Reviewerly/exHarmony huggingface dataset:
Understanding phishers' strategies of mimicking uniform resource locators to leverage phishing attacks: A machine learning approach

Abstract Phishing is a type of social engineering attack with an intention to steal user data, including login credentials and credit card numbers, leading to financial losses for both organizations and individuals. It occurs when an attacker, pretending as a trusted entity, lure a victim into click on a link or attachment in an email, or in a text message. Phishing is often launched via email messages or text messages over social networks. Previous research has revealed that phishing attacks can be identified just by looking at uniform resource locator (URLs). Identifying the techniques which are used by phishers to mimic a phishing URL is rather a challenging issue. At present, we have limited knowledge and understanding of how cyber‐criminals attempt to mimic URLs with the same look and feel of the legitimate ones, to entice people into clicking links. Therefore, this paper investigates the feature selection of phishing URLs (uniform resource locators), aiming to explore the strategies employed by phishers to mimic URLs that can obviously trick people into clicking links. We employed an information gain (IG) and Chi‐Squared feature selection methods in machine learning (ML) on a phishing dataset. The dataset contains a total of 48 features extracted from 5000 phishing and another 5000 legitimate URL from web pages downloaded from January to May 2015 and from May to June 2017. Our results revealed that there were 10 techniques that phishers used to mimic URLs to manipulate humans into clicking links. Identifying these phishing URL manipulation techniques would certainly help to educate individuals and organizations and keep them safe from phishing attacks. In addition, the findings of this research will also help develop anti‐phishing tools, framework or browser plugins for phishing prevention.
Compression or Corruption? A Study on the Effects of Transient Faults on BNN Inference Accelerators

Over past years, the philosophy for designing the artificial intelligence algorithms has significantly shifted towards automatically extracting the composable systems from massive data volumes. This paradigm shift has been expedited by the big data booming which enables us to easily access and analyze the highly large data sets. The most well-known class of big data analysis techniques is called deep learning. These models require significant computation power and extremely high memory accesses which necessitate the design of novel approaches to reduce the memory access and improve power efficiency while taking into account the development of domain-specific hardware accelerators to support the current and future data sizes and model structures. The current trends for designing application-specific integrated circuits barely consider the essential requirement for maintaining the complex neural network computation to be resilient in the presence of soft errors. The soft errors might strike either memory storage or combinational logic in the hardware accelerator that can affect the architectural behavior such that the precision of the results fall behind the minimum allowable correctness. In this study, we demonstrate that the impact of soft errors on a customized deep learning algorithm called Binarized Neural Network might cause drastic image misclassification. Our experimental results show that the accuracy of image classifier can drastically drop by 76.70% and 19.25% in IfcW1A1 and cnvW1A1 networks, respectively across CIFAR-10 and MNIST datasets during the fault injection for the worst-case scenarios.
Running time analysis of the (1+1)-EA for robust linear optimization

Evolutionary algorithms (EAs) have found many successful real-world applications, where the optimization problems are often subject to a wide range of uncertainties. To understand the practical behaviors of EAs theoretically, there are a series of efforts devoted to analyzing the running time of EAs for optimization under uncertainties. Existing studies mainly focus on noisy and dynamic optimization, while another common type of uncertain optimization, i.e., robust optimization, has been rarely touched. In this paper, we analyze the expected running time of the (1+1)-EA solving robust linear optimization problems (i.e., linear problems under robust scenarios) with a cardinality constraint k. Two common robust scenarios, i.e., deletion-robust and worst-case, are considered. Particularly, we derive tight ranges of the robust parameter d or budget k allowing the (1+1)-EA to find an optimal solution in polynomial running time, which disclose the potential of EAs for robust optimization.
Antenna Classification Using Gaussian Mixture Models (GMM) and Machine Learning

Radio frequency fingerprinting (RFF) is the concept arising from classification of wireless emitters due to their unique radio frequency features. RFF has been further extended to applications including both RF devices classification and wireless signal identification. In this paper, we adopt Gaussian Mixture Models (GMM) technique as feature extraction approach and firstly apply it to extract RFF of antennas. 9 classical antennas with 3 different load conditions (open, short, match) were studied in our experiment. Moreover, we also made a theoretical analysis about the reason scattered signal has the unique features. Specifically, we adopt the Random Noise Radar (RNR) technique to obtain reflected RF signals of antenna under test (AUT) and apply the GMM technique to fit RF signals and then extract the RFF of AUT. A support vector machine (SVM) is proposed to recognize the RFF at different signal-to-noise ratio (SNR) environment. Compared with the conventional feature extraction approaches, for example, from variance, skewness and kurtosis (VSK) values, our method demonstrates better performance on large datasets with classification accuracy above 88% using a SVM classifier. Moreover, the accuracy remains higher than 75% even when the Signal to Noise Ratio (SNR) is equal to 0dB, indicating that the proposed approach has the strong capability of noise immunity.
The influence of environmental microseismicity on detection and interpretation of small-magnitude events in a polar glacier setting

