Reviewerly/exHarmony huggingface dataset:
A Novel Approach to Detect Phishing Attack Using Artificial Neural Networks Combined with Pharming Detection

Phishing is the most common yet a major cyber crime. In this fraudulent practice, the perpetrator sends an e-mail to the target posing as a legitimate organization. This email contains a URL link to the phishing website which the user is prompted to visit and is induced to reveal private information, such as passwords, card numbers, etc. The plot of this attack is that the phishing website appears exactly the same as that of the legitimate one to avoid any kind of suspicion. However, the URL features of both websites are different. These differences can be a strong basis for classifying a phishing website accurately and effectively. In our research, we train these features on an Artificial Neural Network(ANN) for accurate classification. Pharming is a special type of phishing attack or DNS poisoning in which the user is redirected to a fake website by changing the IP address at the DNS server. We implement provisions to detect pharming to provide overall protection to the user.
GraphTinker: A High Performance Data Structure for Dynamic Graph Processing

Interest in high performance analytics for dynamic (constantly evolving) graphs has been on the rise in the last decade, especially due to the prevalence and rapid growth of social networks today. The current state-of-the art data structures for dynamic graph processing rely on the adjacency list model of edge blocks in updating graphs. This model suffers from long probe distances when following edges, leading to poor update throughputs. Furthermore, both current graph processing models-the static model that requires reprocessing the entire graph after every batch update, and the incremental model in which only the affected subset of edges need to be processed-suffer drawbacks. In this paper, we present GraphTinker, a new, more scalable graph data structure for dynamic graphs. It uses a new hashing scheme to reduce probe distance and improve edge-update performance. It also better compacts edge data. These innovations improve performance for graph updates as well as graph analytics. In addition, we present a hybrid engine which improves the performance of dynamic graph processing by automatically selecting the most optimal execution model (static vs. incremental) for every iteration, surpassing the performance of both. Our evaluations of GraphTinker shows a throughput improvement of up to 3.3X compared to the stateof-the-art data structure (STINGER) when used for graph updates. GraphTinker also demonstrates a performance improvement of up to 10X over STINGER when used to run graph analytics algorithms. In addition, our hybrid engine demonstrates up to 2X improvement over the incremental compute model and up to 3X improvement over the static model.
Technological Prerequisites and Humanitarian Consequences of Ubiquitous Computing and Networking

Ubiquitous computing and networking prerequisites and consequences are analyzed in the article. Special attention is paid to those of them that have the strongest influence on human being. So, technological, ontological and anthropological aspects of modern ubiquitous computing and networking are discussed. The authors accent humanitarian essence of the modern technologies, study their place and role in the lives of modern humans. From the technological point of view 3D printing, Big Data, Cloud Computing and Cognitive Technologies are analyzed. From the ontological point of view the problems of alternative realities’ formation and the Internet of Things development are considered. From the anthropological point of view the problems of robotization, Artificial Intelligence, genetic engineering and synthetic biology are the point of the special research interest.
A Risk-Aware Pairwise Rank Learning Approach for Visual Discomfort Prediction of Stereoscopic 3D

For visual discomfort prediction (VDP) of stereoscopic 3D images, a common two-stage framework is to first extract features that are predictive of the experienced visual discomfort level when viewing stereoscopic images and then use typical regression tools to learn the mapping from the extracted features to visual discomfort scores. Most existing approaches for stereoscopic 3D VDP focus on the former stage, i.e., feature extraction, while limited efforts have dedicated to exploiting more powerful and robust learning algorithms in this field. In this letter, inspired by the pairwise comparison-based subjective evaluation methodology, we propose a novel Risk-Aware Pairwise Rank Learning (RAPRL) approach to further improve the prediction accuracy. Unlike the traditional VDP approaches using different regression tools for feature-score mapping, our proposed RARL method addresses this problem based on a completely different pairwise rank learning framework with a risk-aware constraint. Experiments have verified the effectiveness and robustness of our proposed VDP model using RAPRL as the learning algorithm.
Real-time algorithm for Poissonian noise reduction in low-dose fluoroscopy: performance evaluation

