Reviewerly/exHarmony huggingface dataset:
Factorizing 2FA: Forensic analysis of two-factor authentication applications

Many sectors such as banking, academia, health care, and others have made Two-Factor Authentication (2FA) mandatory for all their registered users. The growth in the usage of 2FA technology demonstrates the need to understand how 2FA applications operate, the kind of information they store about their users, and the implications, if any, that may arise if malicious actors exploit them. Our work focuses on the forensic analysis of 15 2FA applications used by millions of people. Our analysis includes popular applications such as FreeOTP, Google Authenticator, Microsoft Authenticator, Twilio Authy, and more. The applications were tested on different operating systems (Android, iOS and Windows 10) and used with applications such as Facebook, Twitter and Instagram. Our methodology focused on not just forensically analyzing the devices’ storage, but also the network traffic of all devices and the memory of the Windows machine. Results revealed that the majority of analyzed applications store encrypted/encoded and plain text information, such as secret keys, timestamps, account names, e-mail addresses, the application locking pin, and more. Consequently, we believe that the critical discovery of secret keys allows for the 2FA functionally to be bypassed and it is demonstrated in this work. Our results revealed that 14 of 15 applications stored the name of the social media application/account information, and 14 of 15 applications stored either plain text, or encoded/encrypted secret keys. Finally, 2 of 15 applications stored a pin in plain text used to lock the application and/or encrypt all information on the disk.
Software Architecture is a Set of Abstractions

Software architecture is a set of abstractions that helps you reason about the software you plan to build, or have already built. Our field has had small abstractions for a long time now, but it has taken decades to accumulate larger abstractions, including quality attributes, information hiding, components and connectors, multiple views, and architectural styles. When we design systems, we weave these abstractions together, preserving a chain of intentionality, so that the systems we design do what we want. Twenty years ago, in this magazine, Martin Fowler published the influential essay “Who Needs an Architect?” It’s time for developers to take another look at software architecture and see it as a set of abstractions that helps them reason about software.
Problem Issues and Models of Professional Qualifications Assignment by Higher Education Institutions

The domestic practice of assigning professional qualifications, in particular, by higher education institutions (HEIs), varies and indicates inadequate institutional normalisation of this issue. The objective of our research is to characterise the regulatory and actual procedures for assigning professional qualifications in higher education institutions, to outline possible models for taking professional standards into account when developing educational programmes, and to provide recommendations on procedures for assigning professional qualifications. The research was conducted within the framework of the social constructivist approach and is based on the results of document analysis.&#x0D; The analysis of changes in educational and labour legislation, implemented in 2022, gave grounds to determine the following aspects: the assignment of professional qualifications is possible if there is an occupational standard; the name of the professional qualification is defined in the occupational standard; all learning outcomes/competences stipulated by the occupational standard should be included in the educational programme, in case of partial qualification, the educational programme should specify the specific job function or job functions defined by the occupational standard.&#x0D; The current practice of assigning professional qualifications does not meet these expectations and is characterised by non-systematic and inconsistency, the co-existence of different requirements for the assignment of such qualifications by qualification centres, professional associations, and HEIs.&#x0D; The paper presents two models of implementation of the occupational standards requirements in educational programmes: the first for broad educational specialties, the second for relatively narrow ones (with a few occupational standards per specialty).&#x0D; The paper concludes with recommendations on the normalisation of the process of assigning professional qualifications of HEIs at the micro-level (educational programmes), at the level of HEIs, at the meso-level, as well as at the macrolevel.
An Examination of Occupational Fraud Committed by Information Technology Professionals

ABSTRACT To keep up with the demands from increasingly complex systems, information technology (IT) departments have sought IT professionals and frequently provided these individuals with privileged access to organizational systems early in their tenure. This study uses survey data obtained from the Association of Certified Fraud Examiners (ACFE) Research Institute, to examine occupational frauds committed by IT professionals versus frauds committed by non-IT personnel. The differences between IT and non-IT personnel can have an impact on organizational fraud. Findings show that IT perpetrators engage in fraud earlier in their tenure and generate similar losses to their non-IT counterparts but in shorter time frames. Organizational managers and forensic accountants should be aware of these important differences and consider the use of monitoring controls, such as management reviews of access and user activity, to detect fraud committed by IT perpetrators. This study provides several suggestions for future research in this vein. Data Availability: Data are available from the Association of Certified Fraud Examiners (ACFE). JEL Classifications: M480; L860; L250.
Savitar: an intelligent sign language translation approach for deafness and dysphonia in the COVID-19 era

