Reviewerly/exHarmony huggingface dataset:
Generating Rules to Detect Phishing Websites Using URL Features

Phishing, a well-known type of the cybercrime, is a fraudulent activity created and executed by cyber criminals all over the cyberspace worldwide. Several techniques are used by these cyber criminals or scammers to carry out these attacks on the user to deceive a user. There are also some dangerous types of phishing attacks such as: by using Email messages and the Multimedia Internet Mail Extensions (MIME) attachments with the email messages that are delivered to the targeted users that include newbies as well as old and experienced users. Other types include botnet based attack that is performed using malwares sent either through emails or somehow forcing users to click on the links planted wherein these malicious messages are attached by the scammers in the body of email text or their webpages. Spear attacks are also dangerous kind of phishing attacks. In this kind of attack an individual or a company is targeted by these people to steal the sensitive data over web. Keeping in mind this very purpose, emails and malicious codes are used as their primary weapons. In present paper, Apriori algorithm is used to generate the rules so that these rules can play a vital role in detecting & predicting the phishing and non-phishing website URLs. In order to give the web users a basic idea against this threat, an effective tool is used to spread the word among the users, so that users may get the required knowledge about these and developing a tool to make users able to identify the phishing websites/content while working online.
IoT Security Using Steganography

Internet of things (IoT) is one of those emerging technologies, which are going to rule the world in the next few decades. Due to the advancement of low-cost computing systems and mobile technologies, these physical things are now capable of sharing and collecting data with minimal human interference. However, these devices are exposed to various security threats regarding privacy and data confidentiality as they are openly accessible to all in the network. Moreover, many IoT devices have low processing power and weak security level which could be the main targets for hackers. Lightweight cryptographic schemes are used to meet the security needs in IoT environment. Steganography is used as another security tool for IoT devices. This chapter is an attempt to analyze the various steganography techniques used to strengthen the security needs of IoT devices as per their applications. IoT security schemes using different steganography models and algorithms are outlined here with their relative advantages and disadvantages.
On maximum likelihood estimation of the general projected normal distribution

The general projected normal distribution is a flexible distribution family that is widely used for modelling circular data. It is constructed by projecting the bivariate normal distribution onto the unit circle. Maximum likelihood estimation for the general projected normal distribution was considered for the special cases of Σ=I as well asΣ=σ2I. In this study, we consider the problem of maximum likelihood estimation assuming general form of Σ. EM-algorithm and Newton Raphson method are formulated and used to obtain approximations for the final estimators. A simulation study is conducted to evaluate the performance of the estimated parameters. In general, the obtained estimators show consistent behaviour where EM-algorithm has better performance compared to the Newton Raphson method. Moreover, extension for the regression problem is introduced where EM-algorithm is developed. Two examples are provided to demonstrate the proposed estimators in real applications.
An efficient Real Time Implementation of Motion Estimation in Video Sequences on SOC

Motion estimation is considered among the most famous and important applications in the field of computer vision. Indeed, in the last decade, the optical flow has become an alternative technique to estimate motion on successive frames. However, dense and precise estimates of the optical flow are usually costly in computing time. In this context, our approach is focused upon calculating the moving object's optical flow through the use of the Lucas-Kanade algorithm in real time which is implemented into Raspberry Pi 4. The efficiency of our real time implementation is demonstrated by the experimental results, hence showing better performances in comparison with the conventional calculation.
The Power of D-hops in Matching Power-Law Graphs

This paper studies seeded graph matching for power-law graphs. Assume that two edge-correlated graphs are independently edge-sampled from a common parent graph with a power-law degree distribution. A set of correctly matched vertex-pairs is chosen at random and revealed as initial seeds. Our goal is to use the seeds to recover the remaining latent vertex correspondence between the two graphs. Departing from the existing approaches that focus on the use of high-degree seeds in $1$-hop neighborhoods, we develop an efficient algorithm that exploits the low-degree seeds in suitably-defined D-hop neighborhoods. Specifically, we first match a set of vertex-pairs with appropriate degrees (which we refer to as the first slice) based on the number of low-degree seeds in their D-hop neighborhoods. This approach significantly reduces the number of initial seeds needed to trigger a cascading process to match the rest of graphs. Under the Chung-Lu random graph model with n vertices, max degree Θ(√n), and the power-law exponent 2<β<3, we show that as soon as D>4-β/3-β, by optimally choosing the first slice, with high probability our algorithm can correctly match a constant fraction of the true pairs without any error, provided with only Ω((log n)4-β) initial seeds. Our result achieves an exponential reduction in the seed size requirement, as the best previously known result requires n1/2+ε seeds (for any small constant ε^>0). Performance evaluation with synthetic and real data further corroborates the improved performance of our algorithm.
EyeLogin - Calibration-free Authentication Method for Public Displays Using Eye Gaze

