Reviewerly/exHarmony huggingface dataset:
Phishing in the Free Waters: A Study of Phishing Attacks Created using Free Website Building Services

Free Website Building services (FWBs) provide individuals with a cost-effective and convenient way to create a website without requiring advanced technical knowledge or coding skills. However, malicious actors often abuse these services to host phishing websites. In this work, we propose FreePhish, a scalable framework to continuously identify phishing websites that are created using FWBs. Using FreePhish, we were able to detect and characterize more than 31.4K phishing URLs that were created using 17 unique free website builder services and shared on Twitter and Facebook over a period of six months. We find that FWBs provide attackers with several features that make it easier to create and maintain phishing websites at scale while simultaneously evading anti-phishing countermeasures. Our study indicates that anti-phishing blocklists and browser protection tools have significantly lower coverage and high detection time against FWB phishing attacks when compared to regular (self-hosted) phishing websites. While our prompt disclosure of these attacks helped some FWBs to remove these attacks, we found several others who were slow at removal or did not remove them outright, with the same also being true for Twitter and Facebook. Finally, we also provide FreePhish as a free Chromium web extension that can be utilized to prevent end-users from accessing potential FWB-based phishing attacks.
Not only E.T. Phones Home: Analysing the Native User Tracking of Mobile Browsers

Contemporary browsers constitute a critical component of our everyday interactions with the Web. Similar to a small, but powerful operating system, a browser is responsible to fetch and run web apps locally, on the user's (mobile) device. Even though in the last few years, there has been an increased interest for tools and mechanisms to block potentially malicious behaviours of web domains against the users' privacy (e.g., ad blockers, incognito browsing mode, etc.), it is still unclear if the user can browse the Web in private.
Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique

Abstract In order to achieve facial object detection and tracking in video, a method based on nonlinear sequence Monte Carlo filtering technology is proposed. The algorithm is simple, effective, and easy to operate, which can solve the problems of scale change and occlusion in the process of online learning tracking, so as to ensure the smooth implementation of learning effect evaluation. Experimental methods should be added to the article summary section. The results show that the algorithm in this study outperforms the basic KCF in terms of evaluation accuracy and success rate, as well as outperforms other tracker algorithms in benchmark, achieving scores of 0.837 and 0.705, respectively. In terms of overlapping accuracy, the reason why this study’s algorithm is higher than KCF is that this study determines the tracking status of the current target by calculating the primary side regulated (PSR) value when the target is obscured or lost, which does not make the tracking error to accumulate. The tracking algorithm in this study is not ranked first in the two attributes of motion blur and low resolution, but the rankings of all other nine attributes belong to the first. Compared with the KCF algorithm, the accuracy plots for the three attributes of scale change, occlusion, and leaving the field of view are improved by 10.26, 13.48, and 13.04%, respectively. Thus, it is proved that the method based on nonlinear sequence Monte Carlo filtering technology can achieve video facial object detection and tracking.
Reviving Dead Links on the Web with Fable

The web is littered with millions of links which previously worked but no longer do. When users encounter any such broken link, they resort to looking up an archived copy of the linked page. But, for a sizeable fraction of these broken links, no archived copies exist. Even if a copy exists, it often poorly approximates the original page, e.g., any functionality on the page which requires the client browser to communicate with the page's backend servers will not work, and even the latest copy will be missing updates made to the page's content after that copy was captured.
BaMSGAN: Self-Attention Generative Adversarial Network with Blur and Memory for Anime Face Generation

In this paper, we propose a novel network, self-attention generative adversarial network with blur and memory (BaMSGAN), for generating anime faces with improved clarity and faster convergence while retaining the capacity for continuous learning. Traditional self-attention generative adversarial networks (SAGANs) produce anime faces of higher quality compared to deep convolutional generative adversarial networks (DCGANs); however, some edges remain blurry and distorted, and the generation speed is sluggish. Additionally, common issues hinder the model’s ability to learn continuously. To address these challenges, we introduce a blurring preprocessing step on a portion of the training dataset, which is then fed to the discriminator as fake data to encourage the model to avoid blurry edges. Furthermore, we incorporate regulation into the optimizer to mitigate mode collapse. Additionally, memory data stored in the memory repository is presented to the model every epoch to alleviate catastrophic forgetting, thereby enhancing performance throughout the training process. Experimental results demonstrate that BaMSGAN outperforms prior work in anime face generation, significantly reducing distortion rates and accelerating shape convergence.
Histogram-probabilistic multi-hypothesis tracking with a Poisson mixture measurement process