Quantum noise intrinsically limits the quality of fluoroscopic images. The lower is the X-ray dose the higher is the noise. Fluoroscopy video processing can enhance image quality and allows further patient's dose lowering. This study aims to assess the performances achieved by a Noise Variance Conditioned Average (NVCA) spatio-temporal filter for real-time denoising of fluoroscopic sequences. The filter is specifically designed for quantum noise suppression and edge preservation. It is an average filter that excludes neighborhood pixel values exceeding noise statistic limits, by means of a threshold which depends on the local noise standard deviation, to preserve the image spatial resolution. The performances were evaluated in terms of contrast-to-noise-ratio (CNR) increment, image blurring (full width of the half maximum of the line spread function) and computational time. The NVCA filter performances were compared to those achieved by simple moving average filters and the state-of-the-art video denoising block matching-4D (VBM4D) algorithm. The influence of the NVCA filter size and threshold on the final image quality was evaluated too.For NVCA filter mask size of 5 × 5 × 5 pixels (the third dimension represents the temporal extent of the filter) and a threshold level equal to 2 times the local noise standard deviation, the NVCA filter achieved a 10% increase of the CNR with respect to the unfiltered sequence, while the VBM4D achieved a 14% increase. In the case of NVCA, the edge blurring did not depend on the speed of the moving objects; on the other hand, the spatial resolution worsened of about 2.2 times by doubling the objects speed with VBM4D. The NVCA mask size and the local noise-threshold level are critical for final image quality. The computational time of the NVCA filter was found to be just few percentages of that required for the VBM4D filter.The NVCA filter obtained a better image quality compared to simple moving average filters, and a lower but comparable quality when compared with the VBM4D filter. The NVCA filter showed to preserve edge sharpness, in particular in the case of moving objects (performing even better than VBM4D). The simplicity of the NVCA filter and its low computational burden make this filter suitable for real-time video processing and its hardware implementation is ready to be included in future fluoroscopy devices, offering further lowering of patient's X-ray dose.
Economic geology of southern Saskatchewan potash mines

Canadian commercial potash deposits are located in the southern Saskatchewan area of a Middle Devonian evaporite basin that extends across western Canada and into adjacent areas of northeastern Montana and western North Dakota. There are eleven mines in Saskatchewan that annually produce approximately 23 Mt of KCl from halite-sylvite ore associated with the 60 m thick upper interval of the Prairie Evaporite Formation. Of these, eight mines are conventional underground operations at 900–1100 m depths. Solution mining is used at three sites where the ore is buried to 1500 m depth or where this methodology was a necessary remedial action responding to flooding of an underground dry mine. Two new underground mines are currently under construction. The Prairie Evaporite Formation accumulated during the Middle Devonian along an evaporating equatorial inland sea that extended onto the Laurentia paleocontinent. Development of a 400 km long reef at the northern seaward end barred the basin, permitting accumulation of up to 200 m of halite-dominated evaporite beds across north-central Alberta, southern Saskatchewan and into adjacent areas of the United States. Late cycle potassium-rich brines concentrated within the southern Saskatchewan sub-basin, resulting in accumulation of potash ore zones as the uppermost interval of the 100–200 m thick halite-anhydrite deposit. Early diagenetic processes resulted in widespread concentration of sylvite-rich beds upon leaching of Mg chloride from carnallite-rich deposits, resulting in commercially attractive potash ore. Subsequent post-burial dissolution of halite-dominated beds occurred across large areas of Alberta and southern Saskatchewan, including portions of the potash mining districts. Natural dissolution collapse-subsidence structures up to 10 s km long may have permitted some mining areas to be in contact with basin brines. Brines may have also mixed with descending groundwater sourced from Cretaceous strata up-section if the carbonate seals of the overlying Dawson Bay Formation were compromised by fracturing. Mining methodologies are designed to minimize the mine-scale subsidence upon ore removal and limit potential fracturing of overlying Dawson Bay limestone beds. Brine seeps into the underground mines vary from controllable nuisances to catastrophic flooding of underground workings. Remedial actions such as grouting generally control the seeps, but uncontrollable ingress has resulted in one underground operation being converted into a solution mine.
Polarization Responses of a Solitary and Optically Injected Vertical Cavity Spin Laser

The polarisation properties of a quantum well spin -vertical cavity surface emitting laser (spin -VCSEL), both without injection and with variable polarisation optical injection, are investigated experimentally and compared with the spin flip model (SFM).Without injection, we demonstrate two distinct types of VCSEL-pump response depending on the signs of the linewidth enhancement factor, birefringence and dichroism: firstly where the pump and VCSEL have the same sign of the ellipticity, and secondly where the VCSEL ellipticity, accompanied by the linear polarisation, switches sign.We show that by controlling the injected power, ellipticity or linear angle, near circular polarisation can be obtained.These responses both give insight into the electro-optical injected spin-VCSEL system, and have practical implications for the use of spin VCSELs in unique applications exploiting the ellipticity degree of freedom.
Multi-robot Joint Localization and Target Tracking with Local Sensing and Communication

