Reviewerly/exHarmony huggingface dataset:
Web Phishing Detection using Machine Learning

A web service is one of the most important Internet communications software services. Using fraudulent methods to get personal information is becoming increasingly widespread these days. However, it makes our lives easier, it leads to numerous security vulnerabilities to the Internet’s private structure. Web phishing is just one of the many security risks that web services face. Phishing assaults are usually detected by experienced users however, security is a primary concern for system users who are unaware of such situations. Phishing is the act of portraying malicious web runners as genuine web runners to obtain sensitive information from the end-user. Phishing is currently regarded as one of the most dangerous threats to web security. Vicious Web sites significantly encourage Internet criminal activity and inhibit the growth of Web services. As a result, there has been a tremendous push to build a comprehensive solution to prevent users from accessing such websites. We suggest a literacy-based strategy to categorize Web sites into three categories: benign, spam, and malicious. Our technology merely examines the Uniform Resource Locator (URL) itself, not the content of Web pages. As a result, it removes run-time stillness and the risk of drug users being exposed to cyber surfer-based vulnerabilities. When compared to a blacklisting service, our approach performs better on generality and content since it uses learning techniques.
Research on Channel Modeling and Communication Coverage of Wireless Sensor Networks in Barrier Area of Nuclear Power Plants

In view of the multimetal barrier environment of nuclear power plant, by considering the factors such as transmission power, transmission position, and multipath interference, based on the simulation of metal pipes and equipment, this paper carries out the barrier area channel modeling in logarithmic fading mode and makes quantitative analysis on the channel transmission, path loss, channel power characteristics, and so on under the metal barrier environment. Based on the channel modeling, this paper optimizes the coverage of the network in the obstacle area by using the improved teaching and learning group intelligent algorithm. The simulation results show that the improved teaching and learning algorithm can optimize the network coverage of the obstacle area well, and under the four obstacle modules, 14 nodes can cover the whole area by more than 99%. This provides a solution to the problem of network coverage in the practical application of wireless sensor networks.
PHYSICS TEACHING AND EDUCATIONAL INTERDISCIPLINARITY WITH A.V. USOVA REVISITED

The paper deals with pedagogical heritage of A.V. Usova (1921-2014) that yields insights into problems of interdisciplinary relationship in the school science education system. Her fundamental works were used as teaching guides in the preparation of physics teachers of all pedagogical universities of the Soviet Union and Russia. In this study, professor Usova is not an interdisciplinary researcher; she is a researcher of interdisciplinarity. Here is provided a short and consistent description of her views on teaching physics as a foundation of educational interdisciplinarity. Usova elaborated educational functions, classifications, organizational levels, ways of instantiating, content-related foundations of interdisciplinary relations, recommendations for practical applying interdisciplinary relations. Besides, Usova suggested a unified approach to the study of knowledge and skills through the generalized plans. And we would like not simply clarify, retell or restate the very powerful didactic discoveries of Usova, but consider opportunities of teaching across boundaries for actual educational challenges. The future of the field of didactics, which deals with the study of interdisciplinarity, is very interesting and attractive. This problem will always excite scientists, educators and teachers, and the futurity of science education depends on the ways it is solved.
Completing the Dynamic Deductive System

This chapter continues the development of the Dynamic Deductive System and introduces the operation of Prune, which does the work that <italic>reductio ad absurdum</italic> does in standard theories. Ludlow and Živanović develop an account of universal instantiation and universal generalization, and then prove the completeness of the new system by simulating a Hilbert natural deduction system.
Digital onboarding in finance: a novel model and related cybersecurity risks

Over the last few years, the financial sector has undergone a digital revolution that has had a severe impact on different related areas such as, the entities, the cybersecurity of systems, regulations and, of course, customers. SOTER project takes the challenge providing a complete set of tools to enhance the cybersecurity levels by implementing, in addition to non-technological tools, an innovative onboarding process has been implemented with the goals of increasing security, improving the user experience and integrity in the sector, and facilitating the customer entry into the digital marketplace by combining a set of breakthrough technologies. The cybersecurity research plays a crucial role in the conception and implementation of the onboarding process and for this kind of processes it has to be studied as an individual area by itself, as it is necessary to analyze the possible threats that can affect them, their origin and the solution(s) that can be taken to address them. Therefore, the SOTER project presents a fully digital onboarding process, innovative, adaptable to both current and future market needs and to possible changes in regulation, based on the most advanced technologies available today, and above all guaranteeing the cybersecurity of both entities and end users.
The unexplored terrain of compiler warnings