The histogram-probabilistic multi-hypothesis tracker (H-PMHT) is an efficient parametric mixture fitting approach to the multi target track-before-detect (TBD) problem. It has been shown that it can give comparable performance to other methods by a fraction of the computational costs. In the original derivation of the H-PMHT, the mixing proportions are both coupled and uncorrelated over time, which may not hold true in practical scenarios involving fluctuating target amplitudes. In this paper, the mixing proportions are modeled according to a Poisson mixture measurement process. In contrast to existing approaches, a more general Markov chain prior based on the generalized inverse Gaussian (GIG) distribution is used as a prior of the Poisson mixing rates. The proposed method provides an alternative solution to the data association uncertainty in clutter, giving accurate and robust signal-to-noise ratio (SNR) estimates by utilizing the GIG Markov chain. The results are validated on simulated data.
Boosting Multi-neuron Convex Relaxation for Neural Network Verification

Formal verification of neural networks is essential for their deployment in safety-critical real-world applications, such as autonomous driving and cyber-physical controlling. Multi-neuron convex relaxation is one of the mainstream methods to improve verification precision. However, existing techniques rely on empirically selecting neuron groups before performing multi-neuron convex relaxation, which may yield redundant yet expensive convex hull computations. This paper proposes a volume approximation based approach for selecting neuron groups. We approximate the volumes of convex hulls for all group candidates, without calculating their convex hulls. The group candidates with small volumes are then selected for convex hull computation, aiming at ruling out unnecessary convex hulls with loose relaxation. We implement our approach as the neural network verification tool FaGMR, and evaluate it against state-of-the-art tools including Prima, $$\alpha , \beta $$ -CROWN, and ERAN on neural networks trained by MNIST and CIFAR-10. The experimental results demonstrate that FaGMR is more efficient than these tools, yet with the same or sometimes better verification precision.
Polynomial Analysis of Modular Arithmetic

The modular polynomial abstract domain, MPAD, is proposed, whose invariants are systems of polynomial equations that hold modulo a power of 2. Its domain operations are founded on a closure operation, but unlike conventional polynomial abstractions, MPAD satisfies the ascending chain condition, can model both positive and negative polynomial guards, and can infer invariants previously out of reach.
Sports Video Object Tracking Algorithm Based on Optimized Particle Filter

INTRODUCTION: Particle filter based human motion video target tracking technology has become a trend. This project intends to apply particle filters to image processing of human activities. Firstly, an improved particle filter model is used to track moving video objects. The purpose is to further improve the tracking effect and increase the tracking accuracy. HSV distribution model was used to establish target observation model. The algorithm is combined with the weight reduction algorithm to realize the human motion trajectory detection in the target observation mode. The model was then confirmed by an examination of sports player videos. Experiments show that this method can be used to track people in moving images of sports. Compared with other methods, this method has higher computational accuracy and speed.
Variety of Matrix Galois-like Generators Pseudorandom Number Free from the Berlecamp-Messy Attack

Classical pseudorandom numbers generators (PRN) based on Galois and Fibonacci schemes are constructed, as a rule, using n-bit linear shift registers or corresponding n-order matrices and allowing both hardware and software implementation.The main disadvantage of such generators is their low crypto stability, the reason for which is that if by any means it is possible to obtain 2n bits of the generated sequence taken from any discharge of the generator, then with the help of the Berlecamp-Messy algorithm, it is possible to recover a primitive polynomial of n-degree n f , generating the generator.To increase the cryptostability of the PRN matrix generators is proposed to replace the classical Galois and Fibonacci matrices, uniquely determined by the primitive polynomial n f , at a fixed forming element  , equal to 10, by the so-called generalized Galois or Fibonacci matrices.A distinctive feature of generalized matrices is that the polynomials n f generating them need not be primitive.At the same time, the constituent elements  must be chosen from the subset of primitive elements of the deduction field generated by the polynomial n f .The generalized PRN generators are free from the Berlecamp-Messy attack.The latter property is obtained because the Berlecamp-Messy algorithm solves the problem of computing one single unknown -the primitive polynomial n f , generating the generator.For variants of generalized matrix generators of PRN, there is a need to determine two unknown parameters: both the irreducible polynomial n f and the forming element  , jointly generating the generalized matrix, which turns out to be an unsolvable problem for the Berlecamp-Messy algorithm.
Image compression module based neural network autoencoders

