Reviewerly/exHarmony huggingface dataset:
Hedging Against Sore Loser Attacks in Cross-Chain Transactions

A *sore loser attack* in cross-blockchain commerce rises when one party decides to halt participation partway through, leaving other parties' assets locked up for a long duration. Although vulnerability to sore loser attacks cannot be entirely eliminated, it can be reduced to an arbitrarily low level. This paper proposes new distributed protocols for hedging a range of cross-chain transactions in a synchronous communication model, such as two-party swaps, $n$-party swaps, brokered transactions, and auctions.
Test Suites as a Source of Training Data for Static Analysis Alert Classifiers

Flaw-finding static analysis tools typically generate large volumes of code flaw alerts including many false positives. To save on human effort to triage these alerts, a significant body of work attempts to use machine learning to classify and prioritize alerts. Identifying a useful set of training data, however, remains a fundamental challenge in developing such classifiers in many contexts. We propose using static analysis test suites (i.e., repositories of benchmark programs that are purpose-built to test coverage and precision of static analysis tools) as a novel source of training data. In a case study, we generated a large quantity of alerts by executing various static analyzers on the Juliet C/C++ test suite, and we automatically derived ground truth labels for these alerts by referencing the Juliet test suite metadata. Finally, we used this data to train classifiers to predict whether an alert is a false positive. Our classifiers obtained high precision (90.2%) and recall (88.2%) for a large number of code flaw types on a hold-out test set. This preliminary result suggests that pre-training classifiers on test suite data could help to jumpstart static analysis alert classification in data-limited contexts.
XCycles Backprojection Acoustic Super-Resolution

The computer vision community has paid much attention to the development of visible image super-resolution (SR) using deep neural networks (DNNs) and has achieved impressive results. The advancement of non-visible light sensors, such as acoustic imaging sensors, has attracted much attention, as they allow people to visualize the intensity of sound waves beyond the visible spectrum. However, because of the limitations imposed on acquiring acoustic data, new methods for improving the resolution of the acoustic images are necessary. At this time, there is no acoustic imaging dataset designed for the SR problem. This work proposed a novel backprojection model architecture for the acoustic image super-resolution problem, together with Acoustic Map Imaging VUB-ULB Dataset (AMIVU). The dataset provides large simulated and real captured images at different resolutions. The proposed XCycles BackProjection model (XCBP), in contrast to the feedforward model approach, fully uses the iterative correction procedure in each cycle to reconstruct the residual error correction for the encoded features in both low- and high-resolution space. The proposed approach was evaluated on the dataset and showed high outperformance compared to the classical interpolation operators and to the recent feedforward state-of-the-art models. It also contributed to a drastically reduced sub-sampling error produced during the data acquisition.
Dynamic Markov Model: Password Guessing Using Probability Adjustment Method

In password guessing, the Markov model is still widely used due to its simple structure and fast inference speed. However, the Markov model based on random sampling to generate passwords has the problem of a high repetition rate, which leads to a low cover rate. The model based on enumeration has a lower cover rate for high-probability passwords, and it is a deterministic algorithm that always generates the same passwords in the same order, making it vulnerable to attack. We design a dynamic distribution mechanism based on the random sampling method. This mechanism enables the probability distribution of passwords to be dynamically adjusted and tend toward uniform distribution strictly during the generation process. We apply the dynamic distribution mechanism to the Markov model and propose a dynamic Markov model. Through comparative experiments on the RockYou dataset, we set the optimal adjustment degree α. Compared with the Markov model without the dynamic distribution mechanism, the dynamic Markov model reduced the repetition rate from 75.88% to 66.50% and increased the cover rate from 37.65% to 43.49%. In addition, the dynamic Markov model had the highest cover rate for high-probability passwords. Finally, the model avoided the lack of a deterministic algorithm, and when it was run five times, it reached almost the same cover rate as OMEN.
Interdisciplinary Distance Learning Workshop for IT Students