The authors' industry experiences suggest that compiler warnings, a lightweight version of program analysis, are valuable early bug detection tools. Significant costs are associated with patches and security bulletins for issues that could have been avoided if compiler warnings were addressed. Yet, the industry's attitude towards compiler warnings is mixed. Practices range from silencing all compiler warnings to having a zero-tolerance policy as to any warnings. Current published data indicates that addressing compiler warnings early is beneficial. However, support for this value theory stems from grey literature or is anecdotal. Additional focused research is needed to truly assess the cost-benefit of addressing warnings.
Structure and criteria defining business value in agile software development based on hierarchical analysis

In recent years, agile software development as the most popular development approach has been considered by software teams and companies. In this approach, the business value is one of the concepts that achieving it especially has been emphasized in the agile software project. Despite this emphasis, a formal and appropriate definition of this concept has not been provided. In this study, to solve this challenge, we present a formal structure including necessary values and criteria for defining business value. Using hierarchical analysis, these criteria were weighted. The results of the analysis show what is called business value is often from the customer&#x0027;s point of view that improving competitive position, operational income, and return on investment are the most important components. Data analysis also shows that the customer perspective, financial perspective, and user perspective have a great effect on the definition of business value.
Shortcomings and Flaws in the Mathematical Derivation of the Fundamental Matrix Equation

In stereo vision, the epipolar geometry is the intrinsic projective geometry between the two views. The essential and fundamental matrices relate corresponding points in stereo images. The essential matrix describes the geometry when the used cameras are calibrated, and the fundamental matrix expresses the geometry when the cameras are uncalibrated. Since the nineties, researchers devoted a lot of effort to estimate the fundamental matrix. Although it is a landmark of computer vision, in the current work, three derivations of the essential and fundamental matrices have been revised. The Longuet-Higgins' derivation of the essential matrix where he draws a mapping between the position vectors of a 3D point; however, the one-to-one feature of that mapping is lost when he changed it to a relation between the image points. In the two other derivations, we demonstrate that the authors established a mapping between the image points through the misuse of mathematics.
Highlighting Current Issues in API Usage Mining to Enhance Software Reusability

The sheer amount of open source codes made available in code repositories and code search engines along with the rapidly increasing releases of Application Programming Interfaces (APIs) have made code devel- opment process easier for programmers. However, learning how to use the elements of an API properly is both challenging and requires learning curve. Mining the available client and test codes can help programmers to iden- tify the best practices in using these APIs. In this paper, we investigate the API usage mining to identify open issues for the researchers. In particular, we make a theoretical comparison of the API usage pattern mining and highlight unresolved issues along with proper suggestions to address them.
An Investigation Into The Mathematical and Physical Origins of The Fine-Structure Constant

Abstract The fine-structure constant, α, unites fundamental aspects of electromagnetism, quantum physics, and relativity. As such, it is one of the most important constants in nature. However, why it has the value of approximately 1/137 has been a mystery since it was first identified more than 100 years ago. To date, it is an ad hoc feature of the Standard Model, as it does not appear to be derivable within that body of work — being determined solely by experimentation. This report presents a mathematical formula for α that results in an exact match with the currently accepted value of the constant. The formula requires that a simple corrective term be applied to the value of one of the factors in the suggested equation. Notably, this corrective term, at approximately 0.023, is similar in value to the electron anomalous magnetic moment value, at approximately 0.0023, which is the corrective term that needs to be applied to the g-factor in the equation for the electron spin magnetic moment. In addition, it is shown that the corrective term for the proposed equation for α can be derived from the anomalous magnetic moment values of the electron, muon, and tau particle — values that have been well established through theory and/or experimentation. This supports the notion that the corrective term for the α formula is also a real and natural quantity. The quantum mechanical origins of the lepton anomalous magnetic moment values suggest that there might be a quantum mechanical origin to the corrective term for α as well. This possibility, as well as a broader physical interpretation of the value of α, is explored.
Simulation research on blockchain bidding model