A new approach is proposed to data compression in the form of a neural network module based on the structure of autoencoders, which has the most optimal learning time, compression level and obtains sufficiently clear image reconstruction. The main mechanisms for building the structure of encoder and decoder neural networks, which are used as a module, have been developed. The main data for the reconstruction were selected from the open data set Fashion-MNIST, which allows simplified testing of neural network structures, the process of their training and obtaining results. Approaches to image reproduction using neural network layers of convolution and inverse convolution are analyzed. An analysis of the impact on the quality of the resulting image reconstruction of the structure of the output module, which is used to compress the input image, was carried out. Atypical behavior was found during the increase of layers in the structure of the autoencoder, which did not lead to an increase in the quality of image reproduction. The basic necessity of changing the structural parts of the autoencoder and its application in combination with other technologies to obtain a better reproduction result and the elimination of distortions is highlighted.
Enhancing Autonomous Guided Vehicles with Red-Black TOR Iterative Method

To address an autonomous guided vehicle problem, this article presents extended variants of the established block over-relaxation method known as the Block Modified Two-Parameter Over-relaxation (B-MTOR) method. The main challenge in handling autonomous-driven vehicles is to offer an efficient and reliable path-planning algorithm equipped with collision-free feature. This work intends to solve the path navigation with obstacle avoidance problem explicitly by using a numerical approach, where the mobile robot must project a route to outperform the efficiency of its travel from any initial position to the target location in the designated area. The solution builds on the potential field technique that uses Laplace’s equation to restrict the formation of potential functions across operating mobile robot regions. The existing block over-relaxation method and its variants evaluate the computation by obtaining four Laplacian potentials per computation in groups. These groups can also be viewed as groups of two points and single points if they’re close to the boundary. The proposed B-MTOR technique employs red-black ordering with four different weighted parameters. By carefully choosing the optimal parameter values, the suggested B-MTOR improved the computational execution of the algorithm. In red-black ordering, the computational molecules of red and black nodes are symmetrical. When the computation of red nodes is performed, the updated values of their four neighbouring black nodes are applied, and conversely. The performance of the newly proposed B-MTOR method is compared against the existing methods in terms of computational complexity and execution time. The simulation findings reveal that the red-black variants are superior to their corresponding regular variants, with the B-MTOR approach giving the best performance. The experiment also shows that, by applying a finite difference method, the mobile robot is capable of producing a collision-free path from any start to a given target point. In addition, the findings also verified that numerical techniques could provide an accelerated solution and have generated a smoother path than earlier work on the same issue.
The subject digital educational environment of the teacher in the conditions of the implementation of the FSIS «My school»

One of the priority areas of state policy is the identification, support and development of talents and abilities in children and youth, as well as the digital transformation of education. This is evidenced by the Decree of the President of the Russian Federation The national development goals of the Russian Federation for the period up to 2030 No. 474 dated July 21, 2020. To achieve the national goal aimed at providing opportunities for self-realization and development of talents, the national project Education is being implemented. Through the activities of the national project Education in each region of Russia, the development of the education system in key areas is ensured, one of which is the implementation of the federal project Digital Educational Environment. The development of a digital educational environment as part of the implementation of the federal project Digital Educational Environment implies not only providing the school with modern equipment, but also the introduction of the federal state information system of the Ministry of Education of the Russian Federation My School. The My School system is a Russian educational platform created for effective information support of the bodies and organizations of the education system and citizens in the framework of the processes of organizing education and managing the educational process, as well as to create conditions for the digital transformation of the education system and the effective use of new opportunities for digital technologies. On the other hand, the problem of creating a digital educational environment, its component composition and purpose has been studied in scientific circles for quite a long time. Many researchers emphasize the need for the subject orientation of the process of building a digital educational environment, which is created by the teacher according to his author's intention. In this regard, it becomes necessary to determine the role and place of the subject digital educational environment of the teacher in the context of the implementation of the federal state information system My School and to identify the capabilities of the "My School" platform, which provide teachers with tools for creating digital educational content to organize the process of teaching a school subject.
Pelatihan Pembuatan Film pada Siswa SD dan SMP Taruna Islam Pekanbaru

