Reviewerly/exHarmony huggingface dataset:
Robustness-via-synthesis: Robust training with generative adversarial perturbations

Upon the discovery of adversarial attacks, robust models have become obligatory for deep learning-based systems. Adversarial training with first-order attacks has been one of the most effective defenses against adversarial perturbations to this day. The majority of the adversarial training approaches focus on iteratively perturbing each pixel with the gradient of the loss function with respect to the input image. However, the adversarial training with gradient-based attacks lacks diversity and does not generalize well to natural images and various attacks. This study presents a robust training algorithm where the adversarial perturbations are automatically synthesized from a random vector using a generator network. The classifier is trained with cross-entropy loss regularized with the optimal transport distance between the representations of the natural and synthesized adversarial samples. Unlike prevailing generative defenses, the proposed one-step attack generation framework synthesizes diverse perturbations without utilizing the gradient of the classifier’s loss. The main contributions of the proposed robust training framework are: i) preserving the state-of-the-art generalization performance of the deep model, ii) not requiring an iterative or recursive scheme, and iii) providing robustness that is comparable with the state-of-the-art in literature. Experimental results show that the proposed approach attains comparable robustness with various gradient-based and generative robust training techniques on CIFAR10, CIFAR100, SVHN, and Tiny ImageNet datasets. In addition, compared to the baselines, the proposed robust training framework generalizes well to the natural samples. Code and trained models are available here https://github.com/ALLab-Boun/robustness-via-synthesis.git.
Seismic Phase Association Based on the Maximum Likelihood Method

Phase association is a process that links seismic phases triggered at the stations of a seismic network to declare the occurrence of earthquakes. During phase association, a set of phases from different stations is examined to determine the common origin of phases within a specific region, predominantly on the basis of a grid search and the sum of observations. The association of seismic phases in local earthquake monitoring systems or earthquake early warning systems is often disturbed not only by transient noises, but also by large regional or teleseismic events. To mitigate this disturbance, we developed a seismic phase association method, binder_max , which uses the maximum likelihood method to associate seismic phases. The method is based on the framework of binder_ew , the phase associator of Earthworm, but it uses a likelihood distribution of the arrival information instead of stacking arrival information. Applying binder_max to data from seismic networks of South Korea and Ohio, United States, we found a significant improvement in the robustness of the method against large regional or teleseismic events compared to binder_ew . Our results indicate that binder_max can associate seismic phases of local earthquakes to the same degree as binder_ew as well as can avoid many of the false associations that have limited binder_ew .
Learning Generic Feature Representations with Adversarial Regularization for Person Re-Identification

Many existing person re-identification (Re-ID) methods can achieve human-level accuracy on a single dataset, while most of them can be poorly generalized to other datasets. This is mainly caused by different data distributions between different domains. In this paper, we propose a novel adversarial regularization method to address this issue. Specifically, the features extracted from different datasets will be constrained and focused to follow a more similar distribution during the training process. As a result, our method can learn a feature representation with better inter-domain invariance, which will improve the generalization ability of the resulting model. Besides, our method is flexible and can be combined with any feature learning network. Extensive experiments on both Market1501 and DukeMTMC-reID datasets have demonstrated the effectiveness of our method.
A Gaussian Multivariate Hidden Markov Model for Breast Tumor Diagnosis

The stage of a tumor is sometimes hard to predict, especially early in its development. The size and complexity of its observations are the major problems that lead to false diagnoses. Even experienced doctors can make a mistake in causing terrible consequences for the patient. We propose a mathematical tool for the diagnosis of breast cancer. The aim is to help specialists in making a decision on the likelihood of a patient’s condition knowing the series of observations available. This may increase the patient’s chances of recovery. With a multivariate observational hidden Markov model, we describe the evolution of the disease by taking the geometric properties of the tumor as observable variables. The latent variable corresponds to the type of tumor: malignant or benign. The analysis of the covariance matrix makes it possible to delineate the zones of occurrence for each group belonging to a type of tumors. It is therefore possible to summarize the properties that characterize each of the tumor categories using the parameters of the model. These parameters highlight the differences between the types of tumors.
Sparse And Structured Visual Attention

Visual attention mechanisms are widely used in multimodal tasks, as visual question answering (VQA). One drawback of softmax-based attention mechanisms is that they assign some probability mass to all image regions, regardless of their adjacency structure and of their relevance to the text. In this paper, to better link the image structure with the text, we replace the traditional softmax attention mechanism with two alternative sparsity-promoting transformations: sparsemax, which is able to select only the relevant regions (assigning zero weight to the rest), and a newly proposed Total-Variation Sparse Attention (TVMAX), which further encourages the joint selection of adjacent spatial locations. Experiments in VQA show gains in accuracy as well as higher similarity to human attention, which suggests better interpretability.
Alkali element mobility as a vector for gold mineralization: An example from Central Brazil

