here, we present GraKeL, a library that unifies several graph kernels into a common framework. The library is written in Python and adheres to the scikit-learn interface. It is simple to use and can be naturally combined with scikit-learn's modules to build a complete machine learning pipeline for tasks such as graph classification and clustering. The code is BSD licensed and is available at: https://github.com/ysig/ GraKeL.
Stallion

As video traffic continues to dominate the Internet, interest in near-second low-latency streaming has increased. Existing low-latency streaming platforms rely on using tens of seconds of video in the buffer to offer a seamless experience. Striving for near-second latency requires the receiver to make quick decisions regarding the download bitrate and the playback speed. To cope with the challenges, we design a new adaptive bitrate (ABR) scheme, Stallion, for STAndard Low-LAtency vIdeo cONtrol. Stallion uses a sliding window to measure the mean and standard deviation of both the bandwidth and latency. We evaluate Stallion and compare it to the standard DASH DYNAMIC algorithm over a variety of networking conditions. Stallion shows 1.8x increase in bitrate, and 4.3x reduction in the number of stalls.
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning

What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks? When the desired (test time) goal distribution is too distant to offer a useful learning signal, we argue that the agent should not pursue unobtainable goals. Instead, it should set its own intrinsic goals that maximize the entropy of the historical achieved goal distribution. We propose to optimize this objective by having the agent pursue past achieved goals in sparsely explored areas of the goal space, which focuses exploration on the frontier of the achievable goal set. We show that our strategy achieves an order of magnitude better sample efficiency than the prior state of the art on long-horizon multi-goal tasks including maze navigation and block stacking.
Neural Contours: Learning to Draw Lines From 3D Shapes

This paper introduces a method for learning to generate line drawings from 3D models. Our architecture incorporates a differentiable module operating on geometric features of the 3D model, and an image-based module operating on view-based shape representations. At test time, geometric and view-based reasoning are combined with the help of a neural module to create a line drawing. The model is trained on a large number of crowdsourced comparisons of line drawings. Experiments demonstrate that our method achieves significant improvements in line drawing over the state-of-the-art when evaluated on standard benchmarks, resulting in drawings that are comparable to those produced by experienced human artists.
Fair Generative Modeling via Weak Supervision

Real-world datasets are often biased with respect to key demographic factors such as race and gender. Due to the latent nature of the underlying factors, detecting and mitigating bias is especially challenging for unsupervised machine learning. We present a weakly supervised algorithm for overcoming dataset bias for deep generative models. Our approach requires access to an additional small, unlabeled reference dataset as the supervision signal, thus sidestepping the need for explicit labels on the underlying bias factors. Using this supplementary dataset, we detect the bias in existing datasets via a density ratio technique and learn generative models which efficiently achieve the twin goals of: 1) data efficiency by using training examples from both biased and reference datasets for learning; and 2) data generation close in distribution to the reference dataset at test time. Empirically, we demonstrate the efficacy of our approach which reduces bias w.r.t. latent factors by an average of up to 34.6% over baselines for comparable image generation using generative adversarial networks.
Normalizing Flows on Tori and Spheres

Normalizing flows are a powerful tool for building expressive distributions in high dimensions. So far, most of the literature has concentrated on learning flows on Euclidean spaces. Some problems however, such as those involving angles, are defined on spaces with more complex geometries, such as tori or spheres. In this paper, we propose and compare expressive and numerically stable flows on such spaces. Our flows are built recursively on the dimension of the space, starting from flows on circles, closed intervals or spheres.
Efficient Domain Generalization via Common-Specific Low-Rank Decomposition

Domain generalization refers to the task of training a model which generalizes to new domains that are not seen during training. We present CSD (Common Specific Decomposition), for this setting,which jointly learns a common component (which generalizes to new domains) and a domain specific component (which overfits on training domains). The domain specific components are discarded after training and only the common component is retained. The algorithm is extremely simple and involves only modifying the final linear classification layer of any given neural network architecture. We present a principled analysis to understand existing approaches, provide identifiability results of CSD,and study effect of low-rank on domain generalization. We show that CSD either matches or beats state of the art approaches for domain generalization based on domain erasure, domain perturbed data augmentation, and meta-learning. Further diagnostics on rotated MNIST, where domains are interpretable, confirm the hypothesis that CSD successfully disentangles common and domain specific components and hence leads to better domain generalization.
Differentiable Augmentation for Data-Efficient GAN Training

The performance of generative adversarial networks (GANs) heavily deteriorates given a limited amount of training data. This is mainly because the discriminator is memorizing the exact training set. To combat it, we propose Differentiable Augmentation (DiffAugment), a simple method that improves the data efficiency of GANs by imposing various types of differentiable augmentations on both real and fake samples. Previous attempts to directly augment the training data manipulate the distribution of real images, yielding little benefit; DiffAugment enables us to adopt the differentiable augmentation for the generated samples, effectively stabilizes training, and leads to better convergence. Experiments demonstrate consistent gains of our method over a variety of GAN architectures and loss functions for both unconditional and class-conditional generation. With DiffAugment, we achieve a state-of-the-art FID of 6.80 with an IS of 100.8 on ImageNet 128x128. Furthermore, with only 20% training data, we can match the top performance on CIFAR-10 and CIFAR-100. Finally, our method can generate high-fidelity images using only 100 images without pre-training, while being on par with existing transfer learning algorithms. Code is available at this https URL.
Blockchain-Based Secure E-Voting with the Assistance of Smart Contract

