Reviewerly/exHarmony huggingface dataset:
Geometric shape features extraction using a steady state partial differential equation system

Abstract A unified method for extracting geometric shape features from binary image data using a steady-state partial differential equation (PDE) system as a boundary value problem is presented in this paper. The PDE and functions are formulated to extract the thickness, orientation, and skeleton simultaneously. The main advantage of the proposed method is that the orientation is defined without derivatives and thickness computation is not imposed a topological constraint on the target shape. A one-dimensional analytical solution is provided to validate the proposed method. In addition, two-dimensional numerical examples are presented to confirm the usefulness of the proposed method. Highlights A steady state partial differential equation for extraction of geometrical shape features is formulated. The functions for geometrical shape features are formulated by the solution of the proposed PDE. Analytical solution is provided in one-dimension. Numerical examples are provided in two-dimension.
TRAT: Tracking by attention using spatio-temporal features

Robust object tracking requires knowledge of tracked objects' appearance, motion and their evolution over time. Although motion provides distinctive and complementary information especially for fast moving objects, most of the recent tracking architectures primarily focus on the objects' appearance information. In this paper, we propose a two-stream deep neural network tracker that uses both spatial and temporal features. Our architecture is developed over ATOM tracker and contains two backbones: (i) 2D-CNN network to capture appearance features and (ii) 3D-CNN network to capture motion features. The features returned by the two networks are then fused with attention based Feature Aggregation Module (FAM). Since the whole architecture is unified, it can be trained end-to-end. The experimental results show that the proposed tracker TRAT (TRacking by ATtention) achieves the state-of-the-art performance on most of the benchmarks and it significantly outperforms the baseline ATOM tracker. The source code and pretrained models can be found at https://github.com/Hasan4825/TRAT.
Securing Big Data from Eavesdropping Attacks in SCADA/ICS Network Data Streams through Impulsive Statistical Fingerprinting

While data from Supervisory Control And Data Acquisition (SCADA) systems is sent upstream, it is both the length of pulses as well as their frequency present an excellent opportunity to incorporate statistical fingerprinting. This is so, because datagrams in SCADA traffic follow a poison distribution. Although wrapping the SCADA traffic in a protective IPsec stream is an obvious choice, thin clients and unreliable communication channels make is less than ideal to use cryptographic solutions for security SCADA traffic. In this paper, we propose a smart alternative of data obfuscation in the form of Impulsive Statistical Fingerprinting (ISF). We provide important insights into our research in healthcare SCADA data security and the use of ISF. We substantiate the conversion of sensor data through the ISF into HL7 format and define policies of a seamless switch to a non HL7-based non-secure HIS to a secure HIS.
Language Model Pre-training Method in Machine Translation Based on Named Entity Recognition

Neural Machine Translation (NMT) model has become the mainstream technology in machine translation. The supervised neural machine translation model trains with abundant of sentence-level parallel corpora. But for low-resources language or dialect with no such corpus available, it is difficult to achieve good performance. Researchers began to focus on unsupervised neural machine translation (UNMT) that monolingual corpus as training data. UNMT need to construct the language model (LM) which learns semantic information from the monolingual corpus. This paper focuses on the pre-training of LM in unsupervised machine translation and proposes a pre-training method, NER-MLM (named entity recognition masked language model). Through performing NER, the proposed method can obtain better semantic information and language model parameters with better training results. In the unsupervised machine translation task, the BLEU scores on the WMT’16 English–French, English–German, data sets are 35.30, 27.30 respectively. To the best of our knowledge, this is the highest results in the field of UNMT reported so far.
Formal Language Generation for Fault Diagnosis With Spectral Logic via Adversarial Training

Fault diagnosis with formal languages can be performed in an interpretable way. However, the traditional formal languages cannot deal with noisy environments. Additionally, finding the optimal formal language for fault diagnosis is still a challenge due to the sparse reward issue. This article presents a novel method to find formal languages, written with signal spectral logic (SSL), to describe the fault behaviors among frequency domain for fault diagnosis. The formal language defined by SSL is robust to noise, acts as the fault diagnoser, and provides interpretabilities for human operators. Moreover, the fault diagnoser construction procedure has been formulated as a language generation process and an adversarial training technique is used to find the optimal formal language and avoid sparse reward issue existing in language generation problems. Some experiments with real rolling element bearing data and simulated signals demonstrate that our method is able to find formal languages to diagnose faults efficiently and accurately under noisy environments.
Nudge: Accelerating Overdue Pull Requests toward Completion