The Brazilian Pilar and Três Buracos Paleoproterozoic mid-tier gold deposits are located in the south western Pilar de Goiás greenstone belt, containing 61,688 kg and 7,144 kg of gold, respectively, in addition to approximately 30,000 kg of past production (1736–2018). This research aimed to assess the hypothesis that the mobility of alkali elements may be used to facilitate the discovery of new gold deposits in hydrothermal systems by creating geochemical vectors based on alkali element mobility. The hypothesis was first proposed for the mafic rocks in the West Australian Goldfields and has been adopted in other deposits worldwide (E.g., Andorinhas greenstone belt, Brazil, Pataz gold field, Peru, and Rio das Velhas gold deposit, Brazil). The procedures consisted in the analysis of metasedimentary and mafic rock types from two drill holes from the Pilar de Goiás greenstone belt, central Brazil. These drill holes have intercepted mineralized and barren rock types. The petrographic and energy dispersive spectrometric results indicated that the host rocks around the orebodies present intense sericitization, albitization, and sulfidation. The chemical results reveal that the mobile elements Cs, Rb and Ba tend to be enriched in gold-bearing rock units and depleted in barren rock units. These elements are carried by fluids and can replace K in the structure of the newly formed hydrothermal phyllosilicates forming a halo of alkali-rich elements of up to 50 m around the orebodies. The upper continental crust normalized ratio ((Cs + Rb)/Th)UCC was adopted as an alteration index to evaluate and distinguish possible mineralized zones, with ((Cs + Rb)/Th)UCC > 3, from barren zones, with ((Cs + Rb)/Th)UCC < 3. The ratio ((Cs + Rb)/Th)UCC increases towards mineralization, providing a vector to possible gold mineralization, with a threshold value > 3 indicating high prospectivity.
Strategies of Face Recognition by Humans and Machines

Face recognition by machines has improved markedly over the last decade. Machines now perform some face recognition tasks at the level of untrained humans and forensic face identification experts. In this chapter, first we review recent work on human and machine performance on face recognition tasks. Second, we consider the benefits of statistically fusing human and machine responses to improve performance. Third, we review strategic differences in how humans with various levels of expertise approach face identification tasks. We conclude by considering the challenging problem of human and machine performance on recognition of faces of different races. Understanding how humans and machines perform these tasks can lead to more effective and accurate face recognition in applied settings.
Occlusion-Robust 3D Hand Pose Estimation from a Single RGB Image

We propose an occlusion-robust network for 3D hand pose estimation from a single RGB image. Severe occlusions degrade the estimation accuracy of not only occluded keypoints but also visible keypoints. Since the existing methods based on a deep neural network perform convolutions on all keypoints regardless of visibility, inaccurate features from occluded keypoints affect the localization of visible keypoints. To suppress the influence of occluded keypoints, our proposed deep neural network consists of three modules: a 2D heatmap generator, parallel sub-joints network (PSJNet), and an ensemble network (EN). First, the 2D position of all keypoints in an input image is predicted as a 2D heatmap, similar to the existing methods. Then, PSJNet, which consists of several graph convolutional networks (GCN) in parallel, estimates multiple incomplete 3D poses in which some of the keypoints have been removed. Each GCN performs convolutions on a limited number of keypoints, therefore, features from occluded keypoints do not spread to the whole pose. Finally, EN merges the incomplete poses into a single 3D pose by selecting accurate positions from them. Experimental results on a public dataset RHD demonstrate that the proposed method outperforms the existing methods in the case of both small and severe occlusions.
Abuses of Cryptocurrency in Dark Web and Ways to Regulate Them

The blockchain technology is a distributed ledger system where it is distributed among the users who does the transactions using this technique, it first came in trend after the sudden rise in the value of bitcoin in 2017 and then people get to know about this blockchain system and its working, it provides anonymity and security both to the user and that is why cryptocurrencies like Bitcoin and now Monero are using the blockchain method to ensure the safe, secure and untraceable transactions. Anonymity and security are like two edges of the same sword, they can be used for the great purposes like protecting the privacy of people, fostering, freedom of speech etc on the other hand they can be misused for the illegal activities happening over the internet like cyber terrorism and perpetrators often go unaccounted for their acts. Where there are many qualities of blockchains there are also some downsides too, because of increased security and anonymity it worked as a fuel for the dark web users to illicit transactions and do the illegal activities on the dark web. In this paper we have shown what are the downsides of blockchain, how the transaction happen on the dark web happens and how we can regulate and track the illegal activities on the dark web using regulated and sovereign backed cryptocurrencies.
NEMO-CNN: An Efficient Near-Memory Accelerator for Convolutional Neural Networks

