Reviewerly/exHarmony huggingface dataset:
Creating effective fingerprint artefacts: a cooperative and a non-cooperative method for bypassing capacitive and optical sensors with high success rate

Many Presentation Attack Detection methods (PAD) were introduced recently to enhance the security of biometric sensors, however, most of them are not implemented in commercial devices. In this paper we present both a cooperative and a non-cooperative direct attack method to bypass fingerprint sensors (optical scanners, capacitive scanners) used in modern embedded devices. Using the cooperative method 86%-100% Impostor Attack Presentation Match Rate (IAPMR) can be achieved on optical and capacitive sensors using a combination of Ethylene-Vinyl Acetate -(C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> ) <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> (C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> O <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> - commonly known as “hot glue” and Polyvinyl Acetate -(C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</inf> H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</inf> O <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> ) <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</inf> - commonly known as “white glue” or “school glue”. The cooperative method takes 5–10 minutes and costs no more than $5 which makes it very lucrative and accessible to anyone. We were able to achieve these high success rates on the latest hardware such as the Windows Hello Framework or certified high security devices. Using the non-cooperative method we were able to achieve 98% IAPMR on optical sensors. During the experiment the following devices were tested: Suprema Biostation 2, 2019 Macbook Air, 2017 MacBook Pro, HUAWEI P9, HUAWEI P20, Lexar F35 encrypted USB drive, ASUS Zenbook UX461F Z14, iPhone 8. The whole process is precisely documented and disclosed in this paper for the research community.
Analysis of sysdiagnose in iOS 15 to identify the sending phone number of AirDrop data

Modern cell phones allow for easy communication and transfer of data between devices. Unfortunately, some of the data transferred can be of unwelcomed, illicit, or threatening imagery and other files; digital forensic examiners are often asked to identify the source of these files. In this project, we developed a method to gain insights into the device used to send a file via Apple AirDrop. Our method brute forces the partial SHA256 hash entries found in the receiving Apple device's sysdiagnose logs to reveal the sender's phone number, even if that phone number was not known by the receiving device. This research publishes a method to generate permutations of the partial hash values using potential US area codes to identify the complete phone number of the sending device. In this research project, exemplar photographs were transmitted via AirDrop between Apple devices running iOS 15. A sysdiagnose was then generated on the receiving phone and exported by AirDrop to a MacBook Air for analysis. The analysis of the generated sysdiagnose archive found a partial SHA-256 hash of the sending device's phone number. This research identified a method to generate permutations of the partial SHA-256 hashes using a possible country and area code for the sending device in order to successfully identify the sending device's phone number. As a result, it was found that the sender of an unknown AirDrop file's phone number can be identified from the receiving device's sysdiagnose log files.
Traffic travel service selection based on hybrid optimization algorithm

With the development of information technology in transportation industry, it becomes an important application of intelligent transportation system to provide real-time and effective travel services. The existing traffic travel service composition model is based on the assumption of single request, and it can not build composition according to granularity. Aiming at the problem of requests and granularity, from the two dimensions of transaction and quality of service (QoS), this paper presents an evaluation model of service composition which considers concurrent requests and multi-granularity. Based on the model, the paper proposed a method based on hybrid optimization algorithm. The proposed algorithm combined particle swarm optimization, crossover and mutation operators with priority, simulated annealing algorithm to get the optimal value. Experimental results show that our approach can guarantee QoS of service selection with low time cost.
A Hybrid Approach to Classifying Crime Big Data

Big data opens up new possibilities in a variety of areas. In some instances, having a large dataset available may be beneficial. However, processing an Exabyte of data in its entirety is difficult. This article uses Hybrid Enhanced Artificial Bee Colony to create a hybrid algorithm for obtaining timely and actionable knowledge to expand and use comprehensive extensive data modelling systems and analytics algorithms. Hybrid Enhanced Artificial Bee Colony (HEABC) algorithm Support Vector Machine (SVM) classifiers have been contemplatedto build a hybrid algorithm for obtaining timely and actionable information. Resolving this issue would permit high exactitude data classification, which is particularly important for Big Data, to be completed in a reasonable amount of time. The hybrid algorithm computes an s. The hybrid algorithm simultaneously seeks the kernel function's form. The concept of bees inspired the hybrid EABC algorithm. During the algorithm's implementation, some bees alter the shape of their kernel function to correspond to the particle with the most significant classification accuracy. The proposed EABC algorithm is a mathematical formula that reduces the time spent creating SVM classifiers, which is crucial for solving the big data classification problem. SVM classification work focused on the hybrid EABC algorithm. Experimentation has shown that the Big Data classification approaches provided are adequate.
Body-Face Joint Detection via Embedding and Head Hook