Purpose of research . The purpose of research is to present proposals for the development and use of technologies for automatic checking of problem solutions in the process of forming professional competencies of students of IT-directions in the information educational environment. The article analyzes the authors’ experience gained during the development and operation of a distance learning workshop for IT students, which implements the functions of automatic checking of problem solutions in various specialized disciplines. This approach made it possible to increase the effectiveness of teaching both in the process of classroom studies and in the organization of independent work of students. Methods and materials . The article presents the key points of the creation and development of the distance learning workshop, justifies its concept, according to which the learning workshop is an interdisciplinary resource, in which the function of automatic checking of problem solutions is subordinate to the main function of developing and consolidating the skills and abilities of an IT specialist. An approach to organizing automatic checking by testing the program code developed by a student in the process of solving an educational problem is presented, and an extension of this approach is proposed for organizing automatic checking in cases where the solution is not the program code, but, for example, a diagram, model or text document designed in accordance with the regulated rules. The architectural solution and some features of the implementation of the distance learning workshop are justified, its role in the educational process is determined. Results. The results of the use of the learning workshop in the process of teaching various courses are presented. The results of training in programming are the most significant, since this resource has been used for more than 15 years – during this time, extensive experience has been accumulated in its use, the results of training have been analyzed, and some problems have been identified and solved. An example of using the learning workshop in the course “Mathematical Logic and Theory of Algorithms” is presented, in which the implementation of automatic checking of problem solutions required non-standard solutions to refine the learning workshop. An additional useful function of the learning workshop as the means of developing English-language competence is presented. Conclusion . The results of the study show that the technologies of automatic checking of problem solutions and software based on them are an effective way to develop the professional skills of students of IT areas. Surveys of employers of graduates of IT areas allow us to conclude that there is a relationship between the results of students’ work recorded in a distance learning workshop during training, and their success in professional activities after graduation. Many of the graduates continue to use the learning workshop to further develop and train their professional skills – the resource is open.
A Novel Proof of Concept for Twitter Analytics Using Popular Hashtags: Experimentation and Evaluation

Twitter analytics is a classic research area especially with the widespread presence of Big Data in various online media such as—social network sites, online portals for shopping, e-commerce, forums, chats, recommendation systems, and online services. Ascertaining the sentiment behind, the various types of tweets by different persons can provide great insights on various aspects including behavioral patterns. Besides highlighting the newest trends in the field, we retrieved real-time twitter data pertaining to three currently popular hashtags in the Indian context and carried out extensive experimentation analysis about the prevailing sentiment of a strata of population. Inclusion of current challenges, future trends and applications of sentiment analysis from Twitter data makes this novel work very useful for fellow researchers.
Deepfake Videos Detection Based on Image Segmentation with Deep Neural Networks

With the rapid development of deep learning technology, videos with changed faces generated by deep neural networks (i.e., Deepfake videos) become more and more indistinguishable. As a result, the threat raised by Deepfake videos becomes greater and greater. In literature, there are some convolutional neural networks-based detection algorithms for fake face videos. Although those algorithms perform well when the training set and the testing set are from the same dataset, their performance could deteriorate dramatically in cross-dataset scenario where the training and the testing sets are from different sources. Motivated by the fabrication course of fake face videos, this article attempts to solve the problem of fake faces detection with the way of image splicing detection. A neural network borrowed from image segmentation is adopted for predicting  the tampered face area from which a tampering mask is obtained through denoising and thresholding the probability map. Using the prior knowledge of face tampering that the changing of face mainly happens in face region, a new way is proposed to determine the Face-Intersection over Union (Face-IoU) and to further improve the ratio calculation method. The Face-Intersection over Union with Penalty (Face-IoUP) is used as the classification criterion for deepfake video detection. The proposed method is impletmented using three basic image segmentation neural networks separately and is tested them on datasets of TIMIT, FaceForensics++, Fake Face in the Wild(FFW). Compared with current methods in literature, the HTER (Half Total Error Rate) in cross-dataset test decreases significantly while the detection accuracy in intra-dataset test keeps high. For the Deep Fake Detection(DFD) dataset with higher synthesis quality, the proposed method still performs very well. Experimental results validate the proposed method and demonstrate its good generality.
Security for eHealth system: data hiding in AMBTC compressed images via gradient-based coding