The usage of interactive public displays has increased including the number of sensitive applications and, hence, the demand for user authentication methods. In this context, gaze-based authentication was shown to be effective and more secure, but significantly slower than touch- or gesture-based methods. We implement a calibration-free and fast authentication method for situated displays based on saccadic eye movements. In a user study (n = 10), we compare our new method with CueAuth from Khamis et al. (IMWUT’18), an authentication method based on smooth pursuit eye movements. The results show a significant improvement in accuracy from 82.94% to 95.88%. At the same time, we found that the entry speed can be increased enormously with our method, on average, 18.28s down to 5.12s, which is comparable to touch-based input.
Smart Body Sensor Network for Logging of Activities of Daily Living

This paper presents a novel easy-to-use human activity logger based on a wearable wireless body area network. The activity logger is based on open-source code and includes: 1) two Wear OS smartwatches with the mobile application that continuously acquire smartwatch sensor data (acceleration, orientation, angular velocity, step count, heart rate), 2) a smartphone with an Android application that logs smartwatch data as well as geolocation data (from the smartphone) and activity diary data, 3) a desktop application for data transfer, visualization, and manipulation. The system features are demonstrated in a representative use-case scenario. In addition, the system was validated on three subjects using a state-of-the-art inertial measurement system in a laboratory condition.
Restricted Move – a Smart Contract Description Language to Create and Control Finance Instruments on DFinance Blockchain Platform

Abstract Smart contracts created to issue tokens and control other digital financial assets on the majority of blockchain platforms require knowledge of programming language for specific platforms. This fact imposes a restriction on attracting financial specialists from conventional markets. This paper presents the concept of a Restricted Move language allowing the users to form a smart contract description in financial English, as well as its further development into a high-level digital asset management language, which can be extended by users and can open new opportunities to automate their market operations.
A Web-Based Digital Contact Tracing Strategy Addresses Stigma Concerns Among Individuals Evaluated for COVID-19

Introduction: Conventional contact tracing approaches have not kept pace with the scale of the coronavirus disease 2019 (COVID-19) pandemic and the highly anticipated smartphone applications for digital contact tracing efforts are plagued by low adoption rates attributed to privacy concerns; therefore, innovation is needed in this public health capability. Materials and Methods: This study involved a cross-sectional, nonrepresentative, online survey in the United States of individuals tested for COVID-19. Testing survey items measured the performance of conventional contact tracing programs, quantified the stigma related to the notification of COVID-19 close contacts, and assessed the acceptability of a website service for digital contact tracing. Results: A sample of 668 (19.9%) individuals met the inclusion criteria and consented to participation. Among the 95 participants with COVID-19, results were received after a median of 2 days, 63.2% interacted with a contact tracing program a median of 2 days after receiving test results, 62.1% had close contacts, and 37.1% of participants with COVID-19 and close contacts did not disclose their results to all close contacts. Among all participants, 17% had downloaded a mobile application and 40.3% reported interest in a website service. One hundred and nine participants perceived stigma with the disclosure of COVID-19 test results; of these, 58.7% reported that a website service for close contact notification would decrease this stigma. Discussion: Conventional contact tracing programs did not comprehensively contact individuals who tested positive for COVID-19 nor did so within a meaningful time frame. Digital contact tracing innovations may address these shortcomings; however, the low penetration of mobile application services in the United States indicates that a suite of digital contact tracing tools, including website services, are warranted for a more exhaustive coverage of the population. Conclusions: Public health officials should develop a complementary toolkit of digital contact tracing strategies to enable effective pandemic containment strategies.
Cyber Risk and Financial Stability

The ability of attackers to undermine, disrupt and disable information and communication technology systems used by financial institutions is a threat to financial stability and one that requires additional attention.
Investigation of MRI Prostate Localization using Different MRI Modality Scans