Pull requests are a key part of the collaborative software development and code review process today. However, pull requests can also slow down the software development process when the reviewer(s) or the author do not actively engage with the pull request. In this work, we design an end-to-end service, Nudge, for accelerating overdue pull requests towards completion by reminding the author or the reviewer(s) to engage with their overdue pull requests. First, we use models based on effort estimation and machine learning to predict the completion time for a given pull request. Second, we use activity detection to filter out pull requests that may be overdue, but for which sufficient action is taking place nonetheless. Lastly, we use actor identification to understand who the blocker of the pull request is and nudge the appropriate actor (author or reviewer(s)). The key novelty of Nudge is that it succeeds in reducing pull request resolution time, while ensuring that developers perceive the notifications sent as useful, at the scale of thousands of repositories. In a randomized trial on 147 repositories in use at Microsoft, Nudge was able to reduce pull request resolution time by 60% for 8,500 pull requests, when compared to overdue pull requests for which Nudge did not send a notification. Furthermore, developers receiving Nudge notifications resolved 73% of these notifications as positive. We observed similar results when scaling up the deployment of Nudge to 8,000 repositories at Microsoft, for which Nudge sent 210,000 notifications during a full year. This demonstrates Nudge's ability to scale to thousands of repositories. Lastly, our qualitative analysis of a selection of Nudge notifications indicates areas for future research, such as taking dependencies among pull requests and developer availability into account.
Interconnection Between Darknets

Tor and i2p networks are two of the most popular darknets. Both darknets have become an area of illegal activities highlighting the necessity to study and analyze them to identify and report illegal content to Law Enforcement Agencies (LEAs). This paper analyzes the connections between the Tor network and the i2p network. We created the first dataset that combines information from Tor and i2p networks. The dataset contains more than 49k darknet services. The process of building and analyzing the dataset shows that it is not possible to explore one of the networks without considering the other. Both networks work as an ecosystem and there are clear paths between them. Using graph analysis, we also identified the most relevant domains, the prominent types of services in each network, and their relations. Findings are relevant to LEAs and researchers aiming to crawl and investigate i2p and Tor networks.
Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

The primary purpose of the reported research was to improve the discrete wavelet transform (DWT)-based JP3D compression of volumetric medical images by applying new methods that were only previously used in the compression of two-dimensional (2D) images. Namely, we applied reversible denoising and lifting steps with step skipping to three-dimensional (3D)-DWT and constructed a hybrid transform that combined 3D-DWT with prediction. We evaluated these methods using a test-set containing images of modalities: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). They proved effective for 3D data resulting in over two times greater compression ratio improvements than competitive methods. While employing fast entropy estimation of JP3D compression ratio to reduce the cost of image-adaptive parameter selection for the new methods, we found that some MRI images had sparse histograms of intensity levels. We applied the classical histogram packing (HP) and found that, on average, it resulted in greater ratio improvements than the new sophisticated methods and that it could be combined with these new methods to further improve ratios. Finally, we proposed a few practical compression schemes that exploited HP, entropy estimation, and the new methods; on average, they improved the compression ratio by up to about 6.5% at an acceptable cost.
Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis

Summary Cluster analysis aims at partitioning data into groups or clusters. In applications, it is common to deal with problems where the number of clusters is unknown. Bayesian mixture models employed in such applications usually specify a flexible prior that takes into account the uncertainty with respect to the number of clusters. However, a major empirical challenge involving the use of these models is in the characterisation of the induced prior on the partitions. This work introduces an approach to compute descriptive statistics of the prior on the partitions for three selected Bayesian mixture models developed in the areas of Bayesian finite mixtures and Bayesian nonparametrics. The proposed methodology involves computationally efficient enumeration of the prior on the number of clusters in‐sample (termed as ‘data clusters’) and determining the first two prior moments of symmetric additive statistics characterising the partitions. The accompanying reference implementation is made available in the R package fipp . Finally, we illustrate the proposed methodology through comparisons and also discuss the implications for prior elicitation in applications.
Entropy-Based Approach in Selection Exact String-Matching Algorithms

