Reviewerly/exHarmony huggingface dataset:
Covert channel limitation via special dummy traffic generating

Covert channels in information systems may cause a protected data leakage and lead to violation of data confidentiality or integrity. Moreover, some types of covert channels can function even in case of network data encryption, tunneling or traffic firewall protection. A technique to eliminate such channels is traffic normalization which means sending packets with equal lengths and fixed header fields with equal inter-packets delays that leads to significant decreasing of efficient communication channels capacity and missing of functional capabilities of network protocols. Another way to counteract covert channel is to detect an active channel and limit it capacity. In this paper, we investigate covert channel protection means in packet networks based on their capacity limitation. We suggest a technique to counteract data leakage via covert channel based on dummy traffic generating and estimate maximum residual capacity of covert channel in case of counteracting measures for stream and block encryption of traffic and different distributions for covert channel and dummy traffic. Also we give recommendation for choosing the parameters of counteraction tool.
Lisbon Field: Geology of microseepage

Certain satellite sensors can be used for mineral identification and thus detection of alteration indicative of hydrocarbon microseepage. However, care must be taken when placing these results into context because alteration also can be due to other factors. The authors review the rationale for multiscale and multispectral image selection as well as the benefit of iterative processing, to highlight alteration within a given formation. The authors determine using remote sensing results integrated with other geologic information to confirm that alteration exists, compare results with a conceptual microseepage model, reconcile alteration pattern and extent with the geologic setting and trap timing, and appraise the potential that other factors may have caused alteration. The authors used geologic information from the well-studied Lisbon Field including, but not limited to, geochemistry, petrography and diagenetic studies, and paleo-structure reconstruction. We evaluated whether the alteration model made sense by assessing the thermal history and estimating volumetrics and seepage rate based on the geologic setting and extent of alteration. The results confirm that the alteration at the Lisbon Field is compatible with hydrocarbon microseepage as its principal cause, however, it cannot rule out the possibility that some of it may be due to hydrothermal fluid migration.
A novel method to create long capture volumes for video tracking

This study examined a novel method to create a long and narrow calibrated capture volume for tracking objects. The methodology relies on the reflection of parallel distance-measuring lasers. Images of a board, blocking the lasers as it is moved through the field of interest, were assembled into a virtual calibration fixture. The method accommodates large calibration volumes and can be used with multiple cameras, providing a consistent absolute positional reference that is difficult to achieve with large mechanical calibration boards. This study considered a 17.4 m long tracking volume. A 0.9 m long rod was tracked throughout the calibrated volume where its average tracked length was within 0.2% of its measured length. The speed of balls traveling through the calibrated volume were within 0.1% of independent speed sensors. The average residual error of a ball’s tracked trajectory and a polynomial fit was within 1.5 mm. The method shows promise as an efficient means of calibrating large calibration volumes with multiple camera pairs.
The Detection of Spark Scattering in the Slab Cutting Process Through Deep SVDD Based on Weakly Supervised Learning

In the continuous casting process, which is one of the steel manufacturing processes, a slab is cut by a torch cutter. During cutting, sparks are generated around the slab; if a problem occurs, the sparks are scattered. The failure in recognizing this problem early on can lead to huge losses and serious accidents. Using learning-based network model, this paper proposes a method of spark scattering detection that can be used in the steel manufacturing process. However, since the spark scattering occurs very intermittently, it becomes difficult to secure sufficient data for network learning. To resolve this issue, this study uses an anomaly detection method through Deep SVDD. This study also applies a weakly supervised learning framework using a small amount of spark scattering data so that the network can extract discriminative features for normal and abnormal situations.
Procedural Modelling for Synthetic Data Generation in Automotive Applications