According to the data of World Cancer Research Fund International prostate cancer is the second most common after lung cancer and the fifth most common cause of cancer death amongst men. Prostate cancer is also the fourth most frequent tumor between both genders worldwide. Biopsy is the only way to detect prostate cancer so far. Statistics show that it is able to detect only 70-80% of clinically significant cancer cases. Multi parametric magnetic resonance imaging technique comes to play to help in determining the location to perform biopsy on. The first step to automating the detection of the location is applying prostate segmentation on magnetic resonance images. The fact that there is lack of standardization of signal intensity to acquire those images burdens the problem of automated prostate segmentation. Authors review the results of Prostate MR Image Segmentation (PROMISE12) challenge, designed to evaluate and compare different prostate segmentation algorithms, and provide insights on automated prostate segmentation by applying best open source algorithm under different circumstances. Authors applied selected algorithm on two different datasets and showed how segmentation results can be improved by applying even the most primitive image stretching techniques. Authors also showed that algorithm is promising in segmenting unseen dataset.
Recursive Genetic Micro-Aggregation Technique: Information Loss, Disclosure Risk and Scoring Index

This research investigates the micro-aggregation problem in secure statistical databases by integrating the divide and conquer concept with a genetic algorithm. This is achieved by recursively dividing a micro-data set into two subsets based on the proximity distance similarity. On each subset the genetic operation “crossover” is performed until the convergence condition is satisfied. The recursion will be terminated if the size of the generated subset is satisfied. Eventually, the genetic operation “mutation” will be performed over all generated subsets that satisfied the variable group size constraint in order to maximize the objective function. Experimentally, the proposed micro-aggregation technique was applied to recommended real-life data sets. Results demonstrated a remarkable reduction in the computational time, which sometimes exceeded 70% compared to the state-of-the-art. Furthermore, a good equilibrium value of the Scoring Index (SI) was achieved by involving a linear combination of the General Information Loss (GIL) and the General Disclosure Risk (GDR).
Benchmarking Scientific Image Forgery Detectors

The field of scientific image integrity presents a challenging research bottleneck given the lack of available datasets to design and evaluate forensic techniques. The sensitivity of data also creates a legal hurdle that restricts the use of real-world cases to build any accessible forensic benchmark. In light of this, there is no comprehensive understanding on the limitations and capabilities of automatic image analysis tools for scientific images, which might create a false sense of data integrity. To mitigate this issue, we present an extendable open-source algorithm library that reproduces the most common image forgery operations reported by the research integrity community: duplication, retouching, and cleaning. We create a large scientific forgery image benchmark (39,423 images) with enriched ground truth using this library and realistic scientific images. All figures within the benchmark are synthetically doctored using images collected from creative commons sources. While collecting the source images, we ensured that the they did not present any suspicious integrity problems. Because of the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset, using a new metric that asserts consistent match detection between the source and the copied region. All evaluated methods had a low performance in this dataset, indicating that scientific images might need a specialized copy-move detector. The dataset and source code are available at https://github.com/phillipecardenuto/rsiil .
Effects of Level of Immersion on Virtual Training Transfer of Bimanual Assembly Tasks

The availability of consumer-facing virtual reality (VR) headsets makes virtual training an attractive alternative to expensive traditional training. Recent works showed that virtually trained workers perform bimanual assembly tasks equally well as ones trained with traditional methods. This paper presents a study that investigated how levels of immersion affect learning transfer between virtual and physical bimanual gearbox assembly tasks. The study used a with-in subject design and examined three different virtual training systems i.e., VR training with direct 3D inputs (HTC VIVE Pro), VR training without 3D inputs (Google Cardboard), and passive video-based training. 23 participants were recruited. The training effectiveness was measured by participant’s performance of assembling 3D-printed copies of the gearboxes in two different timings: immediately after and 2 weeks after the training. The result showed that participants preferred immersive VR training. Surprisingly, despite being less favourable, the subjects’ performance of video-based training were similar to training on HTC VIVE Pro. However, video training led to a significant performance decrease in the retention test session 2 weeks after the training.
Fluid dynamics analysis and experimental study for solar radiation error correction of sounding humidity sensor

