Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Web Application Security Parameters: Current Trends and Future Directions

The growing use of the internet has resulted in an exponential rise in the use of web applications. Businesses, industries, financial and educational institutions, and the general populace depend on web applications. This mammoth rise in their usage has also resulted in many security issues that make these web applications vulnerable, thereby affecting the confidentiality, integrity, and availability of associated information systems. It has, therefore, become necessary to find vulnerabilities in these information system resources to guarantee information security. A publicly available web application vulnerability scanner is a computer program that assesses web application security by employing automated penetration testing techniques that reduce the time, cost, and resources required for web application penetration testing and eliminates test engineers’ dependency on human knowledge. However, these security scanners possess various weaknesses of not scanning complete web applications and generating wrong test results. Moreover, intensive research has been carried out to quantitatively enumerate web application security scanners’ results to inspect their effectiveness and limitations. However, the findings show no well-defined method or criteria available for assessing their results. In this research, we have evaluated the performance of web application vulnerability scanners by testing intentionally defined vulnerable applications and the level of their respective precision and accuracy. This was achieved by classifying the analyzed tools using the most common parameters. The evaluation is based on an extracted list of vulnerabilities from OWASP (Open Web Application Security Project).
Hierarchical label-wise attention transformer model for explainable ICD coding

International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents. HiLAT firstly fine-tunes a pretrained Transformer model to represent the tokens of clinical documents. We subsequently employ a two-level hierarchical label-wise attention mechanism that creates label-specific document representations. These representations are in turn used by a feed-forward neural network to predict whether a specific ICD code is assigned to the input clinical document of interest. We evaluate HiLAT using hospital discharge summaries and their corresponding ICD-9 codes from the MIMIC-III database. To investigate the performance of different types of Transformer models, we develop ClinicalplusXLNet, which conducts continual pretraining from XLNet-Base using all the MIMIC-III clinical notes. The experiment results show that the F1 scores of the HiLAT + ClinicalplusXLNet outperform the previous state-of-the-art models for the top-50 most frequent ICD-9 codes from MIMIC-III. Visualisations of attention weights present a potential explainability tool for checking the face validity of ICD code predictions.
Analytical solution for nonadiabatic quantum annealing to arbitrary Ising spin Hamiltonian

Ising spin Hamiltonians are often used to encode a computational problem in their ground states. Quantum Annealing (QA) computing searches for such a state by implementing a slow time-dependent evolution from an easy-to-prepare initial state to a low energy state of a target Ising Hamiltonian of quantum spins, HI. Here, we point to the existence of an analytical solution for such a problem for an arbitrary HI beyond the adiabatic limit for QA. This solution provides insights into the accuracy of nonadiabatic computations. Our QA protocol in the pseudo-adiabatic regime leads to a monotonic power-law suppression of nonadiabatic excitations with time T of QA, without any signature of a transition to a glass phase, which is usually characterized by a logarithmic energy relaxation. This behavior suggests that the energy relaxation can differ in classical and quantum spin glasses strongly, when it is assisted by external time-dependent fields. In specific cases of HI, the solution also shows a considerable quantum speedup in computations.
A Comparison and Evaluation of Stereo Matching on Active Stereo Images

The relationship between the disparity and depth information of corresponding pixels is inversely proportional. Thus, in order to accurately estimate depth from stereo vision, it is important to obtain accurate disparity maps, which encode the difference between horizontal coordinates of corresponding image points. Stereo vision can be classified as either passive or active. Active stereo vision generates pattern texture, which passive stereo vision does not have, on the image to fill the textureless regions. In passive stereo vision, many surveys have discovered that disparity accuracy is heavily reliant on attributes, such as radiometric variation and color variation, and have found the best-performing conditions. However, in active stereo matching, the accuracy of the disparity map is influenced not only by those affecting the passive stereo technique, but also by the attributes of the generated pattern textures. Therefore, in this paper, we analyze and evaluate the relationship between the performance of the active stereo technique and the attributes of pattern texture. When evaluating, experiments are conducted under various settings, such as changing the pattern intensity, pattern contrast, number of pattern dots, and global gain, that may affect the overall performance of the active stereo matching technique. Through this evaluation, our discovery can act as a noteworthy reference for constructing an active stereo system.
Design and Build Academic Website with Digital Certificate Storage Using Blockchain Technology

