Reviewerly/exHarmony huggingface dataset:
Incremental Learning Framework for Mining Big Data Stream

At this current time, data stream classification plays a key role in big data analytics due to its enormous growth. Most of the existing classification methods used ensemble learning, which is trustworthy but these methods are not effective to face the issues of learning from imbalanced big data, it also supposes that all data are pre-classified. Another weakness of current methods is that it takes a long evaluation time when the target data stream contains a high number of features. The main objective of this research is to develop a new method for incremental learning based on the proposed ant lion fuzzy-generative adversarial network model. The proposed model is implemented in spark architecture. For each data stream, the class output is computed at slave nodes by training a generative adversarial network with the back propagation error based on fuzzy bound computation. This method overcomes the limitations of existing methods as it can classify data streams that are slightly or completely unlabeled data and providing high scalability and efficiency. The results show that the proposed model outperforms state-of-the-art performance in terms of accuracy (0.861) precision (0.9328) and minimal MSE (0.0416).
The memory forensic research oriented to segment heap in Windows 10 system

The current forensic research on heaps mainly extracts information from the heap of Linux and the NT heap of Windows. However, the study of how to extract the information on the segment heap in the Windows 10 from dump files is not sufficient. To reproduce the internal information on the segment heap, this paper proposes a method for locating and extracting the internal information on the segment heap in the Windows 10 according to the field offset in the vtype description information of memory object. The method uses the pool scanning technology to locate the process object, obtains the starting position of the process heap and scans the process heap according to the structural information on the process object and the process environment block object. Then it locates the position of the segment heap with its feature values, thereby extracting its internal information. Based on the analysis results, five forensic plugins for extracting the information on the segment heap were developed on the Volatility framework. The experimental results show that this method can effectively extract the information on the address of each segment heap and its internal components in the memory and on the size of committed memory, etc. The information can help investigators to analyze the digital traces left in the memory by cyber criminals or cyber attackers.
Fast stitching of UAV images based on improved SURF algorithm

In order to improve the speed of stitching and information extraction of remote sensing images, based on the UAV (Unmanned Aerial Vehicle) image stitching algorithm, this paper proposes a UAV image matching method based on the combination of SURF (Speeded Up Robust Features) and MASC (M-Estimate Sample Consensus) algorithm. the SURF algorithm is fast, and the MASC algorithm can effectively eliminate mismatch points and has high stability. In this paper, the SIFT (Scale-invariant feature transform) algorithm, SURF algorithm, SURF and RANSAC (Random Sample Consensus) algorithm as well as SURF and MASC algorithm are used to stitch the same two images and compare them respectively. The experimental results show that the lowest image matching error rate is only 2.13% and the matching time is 4.772670s when using the combination of SURF and MASC algorithm for image stitching, which has certain advantages.
An English Online Homework Tutoring System Supported by Internet Database

College English tutoring is an important content of current research, and how to provide more effective methods for teaching tutoring is currently a hot research topic. Therefore, this paper designs an Internet-based online tutoring platform for college English teaching, which is accelerated by using the K-means clustering algorithm. The data processing efficiency and processing methods to improve user satisfaction require more practical experience to summarize. In the business logic layer, the uploaded information is mined through the K-means clustering algorithm to form an online tutoring university English database to provide platform data support, realize the business logic judgment of the data, convert the data in the database, and return to the user interface of other formats, to provide users with browsing and consulting. In the data access layer, the data from the business logic layer is processed. After the processing is completed, data can be added, deleted, modified, and checked in the database. Finally, the operation result of the database is fed back to the business logic layer for processing. Experimental results show that the designed platform has good data mining performance, low connection speed and low response delay, good compatibility, low CPU usage, fast resource sharing speed, and high user satisfaction. It can be connected to different operating systems.
CNN Steganalyzers Leverage Local Embedding Artifacts

While convolutional neural networks have firmly established themselves as the superior steganography detectors, little human-interpretable feedback to the steganographer as to how the network reaches its decision has so far been obtained from trained models. The folklore has it that, unlike rich models, which rely on global statistics, CNNs can leverage spatially localized signals. In this paper, we adapt existing attribution tools, such as Integrated Gradients and Last Activation Maps, to show that CNNs can indeed find overwhelming evidence for steganography from a few highly localized embedding artifacts. We look at the nature of these artifacts via case studies of both modern content-adaptive and older steganographic algorithms. The main culprit is linked to &#x201C;content creating changes&#x201D; when the magnitude of a DCT coefficient is increased (Jsteg, &#x2013;F5), which can be especially detectable for high frequency DCT modes that were originally zeros (J-MiPOD). In contrast, J-UNIWARD introduces the smallest number of locally detectable embedding artifacts among all tested algorithms. Moreover, we find examples of inhibition that facilitate distinguishing between the selection channels of stego algorithms in a multi-class detector. The authors believe that identifying and characterizing local embedding artifacts provides useful feedback for future design of steganographic schemes.
Comparative Analysis of Objective Quality Functions for the Stochastic Gradient Identification Method