The relevance of Deep Learning applications has skyrocketed in the last few years, exposing key weaknesses of traditional Von Neumann hardware architectures. With high amounts of data to be fetched from memory, the efficiency of these systems gets adversely impacted by an order of magnitude for each memory hierarchy that is traversed (e.g., data cache, on- chip SRAM, and off-chip DRAM). Such an issue is even more relevant when we consider that Convolutional Neural Networks (CNNs) are composed of tens of millions of parameters that imply billions of operations per second to achieve an acceptable performance. In order to remove this so-called memory wall problem, we introduce NEMO-CNN: a high-performance hardware accelerator built around the Near-Memory Computing paradigm, i.e., a design methodology based on distributed memory blocks enhanced with nearby processing elements. Coupled with a smart mapping strategy that slices the CNN structure along its depth, our solution drastically reduces the amount of data exchanged between off- and on-chip memories by executing each slice concurrently on dedicated processing elements that only leverage local data. Experimental results using VGG-16, DarkNet-19, and TinyYOLOv2 networks demonstrate that our solution achieves a top efficiency of 60.7 FPS/W, outperforming existing CNN accelerators.
ACHyb: a hybrid analysis approach to detect kernel access control vulnerabilities

Access control is essential for the Operating System (OS) security. Incorrect implementation of access control can introduce new attack surfaces to the OS, known as Kernel Access Control Vulnerabilities (KACVs). To understand KACVs, we conduct our study on the root causes and the security impacts of KACVs. Regarding the complexity of the recognized root causes, we particularly focus on two kinds of KACVs, namely KACV-M (due to missing permission checks) and KACV-I (due to misusing permission checks). We find that over 60% of these KACVs are of critical, high or medium security severity, resulting in a variety of security threats including bypass security checking, privileged escalation, etc. However, existing approaches can only detect KACV-M. The state-of-the-art KACV-M detector called PeX is a static analysis tool, which still suffers from extremely high false-positive rates.
RefactorInsight: Enhancing IDE Representation of Changes in Git with Refactorings Information

Inspection of code changes is a time-consuming task that constitutes a big part of everyday work of software engineers. Existing IDEs provide little information about the semantics of code changes within the file editor view. Therefore developers have to track changes across multiple files, which is a hard task with large codebases.In this paper, we present REFACTORINSIGHT, a plugin for IntelliJ IDEA that introduces a smart diff for code changes in Java and Kotlin where refactorings are auto-folded and provided with their description, thus allowing users to focus on changes that modify the code behavior like bug fixes and new features. REFACTORINSIGHT supports three usage scenarios: viewing smart diffs with auto-folded refactorings and hints, inspecting refactorings in pull requests and in any specific commit in the project change history, and exploring the refactoring history of methods and classes. The evaluation shows that commit processing time is acceptable: on median it is less than 0.2 seconds, which delay does not disrupt developers’ IDE workflows.Refactorinsight is available at https://github.com/JetBrains-Research/RefactorInsight. The demonstration video is available at https://youtu.be/-6L2AKQ66nA.
An Adaptive Row-based Weight Reuse Scheme for FPGA Implementation of Convolutional Neural Networks

There is an increasing need to implement the Convolutional Neural network (CNN) with an FPGA thanks to its design flexibility over an ASIC and low power consumption over a GPU. The size of the network and the resource of the target FPGA board should be considered to deploy the CNN Network successfully. However, previous works use the fixed dataflow which is not optimized for each layer. As a result, high on-chip buffer utilization and frequent memory access are required. The row-based weight reuse scheme is efficient in reducing input/output buffer size. However, it causes resource underutilization for layers with small feature maps size. This paper proposes an adaptive row reuse scheme by applying each level of row-reuse for each layer depending on its characteristic. Finally, the proposed design is implemented with a Xilinx KCU1500 board, and the accelerator achieves 994.74 GOPS of the throughput for U-Net. For general CNN implementation, the proposed scheme achieves 1080.9 GOPS when running VGG16 with 1.7 times less buffer size compared to previous works.
DARM: A Low-Complexity and Fast Modular Multiplier for Lattice-Based Cryptography

The lattice-based cryptography (LBC) has been widely used recently in many compute-intensive applications, such as the post-quantum cryptography (PQC) and privacy-preserving deep learning, where the main task for such applications is to improve the computational efficiency. The modular multiplication operations, mainly involved in the number theoretic transform (NTT), comprise a large proportion of the whole computations required by an LBC. This paper presents a novel "decompose-and-reduce" modular multiplication algorithm (DARM), considering primes with the form of q = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2N</sup> −δ and δ < 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N−2</sup> . The inherent structure of the modulus is exploited and the intermediates’ data widths are reduced. Moreover, a low-complexity and fast multiplier is elaborately devised based on DARM. To further validate the performance of our multiplier, an n-point NTT design with DARM is implemented with various configurations. FPGA implementation results demonstrate that compared with the prior arts, the proposed multiplier has 1.12-1.89× speedups with the least DSP utilization. For the case of ⌈log <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> q⌉ = 60 and n = 4096, the NTT implementation with DARM achieves up to 41.2% and 61.2% reductions in LUTs and DSPs, respectively.
An ontology engineering approach to measuring city education system performance