Abstract Various eHealth applications based on the Internet of Things (IoT) contain a considerable number of medical images and visual electronic health records, which are transmitted through the Internet everyday. Information forensics thus becomes a critical issue. This paper presents a data hiding algorithm for absolute moment block truncation coding (AMBTC) images, wherein secret data, or the authentication code, can be embedded in images to enhance security. Moreover, in view of the importance of transmission efficiency in IoT, image compression is widely used in Internet-based applications. To cope with this challenge, we present a novel compression method named gradient-based (GB) compression, which is compatible with AMBTC compression. Therefore, after applying the block classification scheme, GB compression and data hiding can be performed jointly for blocks with strong gradient effects, and AMBTC compression and data hiding can be performed jointly for the remaining blocks. From the experimental results, we demonstrate that the proposed method outperforms other state-of-the-art methods.
The Revolutionary Media Education Decade: From the UNESCO to the ALFAMED Curriculum for Teacher Training

Nations across the globe are immersed in a technological revolution—intensified by the need to respond to COVID-19 issues. In order to be critical and responsible citizens in the current media ecosystem, it is important that students acquire and develop certain skills when consuming and producing information for and when communicating through the media. This is a major challenge that educational systems worldwide have to face. Hence, new curricula in media education to guide future teachers towards the successful acquisition of new media skills have been proposed. The aims of this work are to conduct a theoretical approach to this worldwide technological and media evolution in the past decade, to make an in-depth comparison between the Curriculum for teachers on media and information literacy published by the UNESCO (2011) and the publication of the new AlfaMed Curriculum for the training of teachers in media education (2021). This framework starts by providing an extensive analysis of the key elements of both curricula and of their corresponding modules, establishing, thus, a constructive comparison while updating them, according to the needs, changes, and realities that have taken place regarding digital literacy in the past decade. Finally, the chapter concludes with the detailing of the challenges and with proposals for teacher training in media and information literacy.
Development of E-Learning in a Modern Technical University

The article is devoted to the increasing role and prospects for the development of e-learning in modern transformation of higher polytechnic education. In the context of preventing the spread of coronavirus infection COVID-19 and the transition of universities to various forms of distance education, this issue is of particular relevance. The aim of the study is to assess the process of introducing e-learning into the educational process of higher technical educational institutions in Russia. The methodological basis of the work was the analytical and systematic approach to the study of the object of research using methods of data systematization, evaluative analysis and forecasting, as well as logical generalization. The authors come to the conclusion that the introduction of e-learning in technical universities in Russia until recently played a supporting role in the reorganization and optimization of the processes of training engineering personnel. The development of e-learning helped to reduce the costs of the educational process, reduce the number of employees, as well as solve the problem of the classroom fund. An important role in the development of e-learning was played by the desire to increase the rating indicators. The desire to expand non-budgetary sources of funding for modern technical universities and the possibility of implementing new learning models were only additional incentives for expanding the e-learning system. The results of this study can be used in further studying the digitalization of higher education and assessing the prospects for the development of professional training in Russia.
Breast tumor segmentation in mammography image via Chan-Vese technique

The accurate segmentation of tumours is a crucial stage of diagnosis and treatment, reducing the damage that breast cancer causes, which is the most common type of cancer among women, especially after the age of forty. The task of segmenting breast tumours in mammograms is very difficult, as its difficulty lies in the lack of contrast between the tumour and the surrounding breast tissue, especially when dealing with small tumours that are not clear boundaries and hidden under the tissues. As algorithms often lose an automatic path toward the boundaries of the tumour at try to determine the site of this type of tumour. The study aims to create a clear contrast between the tumour and the healthy breast area. For this purpose, we used a Gaussian filter as a pre-processing as it works to intensify the low-frequency components while reducing the high-frequency components as the breast structure is enhanced and noise suppression. Then, CLAHE was used to improve the contrast of the image, which increases the contrast between the tumour and the surrounding tissue and sharpens the edges of the tumour. Next, the tumour was segmented by using the Chan-Vese method with appropriate parameters defined. The proposed method was applied to all abnormal mammogram images taken from a publicly available mini-MIAS database. The proposed model was tested in two ways, the first is statistical that got results (90.1, 94.8, 95.5, 92.1, 99.5) for Jaccard, Dice, PF-Score, precision, and sensitivity respectively. And the other is based on the segmented region's characteristics that results showed the algorithm could identify the tumour with high efficiency.
Review of Trajectory Planning for Industrial Robots