A comparative analysis of two objective quality functions effectiveness with usage of the stochastic gradient identification object method for grayscale images is carried out for the situation when the deformation of identified object image in relation to the reference object image correspond to the similarity model.
Near Duplicate Video Detection Algorithm Based on Gray-scale Pyramid and Temporal Features

Near duplicate video detection algorithms based on global feature is sensitive to the brightness and cropping transform of videos. To solve the problem, a near duplicate video detection algorithm based on gray-scale pyramid and temporal feature called GSPOM is proposed. Before extracting the block brightness feature of the video, select the first k frames of the test video and match the gray-scale pyramid feature with the query ones. On the one hand, the dissimilar video will be filtered by matching and the amount of data will be reduced; On the other hand, the editing modes of the test video are estimated by the matching process. The corresponding gray-scale feature of the keyframe in the video library is extracted as the keyframe, and its features are extracted and matched with the feature of the test video. The similarity between them is also calculated. Experiments show that the algorithm has a significant improvement compared with the traditional algorithm based on temporal features.
Semantic and Acoustic-Prosodic Entrainment of Dialogues in Service Scenarios

According to the Communication Accommodation Theory, speakers dynamically adjust their communication behaviors, converging to or diverging from their interlocutors in order to diminish or increase social distance, which is called entrainment. Most of the studies investigated the entrainment of the interlocutors in terms of linguistic and paralinguistic features respectively, but paid less attention to the (dis)entrainment relation between paralinguistic and linguistic ones. In this study, we employed BERT to extract the semantic similarities of turns within dialogues in service scenarios, and found the semantic entrainment. We also found that (dis)entrainments policies were adopted between acoustic-prosodic (paralinguistic) and linguistic (semantic) features. These findings will contribute to fully understanding the mechanism of entrainment in dialogue.
Internet of Things in Cyber Security Scope

IoT represents a technologically bright future where heterogeneously connected devices will be connected to the internet and make intelligent collaborations with other objects to extend the borders of the world with physical entities and virtual components. Despite rapid evolution, this environment is still facing new challenges and security issues that need to be addressed. This chapter will give a comprehensive view of IoT technologies. It will discuss the IoT security scope in detail. Furthermore, a deep analysis of the most recent proposed mechanisms is classified. This study will be a guide for future studies, which direct to three primary leading technologies—machine learning (ML), blockchain, and artificial intelligence (AI)—as intelligent solutions and future directions for IoT security issues.
A fine-grained petri model for SQL time-blind injection

With the background of severe SQL injection attacks, the existing SQL injection modeling methods cannot reflect the process of SQL injection attacks in a fine-grained manner. Based on the discussion of attack technology, this paper takes SQL time-blind injection as an example to model its process with Petri Net. The validity of the model is verified by quantitative analysis and qualitative analysis. Try to inject 10, 20, 30, 40 and 50 times into target aircraft and Petri Net model respectively. The blind injection time is recorded and compared. The results show that the injection time increases with the increase of injection times. Under the same injection times, the Petri Net model takes less time. The sending time in the token can be set. When the sending time is short, the injection speed is fast, and super real-time simulation can be realized, which can realize the rapid prediction of attacks and resource vulnerability effects. When the sending time is long, the injection process slows down. It is beneficial to observe the details of the injection process and whether conflicts occur at a fine-grained level, analyze the purpose of the attack and achieve the purpose of building a patch model. The patch model can effectively take countermeasures against attacks, predict unknown vulnerabilities and ensure network information security.
firm VulSeeker: BERT and Siamese based Vulnerability for Embedded Device Firmware Images

In this paper, we propose firmVulSeeker-a vulnerability search tool for embedded firmware images, based on BERT and Siamese network. It first builds a BERT MLM task to observe and learn the semantics of different instructions in their context in a very large unlabeled binary corpus. Then, a finetune mode based on Siamese network is constructed to guide training and matching semantically similar functions using the knowledge learned from the first stage. Finally, it will use a function embedding generated from the fine-tuned model to search in the targeted corpus and find the most similar function which will be confirmed whether it's a real vulnerability manually. We evaluate the accuracy, robustness, scalability and vulnerability search capability of firmVulSeeker. Results show that it can greatly improve the accuracy of matching semantically similar functions, and can successfully find more real vulnerabilities in real-world firmware than other tools.
AntiTomo: Network topology obfuscation against adversarial tomography-based topology inference