Online bidding has become more and more common in the market economy, but there still exists cheating phenomena, such as rent-seeking and bid-rigging. To study the characteristics of bidding behavior, we designed a bidding game model based on multi-agents to simulate the game between tendering and bidding agents, visualizing and collecting data from multi-rounds of multi-agent game process, quantifying the impact of the existence and non-existence of blockchain to suppress rent-seeking and bid-rigging. The data of the model support the theory that combining e-bidding platform with blockchain can effectively reduce the fraud costs in bidding process. The research provides theoretical and practical evidence for improving the bidding environment.
A Novel Automatic Tracking Method of Moving Image Sequence Marker Points Uses Kinect and Wireless Network Technology

Sports biomechanics research includes the use of various technical methods to collect various sports biomechanical parameters and variables. Sports biomechanics is one of the branches of sports science, which is a new science for studying sports. In the process of teaching, the Kinect somatosensory camera is used for bone tracking to obtain spatial coordinate information of human nodes, and the teaching data for imitation learning is obtained after preprocessing. This paper builds a general sports auxiliary training system and designs some of the system’s functions in detail, using the advantages of Microsoft’s Kinect collector in image acquisition. The automatic tracking method of sports image sequence marker points is studied in this paper using Kinect. Kinect is used to collect bone point information, establish a virtual coordinate system, obtain each joint vector from the node information, calculate the included angle between the vectors, and then obtain the control angle of each joint, providing data support for imitation representation learning. Count and score this movement based on the distance between the jaw and the crossbar as well as the angle at which the arm bends.
Application to Medical Image Processing

Medical images are often corrupted by white noise, blurring and contrast defects. Consequently, important medical information may be degraded or completely masked. Advanced medical diagnostics and pathological analysis utilize information obtained from medical images. Consequently, the best techniques must be applied to capture, compress, store, retrieve and share these images. Recently, the wavelet transform technique has been applied to enhance and compress medical images. This review focuses on the trends of wavelet-based medical image processing techniques. A summary of the application of wavelets to enhance and compress medical images such as magnetic resonance imaging (MRI), computerized tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), and X-ray is provided. Morphological techniques such as closing, thinning and pruning are combined with wavelets methods to extract the features from the medical images.
3D Face Morphing Attacks: Generation, Vulnerability and Detection

Face Recognition systems (FRS) have been found to be vulnerable to morphing attacks, where the morphed face image is generated by blending the face images from contributory data subjects. This work presents a novel direction for generating face-morphing attacks in 3D. To this extent, we introduced a novel approach based on blending 3D face point clouds corresponding to contributory data subjects. The proposed method generates 3D face morphing by projecting the input 3D face point clouds onto depth maps and 2D color images, followed by image blending and wrapping operations performed independently on the color images and depth maps. We then back-projected the 2D morphing color map and the depth map to the point cloud using the canonical (fixed) view. Given that the generated 3D face morphing models will result in holes owing to a single canonical view, we have proposed a new algorithm for hole filling that will result in a high-quality 3D face morphing model. Extensive experiments were conducted on the newly generated 3D face dataset comprising 675 3D scans corresponding to 41 unique data subjects and a publicly available database (Facescape) with 100 data subjects. Experiments were performed to benchmark the vulnerability of the {proposed 3D morph-generation scheme against} automatic 2D, 3D FRS, and human observer analysis. We also presented a quantitative assessment of the quality of the generated 3D face-morphing models using eight different quality metrics. Finally, we propose three different 3D face Morphing Attack Detection (3D-MAD) algorithms to benchmark the performance of 3D face morphing attack detection techniques.
3D Multi-Object Tracking Using Graph Neural Networks with Cross-Edge Modality Attention

Online 3D multi-object tracking (MOT) has witnessed significant research interest in recent years, largely driven by demand from the autonomous systems community. However, 3D offline MOT is relatively less explored. Labeling 3D trajectory scene data at a large scale while not relying on high-cost human experts is still an open research question. In this work, we propose Batch3DMOT which follows the tracking-by-detection paradigm and represents real-world scenes as directed, acyclic, and category-disjoint tracking graphs that are attributed using various modalities such as camera, LiDAR, and radar. We present a multi-modal graph neural network that uses a cross-edge attention mechanism mitigating modality intermittence, which translates into sparsity in the graph domain. Additionally, we present attention-weighted convolutions over frame-wise k-NN neighborhoods as suitable means to allow information exchange across disconnected graph components. We evaluate our approach using various sensor modalities and model configurations on the challenging nuScenes and KITTI datasets. Extensive experiments demonstrate that our proposed approach yields an overall improvement of 3.3% in the AMOTA score on nuScenes thereby setting the new state-of-the-art for 3D tracking and further enhancing false positive filtering.
A Bayesian approach to translators' reliability assessment