In order to monitor the vehicle transportation and notify the individual about the information of the obstacles based on the analysis performed in the MPEG4 video file. The parameters to predict the severity of the impact has also been analyzed. An approach to generate synthetic annotated information about the obstacle has been formulated. The information obtained could then be used to train deep neural networks in computer vision tasks. This procedural modeling approach enables physically accurate image synthesis obtained from hand modeled virtual worlds. A sample MPEG4 Video file of vehicle transportation is provided as initial input to the workspace for experimental purpose. Image analysis has been done and based on specific characteristic feature; the actual object present is identified. The same approach would be used in automotive applications in near future.
A variational level set model with kernel metric induced local image fitting energy

Active contour based methods are effective models for image segmentation. However, they always suffer from the limited performance due to the presence of noise and intensity inhomogeneity. To solve this problem, a kernel metric induced local image fitting (KLIF) variational model is proposed in this paper. Firstly, a kernel metric induced local fitting image (KLFI) is introduced by minimising a kernel metric based energy. The combination of the kernel metric and the local fitting image enables the model to be more robust to the noise and intensity inhomogeneity. And then, using the KLFI, a variational level set model that is a squared l2 distance between the KLFI and the original image is constructed. Two regularisation terms are employed in the model to keep the level set function to be stable during the evolution. At last, an alternating iterative algorithm combining with fixed-point iteration and gradient descent of three-step time-splitting is introduced to solve the proposed model. The experimental results show the effectiveness of the proposed model for image segmentation in the presence of noise and intensity inhomogeneity, and demonstrate the competitive performance over several state-of-the-art variational models in term of accuracy and robustness.
Prediction of Phishing Susceptibility Based on a Combination of Static and Dynamic Features

Phishing is a very serious security problem that poses a huge threat to the average user. Research on phishing prevention is attracting increasing attention. The root cause of the threat of phishing is that phishing can still succeed even when anti-phishing tools are utilized, which is due to the inability of users to correctly identify phishing attacks. Current research on phishing focuses on examining the static characteristics of the phishing behavior phenomenon, which cannot truly predict a user’s susceptibility to phishing. In this paper, a user phishing susceptibility prediction model (DSM) that is based on a combination of dynamic and static features is proposed. The model investigates how the user’s static feature factors (experience, demographics, and knowledge) and dynamic feature factors (design changes and eye tracking) affect susceptibility. A hybrid Long Short-Term Memory (LSTM) and LightGBM prediction model is designed to predict user susceptibility. Finally, we evaluate the prediction performance of the DSM by conducting a questionnaire survey of 1150 volunteers and an eye-tracking experiment on 50 volunteers. According to the experimental results, the correct prediction rate of the DSM is higher than that for individual feature prediction, which reached 92.34%. These research experiments demonstrate the effectiveness of the DSM in predicting users’ susceptibility to phishing using a combination of static and dynamic features.
An algorithm for constructing the two-direction Armlet multiwavelet

In this paper, an algorithm is discussed for constructing the two-direction Armlet multiwavelet. First, the definition of two-direction Armlet multiwavelet is presented in this paper. A two-direction multiwavelet can be changed to a special multi-wavelet. By Two-scale Similar Transform (TST), a transform can be taken on the two-scale matrix symbols of a two-direction multi-wavelets. This transform keeps the orthogonality of the two-direction multi-wavelets. However, the condition is discussed which the two-direction multi-wavelet corresponding to a two-direction multi-scaling function is an Armlet with order n. An approach is given for constructing the transform matrix. Finally, an example is given for discussing the two-direction Armlet multi-wavelet with order 2.
Research on the construction of Internet of Things system integration training room in higher vocational colleges

With the rapid development of Internet of things industry, the Internet of things engineers and technicians were provided a very broad career development prospect. The construction of professional Internet of things system integration training room in higher vocational colleges would improve students' professional practice ability and broaden their employment channels. The construction ideas and objectives of the Internet of things system integration training room were expounded. According to the structure of Internet of things perception layer, network layer and application layer, the construction contents of the Internet of things system integration training room were introduced detailly, which were provided a reference for the construction of Internet of Things training room in higher vocational colleges.
Adaptive Learning Strategy Applicable within “Foreign Language” Discipline at Non-Linguistic University