By using tomography-based topology inference method, attackers can infer the topology of a network without the collaboration of the internal nodes in that network, which can greatly improve the efficiency of their subsequent link flood attack (LFA) behaviors. In order to defend against adversarial tomography-based topology inference, we propose a network topology obfuscation mechanism named AntiTomo, which is a proactive deception based network anti-reconnaissance method. By providing the attackers with well-designed obfuscated path measurement metrics, AntiTomo can lead the attackers to form a fake network topology view, which hides the key elements (i.e., the key links and the key nodes) of the physical network. To generate an obfuscated network topology with high deceptive features efficiently, AntiTomo uses the multi-objective optimization model to construct the obfuscated topology with security and low-cost features. Our experimental analysis based on several typical real network topologies shows that AntiTomo can generate an effective obfuscated network topology with high deceptive, low cost, and high efficiency, which can defend against tomography-based network topology reconnaissance effectively.
Four consecutive primitive elementsin a finite field

For <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="q"> <mml:semantics> <mml:mi>q</mml:mi> <mml:annotation encoding="application/x-tex">q</mml:annotation> </mml:semantics> </mml:math> </inline-formula> an odd prime power, we prove that there are always four consecutive primitive elements in the finite field <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="double-struck upper F Subscript q"> <mml:semantics> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="double-struck">F</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi>q</mml:mi> </mml:mrow> </mml:msub> <mml:annotation encoding="application/x-tex">\mathbb {F}_{q}</mml:annotation> </mml:semantics> </mml:math> </inline-formula> when <inline-formula content-type="math/mathml"> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" alttext="q greater-than 2401"> <mml:semantics> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>&gt;</mml:mo> <mml:mn>2401</mml:mn> </mml:mrow> <mml:annotation encoding="application/x-tex">q&gt; 2401</mml:annotation> </mml:semantics> </mml:math> </inline-formula>.
Keywords Extraction on the Topic "Образование/Education"

The paper aims to identify the keywords features of the thematic field “Образование/Education” in the Russian and English languages. The article describes the stages of the automated parsing of the news articles from the websites of the educational online portals “EDU-Inform” and “Education Today Magazine” for corpus creation. The study also pays significant attention to the linguistic analysis of the extracted keywords. The scientific originality of the research consists in the interdisciplinary consideration of the issue of keywords studying and usage of computer programming instruments for the automated natural language text processing. As a result, the research presents the visualization of the thematic field “Образование/Education” for the Russian and English languages in the form of a word cloud.
Gridlines approach for dynamic analysis in speech ultrasound data: A multimodal app

Having access to efficient technologies is essential for the accurate description and analysis of articulatory speech patterns. In the area of tongue ultrasound studies, the visualization/analysis processes generally require a solid knowledge of programming languages as well as a deep understanding of articulatory phenomena. This demands the use of a variety of programs for an efficient use of the data collected. In this paper we introduce a multimodal app for visualizing and analyzing tongue contours: UVA—Ultrasound Visualization and Analysis. This app combines the computational power of R and the interactivity of Shiny web apps to allow users to manipulate and explore tongue ultrasound data using cutting-edge methods. One of the greatest strengths of the app is that it has the capability of being modified to adapt to users’&amp;nbsp;needs. This has potential as an innovative tool for diverse academic and industry audiences.
RGB-D-Based Method for Measuring the Angular Range of Hip and Knee Joints during Home Care Rehabilitation

Home-based rehabilitation is becoming a gold standard for patient who have undergone knee arthroplasty or full knee replacement, as it helps healthcare costs to be minimized. Nevertheless, there is a chance of increasing adverse health effects in case of home care, primarily due to the patients' lack of motivation and the doctors' difficulty in carrying out rigorous supervision. The development of devices to assess the efficient recovery of the operated joint is highly valued both for the patient, who feels encouraged to perform the proper number of activities, and for the doctor, who can track him/her remotely. Accordingly, this paper introduces an interactive approach to angular range calculation of hip and knee joints based on the use of low-cost devices which can be operated at home. First, the patient's body posture is estimated using a 2D acquisition method. Subsequently, the 3D posture is evaluated by using the depth information coming from an RGB-D sensor. Preliminary results show that the proposed method effectively overcomes many limitations by fusing the results obtained by the state-of-the-art robust 2D pose estimation algorithms with the 3D data of depth cameras by allowing the patient to be correctly tracked during rehabilitation exercises.
Spatial Skills Training Proposal in Virtual Reality Learning Environments