Detecting pedestrians and their associated faces jointly is a challenging task. On one hand, body or face could be absent because of occlusion or non-frontal human pose. On the other hand, the association becomes difficult or even miss-leading in crowded scenes due to the lack of strong correlational evidence. This paper proposes Body-Face Joint (BFJ) detector, a novel framework for detecting bodies and their faces with accurate correspondance. We follow the classical multi-class detector design by detecting body and face in parallel but with two key contributions. First, we propose an Embedding Matching Loss (EML) to learn an associative embedding for matching body and face of the same person. Second, we introduce a novel concept, "head hook", to bridge the gap of matching body and faces spatially. With the new semantical and geometrical sources of information, BFJ greatly reduces the difficulty of detecting body and face in pairs. Since the problem is unexplored yet, we design a new metric named log-average miss matching rate (mMR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> ) to evaluate the association performance and extend the CrowdHuman and CityPersons benchmarks by annotating each face box. Experiments show that our BFJ detector can maintain state-of-the-art performance in pedestrian detection on both one-stage and two-stage structures while greatly outperform various body-face association strategies. Code will be available at: https://github.com/AibeeDetect/BFJDet.
Assessing the Learning Outcomes of Using Mobile Game Integration in Teaching English Vocabulary

English language is taught universally and is therefore also in Saudi Arabia since 1958. But the student’s achievements have not been satisfactory, hence the imperative to research new teaching methods. The purpose of this study is to investigate the potential and effects of using mobile device games on learning English as a Foreign Language (EFL) vocabulary for student achievement in a Saudi female public high school. After conducting pilot studies of five mobile games with teachers and students, The English Bee, an original game specifically designed for this research project, was selected. The study comprised one participant cohort who were taught two modules; one through The English Bee in and the other module was taught by traditional methods. It employed a mixed methodology of a number of collection techniques for both qualitative and quantitative data, namely, pre- and post-tests, focus group discussions, interviews and reflective essays.
Anisotropic Gaussian Side Windows Guided Filtering

The guided filtering and its different variants have good edge preserving characteristics, widely used in many computer vision applications such as Matting, Dhaze, but halos still appear near the edges and it is difficult to identify high-resolution edges. Therefore, we propose an Anisotropic Gaussian Side Window Guided Filtering (ANGKSWGF). This method has three advantages: (1) We efficiently combine Anisotropic Gaussian-shaped Kernel with side window technology to suppress edge blur and halo effects; (2) We use anisotropic Gaussian-shaped kernel weights The factor effectively weights and fuses different pixel information in the window to produce more accurate guided filtering results; (3) The multi-directional anisotropic Gaussian kernel with large-scale shape parameters can effectively improve the enhancement effect of fine edges. The proposed filtering algorithm can be applied to image processing applications, such as image smoothing and detail enhancement. The experimental results on synthetic ideal edge and actual natural images show that the edge preservation and detail enhancement of the proposed algorithm is significantly better than the other algorithms.
The strategy of indirect actions in educational technologies

The article deals with modern educational technologies, analyzes approaches to their classification. In particular, it considers such a classification criterion as the features of cognitive activity management. It is noted that the cognitive activity management in a number of educational technologies can be characterized as a «strategy of indirect actions». The term of «indirect action strategy» was introduced by B.G. Liddell Hurt, an outstanding military theorist and historian. This term was previously used in military affairs and related to interstate relations, not in pedagogy. However, this term best describes goal setting in many educational technologies. As applied to educational technologies, the «indirect action strategy» is that the student consciously pursues one goal, but actually achieves another one. The goal that seems to the student to be the most significant in the educational process, in fact, is of a subordinate, service nature. This shows such a common property of hierarchical systems as the ability to form system inversions. In the case of system inversion, the lowest, subordinate element in the hierarchy takes on the dominant value, while remaining in its former low hierarchical position. In other words, there is a contradiction between the formal position of an element in the system and its actual role in it. For example, this situation develops when using game educational technologies. The learner, immersed in the learning game, does not perceive and is not aware of anything other than the game itself; however, in this game he acquires a number of important professional skills. This is due to the very nature of the game activity, in which it is not the external goal that is crucial, but the game process itself. A similar situation develops when using problem-based learning. The student solves the educational problem, which in the course of activity seems to him something very important. However, it is not the problem itself that is really important, but the competencies that the student acquires in the process of solving it. Similarly, the strategy of indirect actions is manifested in the application of the project method, as well as in the use of case study technology.
Kalman Filter Tracking Algorithm Simulation Based on Angle Expansion