Purpose In the COVID-19 era, sign language (SL) translation has gained attention in online learning, which evaluates the physical gestures of each student and bridges the communication gap between dysphonia and hearing people. The purpose of this paper is to devote the alignment between SL sequence and nature language sequence with high translation performance. Design/methodology/approach SL can be characterized as joint/bone location information in two-dimensional space over time, forming skeleton sequences. To encode joint, bone and their motion information, we propose a multistream hierarchy network (MHN) along with a vocab prediction network (VPN) and a joint network (JN) with the recurrent neural network transducer. The JN is used to concatenate the sequences encoded by the MHN and VPN and learn their sequence alignments. Findings We verify the effectiveness of the proposed approach and provide experimental results on three large-scale datasets, which show that translation accuracy is 94.96, 54.52, and 92.88 per cent, and the inference time is 18 and 1.7 times faster than listen-attend-spell network (LAS) and visual hierarchy to lexical sequence network (H2SNet) , respectively. Originality/value In this paper, we propose a novel framework that can fuse multimodal input (i.e. joint, bone and their motion stream) and align input streams with nature language. Moreover, the provided framework is improved by the different properties of MHN, VPN and JN. Experimental results on the three datasets demonstrate that our approaches outperform the state-of-the-art methods in terms of translation accuracy and speed.
Retrieving space-dependent polarization transformations via near-optimal quantum process tomography

An optical waveplate rotating light polarization can be modeled as a single-qubit unitary operator. This analogy can be exploited to experimentally retrieve a polarization transformation within the paradigm of quantum process tomography. Standard approaches to tomographic problems rely on the maximum-likelihood estimation, providing the most likely transformation to yield the same outcomes as a set of experimental projective measurements. The performances of this method strongly depend on the number of input measurements and the numerical minimization routine that is adopted. Here we investigate the application of genetic and machine learning approaches to this problem, finding that both allow for accurate reconstructions and fast operations when processing a set of projective measurements very close to the minimal one. We apply these techniques to the case of space-dependent polarization transformations, providing an experimental characterization of the optical action of spin-orbit metasurfaces having patterned birefringence. Our efforts thus expand the toolbox of methodologies for optical process tomography. In particular, we find that the neural network-based scheme provides a significant speed-up, that may be critical in applications requiring a characterization in real-time. We expect these results to lay the groundwork for the optimization of tomographic approaches in more general quantum processes, including non-unitary gates and operations in higher-dimensional Hilbert spaces.
A Multiphase Semistatic Training Method for Swarm Confrontation Using Multiagent Deep Reinforcement Learning

In this paper, we propose a multiphase semistatic training method for swarm confrontation using multi-agent deep reinforcement learning. In particular, we build a swarm confrontation game, the 3V3 tank fight, based on the Unity platform and train the agents by a MDRL algorithm called MA-POCA, coming with the ML-Agent toolkit. By multiphase learning, we split the traditional single training phase into multiple consecutive training phases, where the performance level of the strong team for each phase increases in an incremental way. On the other hand, by semistatic learning, the strong team in all phases will stop learning when fighting against the weak team, which reduces the possibility that the weak team keeps being defeated and learns nothing at all. Comprehensive experiments prove that, in contrast to the traditional single-phase training method, the multiphase semistatic training method proposed in this paper can significantly increase the training efficiency, shedding lights on how the weak could learn from the strong with less time and computational cost.
Designing and creating MOOCs: the experience of the University of Bayreuth