A certificate is a form of award that is obtained by someone after completing a competency test or certain learning. Certificates must be generated and stored in a safe and secure manner to prevent alteration of content or even falsification. Blockchain technology is a technology that allows secure storage processes at low costs. Security is guaranteed because everyone can take part in storing data with a distributed ledger. Based on the results of research and system design, it can be concluded that the process of making blockchain technology as a medium for issuing certificates and their validation can be made using Ethereum's program, namely Geth, and storing data using smart contracts issued on the blockchain network. The results of the reliability testing of the system show that the system has successfully processed 200 transactions in approximately 8 seconds. For scalability testing, it is estimated that 10 million blocks require a storage capacity of 22.6 GB to become a node or miner on this blockchain network.
Matrix eigenvalue solver based on reconfigurable photonic neural network

Abstract The solution of matrix eigenvalues has always been a research hotspot in the field of modern numerical analysis, which has important value in practical application of engineering technology and scientific research. Despite the fact that currently existing algorithms for solving the eigenvalues of matrices are well-developed to try to satisfy both in terms of computational accuracy and efficiency, few of them have been able to be realized on photonic platform. The photonic neural network not only has strong judgment in solving inference tasks due to the superior learning ability, but also makes full use of the advantages of photonic computing with ultrahigh speed and ultralow energy consumption. Here, we propose a strategy of an eigenvalue solver for real-value symmetric matrices based on reconfigurable photonic neural networks. The strategy shows the feasibility of solving the eigenvalues of real-value symmetric matrices of n × n matrices with locally connected networks. Experimentally, we demonstrate the task of solving the eigenvalues of 2 × 2, 3 × 3, and 4 × 4 real-value symmetric matrices based on graphene/Si thermo-optical modulated reconfigurable photonic neural networks with saturated absorption nonlinear activation layer. The theoretically predicted test set accuracy of the 2 × 2 matrices is 93.6% with the measured accuracy of 78.8% in the experiment by the standard defined for simplicity of comparison. This work not only provides a feasible solution for the on-chip integrated photonic realization of eigenvalue solving of real-value symmetric matrices, but also lays the foundation for a new generation of intelligent on-chip integrated all-optical computing.
Predicting the outputs of finite deep neural networks trained with noisy gradients

A recent line of works studied wide deep neural networks (DNNs) by approximating them as Gaussian processes (GPs). A DNN trained with gradient flow was shown to map to a GP governed by the neural tangent kernel (NTK), whereas earlier works showed that a DNN with an i.i.d. prior over its weights maps to the so-called neural network Gaussian process (NNGP). Here we consider a DNN training protocol, involving noise, weight decay, and finite width, whose outcome corresponds to a certain non-Gaussian stochastic process. An analytical framework is then introduced to analyze this non-Gaussian process, whose deviation from a GP is controlled by the finite width. Our contribution is threefold: (i) In the infinite width limit, we establish a correspondence between DNNs trained with noisy gradients and the NNGP, not the NTK. (ii) We provide a general analytical form for the finite width correction (FWC) for DNNs with arbitrary activation functions and depth and use it to predict the outputs of empirical finite networks with high accuracy. Analyzing the FWC behavior as a function of n, the training set size, we find that it is negligible for both the very small n regime, and, surprisingly, for the large n regime [where the GP error scales as O(1/n)]. (iii) We flesh out algebraically how these FWCs can improve the performance of finite convolutional neural networks (CNNs) relative to their GP counterparts on image classification tasks.
POLITICS: Pretraining with Same-story Article Comparison for Ideology Prediction and Stance Detection

Ideology is at the core of political science research.Yet, there still does not exist generalpurpose tools to characterize and predict ideology across different genres of text.To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies.We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining.Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks.Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.* Equal contribution by the first two authors.News Story: Donald Trump tests positive for COVID-19.Daily Kos (left): It's now clear that Donald Trump lied to the nation about when he received a positive test for COVID-19. . .
Context-Sensitive and Directional Concurrency Fuzzing for Data-Race Detection