In the position tracking of a uniform linear moving target affected by noise, when the extended Kalman filter adopts distance as the correction amount, and the performance is greatly affected by the noise. The angle variable is used as the second-dimensional variable to form a gain matrix, so that distance and the angle work together to track the target position. By introducing the current position angle observations that are away from the sensor into the observation matrix Z. and introducing the horizontal and vertical angle change ratio under the action of the prior value into Jacobian matrix H; the second dimension is expanded. Simulation results and analysis show that the proposed method has a smaller position tracking deviation and a better overall performance under certain conditions in comparison with Kalman filter method with only distance correction, the new method has a smaller position tracking deviation and a better overall performance under certain conditions.
Information Retrieval for Cloud Forensics

The use of cloud computing and cloud-based services has increased sharply in the past decade. Regardless of many advantages, the extensive use of the cloud has also created a large attack platform, frequently exploited by cybercriminals, requiring real-time, automated detection and competent forensics tools for investigations. Evidence identification so far has been limited to performance studies on datasets that were created a long time ago and are not specific to cloud environments. In this paper, we introduce a novel dataset for cloud-specific evidence detection. The dataset has two categories: the monitoring database and the evidence database. The monitoring database has 43 features and 9610 records, whereas the evidence database has 360 memory dump files of around 280 GB which contain memory dumps of benign virtual machines and a hostile virtual machine. The dataset will be an important resource for evidence identification research in the cloud environment.
Authoring With Virtual Reality Nuggets—Lessons Learned

Virtual Reality (VR) systems and VR content are complex, and their creation can mainly be conducted by experts in VR and related areas. That makes the use of VR challenging for experts from other domains, such as educators. In this paper, we build up on existing work and investigate the VR nugget concept—small self-contained VR systems that are built from educational design patterns. Particularly, we extend this concept and introduce structured authoring processes based on VR nuggets that show how standalone, combinable, and reusable VR software can serve as a meta-level guide for various VR applications and involve educators as domain experts. We conduct a user study with VR Forge, a VR-nugget-based authoring software tool, to draw conclusions on how pattern-based VR authoring tools should be designed to support domain experts. We compare our results with those of a related study of the VR nugget tool IN Tiles. Based on the comparative results of usability and hedonic quality measures, both anecdotal evidence and statistically significant results support the concept’s potential for VR authoring conducted by practitioners who are not experts in VR. We derive the recommendation that the design of a VR-nugget-based authoring environment will benefit from using both immersive and desktop user interface (UI) technologies and that the authoring workflow will need authors to frequently alternate between the technologies. We state findings and lessons learned from the development and the studies. We contribute insights in developing reusable and use-case-specific VR content and tools and propose authoring processes that focus on the tasks and goals of domain experts as the primary authoring role within educational VR development. Finally, the relevance of VR-nugget-based authoring is supported by anecdotal evidence gathered from over 3 years of investigating and applying it within three educational institutions and a company providing education services.
Cascade R-CNN for MIDOG Challenge

Mitotic counts are one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counting is still a difficult problem and is labourious. Automated methods have been proposed for this task, but are usually dependent on the training images and show poor performance on unseen domains. In this work, we present a multi-stage mitosis detection method based on a Cascade R-CNN developed to be sequentially more selective against false positives. On the preliminary test set, the algorithm scores an F $$_1$$ score of 0.7492.
Representation select Deep Neural Network for Mammographic Image Classification

