Reviewerly/exHarmony huggingface dataset:
How to use statistical models and methods for clinical prediction

One of the main aims of statistics is to control and model variability in observed phenomena. A second important aim is to translate the results of such modelling into clinical decision-making, e.g., by constructing appropriate prediction models. Currently, model-based individualized predictions play an important role in the era of personalized medicine, where diagnosis and prognosis of a clinical outcome are based on a large number of observed clinical, individual and genetic characteristics (1).
A Tree Adjoining Grammar representation for models of stochastic dynamical systems

Model structure and complexity selection remains a challenging problem in system identification, especially for parametric non-linear models. Many Evolutionary Algorithm (EA) based methods have been proposed in the literature for estimating model structure and complexity. In most cases, the proposed methods are devised for estimating structure and complexity within a specified model class and hence these methods do not extend to other model structures without significant changes. In this paper, we propose a Tree Adjoining Grammar (TAG) for stochastic parametric models. TAGs can be used to generate models in an EA framework while imposing desirable structural constraints and incorporating prior knowledge. In this paper, we propose a TAG that can systematically generate models ranging from FIRs to polynomial NARMAX models. Furthermore, we demonstrate that TAGs can be easily extended to more general model classes, such as the non-linear Box–Jenkins model class, enabling the realization of flexible and automatic model structure and complexity selection via EA.
A First Look at the Misuse and Abuse of the IPv4 Transfer Market

The depletion of the unallocated IPv4 addresses and the slow pace of IPv6 deployment have given rise to the IPv4 transfer market, the trading of allocated IPv4 prefixes between organizations. Despite the policies established by RIRs to regulate the IPv4 transfer market, IPv4 transfers pose an opportunity for malicious networks, such as spammers and bulletproof ASes, to bypass reputational penalties by obtaining “clean” IPv4 address space or by offloading blacklisted addresses. Additionally, IP transfers create a window of uncertainty about the legitimate ownership of prefixes, which leads to inconsistencies in WHOIS records and routing advertisements. In this paper we provide the first detailed study of how transferred IPv4 prefixes are misused in the wild, by synthesizing an array of longitudinal IP blacklists, honeypot data, and AS reputation lists. Our findings yield evidence that transferred IPv4 address blocks are used by malicious networks to address botnets and fraudulent sites in much higher rates compared to non-transferred addresses, while the timing of the attacks indicate efforts to evade filtering mechanisms.
Conditional Generative Adversarial Networks for the Prediction of Cardiac Contraction from Individual Frames

Cardiac anatomy and function are interrelated in many ways, and these relations can be affected by multiple pathologies. In particular, this applies to ventricular shape and mechanical deformation. We propose a machine learning approach to capture these interactions by using a conditional Generative Adversarial Network (cGAN) to predict cardiac deformation from individual Cardiac Magnetic Resonance (CMR) frames, learning a deterministic mapping between end-diastolic (ED) to end-systolic (ES) CMR short-axis frames. We validate the predicted images by quantifying the difference with real images using mean squared error (MSE) and structural similarity index (SSIM), as well as the Dice coefficient between their respective endo- and epicardial segmentations, obtained with an additional U-Net. We evaluate the ability of the network to learn “healthy” deformations by training it on $$\sim $$ 33,500 image pairs from $$\sim $$ 12,000 subjects, and testing on a separate test set of $$\sim $$ 4,500 image pairs from the UK Biobank study. Mean MSE, SSIM and Dice scores were 0.0026 ± 0.0013, 0.89 ± 0.032 and 0.89 ± 0.059 respectively. We subsequently re-trained the network on specific patient group data, showing that the network is capable of extracting physiologically meaningful differences between patient populations suggesting promising applications on pathological data.
Two-Sample Testing for Event Impacts in Time Series

In many application domains, time series are monitored to detect extreme events like technical faults, natural disasters, or disease outbreaks. Unfortunately, it is often non-trivial to select both a time series that is informative about events and a powerful detection algorithm: detection may fail because the detection algorithm is not suitable, or because there is no shared information between the time series and the events of interest. In this work, we thus propose a non-parametric statistical test for shared information between a time series and a series of observed events. Our test allows identifying time series that carry information on event occurrences without committing to a specific event detection methodology. In a nutshell, we test for divergences of the value distributions of the time series at increasing lags after event occurrences with a multiple two-sample testing approach. In contrast to related tests, our approach is applicable for time series over arbitrary domains, including multivariate numeric, strings or graphs. We perform a large-scale simulation study to show that it outperforms or is on par with related tests on our task for univariate time series. We also demonstrate the real-world applicability of our approach on datasets from social media and smart home environments.
Enhanced calibration for freeform surface misalignments in non-null interferometers by convolutional neural network