In this paper, we study the problem of target state estimation using a mobile robot network, where a team of robots jointly localize themselves and track multiple targets with onboard sensor measurements. We consider a general case, where 1) Absolute measurements might be accessible intermittently; 2) Any robot might detect (or be detected by) multiple robots synchronously; 3) There exists a time-varying communication topology between robots; 4) The robots not directly sensing the targets might change with time. A fully Distributed Hybrid Extended Information Fusion(DHEIF) algorithm is introduced. Unlike most existing works, we do not assume that each robot has good knowledge of its own state. Hence, each robot establishes a filter bank to estimate its own state (localization) and the target states (tracking). To avoid the measurements being used by one robot more than once, the Interleaved Update(IU) technique is adopted in the localization part to compute a consistent estimate. Through communicating with its neighbors, each robot maintains consistent state estimates of the targets even if they are outside the visibility range. The performance of our algorithm is tested in simulations.
Reliability maintenance aspects of deck machinery for ageing/aged fishing vessels

This paper addresses the reliability aspects regarding deck machinery on ageing/aged fishing vessels. A reliability and degradation analysis for ageing fishing vessel deck machinery enables an evaluation of the reliability trend for the remaining useful life of the deck machinery. The research was limited to the maintenance of cranes, winches and capstans and failure data for fishing vessels whose age was 24 years and above. The reliability and degradation of deck machinery for ageing fishing vessels was analysed using Weibull and Gamma distributions. Deck machinery maintenance and failure data collected from fishing vessels at Walvis Bay were statistically fitted using ReliaSoft from which an analysis of deck machinery reliability was done. Reliability and degradation results of deck machinery indicate that the majority of the fishing vessels are using deck machinery beyond their design life. For the two mission end times 4700 h and 23300 h, winches and cranes have lower reliability results, less than 50% compared to capstans. This is attributed to the age of the fishing vessels and maintenance laxity towards deck machinery which poses risks to both operators and the vessel crew. The maintenance of ageing fishing vessel deck machinery should be given priority in order to extend their time available for the better fishing operations.
An Empirical Approach to Phishing Countermeasures Through Smart Glasses and Validation Agents

Phishing attacks have been persistent for more than two decades despite mitigation efforts from academia and industry.We believe that users fall victim to attacks not only because of lack of knowledge and awareness, but also because they are not attentive enough to security indicators and visual abnormalities on the webpages they visit.This is also probably why smart device users, who have more limited screen size and device capabilities compared to desktop users, are three times more likely to fall victim to phishing attacks.To assert our claim, we first investigated general phishing awareness among different groups of smartphone users.We then used smart eyeglasses (electro-oculographic) to experimentally measure the mental effort and vigilance exhibited by users while surfing a website and while playing an Android phishing game that we developed.The results showed that knowledge and awareness about phishing do not seem to have a significant impact on security behaviours, as knowledgeable participants exhibited insecure behaviours such as opening email attachments from unfamiliar senders.However, attentiveness was important as even participants with low cybersecurity knowledge could effectively identify attacks if they were reasonably attentive.Based on these results, we asserted that users are more likely to continue falling victim to phishing attacks due to insecure behaviours, unless tools to lessen the identification burden are provided.We thus recommended implementing a lightweight algorithm into a custom Android browser for detecting phishing sites deceptively without a user interaction.We used fake login credentials as validation agents and monitor the destination server HTTP responses to determine the authenticity of a webpage.We also presented initial evaluation results of this algorithm.
A Double-Variational Bayesian Framework in Random Fourier Features for Indefinite Kernels

Random Fourier features (RFFs) have been successfully employed to kernel approximation in large-scale situations. The rationale behind RFF relies on Bochner's theorem, but the condition is too strict and excludes many widely used kernels, e.g., dot-product kernels (violates the shift-invariant condition) and indefinite kernels [violates the positive definite (PD) condition]. In this article, we present a unified RFF framework for indefinite kernel approximation in the reproducing kernel Kreĭn spaces (RKKSs). Besides, our model is also suited to approximate a dot-product kernel on the unit sphere, as it can be transformed into a shift-invariant but indefinite kernel. By the Kolmogorov decomposition scheme, an indefinite kernel in RKKS can be decomposed into the difference of two unknown PD kernels. The spectral distribution of each underlying PD kernel can be formulated as a nonparametric Bayesian Gaussian mixtures model. Based on this, we propose a double-infinite Gaussian mixture model in RFF by placing the Dirichlet process prior. It takes full advantage of high flexibility on the number of components and has the capability of approximating indefinite kernels on a wide scale. In model inference, we develop a non-conjugate variational algorithm with a sub-sampling scheme for the posterior inference. It allows for the non-conjugate case in our model and is quite efficient due to the sub-sampling strategy. Experimental results on several large classification data sets demonstrate the effectiveness of our nonparametric Bayesian model for indefinite kernel approximation when compared to other representative random feature-based methods.
Mining Multispectral Aerial Images for Automatic Detection of Strategic Bridge Locations for Disaster Relief Missions