Voting is a very important issue that can be beneficial in terms of choosing the right leader in an election. A good leader can bring prosperity to a country and also can lead the country in the right direction every time. However, elections are surrounded by ballot forgery, coercion, and multiple voting issues. Moreover, while giving votes, a person has to wait in a long queue and it is a very time-consuming process. Blockchain is a distributed database in which data are shared with the participant of the node and each participant holds the same copy of the data. Blockchain has properties like transparency, pseudonymity, and data integrity. In this paper, a fully decentralized e-voting system based on blockchain technology is proposed. This protocol utilizes smart contracts in the e-voting system to deal with security issues, accuracy, and voters’ privacy during the vote. The protocol results in a transparent, non-editable, and independently verifiable procedure. The protocol discards all the intended fraudulent activities occurring during the election process by removing the least participation of the third party. Both transparency and coercion are obtained at the same time.
Distilling Neural Networks for Greener and Faster Dependency Parsing

The carbon footprint of natural language processing research has been increasing in recent years due to its reliance on large and inefficient neural network implementations. Distillation is a network compression technique which attempts to impart knowledge from a large model to a smaller one. We use teacher-student distillation to improve the efficiency of the Biaffine dependency parser which obtains state-of-the-art performance with respect to accuracy and parsing speed (Dozat and Manning, 2017). When distilling to 20% of the original model’s trainable parameters, we only observe an average decrease of ∼1 point for both UAS and LAS across a number of diverse Universal Dependency treebanks while being 2.30x (1.19x) faster than the baseline model on CPU (GPU) at inference time. We also observe a small increase in performance when compressing to 80% for some treebanks. Finally, through distillation we attain a parser which is not only faster but also more accurate than the fastest modern parser on the Penn Treebank.
Expressivity of Deep Neural Networks

In this chapter, we give a comprehensive overview of the large variety of approximation results for neural networks. Approximation rates for classical function spaces as well as the benefits of deep neural networks over shallow ones for specifically structured function classes are discussed. While the main body of existing results is for general feedforward architectures, we also review approximation results for convolutional, residual and recurrent neural networks.
Drill-Core Hyperspectral and Geochemical Data Integration in a Superpixel-Based Machine Learning Framework

The analysis of drill-core samples is one of the most important steps in the mining industry for the exploration and discovery of mineral resources.Geochemical assays are a common approach to represent the abundance of different chemical elements and aid at quantifying the concentrations of the important ore accumulations.However, their acquisition is time-consuming and usually averages of long core portions.Hyperspectral data are increasingly being used in the mining industry to complement the analysis of drill-cores due to their efficiency and fast turn-around time.Moreover, hyperspectral imaging is a technique able to provide data with high spatial resolution.In this article, we propose to integrate the complementary information derived from hyperspectral and geochemical data via a superpixel-based machine learning framework.This framework considers the difference in spatial resolution through segmentation.We extract labels from the geochemical assays and select, from the hyperspectral data, representative samples for each measurement.A supervised machine learning classification (composite kernel support vector machine) is then used to extrapolate the elements relative abundance to the entire core length.We propose an innovative integration of hyperspectral data covering different regions of the electromagnetic spectrum in a kernel-based framework to facilitate the identification of a larger amount of elements.A qualitative and quantitative evaluation of the results demonstrates the capabilities of the proposed method, which provides approximately 20% more accurate results than the pixel-based approach.Results also imply that the method could be beneficial for the reduction of geochemical assays needed for the detailed analysis of the cores.
New quantum codes from constacyclic codes over the ring $ R_{k,m} $

&lt;p style='text-indent:20px;'&gt;For any odd prime &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ p $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, we study constacyclic codes of length &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ n $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; over the finite commutative non-chain ring &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ R_{k,m} = \mathbb{F}_{p^m}[u_1,u_2,\dots,u_k]/\langle u^2_i-1,u_iu_j-u_ju_i\rangle_{i\neq j = 1,2,\dots,k} $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;, where &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ m,k\geq 1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; are integers. We determine the necessary and sufficient condition for these codes to contain their Euclidean duals. As an application, from the dual containing constacyclic codes, several MDS, new and better quantum codes compare to the best known codes in the literature are obtained.&lt;/p&gt;
Comparison of D-Wave Quantum Annealing and Classical Simulated Annealing for Local Minima Determination

Restricted Boltzmann Machines trained with different numbers of iterations were used to provide a large diverse set of energy functions each containing many local valleys (LVs). They were used to confirm the property of the D-Wave quantum annealer (QA) to find potentially important LVs in the energy functions of Markov Random Fields that may be missed by classical searches. Even after a prohibitively long classical search by simulated annealing (SA), as many as 30-50% of the QA-found LVs remained not found by the SA. In order to establish if those LVs represent potentially important regions of the configuration space, they were compared to LVs that were found by both techniques. With respect to most of the important LV parameters, the LVs found only by the QA (missed by SA) were distributed in a wide range of the parameters' values. In an attempt to explain which LVs could not be found easily by the SA, it was established that for large or small, shallow or deep, wide or narrow LVs, the LVs found only by the QA are distinguished by a few-times smaller size of the LV basin of attraction (BoA). Apparently, the size of the BoA is much less important for QA search compared to the classical search, allowing QA to easily find many potentially important (e.g., wide and deep) LVs missed by even prohibitively lengthy classical searches.