The early detection and timely treatment of breast cancer can save lives. With the development of computer-aided diagnosis systems, the algorithms based deep neural network have been widely used in breast cancer screen. However, the most of the existing methods use deep convolutional neural network to extract feature maps for image classification, which lack attention to the representations of regions with a high probability of containing malignant lesions. In this paper, a strategy is proposed to train the neural network and select the most important representations to make the final classification decision. Specifically, the extracted feature maps are divided into several mutually exclusive parts, and for each part, a trainable filter is used to compute the importance of each element. Then, the most important k representations where most likely to contain malignant lesions are selected from each part and is concatenated as the final decision selected representations. Finally, a new classifier is trained to make the final decision based on the selected representations. Several experiments are conducted and this strategy’s performance proposed in this paper is compared with other similar algorithms’ performance on a public mammographic image dataset, the experimental results show the proposed structure can achieve the stat-of-the-art AUC score.
A system of configurable 3D indoor scene synthesis via semantic relation learning

When describing a complicated scene, natural language usually has extra meanings beyond the actual descriptions based on the context of objects and relations. Thus, synthesizing 3D scene from natural language description can be regarded as an ill-posed problem. To solve this challenge, we build a novel system named Text to Scene (T2S) via relation learning for the language-driven scene synthesis. In our paper, we propose a novel graph-based contextual completion method Contextual ConvE(CConvE) to enrich the 3D indoor scene and visualize the graph by arranging 3D models under an object location protocol. Besides, we integrate them into T2S system which synthesizes 3D scene from text. Given a text, T2S system will organize inclusive semantic message to a graph template, complete the graph with CConvE and visualize the graph by retrieving and arranging 3D models under the protocol. CConvE is a convolutional neural network which can infer object categories and spatial relations from the contextual message passing. The experimental result shows the competitive performance of CConvE compared with the state-of-the-art approach and proves that introducing semantic object relation learning method to 3D scene synthesis task can make the virtual visualized results in line with real life.
Similarity Measure for Smart Contract Bytecode Based on CFG Feature Extraction

As the mainstream of smart contract research, most Ethereum smart contracts do not open their source code, and the bytecode of smart contracts has attracted the attention of researchers. Based on the similarity measurement of smart contract bytecode, a series of tasks such as vulnerability mining, contract upgrading and malicious contract detection can be carried out. This paper proposes a method to measure the similarity of smart contract bytecode. Firstly, the key opcode combination of smart contract is summarized. When traversing the CFG(control flow graph) constructed by decompilation of smart contract bytecode, the opcodes in the basic block are pattern matched, and the features between the basic blocks are extracted according to the in-out degree, so as to enhance the similarity measurement effect of contract semantics in vector space. The experimental results show that the proposed method is greatly improved compared with the baseline.
Threats and data trading detection methods in the dark web

The dark web has become a major trading platform for cybercriminals, with its anonymity and encrypted content nature make it possible to exchange hacked information and sell illegal goods without being traced. The types of items traded on the dark web have increased with the number of users and demands. In recent years, in addition to the main items sold in the past, including drugs, firearms and child pornography, a growing number of cybercriminals are targeting various types of private information, including different types of account data, identity information and visual data etc. This paper will further discuss the issue of threat detection in the dark web by reviewing the past literature on the subject. An approach is also proposed to identify criminals who commit crimes offline or on the surface network by using private information purchased from the dark web and the original sources of information on the dark web by building a database based on historical victim records for keyword matching and traffic analysis.
The Role of Quantum and Post-Quantum Techniques in Wireless Network Security - Status, Challenges and Future Trends

One of the most essential ways of communication is the wireless network. As a result, ensuring the security of information transmitted across wireless networks is a critical concern. For wireless networks, classical cryptography provides conditional security with several loopholes, but quantum cryptography claims to be unconditionally safe. People began to consider beyond classical cryptosystems for protecting future electronic communication when quantum computers became functional. With all of these flaws in classical cryptosystems in mind, people began to consider beyond it for protecting future electronic communication. Quantum cryptography addresses practically all flaws in traditional cryptography.
Enhanced Speed Control of Separately Excited DC Motor Using Fuzzy-Neural Networks Controller

The dynamic behavior of a PID-type fuzzy logic control depends on the appropriate choice of its scaling factors. Fixed scaling factors cannot provide adequate control performance under a wide range of operating conditions. This paper proposes a control strategy for separately excited dc motor (SEDCM) speed control based on fuzzy logic and neural networks. The function of the neural networks is to adapt the scaling factors at the inputs and output of the fuzzy logic controller. Using MATLAB/Simulink, the performance of the proposed controller is highlighted in comparison with anti-windup proportional-integral (PI) and sliding mode controllers under variable speed reference, disturbances, and armature resistance variation.