Reviewerly/exHarmony huggingface dataset:
High resolution record of heavy metals from estuary sediments of Nankan River (Taiwan) assessed by rigorous multivariate statistical analysis

This study presents a proof-of-concept data reduction and analysis protocol that can be applied to the study of polluted sediments. Sediment cores from the Nankan River estuary are used as an example of how the protocol can be employed to quantify temporal heavy metal variability. The measurement protocol produces more detailed elemental profiles than conventional techniques using a combination of data transformation techniques and multivariate analysis. Conventional sediment analyses are used to confirm the robustness of the protocol by comparisons of heavy metal concentrations. X-ray fluorescence (XRF) core scanning provides rapid, high-resolution elemental profiles from sediment cores. The technique relies on a variety of calibration methods (ratio, additive and centred log-ratio) to transform the raw data and reduce bias caused by matrix and closed-sum effects. We further test all these calibration approaches since the transformation process is an essential step for the follow up multivariate analyses. The combination of principal component and cluster analysis objectively assesses the information implicit in the dataset. The settings in each procedure are optimized to account for the variance of the dataset. This optimization protocol explains the heavy metal trends using the sediment characteristics of the cores. Heavy metal pollution is characterized by three periods and classified by their oxidation states. We show that heavy metals have an affinity with fine-grained sediments and Mn. The interpretation is confirmed by grain size analysis and inductively coupled plasma optical emission spectrometry (ICP-OES) measurements. This study provides an impartial, cost- and time-effective protocol suitable for the analysis of other heavy metal polluted sites and further studies using sediment cores as archives.
Bilingual Dissection of Words with Biblical Correlation

In the language of China, GOD神 was initially written with two hands holding worshiping item.Its translated words related to GOD also show duality.We allocate the cause of such duality to the covenant of GOD with Abram, when "Abram brought all these to him, cut them in two and arranged the halves opposite each other".Astonishingly, we notice that numerous words match this verse and much more extensively, the whole holy bible including Genesis, Exodus, Leviticus and Numbers.We present examples of such matching to cutting offering, burning offering and divine appearance of LORD in cloud.The widespread coincidences of bi-linguistic match and biblical match can only be achieved by GOD.
Complexities in Projection-Free Stochastic Non-convex Minimization

For constrained nonconvex minimization problems, we propose a meta stochastic projection-free optimization algorithm, named Normalized Frank Wolfe Updating, that can take any Gradient Estimator (GE) as input. For this algorithm, we prove its convergence rate, regardless of the choice of GE. Using a sophisticated GE, this algorithm can significantly improve the Stochastic First order Oracle (SFO) complexity. Further, a new second order GE strategy is proposed to incorporate curvature information, which enjoys theoretical advantage over the first order ones. Besides, this paper also provides a lower bound of Linear-optimization Oracle (LO) queried to achieve an approximate stationary point. Simulation studies validate our analysis under various parameter settings.
An Inherent Difficulty in the Aggregation of Multidimensional Data

In the field of information fusion, the problem of data aggregation has been formalized as an order-preserving process that builds upon the property of monotonicity.However, fields such as computational statistics, data analysis, and geometry usually emphasize the role of equivariances to various geometrical transformations in aggregation processes.Admittedly, if we consider a unidimensional data fusion task, both requirements are often compatible with each other.Nevertheless, in this paper, we show that, in the multidimensional setting, the only idempotent functions that are monotone and orthogonal equivariant are the over-simplistic weighted centroids.Even more, this result still holds after replacing monotonicity and orthogonal equivariance by the weaker property of orthomonotonicity.This implies that the aforementioned approaches to the aggregation of multidimensional data are irreconcilable, and that, if a weighted centroid is to be avoided, we must choose between monotonicity and a desirable behavior with regard to orthogonal transformations.
Finding and Tracking Multi-Density Clusters in Online Dynamic Data Streams

Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and tracking</i> change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream. Density-based clustering identifies clusters as areas of high density separated by areas of low density. This paper proposes a Multi-Density Stream Clustering (MDSC) algorithm to address these two problems; the multi-density problem and the problem of discovering and tracking changes in a dynamic stream. MDSC consists of two on-line components; discovered, labelled clusters and an outlier buffer. Incoming points are assigned to a live cluster or passed to the outlier buffer. New clusters are discovered in the buffer using an ant-inspired swarm intelligence approach. The newly discovered cluster is uniquely labelled and added to the set of live clusters. Processed data is subject to an ageing function and will disappear when it is no longer relevant. MDSC is shown to perform favourably to state-of-the-art peer stream-clustering algorithms on a range of real and synthetic data-streams. Experimental results suggest that MDSC can discover qualitatively useful patterns while being scalable and robust to noise.
Advances and applications for automated drones in underground mining operations