Most tested surface calibration methods in interferometers, such as the direct coefficients removing method, the sensitive matrix (SM) method, and deep neural network (DNN) calibration method, rely on Zernike coefficients. However, due to the inherent rotationally non-symmetric aberrations in a non-null freeform surface interferometer, the interferograms are usually non-circular even if the surface apertures are circular. The Zernike coefficients based methods are inaccurate due to the non-orthogonality of Zernike polynomials in the non-circular area. A convolutional neural network (CNN)-based misalignment calibration method is proposed. Instead of Zernike coefficients, the well-trained CNN treats the interferogram directly to estimate the specific misalignments. Simulations and experiments are carried out to validate the high accuracy.
Current Nanotechnology Based Solutions for Sustainable Wastewater Treatment

Background: Industrialization plays an important role in the growth of a nation. But it is also one of the causes of the deteriorating condition of our ecosystem. The pollution, be it aquatic, terrestrial, or air-borne, has affected our environment drastically, and industrial and domestic wastewater plays a major role in it. As the Earth transforms into urban sprawl, industries flourish, pollution increases and the natural resources deplete. Recently nano-engineering based technologies have been explored for the purpose of wastewater treatment, which helps in the detection and remediation of the pollutants present in wastewater. Various nano-material based technologies deployed in wastewater treatment are discussed in this article. Methods: A thorough survey of the literature was effectuated, and the study was focused mainly on the different types of nanomaterials applied for the purpose of wastewater management and the diverse treatment methods related to them. Literature was also studied to confirm the functionalization of nanomaterials as pollution sensors. Results: There are four main kinds of nano-materials employed for the purpose of wastewater remediation, i.e. metallic nanomaterials, carbon-based nanomaterials, nanocomposites, and dendrimers. The treatment technologies utilizing these materials include nanofiltration, nanoadsorption, nano-photocatalysis, and disinfection. Conclusion: Nanomaterials are quite efficient in removing pollutants from different kinds of wastewater. But drawbacks such as expenditure and effect of the materials in the environment make it difficult for real-time utilization. Since the nano-scaled elements behave differently than their standard-sized counterparts, the consequence of these materials in the human life cycle is unknown. This knowledge gap should be filled so that these materials can be adopted worldwide.
Collective Entity Disambiguation Based on Hierarchical Semantic Similarity

Entity disambiguation involves mapping mentions in texts to the corresponding entities in a given knowledge base. Most previous approaches were based on handcrafted features and failed to capture semantic information over multiple granularities. For accurately disambiguating entities, various information aspects of mentions and entities should be used in. This article proposes a hierarchical semantic similarity model to find important clues related to mentions and entities based on multiple sources of information, such as contexts of the mentions, entity descriptions and categories. This model can effectively measure the semantic matching between mentions and target entities. Global features are also added, including prior popularity and global coherence, to improve the performance. In order to verify the effect of hierarchical semantic similarity model combined with global features, named HSSMGF, experiments were carried out on five publicly available benchmark datasets. Results demonstrate the proposed method is very effective in the case that documents have more mentions.
State preparation robust to modulation signal degradation by use of a dual parallel modulator for high-speed BB84 quantum key distribution systems

Security certification of quantum key distribution systems with a practical device is essential for their social deployment. Considering the transmitter, we investigate quantum state generation affected by degraded electrical signals from practical bandwidth-limited devices on high-speed phase-encoding BB84 quantum key distribution systems. The state preparation flaw caused by this degradation undesirably enhances the distinguishability between the two bases for the BB84 protocol and decreases the key generation rate. We propose the state preparation with a dual parallel modulator for increasing the robustness to signal degradation. To verify the effectiveness of the dual parallel modulator, we characterize the generated states using state tomography and estimate the key generation rate based on the Gottesman–Lo–Lütkenhaus–Preskill theory with fidelity derived from the estimated density matrices. Simulation results show that the key generation rate remains unaffected by modulation voltage shifts up to 20%.
Continuous Motion Numeral Recognition Using RNN Architecture in Air-Writing Environment

Air-writing, defined as character tracing in a three dimensional free space through hand gestures, is the way forward for peripheral-independent, virtual interaction with devices. While single unistroke character recognition is fairly simple, continuous writing recognition becomes challenging owing to absence of delimiters between characters. Moreover, stray hand motion while writing adds noise to the input, making accurate recognition difficult. The key to accurate recognition of air-written characters lies in noise elimination and character segmentation from continuous writing. We propose a robust and hardware-independent framework for multi-digit unistroke numeral recognition in air-writing environment. We present a sliding window based method which isolates a small segment of the spatio-temporal input from the air-writing activity for noise removal and digit segmentation. Recurrent Neural Networks (RNN) show great promise in dealing with temporal data and is the basis of our architecture. Recognition of digits which have other digits as their sub-shapes is challenging. Capitalizing on how digits are commonly written, we propose a novel priority scheme to determine digit precedence. We only use sequential coordinates as input, which can be obtained from any generic camera, making our system widely accessible. Our experiments were conducted on English numerals using a combination of MNIST and Pendigits datasets along with our own air-written English numerals dataset (ISI-Air Dataset). Additionally, we have created a noise dataset to classify noise. We observe a drop in accuracy with increase in the number of digits written in a single continuous motion because of noise generated between digit transitions. However, under standard conditions, our system produced an accuracy of 98.45% and 82.89% for single and multiple digit English numerals, respectively.
Towards a Low Complexity Scheme for Medical Images in Scalable Video Coding