Translation Quality Assessment (TQA) is a process conducted by human translators and is widely used, both for estimating the performance of (increasingly used) Machine Translation, and for finding an agreement between translation providers and their customers. While translation scholars are aware of the importance of having a reliable way to conduct the TQA process, it seems that there is limited literature that tackles the issue of reliability with a quantitative approach. In this work, we consider the TQA as a complex process from the point of view of physics of complex systems and approach the reliability issue from the Bayesian paradigm. Using a dataset of translation quality evaluations (in the form of error annotations), produced entirely by the Professional Translation Service Provider Translated SRL, we compare two Bayesian models that parameterise the following features involved in the TQA process: the translation difficulty, the characteristics of the translators involved in producing the translation, and of those assessing its quality - the reviewers. We validate the models in an unsupervised setting and show that it is possible to get meaningful insights into translators even with just one review per translation; subsequently, we extract information like translators' skills and reviewers' strictness, as well as their consistency in their respective roles. Using this, we show that the reliability of reviewers cannot be taken for granted even in the case of expert translators: a translator's expertise can induce a cognitive bias when reviewing a translation produced by another translator. The most expert translators, however, are characterised by the highest level of consistency, both in translating and in assessing the translation quality.
A Brief Introduction to POVM Measurement in Quantum Communications

This paper gives a brief introduction to Positive-Operator Valued Measure (POVM) of quantum communications. The Projection-Valued Measure (PVM) is first introduced and then the POVM. The relation between POVM and PVM is discussed and an example of POVM in practical measurement is given. This paper provides some insight of POVM measurement for quantum communications.
ABSNFT: Securitization and Repurchase Scheme for Non-Fungible Tokens Based on Game Theoretical Analysis

The Non-Fungible Token (NFT) is viewed as one of the important applications of blockchain technology. Although NFT has a large market scale and multiple practical standards, several limitations of the existing mechanism in NFT markets exist. This work proposes a novel securitization and repurchase scheme for NFT to overcome these limitations. We first provide an Asset-Backed Securities (ABS) solution to settle the limitations of non-fungibility of NFT. Our securitization design aims to enhance the liquidity of NFTs and enable Oracles and Automatic Market Makers (AMMs) for NFTs. Then we propose a novel repurchase protocol for a participant owing a portion of NFT to repurchase other shares to obtain the complete ownership. As participants may strategically bid during the acquisition process, our repurchase process is formulated as a Stackelberg game to explore the equilibrium prices. We also provide solutions to handle difficulties at market such as budget constraints and lazy bidders.
Accelerated Gradient Flow: Risk, Stability, and Implicit Regularization

Acceleration and momentum are the de facto standard in modern applications of machine learning and optimization, yet the bulk of the work on implicit regularization focuses instead on unaccelerated methods. In this paper, we study the statistical risk of the iterates generated by Nesterov's accelerated gradient method and Polyak's heavy ball method, when applied to least squares regression, drawing several connections to explicit penalization. We carry out our analyses in continuous-time, allowing us to make sharper statements than in prior work, and revealing complex interactions between early stopping, stability, and the curvature of the loss function.
Accelerated SGD for Non-Strongly-Convex Least Squares

We consider stochastic approximation for the least squares regression problem in the non-strongly convex setting. We present the first practical algorithm that achieves the optimal prediction error rates in terms of dependence on the noise of the problem, as $O(d/t)$ while accelerating the forgetting of the initial conditions to $O(d/t^2)$. Our new algorithm is based on a simple modification of the accelerated gradient descent. We provide convergence results for both the averaged and the last iterate of the algorithm. In order to describe the tightness of these new bounds, we present a matching lower bound in the noiseless setting and thus show the optimality of our algorithm.
Accurate smartphone camera simulation using 3D scenes

We assess the accuracy of a smartphone camera simulation. The simulation is an end-to-end analysis that begins with a physical description of a high dynamic range 3D scene and includes a specification of the optics and the image sensor. The simulation is compared to measurements of a physical version of the scene. The image system simulation accurately matched measurements of optical blur, depth of field, spectral quantum efficiency, scene inter-reflections, and sensor noise. The results support the use of image systems simulation methods for soft prototyping cameras and for producing synthetic data in machine learning applications.