Abstract More and more industrial robots need high-precision trajectory control, but the traditional PID control is difficult to achieve. The simulation diagram and pose of the collision free mechanism are drawn by using rosty software. At the same time, through the experiment of MOTOMAN-HP3 robot, using VC + + application program to call the external interface function of motocom32 software, the application program planning is realized. The linear and circular trajectory planning of industrial robot in Cartesian space is studied. Linear interpolation algorithm based on spatial parabola transformation and circular interpolation algorithm based on local coordinates are proposed. The algorithm is applied to a self-designed 5-DOF spraying robot. An evolutionary algorithm based optimal trajectory planning method for PUMA560 robot is proposed. The method is based on the elite non dominated sorting genetic algorithm (NSGA-II). The results show that the minimum angular displacement, angular velocity and angular acceleration of X3 parallel robot are 56, and the maximum angular displacement, angular velocity and angular acceleration are 89.
Ensuring sustainable development of higher education on the basis of digital educational services

In the context of national economy digital transformation and global challenges impact, the development trends of digital educational services are becoming more and more relevant. In the given article, scientific papers on various aspects of educational services digitization are reviewed. The authors proposed an original approach to the category of “digital educational service”, and revealed stages of digital educational services formation. To identify trends in the digital educational services development and the impact of the novel Covid-19 coronavirus infection pandemic on this process the authors analysed digital learning services development in the higher education market from2016 through 2020. Based on the research results, main trends of higher education development were revealed and prospects for digital educational services development in the medium term were identified.
Groups whose orders factorise into at most four primes

The groups whose orders factorise into at most four primes have been described (up to isomorphism) in various papers. Given such an order n, this paper exhibits a new explicit and compact determination of the isomorphism types of the groups of order n together with effective algorithms to enumerate, construct, and identify these groups. The algorithms are implemented for the computer algebra system GAP.
A Study and Analysis of Improved Binary Pattern Technique in Dynamic Images

A dynamic image has a distinct quantity of object movement from one to another. It can be any object such as a car, person, an object moving from one point X to another point Y. Image consists of a sense of movement. Applications of object tracking are biometrics tracking, AR uses, video surveillance, passage monitoring, vehicle navigation, etc. Challenges in tracking multifaceted objects are fast movement, geometric conversion, blurring, messy background, artifacts, etc. To resolve this problem by merge all small features with nearby texture features. Texture feature describes the plane space and configuration of an area. A mixture of color and texture feature improves the object details and to increase the strength of the object's illustration. In Existing methods such as binary pattern method all object features are removed, so it is difficult to predict the exact pixel movement. The proposed method of improved binary pattern is also tracking the small changes in the pixel difference in one frame to other. Compared with the existing algorithms, IBP method measures the spatial arrangement of local image texture which reduces the overall processing cost and improves the strength of objective image. To track the similarities and difference of the object in each and every frame efficiently and effectively Improved Local Binary Pattern tracking algorithm was proposed. This proposed technique is an effective way to analysis complicated real time situations compared with other methods.
Decoupled Kalman Filter Based Identification of Time-Varying FIR Systems

When system parameters vary at a fast rate, identification schemes based on model-free local estimation approaches do not yield satisfactory results. In cases like this, more sophisticated parameter tracking procedures must be used, based on explicit models of parameter variation (often referred to as hypermodels), either deterministic or stochastic. Kalman filter trackers, which belong to the second category, are seldom used in practice due to difficulties in adjusting their internal parameters such as the smoothness coefficient and the order of the hypermodel. The paper presents a new solution to this problem, based on the concept of preestimation of system parameters. The resulting identification algorithms, which can be characterized as decoupled Kalman trackers, are computationally attractive, easy to tune and can be optimized in an adaptive fashion using the parallel estimation approach. The decoupled KF algorithms can be regarded as an attractive alternative to the state-of-the-art algorithms which are much more computationally demanding.