The string-matching paradigm is applied in every computer science and science branch in general. The existence of a plethora of string-matching algorithms makes it hard to choose the best one for any particular case. Expressing, measuring, and testing algorithm efficiency is a challenging task with many potential pitfalls. Algorithm efficiency can be measured based on the usage of different resources. In software engineering, algorithmic productivity is a property of an algorithm execution identified with the computational resources the algorithm consumes. Resource usage in algorithm execution could be determined, and for maximum efficiency, the goal is to minimize resource usage. Guided by the fact that standard measures of algorithm efficiency, such as execution time, directly depend on the number of executed actions. Without touching the problematics of computer power consumption or memory, which also depends on the algorithm type and the techniques used in algorithm development, we have developed a methodology which enables the researchers to choose an efficient algorithm for a specific domain. String searching algorithms efficiency is usually observed independently from the domain texts being searched. This research paper aims to present the idea that algorithm efficiency depends on the properties of searched string and properties of the texts being searched, accompanied by the theoretical analysis of the proposed approach. In the proposed methodology, algorithm efficiency is expressed through character comparison count metrics. The character comparison count metrics is a formal quantitative measure independent of algorithm implementation subtleties and computer platform differences. The model is developed for a particular problem domain by using appropriate domain data (patterns and texts) and provides for a specific domain the ranking of algorithms according to the patterns’ entropy. The proposed approach is limited to on-line exact string-matching problems based on information entropy for a search pattern. Meticulous empirical testing depicts the methodology implementation and purports soundness of the methodology.
Focal Frequency Loss for Generative Models

Despite the remarkable success of generative models in creating photorealistic images using deep neural networks, gaps could still exist between the real and generated images, especially in the frequency domain. In this study, we find that narrowing the frequency domain gap can ameliorate the image synthesis quality further. To this end, we propose the focal frequency loss, a novel objective function that brings optimization of generative models into the frequency domain. The proposed loss allows the model to dynamically focus on the frequency components that are hard to synthesize by down-weighting the easy frequencies. This objective function is complementary to existing spatial losses, offering great impedance against the loss of important frequency information due to the inherent crux of neural networks. We demonstrate the versatility and effectiveness of focal frequency loss to improve various baselines in both perceptual quality and quantitative performance.
YOLOv3 Object Detection Algorithm with Feature Pyramid Attention for Remote Sensing Images

In object detection in remote sensing images, owing to the complex background environment, there are problems of poor robustness to interference and low detection accuracy for small objects.The algorithm proposed in this paper combines the attention mechanism with the spatial pyramid structure to improve the You-only-look-once algorithm version 3 (YOLOv3), and it also includes the pyramid attention module to improve the performance of the detection model.The feature pyramid attention module is introduced into deep features, and the feature pyramid attention structure is combined with global context information to better learn object features.The global attention upsampling module is introduced into low-level features, and the global information provided by global pooling is used as a guide to select low-level features.The object detection model can more fully acquire the features of important information and selectively suppress irrelevant features, thereby improving the detection accuracy of the algorithm.To verify the performance of the proposed algorithm, it is used to detect airplanes, storage tanks, ships, baseball diamonds, and running tracks in remote sensing images, and its performance is compared with that of other algorithms.Experiments prove that the proposed algorithm has better detection performance and can improve the detection accuracy of each object in remote sensing images.
Dissipativity-Based Filtering of Time-Varying Delay Interval Type-2 Polynomial Fuzzy Systems Under Imperfect Premise Matching

This paper investigates the dissipativity-based filtering problem for the nonlinear systems subject to both uncertainties and time-varying delay in the time-delay interval type-2 (IT2) polynomial fuzzy framework.Filter design is a challenging issue for complex nonlinear systems especially when the uncertainties and time delay exist.IT2 polynomial fuzzy model is an effective and powerful approach to analyze and synthesize uncertain nonlinear systems.This is the first attempt to design both the fullorder and reduced-order IT2 polynomial fuzzy filters to ensure that the filtering error system is asymptotically stable under the dissipativity constraint.The design of filtering is based on the imperfect premise matching scheme where the number of fuzzy rules and shapes of membership functions of the designed fuzzy filter can differ from those of IT2 polynomial fuzzy model, to provide greater design flexibility and lower implementation burden.By utilizing the Lyapunov-Krasovskii functional based approach, the information of membership functions, time delay and system states is taken into account in the design process to develop the relaxed membership-function-dependent (MFD) and delay-dependent filtering existence criteria.Finally, simulation results are presented to illustrate the effectiveness of the filtering algorithm reported in this paper.
Cross-Dataset Face Manipulation Detection

Easily available recent face image/video manipulation techniques and tools are now being utilized to generate highly realistic manipulated videos known as DeepFakes, which can fool face recognition systems and humans. Thus, it is vital to devise precise manipulation detection methods. Despite the progress, existing mechanisms are limited to the datasets or manipulation types. In this paper, to increase the performance under unseen data and manipulations, a DeepFakes detection framework using metric learning and steganalysis rich models is presented. Extensive empirical analysis on three publicly available datasets, namely, FaceForensics++, CelebDF, and DeepFakeTIMIT, were carried out to evaluate the generalization capability of the proposed approach. The framework attained 5% to 15% accuracy gains under unseen manipulations.
Quotients, inductive types, and quotient inductive types