Abstract Glacial environments exhibit temporally variable microseismicity. To investigate how microseismicity influences event detection, we implement two noise-adaptive digital power detectors to process seismic data from Taylor Glacier, Antarctica. We add scaled icequake waveforms to the original data stream, run detectors on the hybrid data stream to estimate reliable detection magnitudes and compare analytical magnitudes predicted from an ice crack source model. We find that detection capability is influenced by environmental microseismicity for seismic events with source size comparable to thermal penetration depths. When event counts and minimum detectable event sizes change in the same direction (i.e. increase in event counts and minimum detectable event size), we interpret measured seismicity changes as ‘true’ seismicity changes rather than as changes in detection. Generally, one detector (two degree of freedom (2dof)) outperforms the other: it identifies more events, a more prominent summertime diurnal signal and maintains a higher detection capability. We conclude that real physical processes are responsible for the summertime diurnal inter-detector difference. One detector (3dof) identifies this process as environmental microseismicity; the other detector (2dof) identifies it as elevated waveform activity. Our analysis provides an example for minimizing detection biases and estimating source sizes when interpreting temporal seismicity patterns to better infer glacial seismogenic processes.
Deep Learning Based Gesture Classification for Hand Physical Therapy Interactive Program

In this paper, we propose using the Google Colab deep learning framework to create and train convolutional neural networks from scratch. The trained network is part of a core artificial intelligent feature of our interactive software game, aiming to encourage white-collar workers to exercise hands and wrists frequently through playing the game. At this moment, the network is trained with our self-collected dataset of 12,000 bare-hand gesture images shot against a static dark background. The network focuses on classifying a still image into one of the six predefined classes of gestures and it seems to cope well with slight variation in size, skin tone, position and orientation of hand. This network is designed to be light in computation with real-time running time even on CPU. The network yields 99.68% accuracy on the validation set and 78% average accuracy when being tested with 50 different users. Our experiment on actual users reveals useful insight about problems using a deep learning based classifier in a real-time interactive system.
Improving U-Net Segmentation with Active Contour Based Label Correction

Deterministic deep learning methods for image segmentation require very precise ground-truth labels. However, obtaining perfect segmentations for medical image analysis is highly time-consuming and usually not feasible. In ultrasound imaging this problem is especially pronounced, as ultrasound scans are challenged by low contrast, speckle and shadow artifacts, all contributing to imperfect manual labelling. To overcome the problem of imperfect labels, we propose a label correction step which can correct the imperfect ground-truth labels in the training set by applying active contours. This forces the ground-truth segmentations towards regions which coincide with edges in the original volume (and thus object boundaries). We demonstrated the proposed active contour correction with a standard U-Net on the boundary segmentation of the cavum septum pellucidum in 3D fetal brain ultrasound and on the segmentation of the left ventricle in 2D ultrasound scans. The active contour label correction yielded more precise boundary predictions, suggesting that this simple correction step can improve boundary segmentation with imperfect labels.
STARS

Customers of virtually all online marketplaces rely upon reviews in order to select the product or service they wish to buy. These marketplaces in turn deploy review fraud detection systems so that the integrity of reviews is preserved. A well-known problem with review fraud detection systems is their underlying assumption that the majority of reviews are honest-this assumption leads to a vulnerability where an attacker can try to generate many fake reviews of a product. In this article, we consider the case where a company wishes to fraudulently promote its product through fake reviews and propose the Sockpuppet-based Targeted Attack on Reviewing Systems (STARS for short). STARS enables an attacker to enter fake reviews for a product from multiple, apparently independent, sockpuppet accounts. We show that the STARS attack enables companies to successfully promote their product against seven recent, well-known review fraud detectors on four datasets (Amazon, Epinions, and the BitcoinAlpha and OTC exchanges) by significant margins. To protect against the STARS attack, we propose a new fraud detection algorithm called RTV. RTV introduces a new class of users (called trusted users) and also considers reviews left by verified users which were not considered in existing review fraud detectors. We show that RTV significantly mitigates the impact of the STARS attack across the four datasets listed above.
A4: A Lightweight Stream Cipher