This paper addresses the problem of how to represent education system measurement definitions and the data used to derive them. ISO 37120 is standard for measuring city performance. It defines 100 indicators of which seven focus on Education. As cities adopt the standard and begin publishing their indicator values, citizens, city bureaucrats and politicians will be able to see how well their education systems perform relative to other cities. Hence, education systems will be subject to greater scrutiny and will have to provide evidence-based explanations for their performance. In order to enable the automated analysis of city performance, four problems must be solved: 1) how are indicator definitions represented? 2) how is the data used to derive an indicator value represented? 3) how is educational system specific knowledge represented, and 4) how is city specific educational system knowledge represented? In this paper we present an Education Ontology designed to represent ISO 37120 education indicators and the information delineated above. The ontology is designed to enable an intelligent agent to perform consistency and root cause analysis of city indicators. It can also be used by cities to publish their education indicators and data, using Semantic Web standards, on their Open Data portals.
A Generalized Stacked Reinforcement Learning Method for Sampled Systems

A common setting of reinforcement learning (RL) is a Markov decision process (MDP) in which the environment is a stochastic discrete-time dynamical system. Whereas MDPs are suitable in such applications as video games or puzzles, physical systems are time continuous. A general variant of RL is of digital format, where updates of the value (or cost) and policy are performed at discrete moments in time. The agent–environment loop then amounts to a sampled system, whereby sample-and-hold is a specific case. In this article, we propose and benchmark two RL methods suitable for sampled systems. Specifically, we hybridize model predictive control with critics learning the optimal Q- and value (or cost-to-go) function. Optimality is analyzed and performance comparison is done in an experimental case study with a mobile robot.
Plasma Real-time Diagnostics Based on Visual Image Recognition

Real-time diagnostics has always been a key point of plasma research. Optical emission spectroscopy is the most commonly used method to obtain the plasma parameters through fitting of the emission curves. But the process is time-consuming, making it almost impossible to get the data in real-time. In this paper, we propose a method using image recognition for real-time diagnostics. The relationship between visual images and rotational and vibrational temperatures was established. A convolutional neural network model was used for image recognition. After training, the test accuracy was over 0.99. The model was proved to be able to predict the temperature of a new input image, indicating potential applications of image recognition in real-time plasma diagnostics.
Fast Algorithms for Computing the Statistics of Pattern Matching

Pattern matching is a fundamental problem in theoretical computer science.The algorithms for pattern matching and the study on the statistics of pattern matching have found enormous applications in practical fields.In this paper, we revisit the Markov embedding approach for studying pattern matching in repeated experiments.For any pattern of length m over alphabet Σ, we show that the mean and variance of the waiting time of the pattern in iid experiments can be computed in O(m) time based on Markov embedding technique, improving over the O(|Σ| • m) and O(m 2 ) naïve bounds.Our method extends to computing the k-th moment of the waiting time, and it extends to computing other related statistics about pattern matching in repeated experiments, and it also extends to the case of Markov dependent experiments.
Multiparameter discrete transforms based on discrete orthogonal polynomials and their application to image watermarking

Applications of discrete orthogonal polynomials (DOPs) in image processing have been recently emerging. In particular, Krawtchouk, Chebyshev, and Charlier DOPs have been applied as bases for image analysis in the frequency domain. However, fast realizations and fractional-type generalizations of DOP-based discrete transforms have been rarely addressed. In this paper, we introduce families of multiparameter discrete fractional transforms via orthogonal spectral decomposition based on Krawtchouk, Chebyshev, and Charlier DOPs. The eigenvalues are chosen arbitrarily in both unitary and non-unitary settings. All families of transforms, for varieties of eigenvalues, are applied in image watermarking. We also exploit recently introduced fast techniques to reduce complexity for the Krawtchouk case. Experimental results show the robustness of the proposed transforms against watermarking attacks.
A New Total Variation Denoising Algorithm for Piecewise Constant Signals Based on Non-convex Penalty

Total variation signal denoising is an effective nonlinear filtering method, which is suitable for the restoration of piecewise constant signals disturbed by white Gaussian noises. Towards efficient denoising of piecewise constant signals, a new total variation denoising algorithm based on an improved non-convex penalty function is proposed in this paper. While ensuring the objective function is convex, a new non-convex penalty is designed. The denoising efficiency is improved by updating the dynamic total variational adjustment in the penalty function to the static adjustment. Experimental results demonstrated that the proposed denoising algorithm improved the denoising efficiency without affecting the denoising effect. The overall performance was shown better than the current excellent denoising algorithm.