The development and current state of the Hovermap autonomous flight system in underground and GPS-denied areas is discussed, with examples obtained during the development and early adoption of the system.The current performance of the system and subsequent data interpretation suggest some scenarios in which Hovermap deployment is appropriate and have been proven.The examples focus principally on improving safety through a better understanding of the rock mass behaviour and failure mechanisms commonly encountered in deep and high-stress mining conditions, and from feeding these insights back into the design process.Recent and future developments in the hardware and software platforms and the associated data analytics are also outlined
Randomized polynomial-time root counting in prime power rings

Suppose $k,p\!\in \!\mathbb {N}$ with $p$ prime and $f\!\in \!\mathbb {Z}[x]$ is a univariate polynomial with degree $d$ and all coefficients having absolute value less than $p^k$. We give a Las Vegas randomized algorithm that computes the number of roots of $f$ in $\mathbb {Z}/\!\left (p^k\right )$ within time $d^3(k\log p)^{2+o(1)}$. (We in fact prove a more intricate complexity bound that is slightly better.) The best previous general algorithm had (deterministic) complexity exponential in $k$. We also present some experimental data evincing the potential practicality of our algorithm.
Explaining vulnerabilities of deep learning to adversarial malware binaries

Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that enable misleading classification. Although this has questioned their suitability for this task, it is not yet clear why such algorithms are easily fooled also in this particular application domain. In this work, we take a first step to tackle this issue by leveraging explainable machine-learning algorithms developed to interpret the black-box decisions of deep neural networks. In particular, we use an explainable technique known as feature attribution to identify the most influential input features contributing to each decision, and adapt it to provide meaningful explanations to the classification of malware binaries. In this case, we find that a recently-proposed convolutional neural network does not learn any meaningful characteristic for malware detection from the data and text sections of executable files, but rather tends to learn to discriminate between benign and malware samples based on the characteristics found in the file header. Based on this finding, we propose a novel attack algorithm that generates adversarial malware binaries by only changing few tens of bytes in the file header. With respect to the other state-of-the-art attack algorithms, our attack does not require injecting any padding bytes at the end of the file, and it is much more efficient, as it requires manipulating much fewer bytes.
DeepSeed Local Graph Matching for Densely Packed Cells Tracking

The tracking of densely packed plant cells across microscopy image sequences is very challenging, because their appearance change greatly over time. A local graph matching algorithm was proposed to track such cells by exploiting the tight spatial topology of neighboring cells, and then an iterative searching strategy was used to grow the correspondence from a seed cell pair. Thus, the performance of the existing tracking approach heavily relies on the robustness of finding seed cell pair. However, the existing local graph matching algorithm cannot guarantee the correctness of the seed cell pair, especially in unregistered image sequences or image sequences with large time intervals. In this paper, we propose a DeepSeed local graph matching model to find seed cell pair robustly, by combining local graph matching and CNN-based similarity learning, which uses cells’ spatial-temporal contextual information and cell pairs’ similarity information. The CNN-based similarity learning is designed to learn cells’ deep feature and measure cell pairs’ similarity. Compared with the existing plant cell matching methods, the experimental results show that the DeepSeed local graph matching method can track most cells in unregistered image sequences. Moreover, the DeepSeed tracking algorithm can accurately track cells across image sequences with large time intervals.
A Phish Scale: Rating Human Phishing Message Detection Difficulty

As organizations continue to invest in phishing awareness training programs, many Chief Information Security Officers (CISOs) are concerned when their training exercise click rates are high or variable, as they must justify training budgets to those who question the efficacy of training when click rates are not declining.We argue that click rates should be expected to vary based on the difficulty of the phishing email for a target audience.Past research has shown that when the premise of a phishing email aligns with a user's work context, it is much more challenging for users to detect a phish.Given this, we propose a Phish Scale, so CISOs and phishing training implementers can easily rate the difficulty of their phishing exercises and help explain associated click rates.We based our scale on past research in phishing cues and user context, and applied it to previously published data and new data from organization-wide phishing exercises targeting approximately 5 000 employees.The Phish Scale performed well with the current phishing dataset, but future work is needed to validate it with a larger variety of phishing emails.The Phish Scale shows great promise as a tool to help frame data sharing on phishing exercise click rates across sectors.
Automatic Segmentation of the Left Ventricle From Cardiac MRI Using Deep Learning and Double Snake Model