In order to correct the solar radiation error of relative humidity, the mainstream capacitive sounding humidity sensor HC103M2 is selected and investigated by simulation analysis and experimental verification. First, the basic theories for solar radiation error and sensor error itself are elaborated, and simulational and experimental platforms are introduced. The computational fluid dynamics (CFD) method is utilized to theoretically investigate the dry error of the humidity sensor caused by solar radiation heating, which is related to radiation intensity, altitude, and solar elevation angle as well as reflectivity, thickness, and shape of the shield. Then, in order to verify the accuracy of the simulation, an experimental platform including a humidity sensor and two temperature sensors to measure the solar radiation heating is built to analyze the relative error of humidity obtained by the CFD simulation and experiment. It is found that their maximum deviation is 3.30% and the average error is 1.94%, which indicates that the calculation using the CFD method is accurate and feasible. In order to easily and operationally predict the solar radiation heating of the humidity sensor, a back propagation (BP) neural network fusion algorithm based on three inputs of radiation intensity, air pressure, and solar elevation angle is proposed. Compared with the solar radiation heating obtained by CFD simulation, the maximum absolute error is about 0.2 K, and the relative error of humidity is about ±1.30%. Finally, a case of vertical humidity profile correction considering the temperature-sensitive error of HC103M2 is analyzed. The response time of sensor measurement and the airflow into the shield are discussed as well. The corrected results after taking solar radiation heating into account are more similar to those measured by RS92 and cryogenic frost point hygrometer (CFH). This result shows that the prediction model is accurate, which may be applied to correct the dry error and further improve the accuracy of humidity measurement.
Loop invariance with break and continue

This paper presents a formal approach to program derivation using break and continue statements within a loop. We define the break and continue semantics under the framework of weakest precondition. The underpinning theory centers around a miraculous statement re-discovered to model those jump statements in a novel manner. Our focus is on introducing break and continue within a loop to derive a program hand in hand with its correctness. With break and continue so defined, we can naturally incorporate them into the well-known loop invariance theorem. Several program derivation case studies conducted in the paper show that non-trivial programs using those jump statements can be effectively and formally derived without knowing details of underlying formalism. Those case studies should provide convincing evidence that there is no distinction between deriving programs with or without the jump statements but using the jump statements to solve certain types of problems can make the work much simpler and the result more comprehensible.
Multi-Armed Bandits for <i>Minesweeper</i>: Profiting From Exploration–Exploitation Synergy

A popular computer puzzle, the game of Minesweeper requires its human players to have a mix of both luck and strategy to succeed. Analyzing these aspects more formally, in our research we assessed the feasibility of a novel methodology based on Reinforcement Learning as an adequate approach to tackle the problem presented by this game. For this purpose we employed Multi-Armed Bandit algorithms which were carefully adapted in order to enable their use to define autonomous computational players, targeting to make the best use of some game peculiarities. After experimental evaluation, results showed that this approach was indeed successful, especially in smaller game boards, such as the standard beginner level. Despite this fact the main contribution of this work is a detailed examination of Minesweeper from a learning perspective, which led to various original insights which are thoroughly discussed.
Transducer Adaptive Ultrasound Volume Reconstruction

Reconstructed 3D ultrasound volume provides more context information than a sequence of 2D scanning frames, which is desirable for various clinical applications such as ultrasound-guided prostate biopsy. Nevertheless, 3D volume reconstruction from freehand 2D scans is very challenging, especially without external tracking devices. Recent deep learning-based methods demonstrate the potential of directly estimating inter-frame motion between consecutive ultrasound frames. However, such algorithms are specific to particular transducers and scanning trajectories associated with the training data, which may not be generalized to other image acquisition settings. In this paper, we formulate the difference in data acquisition as domain shift and propose a novel domain adaptation strategy to adapt deep learning algorithms to data acquired using a different transducer. Specifically, feature extractors generating transducer-invariant features get trained by minimizing the discrepancy between image features of paired samples in a latent space. Our results show that the proposed domain adaptation method can successfully align different feature distributions while preserving the transducer-specific information for universal freehand ultrasound volume reconstruction.
Smocam: Smooth Conditional Attention Mask For 3d-Regression Models

Despite the pervasive growth of deep neural networks in medical image analysis, methods to monitor and assess network outputs, such as segmentation or regression, remain limited. In this paper, we introduce SMOCAM (SMOoth Conditional Attention Mask), an optimization method that reveals the specific regions of the input image taken into account by the prediction of a trained neural network. We developed SMOCAM explicitly to perform saliency analysis for complex regression tasks in 3D medical imagery. Our formulation optimises an 3D-attention mask at a given layer of a convolutional neural network (CNN). Unlike previous attempts, our method is relatively fast (40s per output) and is suitable for large data such as 3D MRI. We applied SMOCAM on a CNN that predicts Brain morphometry from 3D MRI which was trained using more than 5000 3D brain MRIs. We show that SMOCAM highlights neural network's limitations when cases are underrepresented and in cases with large volume asymmetry.