Fuzzing is popular for bug detection and vulnerability discovery nowadays.To adopt fuzzing for concurrency problems like data races, several recent concurrency fuzzing approaches consider concurrency information of program execution, and explore thread interleavings by affecting thread scheduling at runtime.However, these approaches are still limited in data-race detection.On the one hand, they fail to consider the execution contexts of thread interleavings, which can miss real data races in specific runtime contexts.On the other hand, they perform random thread-interleaving exploration, which frequently repeats already covered thread interleavings and misses many infrequent thread interleavings.In this paper, we develop a novel concurrency fuzzing framework named CONZZER, to effectively explore thread interleavings and detect hard-to-find data races.The core of CONZZER is a context-sensitive and directional concurrency fuzzing approach for thread-interleaving exploration, with two new techniques.First, to ensure context sensitivity, we propose a new concurrencycoverage metric, concurrent call pair, to describe thread interleavings with runtime calling contexts.Second, to directionally explore thread interleavings, we propose an adjacency-directed mutation to generate new possible thread interleavings with already covered thread interleavings and then use a breakpointcontrol method to attempt to actually cover them at runtime.With these two techniques, this concurrency fuzzing approach can effectively cover infrequent thread interleavings with concrete context information, to help discover hard-to-find data races.We have evaluated CONZZER on 8 user-level applications and 4 kernel-level filesystems, and found 95 real data races.We identify 75 of these data races to be harmful and send them to related developers, and 44 have been confirmed.We also compare CONZZER to existing fuzzing tools, and CONZZER continuously explores more thread interleavings and finds many real data races missed by these tools.
Building separable approximations for quantum states via neural networks

Finding the closest separable state to a given target state is a notoriously difficult task, even more difficult than deciding whether a state is entangled or separable. To tackle this task, we parametrize separable states with a neural network and train it to minimize the distance to a given target state, with respect to a differentiable distance, such as the trace distance or Hilbert--Schmidt distance. By examining the output of the algorithm, we obtain an upper bound on the entanglement of the target state, and construct an approximation for its closest separable state. We benchmark the method on a variety of well-known classes of bipartite states and find excellent agreement, even up to local dimension of $d=10$, while providing conjectures and analytic insight for isotropic and Werner states. Moreover, we show our method to be efficient in the multipartite case, considering different notions of separability. Examining three and four-party GHZ and W states we recover known bounds and obtain additional ones, for instance for triseparability.
3D reconstruction from structured-light profilometry with dual-path hybrid network

Abstract With the rapid development of high-speed image sensors and optical imaging technology, these have effectively promoted the improvement of non-contact 3D shape measurement. Among them, striped structured-light technology has been widely used because of its high measurement accuracy. Compared with classical methods such as Fourier transform profilometry, many deep neural networks are utilized to restore 3D shape from single-shot structured light. In actual engineering deployments, the number of learnable parameters of convolution neural network (CNN) is huge, especially for high-resolution structured-light patterns. To this end, we proposed a dual-path hybrid network based on UNet, which eliminates the deepest convolution layers to reduce the number of learnable parameters, and a swin transformer path is additionally built on the decoder to improve the global perception of this network. The experimental results show that the learnable parameters of the model are reduced by 60% compared with the UNet, and the measurement accuracy is not degraded at the same time. The proposed dual-path hybrid network provides an effective solution for structured-light 3D reconstruction and its practice in engineering.
Unsupervised Domain Adaptive Salient Object Detection through Uncertainty-Aware Pseudo-Label Learning

Recent advances in deep learning significantly boost the performance of salient object detection (SOD) at the expense of labeling larger-scale per-pixel annotations. To relieve the burden of labor-intensive labeling, deep unsupervised SOD methods have been proposed to exploit noisy labels generated by handcrafted saliency methods. However, it is still difficult to learn accurate saliency details from rough noisy labels. In this paper, we propose to learn saliency from synthetic but clean labels, which naturally has higher pixel-labeling quality without the effort of manual annotations. Specifically, we first construct a novel synthetic SOD dataset by a simple copy-paste strategy. Considering the large appearance differences between the synthetic and real-world scenarios, directly training with synthetic data will lead to performance degradation on real-world scenarios. To mitigate this problem, we propose a novel unsupervised domain adaptive SOD method to adapt between these two domains by uncertainty-aware self-training. Experimental results show that our proposed method outperforms the existing state-of-the-art deep unsupervised SOD methods on several benchmark datasets, and is even comparable to fully-supervised ones.
Eurasian oystercatcher optimiser: New meta-heuristic algorithm