The left ventricle segmentation (LVS) is of great important for the evaluation of cardiac function.This study aimed to establish new segmentation algorithms that can enhance the accuracy and robustness of automatic LVS on magnetic resonance images.The datasets involved 45 subjects, including 12 heart failure patients with ischemia, 12 heart failure patients without ischemia, 12 hypertrophy patients and 9 normal individuals.The experiments consisted of three important steps.At first, deep learning was employed for the coarse LVS on myocardial images.Next, a double snake model was applied to assess the endo-and epi-cardial boundaries.Finally, the optimal epicardial boundary was obtained by adopting radial region growing method.Additionally, the performance of the developed LVS method was evaluated by the previously established software.Furthermore, the developed LVS method was validated by applying the datasets of 45 subjects.The results showed that the good contours, overlapping dice metric and average perpendicular distance of both epi-and endo-cardial contours were approximately 97%, 0.97 and 1.8 mm respectively.The regression coefficient and coefficient of determination between the proposed method and clinical experts were 0.96 and 1.039, respectively for ejection fraction, while 0.92 and 0.994 for left ventricle mass.These findings reveal that the developed method can enhance the accuracy and robustness of LVS.This novel LVS approach exhibits outstanding performance and possesses promising potential to increase the reliability of computer-aided imaging detection system for cardiovascular disease.
An Attack-Based Evaluation Method for Differentially Private Learning Against Model Inversion Attack

As the amount of data and computational power explosively increase, valuable results are being created using machine learning techniques.In particular, models based on deep neural networks have shown remarkable performance in various domains.On the other hand, together with the development of neural network models, privacy concerns have been raised.Recently, as privacy breach attacks on training datasets of neural network models have been proposed, research on privacy-preserving neural networks have been conducted.Among the privacy-preserving approaches, differential privacy provides a strict privacy guarantee, and various differentially private mechanisms have been studied for neural network models.However, it is not clear how appropriate privacy parameters should be chosen, considering the model's performance and the degree of privacy guarantee.In this paper, we study how to set appropriate privacy parameters to preserve differential privacy based on the resistance to privacy breach attacks in neural networks.In particular, we focus on the model inversion attack for neural network models, and study how to apply differential privacy as a countermeasure against this attack while retaining the utility of the model.In order to quantify the resistance to the model inversion attack, we introduce a new attack performance metric, instead of a survey-based approach, by leveraging a deep learning model, and capture the relationship between attack probability and the degree of privacy guarantee.
Robust and efficient data transmission over noisy communication channels using stacked and denoising autoencoders

We study the effects of quantization and additive white Gaussian noise (AWGN) in transmitting latent representations of images over a noisy communication channel. The latent representations are obtained using autoencoders (AEs). We analyze image reconstruction and classification performance for different channel noise powers, latent vector sizes, and number of quantization bits used for the latent variables as well as AEs' parameters. The results show that the digital transmission of latent representations using conventional AEs alone is extremely vulnerable to channel noise and quantization effects. We then propose a combination of basic AE and a denoising autoencoder (DAE) to denoise the corrupted latent vectors at the receiver. This approach demonstrates robustness against channel noise and quantization effects and enables a significant improvement in image reconstruction and classification performance particularly in adverse scenarios with high noise powers and significant quantization effects.
Smartphones Identification Through the Built-In Microphones With Convolutional Neural Network

The use of mobile phones or smartphones has become so widespread that most people rely on them for many services and applications like sending e-mails, checking the bank account, accessing cloud platforms, health monitoring, buying on-line and many other applications where sharing sensitive data is required.As a consequence, security functions are important in the use of smartphones, especially because most of the applications require the identification and authentication of the device in mobility.This is usually achieved through cryptographic systems but recent research studies have also investigated alternative or complementary authentication mechanisms which can be used to strengthen cryptographic methods with multi-factor authentication.In this paper, we investigate the identification and the authentication of smartphones using the intrinsic physical properties of the mobile phones built-in microphones.The possibility to identify a microphone on the basis of features extracted from audio recordings is well known in literature but it is mostly used in forensics studies and usually relies on human voice recordings.On the contrary this paper proposes a smartphone identification and authentication approach by stimulating the built-in microphone with non-voice sounds at different frequencies.An extensive data set of 32 phones was used to evaluate experimentally the proposed approach.On the basis of the proven performance of deep learning techniques, a new Convolutional Neural Network architecture is proposed both for the identification and the authentication purposes.Its performance, in comparison to other machine learning algorithms, is demonstrated in presence of different types of noises (e.g., Gaussian White noise, Babble noise and Street noise).Satisfactory results have been obtained showing that the exploitation of a fingerprint from the microphone sensor is a good choice to assess smartphone distinctiveness.
Variation in attention at encoding: Insights from pupillometry and eye gaze fixations.