The impact of modern digital technologies on the forms and methods of teaching in a modern university has been analysed. The characteristics of massive online open courses are described: they are regarded as a part of the digital and educational environment of a modern university, as well as a universal form of communication between society and the institution.&#x0D; The experience of Zentrums für Hochschullehre (Ukrainian analogue — a teacher training centre), whose employees create massive online courses, is described with the help of general scientific methods and the interview method. The factors that influenced the need to develop massive online courses at the university are substantiated and the philosophy of the university regarding massive online courses is characterised as an opportunity to attract a wide range of students, including potential applicants, who want to receive high-quality modern education. Also, the functions of the team that creates massive open courses are characterised.&#x0D; In the example of the case study of the University of Bayreuth, namely the course “Cellular Polymers”, the production cycle of creating massive open online courses, which consists of eight stages and takes from 8-10 months to a year, is examined in detail. Difficulties that may arise when creating massive open online courses are highlighted.&#x0D; The results of the internal audit of Zentrums für Hochschullehre to determine the effectiveness of massive online courses and the results of the author’s research on the perception of massive online courses by Ukrainian students are described. The interest of young people in taking such courses is proven, and typical problems that arise during training are presented (lack of motivation to complete the course, insufficient knowledge of the language, complex interface of the educational platform, etc.).&#x0D; It is proposed to gradually introduce massive open online courses into the activities of Ukrainian universities, primary recommendations are provided for those teachers who personally want to initiate the process of creating open online courses.
Expanding digital forensics education with artifact curation and scalable, accessible exercises via the Artifact Genome Project

Digital Forensics (DF) is a multidisciplinary domain that involves computing, law, criminology and other disciplines. At the core of the domain, however, is the Acquisition, Authentication and Analysis (AAA) of digital evidence. In the real world, practitioners typically find data of forensic value in DF artifacts. While this is true, educational programs and resources have not kept up with DF artifacts - which are the cornerstone of real-world investigations. Our work transforms and expands DF education by focusing the community's attention to artifacts. By leveraging our past work on the Artifact Genome Project (AGP), we expanded the platform to house educational modules that can be created and taken by any user or organization that has been vetted and met our standards to do so. Hundreds of curated DF artifacts have been added to the platform, and along with other educational resources, they have been employed to design scalable, self-paced, open, online DF educational modules. Our work was tested and put into practice in real-world scenarios around the world. This includes using the AGP platform to host the first DF challenge at the 2020 Interpol Digital Forensics Expert Group (DFEG) Conference and the 2021 DFRWS APAC Conference's annual Forensics Rodeo. Furthermore, we implemented the platform in DF courses at our own university. Feedback from these experiences was collected through surveys and in summary, the results show that there is a need for these type of educational resources in our community. While we observed that some improvements needed to be made either in the materials or platform, overall, participants benefited from the experience regardless of having zero to many years practicing on the field.
Trustworthy artificial intelligence classification-based equivalent bandwidth control

Nowadays, machine learning (ML) is a viable solution for the allocation of equivalent bandwidth (EqB) in telecommunication networks, i.e. the minimum service rate required by a traffic buffer to guarantee a satisfactory Quality of Service (QoS). Moreover, trustworthy artificial intelligence (AI) is gaining importance in regulating the implementation of ML models, requiring explainable AI (XAI) and uncertainty handling. The paper extends prior works on the combined usage of control and rule-based classification for the EqB allocation, by adding the perspective of trustworthy AI. Simulation-based data collection is performed under a large setting of traffic conditions. Clopper–Pearson generalization bound is used as an efficient tool to select a rule-based model with adequate performance, also determining the minimum amount of data required for model training, which resulted in 3000 samples (≈ 3.3 h of simulation). Also, robustness in terms of the model’s capability to recognize out-of-distribution samples is studied, by comparing the different rates of satisfaction of rules in presence of training or operational data, which is quantified via mutual information, ℓ1 and ℓ2 norms. Results show that, while norms are more likely to capture the difference between training and operational data distribution, regardless its entity, mutual information seems sensitive to the entity of the separation between the training and the operational domains.
Soccer line mark segmentation and classification with stochastic watershed transform

Augmented reality applications are beginning to change the way sports are broadcast, providing richer experiences and valuable insights to fans. The first step of augmented reality systems is camera calibration, possibly based on detecting the line markings of the playing field. Most existing proposals for line detection rely on edge detection and Hough transform, but radial distortion and extraneous edges cause inaccurate or spurious detections of line markings. We propose a novel strategy to automatically and accurately segment and classify line markings. First, line points are segmented thanks to a stochastic watershed transform that is robust to radial distortions, since it makes no assumptions about line straightness, and is unaffected by the presence of players or the ball. The line points are then linked to primitive structures (straight lines and ellipses) thanks to a very efficient procedure that makes no assumptions about the number of primitives that appear in each image. The strategy has been tested on a new and public database composed by 60 annotated images from matches in five stadiums. The results obtained have proven that the proposed strategy is more robust and accurate than existing approaches, achieving successful line mark detection even under challenging conditions.
Delving NFT vulnerabilities, a sleepminting prevention system