Students in Taruna Islam's elementary and junior high schools, whose ages ranged from twelve to thirteen, were chosen as community service partners. The students on average come from middle to lower-economic families. This school is managed by the Islamic Taruna Foundation by employing twenty-four teachers, most of whom are honorary teachers. All teachers have not received certification. The facilities offered by this school are not many, however, the school's accreditation is very good with a grade of A. Based on daring investigations, there is little information about this school, such as student achievements and school activities. Responding to this condition can be interpreted that there are not many innovations for students that are carried out by this school, and this has an impact on the growth and development of children's creativity. In addition, with current technological developments, many children only use technology passively, rarely using technology to create positively and innovatively. The existing technology at home and school is less able to accommodate children's creativity. From this situation, we took the initiative to create activities to explore children's creativity in the form of film-making training. This service aims to make the film a medium that can be used as a space for students' creative aspirations and expressions by prioritizing Islamic values in their work. To achieve this goal, it is necessary to carry out a series of activities including (1) enrichment classes; (2) shooting (recording); (3) post-production; (4) movie screening.
Not Using Standard Phraseology: Misunderstandings and Delays

: In order to ensure safety, international aviation radio communication is conducted in a restricted language, based on English but with significant constraints and specific characteristics. Deviations from standard phraseology are tolerated to a certain extent and are to be expected in non-routine situations where no exact phraseology has been defined. Some relational language is also accepted as helping smooth interactions. But any additional word increases the length of the speech signal and may make it more difficult for the recipient to identify the important words that need to be attended to, so the use of relational language can also have a detrimental effect. Clarity and lack of ambiguity are particularly necessary when the interlocutors do not share the same language background. This paper analyses the communications during an incident at Narita (Japan) where a native English-speaking pilot used unnecessary words that are not part of the expected phraseology to communicate an emergency, making it difficult for the Japanese ATC to extract the crucial words ‘vector back to Narita’ from the pilot’s transmission. Supported by a close linguistic study of the complete interaction in this incident and by interviews with aviation experts, the analysis employs the Community of Practice framework to investigate the series of misunderstandings. It shows how non-standard phraseology and unnecessary verbosity can be detrimental to successful aviation communication, even if the additional words are intended to build rapport and help the interaction.
Research on Aviation English from a LHUFT (Language as a Human Factor) Perspective

This article is based on a presentation on recent research into Aviation English within a LHUFT perspective conducted in a private university in Brazil. The article describes this perspective to Aviation English and illustrates how it is being used with undergraduate student pilots in the Aeronautical Science Program. Miscommunication episodes in aviation have historically caused accidents, incidents, and near misses and so need to be addressed in a way that encompasses aspects closely associated with language factors, such as technical, procedural, and cultural factors. Linguistic issues are the starting point and can be analyzed in a more thorough manner if considered along with underlying elements that account for a given communicative behavior. The following research topics fit into this proposal: Cross-cultural factors underlying air-ground communications between native and non-native speakers of English; miscommunication issues in the SBPA radiotelephony between the ATCO and foreign pilots and a taxonomy-based classification and analysis of aeronautical accidents considering language as a human factor in aviation. We conclude that a number of challenges can be overcome with improvements in pilot training regarding a wider perspective of aeronautical communication, and that can be done through research. The LHUFT perspective seems to allow for a broader approach to better understand miscommunication episodes and offer tools to be used in training.
Visual-semantic network: a visual and semantic enhanced model for gesture recognition

Abstract Gesture recognition has attracted considerable attention and made encouraging progress in recent years due to its great potential in applications. However, the spatial and temporal modeling in gesture recognition is still a problem to be solved. Specifically, existing works lack efficient temporal modeling and effective spatial attention capacity. To efficiently model temporal information, we first propose a long- and short-term temporal shift module (LS-TSM) that models the long-term and short-term temporal information simultaneously. Then, we propose a spatial attention module (SAM) that focuses on where the change primarily occurs to obtain effective spatial attention capacity. In addition, the semantic relationship among gestures is helpful in gesture recognition. However, this is usually neglected by previous works. Therefore, we propose a label relation module (LRM) that takes full advantage of the relationship among classes based on their labels’ semantic information. To explore the best form of LRM, we design four different semantic reconstruction methods to incorporate the semantic relationship information into the class label’s semantic space. We perform extensive ablation studies to analyze the best settings of each module. The best form of LRM is utilized to build our visual-semantic network (VS Network), which achieves the state-of-the-art performance on two gesture datasets, i.e., EgoGesture and NVGesture.