Abstract Modern optimisation is increasingly relying on meta-heuristic methods. This study presents a new meta-heuristic optimisation algorithm called Eurasian oystercatcher optimiser (EOO). The EOO algorithm mimics food behaviour of Eurasian oystercatcher (EO) in searching for mussels. In EOO, each bird (solution) in the population acts as a search agent. The EO changes the candidate mussel according to the best solutions to finally eat the best mussel (optimal result). A balance must be achieved among the size, calories, and energy of mussels. The proposed algorithm is benchmarked on 58 test functions of three phases (unimodal, multimodal, and fixed-diminution multimodal) and compared with several important algorithms as follows: particle swarm optimiser, grey wolf optimiser, biogeography based optimisation, gravitational search algorithm, and artificial bee colony. Finally, the results of the test functions prove that the proposed algorithm is able to provide very competitive results in terms of improved exploration and exploitation balances and local optima avoidance.
SFGAN: Unsupervised Generative Adversarial Learning of 3D Scene Flow from the 3D Scene Self

Scene flow tracks the 3D motion of each point in adjacent point clouds. It provides fundamental 3D motion perception for autonomous driving and server robot. Although red green blue depth (RGBD) camera or light detection and ranging (LiDAR) capture discrete 3D points in space, the objects and motions usually are continuous in the macroworld. That is, the objects keep themselves consistent as they flow from the current frame to the next frame. Based on this insight, the generative adversarial networks (GAN) is utilized to self‐learn 3D scene flow without ground truth. The fake point cloud is synthesized from the predicted scene flow and the point cloud of the first frame. The adversarial training of the generator and discriminator is realized through synthesizing indistinguishable fake point cloud and discriminating the real point cloud and the synthesized fake point cloud. The experiments on Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset show that our method realizes promising results. Just as human, the proposed method can identify the similar local structures of two adjacent frames even without knowing the ground truth scene flow. Then, the local correspondence can be correctly estimated, and further the scene flow is correctly estimated. An interactive preprint version of the article can be found here: https://www.authorea.com/doi/full/10.22541/au.163335790.03073492 .
Adaptive Set-Membership State Estimation for Nonlinear Systems Under Bit Rate Allocation Mechanism: A Neural-Network-Based Approach

In this article, the adaptive neural-network-based (NN-based) set-membership state estimation problem is studied for a class of nonlinear systems subject to bit rate constraints and unknown-but-bounded noises. The measurement output signals are transmitted from sensors to a remote estimator via a bit rate constrained communication channel. To relieve the communication burden and ameliorate the state estimation accuracy, a bit rate allocation mechanism is put forward for the sensor nodes by solving a constrained optimization problem. Subsequently, through the NN learning method, an NN-based set-membership estimator is designed to determine an ellipsoidal set that contains the system state, where the proposed estimator relies upon a prediction-correction structure. With the help of the mathematical induction technique and the set theory, sufficient conditions are obtained to ensure the existence of both the adaptive tuning parameters and the set-membership estimators, and then, the corresponding parameters and estimator gains are calculated by solving a set of optimization problems. In addition, the monotonicity of the upper bound on the squared estimation error with respect to the bit rate and the convergence of the NN weight are analyzed, respectively. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed state estimation algorithm.
Boosting modern and historical handwritten text recognition with deformable convolutions

Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant boost to the digitization of handwritten documents and reuse of their content. The task becomes even more challenging when dealing with historical documents due to the variability of the writing style and degradation of the page quality. State-of-the-art HTR approaches typically couple recurrent structures for sequence modeling with Convolutional Neural Networks for visual feature extraction. Since convolutional kernels are defined on fixed grids and focus on all input pixels independently while moving over the input image, this strategy disregards the fact that handwritten characters can vary in shape, scale, and orientation even within the same document and that the ink pixels are more relevant than the background ones. To cope with these specific HTR difficulties, we propose to adopt deformable convolutions, which can deform depending on the input at hand and better adapt to the geometric variations of the text. We design two deformable architectures and conduct extensive experiments on both modern and historical datasets. Experimental results confirm the suitability of deformable convolutions for the HTR task.