We propose in this paper an image mining technique based on multispectral aerial images for automatic detection of strategic bridge locations for disaster relief missions. Bridge detection from aerial images is a key landmark that has vital importance in disaster management and relief missions. UAVs have been increasingly used in recent years for various relief missions during the natural disasters such as floods and earthquakes and a huge amount of multispectral aerial images are generated by UAVs in the missions. Being a multi- stage technique, our method utilizes these multispectral aerial images for identifying patterns for effective mining of bridge locations. Experimental results on real-world and synthetic images are conducted to demonstrate the effectiveness of our proposed method, showing that it is 40% faster than the existing Automatic Target Recognition (ATR) systems and can achieve a 95% accuracy. Our technique is believed to be able to help accelerate and enhance the effectiveness of the relief missions carried out during disasters.
Embedding Distortion Analysis in Wavelet-domain Watermarking

Imperceptibility and robustness are two complementary fundamental requirements of any watermarking algorithm. Low-strength watermarking yields high imperceptibility, but exhibits poor robustness. High-strength watermarking schemes achieve good robustness but often infuse distortions resulting in poor visual quality in host images. This article analyses the embedding distortion for wavelet-based watermarking schemes. We derive the relationship between distortion, measured in mean square error (MSE), and the watermark embedding modification and propose the linear proportionality between MSE and the sum of energy of the selected wavelet coefficients for watermark embedding modification. The initial proposition assumes the orthonormality of discrete wavelet transform. It is further extended for non-orthonormal wavelet kernels using a weighting parameter that follows the energy conservation theorems in wavelet frames. The proposed analysis is verified by experimental results for both non-blind and blind watermarking schemes. Such a model is useful to find the optimum input parameters, including the wavelet kernel, coefficient selection, and subband choices for wavelet domain image watermarking.
Automatically Identifying Security Checks for Detecting Kernel Semantic Bugs

OS kernels enforce a large number of security checks to validate system states. We observe that security checks are in fact very informative in inferring critical semantics in OS kernels. Specifically, security checks can reveal (1) whether an operation or a variable is critical but can be erroneous, (2) what particular errors may occur, and (3) constraints that should be enforced for the uses of a variable or a function. Such information is particularly valuable for detecting kernel semantic bugs because the detection typically requires understanding critical semantics. However, identifying security checks is challenging due to not only the lack of clear criteria but also the diversity of security checks. In this paper, we first systematically study security checks and propose a mostly-automated approach to identify security checks in OS kernels. Based on the information offered by the identified security checks, we then develop multiple analyzers that detect three classes of common yet critical semantic bugs in OS kernels, including NULL-pointer dereferencing, missing error handling, and double fetching. We implemented both the identification and the analyzers as LLVM passes and evaluated them using the Linux kernel and the FreeBSD kernel. Evaluation results show that our security-check identification has very low false-negative and false-positive rates. We also have found 164 new semantic bugs in both kernels, 88 of which have been fixed with our patches. The evaluation results confirm that our system can accurately identify security checks, which helps effectively identify numerous critical semantic bugs in complex OS kernels.
Potential of VNIR spectroscopy for prediction of clay mineralogy and magnetic properties, and its paleoclimatic application to two contrasting Quaternary soil deposits

The properties of phyllosilicates, iron oxides (oxyhydroxides), and magnetic minerals in soils can respond to changes in pedogenic weathering intensity and past climate. Numerous paleoclimate reconstructions using soil mineralogy have made use of only one or two of these mineral types, generally in order to limit analytical effort or expense, thereby foregoing comparisons between different mineralogic proxies. Quaternary soil deposits in China afford a unique opportunity for assessing the potential of visible and near-infrared reflectance (VNIR) spectroscopy for analysis of soil minerals, chemical weathering, and paleoclimate reconstruction. Here, we investigated two typical Quaternary soil types exhibiting different weathering degrees, i.e., a loess-paleosol sequence in northern China and a red earth deposit in southern China. Clay and iron mineralogy from the soils was compared with VNIR data using partial least squares regression (PLSR) and continuum-removal-based multiple linear regression (MLRCR) approaches. VNIR spectra show potential for analysis of pedogenic weathering and paleoclimate reconstruction in weakly to highly weathered soils in that spectral behavior is strongly related to both clay-mineral composition and magnetic properties. Both higher concentrations of minerals and their wider variability can improve model predictive ability. Phyllosilicates and non-magnetic Fe-oxides are predicted more accurately for highly weathered red earth deposits, whereas soil magnetic properties are predicted more accurately for loess-paleosols. The predictors identified by the MLRCR method (D2200, D680, and D900) and the ranges constrained by the PLSR method (650–700 and 2150–2250 nm) deserve more attention in future studies of spectral pedology and chemical weathering. We suggest that VNIR spectroscopy has the potential to be an alternative and broadly applicable tool for reconstructing paleoclimate using soil-mineral properties, substituting for a series of common mineralogic analyses. This would significantly reduce the workload and expense of experiments and improve the resolution of paleoclimate studies.