In the last decade, Virtual Reality (VR) technologies have become a driving force for Information and Communication Technologies (ICT) educational change. Several authors and Computers and Education publications state that immersive experiences provide better assimilation of the concepts studied. The educational goal of ICT such as VR is to deliver tools that enable the learning process as it could happen in the classroom, through three-dimensional simulations where information is organized and presented to the user who interacts directly with it. These simulations are referred as Virtual Reality Learning Environments (VRLE). This paper proposal is an immersive VRLE where the user performs tasks through direct interaction with 3D objects to train and enhance spatial skills demanded for Engineering undergraduates in the Graphic Expression area, including identification of 3D object projections, translation, rotation and cutting plane operations. This VR based method aims at optimizing time dedication to learn traditional Computer-Aided Design (CAD) software for training spatial skills set and motivate students to participate in a program to improve and evaluate competences required in engineering occupations. This methodology allows telematic learning via VR head-mounted displays with connectivity, that has become more relevant in the light of lockdown scenarios experienced during pandemic in the year 2020.
Representation of the Mechanism for Constructing an Integrated Method of Solving a Diagnostic Problem and Results of Its Work

In the paper, we present the results of the development of the language of schemes of role-based visual relations for representation of “an integrated method of solving a diagnostic problem” and the heuristic mechanism of solving problems by dynamically restructuring the whole into a decomposition of a related task.
Arabic Sign Language Recognition System by Using Surface Intelligent EMG Signal

Abstract— Sign language is the way deaf persons use to interact with other people. The problem is that sign language like any other language is not international. That means a reliable and efficient device that works for one language does not work for another language, so there is a dire need for designing various sign language recognition techniques based on the structure and complexity of each language. Although variety of programs and techniques both conventional and smart based were designed for the English language , other languages like Arabic had not attracted enough research and research funding, and there is a need for both academic research and practical engineering designs. In this paper an Arabic sign language recognition system is designed by utilizing Electromyography (EMG) and artificial intelligence (IEMG), specifically artificial neural networks (ANN) combined with LabVIEW and sensors designed specifically for this system. After training the designed system with variety of data validation and testing different data entries yield high percentage of reliability and efficiency.
Comic Image Inpainting via Distance Transform

Inpainting techniques for natural images have progressed significantly. However, if these methods are applied to comic images, the results are not satisfactory because of very noticeable artifacts, especially around line drawings. Line drawings are challenging to inpaint because of their high-frequency components. In this paper, we propose a novel method for inpainting comic images in the distance transform domain. In this method, we first convert a line drawing into a distance image and then inpaint the distance image. By transforming line drawings into distance images, we can eventually reduce the high-frequency components, which leads to improve inpainting performance. We compared the results of our proposed method with those of the conventional methods. The results showed that the proposed method achieved 0.1% lower l1 loss, 0.5dB higher PSNR, and 0.5% higher SSIM than those of the conventional inpainting methods.
Research Study on Edge Computing

Driven by the rapid development of the Internet, the Internet of Things and mobile devices are rapidly updated, generating a large amount of data all the time. The traditional computing model is difficult to meet the computing requirements, which promotes the development of edge computing. Many Internet companies now offer independent computing platforms and architectures. This paper introduces the background of edge computing and summarizes the current mainstream edge computing methods. The framework and key technologies of edge computing are described. In addition, several typical scenarios using edge computing were introduced. Finally, the challenges and possible solutions of edge computing are summarized according to the related literatures of edge computing.
TSP Solving Utilizing Improved Ant Colony Algorithm

Abstract To solve the premature issue of TSP solving using the ant colony optimization algorithm (ACO), this paper proposes an improved ACO using particle swarm optimization (PSO) to solve the classic traveling salesman problem (TSP). The algorithm’s strategy includes three stages: firstly, establishing a mathematical model according to the optimization objective, and then solving the optimal path obtained by the particle swarm optimization algorithm. Finally, the pheromone concentration of this path in the ant colony mathematical model is enhanced according to the particle swarm optimization algorithm’s optimal path. A classic TSP case is used to compare the PSO and ACO. The results show that the proposed improved algorithm has a faster convergence speed and can converge to the optimal global solution, and its performance is better than that of ACO and PSO.