Medical imaging has become of vital importance for diagnosing diseases and conducting noninvasive procedures. Advances in eHealth applications are challenged by the fact that Digital Imaging and Communications in Medicine (DICOM) requires high-resolution images, thereby increasing their size and the associated computational complexity, particularly when these images are communicated over IP and wireless networks. Therefore, medical research requires an efficient coding technique to achieve high-quality and low-complexity images with error-resilient features. In this study, we propose an improved coding scheme that exploits the content features of encoded videos with low complexity combined with flexible macroblock ordering for error resilience. We identify the homogeneous region in which the search for optimal macroblock modes is early terminated. For non-homogeneous regions, the integration of smaller blocks is employed only if the vector difference is less than the threshold. Results confirm that the proposed technique achieves a considerable performance improvement compared with existing schemes in terms of reducing the computational complexity without compromising the bit-rate and peak signal-to-noise ratio.
Compliance Cautions: Investigating Security Issues Associated with U.S. Digital-Security Standards

Digital security compliance programs and policies serve as powerful tools for protecting organizations' intellectual property, sensitive resources, customers, and employees through mandated security controls.Organizations place a significant emphasis on compliance and often conflate high compliance audit scores with strong security; however, no compliance standard has been systemically evaluated for security concerns that may exist even within fully-compliant organizations.In this study, we describe our approach for auditing three exemplar compliance standards that affect nearly every person within the United States: standards for federal tax information, credit card transactions, and the electric grid.We partner with organizations that use these standards to validate our findings within enterprise environments and provide first-hand narratives describing impact.We find that when compliance standards are used literally as checklists -a common occurrence, as confirmed by compliance experts -their technical controls and processes are not always sufficient.Security concerns can exist even with perfect compliance.We identified 148 issues of varying severity across three standards; our expert partners assessed 49 of these issues and validated that 36 were present in their own environments and 10 could plausibly occur elsewhere.We also discovered that no clearly-defined process exists for reporting security concerns associated with compliance standards; we report on our varying levels of success in responsibly disclosing our findings and influencing revisions to the affected standards.Overall, our results suggest that auditing compliance standards can provide valuable benefits to the security posture of compliant organizations.
Evaluating the effectiveness of Google, Parsijoo, Rismoon, and Yooz to retrieve Persian documents

Purpose The purpose was to evaluate the effectiveness of Google (as an international search engine) as well as of Parsijoo, Rismoon, and Yooz (as Persian search engines). Design/methodology/approach In this research, Google search engine as an international search engine, and three local ones, Parsijoo, Rismoon, and Yooz, were selected for evaluation. Likewise, 32 subject headings were selected from the Persian Subject Headings List, and then simulated work tasks were assigned based on them. A total of 192 students from Ferdowsi University of Mashhad were asked to search for the information needed for simulated work tasks in the selected search engines, and then to copy the relevant website URLs in the search form. Findings The findings indicated that Google, Parsijoo, Rismoon, and Yooz had a significant difference in the precision, recall, and normalized discounted cumulative gain. There was also a significant difference in the effectiveness (average of precision, recall, and NDCG) of these four search engines in the retrieval of the Persian resources. Practical implications Users using an efficient search engine will attain more relevant documents, and Google search engine was more efficient in retrieving the Persian resources. It is recommended to use Google as it has a more efficient search. Originality/value In this research, for the first time, Google has been compared with local Persian search engines considering the new approach (simulated work tasks).
Steganalysis of Adaptive Multi-Rate Speech Based on Extreme Gradient Boosting

Steganalysis of adaptive multi-rate (AMR) speech is a hot topic for controlling cybercrimes grounded in steganography in related speech streams. In this paper, we first present a novel AMR steganalysis model, which utilizes extreme gradient boosting (XGBoost) as the classifier, instead of support vector machines (SVM) adopted in the previous schemes. Compared with the SVM-based model, this new model can facilitate the excavation of potential information from the high-dimensional features and can avoid overfitting. Moreover, to further strengthen the preceding features based on the statistical characteristics of pulse pairs, we present the convergence feature based on the Markov chain to reflect the global characterization of pulse pairs, which is essentially the final state of the Markov transition matrix. Combining the convergence feature with the preceding features, we propose an XGBoost-based steganalysis scheme for AMR speech streams. Finally, we conducted a series of experiments to assess our presented scheme and compared it with previous schemes. The experimental results demonstrate that the proposed scheme is feasible, and can provide better performance in terms of detecting the existing steganography methods based on AMR speech streams.
Self-Structured Cortical Learning Algorithm by Dynamically Adjusting Columns and Cells