In 2 experiments, eye-tracking was used to examine individual differences in attention during encoding and their relation to associative learning. Pupillary responses were used as an indicator of the amount of attention devoted to items, whereas eye fixations provided a means of assessing attentional focus among items within each to-be-remembered word pair. In both experiments, participants performed a paired associates (PA) cued recall task while pupil diameter and eye fixations were simultaneously recorded. Results from Experiment 1 revealed that larger pupillary responses at encoding, as well as more alternating fixations between the Cue and Target items for each word pair (i.e., switches), was associated with increased learning ability. Critically, while Experiment 2 revealed that effective strategy use partially accounted for the relationship between pupillary responses and PA recall accuracy, pupillary responses still explained unique variance in PA recall accuracy-a result that held even when controlling for the influence of working memory capacity and long-term memory ability. Collectively, the results suggest attentional abilities at encoding are vital for successful learning. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
ROI-Based Intraoperative MR-CT Registration for Image-Guided Multimode Tumor Ablation Therapy in Hepatic Malignant Tumors

Image-guided ablation therapy has been widely used as a minimally invasive treatment for hepatic malignant tumors. Image registration is very useful during such operations, especially for multimode tumor ablation therapy. This research proposes a novel idea for performing MR-CT registration in multimode tumor ablation therapy based on liver segmentation. First, the liver is segmented from the preoperative 3D MR image and the intraoperative 3D CT image using a deep learning method based on a modified UNet++ architecture. Then, the preoperative MR image and the intraoperative CT image are coregistered using rigid and nonrigid registration methods with the segmented liver as the region of interest. The segmented binary images, rather than gray-level images, are aligned in the rigid registration step, which proves to be faster and more accurate than the registration method based on gray information. For the nonrigid registration, a multilevel free-form deformation method is applied to correct tiny misalignments. Finally, our method was validated using clinical data from 15 patients. The proposed method achieved an average Dice coefficient and target registration error of 93.36±1.21% and 4.42±2.35 mm, respectively, and it can help interventional radiologists adjust the probe position in clinical work.
RNNs Incrementally Evolving on an Equilibrium Manifold: A Panacea for Vanishing and Exploding Gradients?

Recurrent neural networks (RNNs) are particularly well-suited for modeling long-term dependencies in sequential data, but are notoriously hard to train because the error backpropagated in time either vanishes or explodes at an exponential rate. While a number of works attempt to mitigate this effect through gated recurrent units, skip-connections, parametric constraints and design choices, we propose a novel incremental RNN (iRNN), where hidden state vectors keep track of incremental changes, and as such approximate state-vector increments of Rosenblatt's (1962) continuous-time RNNs. iRNN exhibits identity gradients and is able to account for long-term dependencies (LTD). We show that our method is computationally efficient overcoming overheads of many existing methods that attempt to improve RNN training, while suffering no performance degradation. We demonstrate the utility of our approach with extensive experiments and show competitive performance against standard LSTMs on LTD and other non-LTD tasks.
Social network analysis for personalized characterization and risk assessment of alcohol use disorders in adolescents using semantic technologies

Alcohol Use Disorder (AUD) is a major concern for public health organizations worldwide, especially as regards the adolescent population. The consumption of alcohol in adolescents is known to be influenced by seeing friends and even parents drinking alcohol. Building on this fact, a number of studies into alcohol consumption among adolescents have made use of Social Network Analysis (SNA) techniques to study the different social networks (peers, friends, family, etc.) with whom the adolescent is involved. These kinds of studies need an initial phase of data gathering by means of questionnaires and a subsequent analysis phase using the SNA techniques. The process involves a number of manual data handling stages that are time consuming and error-prone. The use of knowledge engineering techniques (including the construction of a domain ontology) to represent the information, allows the automation of all the activities, from the initial data collection to the results of the SNA study. This paper shows how a knowledge model is constructed, and compares the results obtained using the traditional method with this, fully automated model, detailing the main advantages of the latter. In the case of the SNA analysis, the validity of the results obtained with the knowledge engineering approach are compared to those obtained manually using the UCINET, Cytoscape, Pajek and Gephi to test the accuracy of the knowledge model.