This paper introduces an expressive class of indexed quotient-inductive types, called QWI types, within the framework of constructive type theory. They are initial algebras for indexed families of equational theories with possibly infinitary operators and equations. We prove that QWI types can be derived from quotient types and inductive types in the type theory of toposes with natural number object and universes, provided those universes satisfy the Weakly Initial Set of Covers (WISC) axiom. We do so by constructing QWI types as colimits of a family of approximations to them defined by well-founded recursion over a suitable notion of size, whose definition involves the WISC axiom. We developed the proof and checked it using the Agda theorem prover.
Fiber-compatible photonic feed-forward with 99% fidelity

Both photonic quantum computation and the establishment of a quantum internet require fiber-based measurement and feed-forward in order to be compatible with existing infrastructure. Here we present a fiber-compatible scheme for measurement and feed-forward, whose performance is benchmarked by carrying out remote preparation of single-photon polarization states at telecom-wavelengths. The result of a projective measurement on one photon deterministically controls the path a second photon takes with ultrafast optical switches. By placing well-calibrated bulk passive polarization optics in the paths, we achieve a measurement and feed-forward fidelity of (99.0 ± 1)%, after correcting for other experimental errors. Our methods are useful for photonic quantum experiments including computing, communication, and teleportation.
Generation of <i>N</i>‐particle <i>W</i> State with Trapped Λ‐Type Ions by Transitionless Quantum Driving

Abstract An efficient scheme to generate multiparticle W state based on transitionless quantum driving (TQD) with trapped ions is proposed. In the TQD scheme, the effective Hamiltonian can be obtained with the help of quantum Zeno dynamics and large off‐resonance condition, and the multiparticle W state can be generated with high fidelity. The influence of decoherence induced by the ionic spontaneous emission and the ambient heating, and the sensitivity of the TQD scheme to some experimental errors are discussed by numerical simulation. The result demonstrates that the TQD scheme is faster than that only via the fractional stimulated Raman adiabatic passage and robust against decoherence and operation imperfection.
Research on the Influence of New Media Technology on Internet Short Video Content Production under Artificial Intelligence Background

With the rapid development of the Internet and smart phone technology, a large number of short videos are shared through social platforms. Therefore, video content analysis is a very important and popular work in machine learning and artificial intelligence currently. However, it is very difficult to analyze all aspects of video content originally produced by large-scale users. How to screen out bad and illegal content from short videos published by a large number of users, select high-quality videos to share with other users, and improve the quality of video on the distribution platform of the entire user is a top priority. Based on this background, this paper focuses on optimizing video auditing to provide basic features for algorithm judgment, supporting original content and increasing the distribution of new content, and strengthening manual intervention combining algorithm recommendation with manual recommendation. Four major aspects of the artificial training algorithm model discuss the optimization effect of artificial intelligence on the algorithm in order to provide some guidance for the sustainable and healthy development of mobile short video.
Colour image segmentation based on a convex K‐means approach

Image segmentation is a fundamental and challenging task in image processing and computer vision. The colour image segmentation is attracting more attention as the colour image provides more information than the grey image. A variational model based on a convex K-means approach to segment colour images is proposed. The proposed variational method uses a combination of l1 and l2 regularizers to maintain edge information of objects in images while overcoming the staircase effect. Meanwhile, our one-stage strategy is an improved version based on the smoothing and thresholding strategy, which contributes to improving the accuracy of segmentation. The proposed method performs the following steps. First, the colour set which can be determined by human or the K-means method is specified. Second, a variational model to obtain the most appropriate colour for each pixel from the colour set via convex relaxation and lifting is used. The Chambolle–Pock algorithm and simplex projection are applied to solve the variational model effectively. Experimental results and comparison analysis demonstrate the effectiveness and robustness of the method.
Single-tone pulse sequences and robust two-tone shaped pulses for three silicon spin qubits with always-on exchange

Quantum computation requires high-fidelity single-qubit and two-qubit gates on a scalable platform. Silicon spin qubits are a promising platform toward realization of this goal. In this paper we show how to perform single-qubit and controlled-Z (cz) gates in a linear chain of three spin qubits with always-on exchange coupling, which is relevant for certain dot- and donor-based silicon devices. We also show how to make the cz gate robust against both charge noise and pulse length error using a two-tone pulse shaping method. The robust pulse maintains a fidelity of 99.99% at 3.5% fluctuations in exchange or pulse amplitude, which is an improvement over the uncorrected pulses where this fidelity can only be maintained for fluctuations in exchange up to 2% or up to 0.2% in amplitude.