Lightweight ciphers are algorithms with low computational and spacial complexity. In the modern world of miniaturization, a lightweight cipher is used in constrained devices such as RFID tags, fire and security detectors, devices for wireless communications and other IoT devices. Stream ciphers are symmetric ciphers which encrypts the plain text bit stream with corresponding key stream to generate cipher text. Hence a stream cipher with low computational complexity and maximum security can be termed as a lightweight stream cipher. Many light weight stream ciphers are already existing. Each has its own vulnerabilities and spacial requirement. This paper has successfully developed, implemented, and analyzed a lightweight stream cipher - A4. Along with low computational cost, A4 also ensures paramount security and is less prone to the emerging cryptographic attacks.
Effects of Avatar Cuteness on Users’ Perceptions of System Errors in Anthropomorphic Interfaces

Anthropomorphic design has been widely used in human-computer interactions. Anchored on the baby schema (Kindchenschema) theory, we propose that integrating cuteness elements in an anthropomorphic system can significantly alleviate the users’ negative perceptions of system errors. The results of a laboratory experiment reveal that avatar cuteness can significantly reduce users’ perceived severity of software errors taking place during human-computer interactions. This study not only explores the factor of avatar age in anthropomorphic interface design, but it also introduces the concept of cuteness into HCI research. Practical implications of these findings for human-computer interface design are discussed.
Finger Recognition and Gesture based Virtual Keyboard

Hand motion acknowledgment is critical for human-PC connection. Right now, present a novel constant strategy for hand motion recognition. The proposed framework is vision based, which uses AI methods and contributions from a PC webcam. Vision based signal acknowledgment following and motion acknowledgment In our structure, the hand area is separated from the foundation with the foundation subtraction technique. At that point, fingers are portioned in order to identify and perceive the fingers. At long last, a standard classifier is applied to anticipate the names of hand motions. The examinations on the informational index of 1300 pictures show that our strategy performs well and is exceptionally productive. Besides, our technique shows preferred execution over a condition of-workmanship strategy on another informational collection of hand motions.
Enhanced character-level deep convolutional neural networks for cardiovascular disease prediction

Abstract Background Electronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disease (CVD), and are able to use them to predict CVD, we have the ability to automatically process clinical texts, resulting in an improved accuracy of supporting doctors for the clinical diagnosis of CVD. In the case where CVD is becoming more worldwide, predictive CVD based on EMRs has been studied by many researchers to address this important aspect of improving diagnostic efficiency. Methods This paper proposes an Enhanced Character-level Deep Convolutional Neural Networks (EnDCNN) model for cardiovascular disease prediction. Results On the manually annotated Chinese EMRs corpus, our risk factor identification extraction model achieved 0.9073 of F-score, our prediction model achieved 0.9516 of F-score, and the prediction result is better than the most previous methods. Conclusions The character-level model based on text region embedding can well map risk factors and their labels as a unit into a vector, and downsampling plays a crucial role in improving the training efficiency of deep CNN. What’s more, the shortcut connections with pre-activation used in our model architecture implements dimension-matching free in training.
Least-Squares Fuzzy Transforms and Autoencoders: Some Remarks and Application

In this article, analogies and differences between a type of fuzzy transform and a type of autoencoder, both based on a least-squares optimization, will be discussed. Such schemes have been recently introduced in the literature in different contexts. In particular, in this article, the data compression application will be considered. As it will be shown, the least-squares fuzzy transform can be regarded as a kind of autoencoder with a lower computational cost, without losing accuracy. The numerical comparison against existing results for the considered application shows the good performance of the fuzzy transform based approach.
Seeing and Viewing Through a Postdigital Pandemic: Shifting from Physical Proximity to Scopic Mediation

This paper addresses a particular area of concern regarding our habits that pertains to embodied experience and education through the Covid-19 pandemic: the shift from comfort to discomfort regarding face-to-face social interaction within the same physical space. To explore this transition, I use the related concepts of seeing and viewing from Isaac Asimov’s novel, The Naked Sun (1957), which are useful tools for investigating a probable collateral effect of rapid social distancing for the sake of avoiding contagion and includes replacing physically proximate interaction and procedures with a scopic mediation. Seeing and viewing provide concepts for understanding how values change in the midst of fears concerning contagion through physical contact that are mollified through the use of technology analogous to video conferencing. Postdigital education and the concepts of we-think, we-learn, and we-act provide critical tools for helping us understand this transition of perspective regarding educational and social practices in the midst of a pandemic.
Simple Dataset for Proof Method Recommendation in Isabelle/HOL

Recently, a growing number of researchers have applied machine learning to assist users of interactive theorem provers. However, the expressive nature of underlying logics and esoteric structures of proof documents impede machine learning practitioners, who often do not have much expertise in formal logic, let alone Isabelle/HOL, from achieving a large scale success in this field. In this data description, we present a simple dataset that contains data on over 400k proof method applications along with over 100 extracted features for each in a format that can be processed easily without any knowledge about formal logic. Our simple data format allows machine learning practitioners to try machine learning tools to predict proof methods in Isabelle/HOL without requiring domain expertise in logic.
Tracking Museum Visitors through Convolutional Object Detectors