Abstract The rise of Non-Fungible Tokens (NFTs) is beginning to revolutionize the digital world thanks to the unique property of these tokens. Indeed, they can represent the ownership of physical or digital assets. They are implemented using smart contracts, therefore if the code of the smart contract contains bugs, an attacker can exploit its vulnerabilities to perform an attack called sleepminting. Sleepminting consists of transferring NFTs owned by an address, without the owner’s consent. In this paper, we provide a detailed analysis of the sleepminting attack and, thanks to the insights gained, we propose a prevention system to reduce the number of sleepminting attacks. Our prevention system is based on analysing the transactions included in new blocks, detecting those that are related to sleepminting attacks and keeping track of the addresses that are involved in these transactions. A dictionary-like data structure can be used to keep track of the addresses involved, where the key is the address and the value acts as a counter for the number of times the address is involved in sleepminting. With this information, block-creating nodes can add another verification step before adding a transaction to a block, which consists of blocking transactions when the addresses involved appear in sleepminting attacks a number of times greater than a threshold. The evaluation shows that sleepminting is a relevant phenomenon, and now it involves NFT transfers rather than NFT minting. Our proposed prevention system is able to block up to 87% of attacks.
Analysis of The Needs for Podcasts As Learning Media for Visually Impaired Students

The formulation of the problem from this research is what is the need for podcasts as a media for learning the visually impaired students? The purpose of this study was to analyze the need for podcasts as learning media for visually impaired students and their benefits for developing podcasts as learning media for visually impaired students. The research method is quantitative and qualitative, namely in the form of a list of tables that are converted into percentages to calculate how many items in a questionnaire to answer a study. While the qualitative is the elaboration of the quantitative data obtained by the researcher. Data collection techniques using questionnaires or questionnaires. The results of the research and discussion data presented can be concluded that the need for podcasts as a learning medium for the blind is indicated by a percentage of 100% meaning that students need learning media that suits their learning style.
Age and Gender Prediction using Gated Residual Attention Network

While a great deal of researchers has addressed the challenge of determining the age and sex from face images, these have received significantly fewer spotlights than some other challenges that are particularly linked with face recognition. Relatively to certain other face recognition concerns, the level of performance in this area has not increased significantly. Despite the progress made in age and gender prediction, several challenges like Facial variability, Data bias persist. Each language around the world includes its own lexicons and grammar standards which are aimed at helping individuals of various generations to interact. The decision to adopt is dependent upon the human capability to quickly identify specific characteristics like age and gender from the individual external appearance. This study assumed that AI technologies become even more widespread in variety of sectors and its decision-making abilities might mimic human mind. Besides, this study has created a GRA NET computational intelligence algorithm to Figure out the person’s age and sex.The residual attention network has already been improved and enhanced as well as the infrastructure now encompasses the principle of gate. Age forecasting is a regression issue whereas gender identity is a binary classification issue. Investigations have been done on the publicly available UTK Face dataset. The results obtained have already shown their importance with respect to both gender and age classification, it serves as a strong factor when compared to other cutting edge technologies.
Fake Profile Detection on Social Networking Websites using Machine Learning

These days, social media has a significant impact on everyone’s life. Most people frequently utilize social media platforms. Each of these social media platforms offers benefits and drawbacks, as well as security risks for our information. To determine who poses threats on these platforms, it is necessary to distinguish between the real and fake social media profiles. There are traditionally used various methods for identifying fake social media accounts. But these platforms need to be better at identifying phoney accounts. The accuracy rate of identifying fake accounts utilising timestamp data types is improved in this proposed work employing high gradient boosting algorithms and Natural Language Processing. In order to investigate the relationship between various machine learning methods and multi-features in time series, this study employs a variety of machine learning techniques.