The cortical learning algorithm (CLA) is a type of time-series data prediction algorithm based on the human neocortex. CLA uses multiple columns to represent an input data value at a timestep, and each column has multiple cells to represent the time-series context of the input data. In the conventional CLA, the numbers of columns and cells are user-defined parameters. These parameters depend on the input data, which can be unknown before learning. To avoid the necessity for setting these parameters beforehand, in this work, we propose a self-structured CLA that dynamically adjusts the numbers of columns and cells according to the input data. The experimental results using the time-series test inputs of a sine wave, combined sine wave, and logistic map data demonstrate that the proposed self-structured algorithm can dynamically adjust the numbers of columns and cells depending on the input data. Moreover, the prediction accuracy is higher than those of the conventional long short-term memory and CLAs with various fixed numbers of columns and cells. Furthermore, the experimental results on a multistep prediction of real-world power consumption show that the proposed self-structured CLA achieves a higher prediction accuracy than the conventional long short-term memory.
Guarded Deep Learning using Scenario-based Modeling

Deep neural networks (DNNs) are becoming prevalent, often outperforming manually-created systems. Unfortunately, DNN models are opaque to humans, and may behave in unexpected ways when deployed. One approach for allowing safer deployment of DNN models calls for augmenting them with hand-crafted override rules, which serve to override decisions made by the DNN model when certain criteria are met. Here, we propose to bring together DNNs and the well-studied scenario-based modeling paradigm, by expressing these override rules as simple and intuitive scenarios. This approach can lead to override rules that are comprehensible to humans, but are also sufficiently expressive and powerful to increase the overall safety of the model. We describe how to extend and apply scenario-based modeling to this new setting, and demonstrate our proposed technique on multiple DNN models.
Influence of projector pixel shape on ultrahigh-resolution 3D shape measurement

The state-of-art three-dimensional (3D) shape measurement with digital fringe projection (DFP) techniques assume that the influence of projector pixel shape is negligible. However, our research reveals that when the camera pixel size is much smaller than the projector pixel size in object space (e.g., 1/5), the shape of projector pixel can play a critical role on ultimate measurement quality. This paper evaluates the performance of two shapes of projector pixels: rectangular and diamond shaped. Both simulation and experimental results demonstrated that when the camera pixel size is significantly smaller than the projector pixel size, it is advantageous for ultrahigh resolution 3D shape measurement system to use a projector with rectangular-shaped pixels than a projector with diamond-shaped pixels.
On the number of solutions of two-variable diagonal quartic equations over finite fields

Let $p$ be a odd prime number and let $\mathbb{F}_q$ be the finite field of characteristic $p$ with $q$ elements. In this paper, by using the Gauss sum and Jacobi sum, we give an explicit formula for the number $N(x_1^4+x_2^4 = c)$ of solutions of the following two-variable diagonal quartic equations over $\mathbb{F}_q$: $x_1^4+x_2^4 = c$ with $c\in\mathbb{F}_q^*$. From this result, one can deduce that $N(x_1^4+x_2^4 = c) = q+O(q^{\frac{1}{2}}).$
Wind Profile Tracking in MST Radar Using Viterbi Data Association

The mesosphere-stratosphere-troposphere (MST) radar is a very high frequency (VHF) pulsed coherent Doppler radar used for ground-based remote sensing. In an MST radar, the backscattered pulses due to fluctuations in atmospheric refractive index are observed at the receiver. Echoes from higher altitudes are typically weaker, resulting in a low signal-to-noise ratio (SNR). They could also be contaminated with clutter and interference, making conventional spectral estimation techniques in the Fourier domain unreliable. In this article, we propose an algorithm to estimate the atmospheric wind profile using a dynamic programming approach employing the Viterbi data association (VDA) algorithm. Progressively for each range bin, the proposed algorithm chooses a set of probable wind speeds as nodes in a trellis. A branch metric incorporating the kinematic parameters inclusive of the differential wind shear is then constructed for state transitions within the trellis, from nodes in one range bin to the subsequent ones. A dynamic programming approach then chooses the optimal wind profile across altitudes. The proposed algorithm is experimented on simulated as well as real MST radar data sets to verify its robustness. Our experiments show that the suggested method has improved performance when compared to previously established techniques.