Tracking museum visitors may provide useful insights about them, thus enabling curators and personnel to better manage the flows and the arrangement of the museum's works and to develop a recommender system as well. Tracking of visits in a museum environment is an expensive task if performed without automatic tracking systems. For this reason, many automatic tracking systems are proposed in the research literature. However, some of them are expensive (e.g., systems based on light detection and ranging (LIDAR) technology), or require active collaboration from visitors (e.g., systems based on wearable devices). In this work, we propose a deep learning object detection approach to the problem of tracking visitors. The proposed system can accurately detect specific objects in videos, thus allowing for the careful measurement of the spatial and temporal movements of a visitor in a museum scenario. The system requires only off-the-shelf inexpensive devices and deep learning models for object detection and recognition.
Hybridization of Constriction Coefficient-Based Particle Swarm Optimization and Chaotic Gravitational Search Algorithm for Solving Engineering Design Problems

The Constriction Coefficient-based Particle Swarm Optimization and Chaotic Gravitational Search Algorithm (CPSOCGSA) is a stochastic hybrid optimization technique. It is a combination of PSO which is inspired by the simulated behavior of bird flocking and GSA that is a physics-based heuristic technique inspired by Newton’s law of universal gravitation. It combines the exploitation and exploration capabilities of PSO and GSA, respectively, to obtain the optimal result. Further, it uses ten chaotic maps for the proper balance between exploration and exploitation processes. The advantages of CPSOCGSA have been incorporated in various mechanical and civil engineering design frameworks which include Welded Beam Design (WBD), Compression Spring Design (CSD), Pressure Vessel Design (PVD), and Three Bar Truss Design (TBTD). The CPSOCGSA has been compared with seven stochastic algorithms, particularly Standard Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm (CPSOGSA), Gravitational Search Algorithm (GSA), Classical Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), Continuous Genetic Algorithm (GA), Differential Evolution (DE), and Ant Colony Optimization (ACO). The experimental results indicate that CPSOCGSA was successful in minimizing the cost function and handling the constraints of the design frameworks efficiently as compared to other algorithms.
Bombalytics: Visualization of Competition and Collaboration Strategies of Players in a Bomb Laying Game

Abstract Competition and collaboration form complex interaction patterns between the agents and objects involved. Only by understanding these interaction patterns, we can reveal the strategies the participating parties applied. In this paper, we study such competition and collaboration behavior for a computer game. Serving as a testbed for artificial intelligence, the multiplayer bomb laying game Pommerman provides a rich source of advanced behavior of computer agents. We propose a visualization approach that shows an overview of multiple games, with a detailed timeline‐based visualization for exploring the specifics of each game. Since an analyst can only fully understand the data when considering the direct and indirect interactions between agents, we suggest various visual encodings of these interactions. Based on feedback from expert users and an application example, we demonstrate that the approach helps identify central competition strategies and provides insights on collaboration.
Vulnerability-Based Impact Criticality Estimation for Industrial Control Systems

Cyber threats directly affect the critical reliability and availability of modern Industry Control Systems (ICS) in respects of operations and processes. Where there are a variety of vulnerabilities and cyber threats, it is necessary to effectively evaluate cyber security risks, and control uncertainties of cyber environments, and quantitative evaluation can be helpful. To effectively and timely control the spread and impact produced by attacks on ICS networks, a probabilistic Multi-Attribute Vulnerability Criticality Analysis (MAVCA) model for impact estimation and prioritised remediation is presented. This offer a new approach for combining three major attributes: vulnerability severities influenced by environmental factors, the attack probabilities relative to the vulnerabilities, and functional dependencies attributed to vulnerability host components. A miniature ICS testbed evaluation illustrates the usability of the model for determining the weakest link and setting security priority in the ICS. This work can help create speedy and proactive security response. The metrics derived in this work can serve as sub-metrics inputs to a larger quantitative security metrics taxonomy; and can be integrated into the security risk assessment scheme of a larger distributed system.
Public Health Informatics: An Introduction

Public health informatics applies the theories and methods of computer and information science to support the work of public health organizations, which is to improve health outcomes for populations and communities. Many public health challenges require the use of advanced computing and information systems. This chapter describes the background, principles, and current state of public health informatics, public health, and public health information systems. Furthermore, the chapter discusses some current drivers of change in public health informatics and information systems: health care reform, precision public health, mHealth and uHealth, and Public Health 3.0.