The aim of the study is to develop a strategy for teaching the discipline “Foreign Language” to first-year students at non-linguistic university in the light of the challenges of the modern educational environment and the labour market. The article deals with the reasons and conditions for the complication and transformation of work in modern higher education, and also analyses the pedagogical potential of the individual and group project activity of first-year students within the framework of the discipline “Foreign Language”. The scientific novelty of the research lies in the development and substantiation of an educational activity strategy, which we called “adaptive learning strategy”: the use of predominantly interactive and project group tasks, as well as tasks with an open structure within the framework of “Foreign Language” classes in groups with a high level of linguistic competence in order to develop the students’ non-linguistic competencies, as well as to help them to adapt to the university. As a result, it has been proven that the use of group and individual creative tasks can help students with a high linguistic competence develop non-linguistic competencies, successfully adapt to new learning conditions and improve their knowledge of a foreign language.
Enhancing Automatic Attendance System using Face Recognition

Designing an automated class attendance system requires the feature of uniquely detecting each person of the class room. Furthermore, such system should be resistant to varied illumination in the classroom. The recent works on facenet and vggface2 provide promising results in producing high quality facial embeddings as feature vectors. The paper discusses the methodology to identify individual students uniquely. To achieve this, the Support Vector Classifiers are used. The dataset of about 11 classes each with about 1000 faces are used. From the original dataset, two more datasets are created: one with varied contrast and the other with varied contrast and noise. Further, embeddings of these datasets are created and supplied to four proposed models built using Support Vector Classifiers and Neural Network: Tuned SVC with RBF Kernel, SVC with linear kernel, Simple NN and CNN. Although all four models performed similarly on the clean dataset, tuned SVC with RBF kernel is more resilient to noise and variation in contrast. The accuracies of tuned SVC model for dataset-1, dataset-2 and dataset-3 were found to be 0.99334, 0.98990 and 0.90619 respectively.
Mouse Dynamics Analysis Using Machine Learning to Prevent Account Stealing in Web Systems

This paper is dedicated to development of algorithm of website users authentication based on their mouse movement dynamics. The algorithm of mouse activity data collection, feature extraction and mouse dynamics sessions classification to legal and illegal ones using machine learning and deep learning methods is proposed. The user authentication problem is considered as one-class classification and binary classification problem. For each user, the classifier that provides maximum session classification quality is automatically chosen. The performance of the algorithm is evaluated on mouse dynamics dataset gathered on a public website and compared with the results of the state-of-the-art solutions. The quality of mouse dynamics sessions classification using classifiers selected independently for each user is significantly higher than one when using the same session classifier for all system users.
A Deconvolutional Deblurring Algorithm Based on Dual-Channel Images

Aiming at the motion blur restoration of large-scale dual-channel space-variant images, this paper proposes a dual-channel image deblurring method based on the idea of block aggregation, by studying imaging principles and existing algorithms. The study first analyzed the model of dual-channel space-variant imaging, reconstructed the kernel estimation process using the side prior information from the correlation of the two-channel images, and then used a clustering algorithm to classify kernels and restore the images. In the kernel estimation process, the study proposed two kinds of regularization terms. One is based on image correlation, and the other is based on the information from another channel input. In the image restoration process, the mean-shift clustering algorithm was used to calculate the block image kernel weights and reconstruct the final restored image according to the weights. As the experimental section shows, the restoration effect of this algorithm was better than that of the other compared algorithms.
A Nonparametric Weighted Cognitive Diagnosis Model and Its Application on Remedial Instruction in a Small-Class Situation

CDMs can provide a discrete classification of mastery skills to diagnose relevant conceptions immediately for Education Sustainable Development. Due to the problem of parametric CDMs with only a few training sample sizes in small classroom teaching situations and the lack of a nonparametric model for classifying error patterns, two nonparametric weighted cognitive diagnosis models, NWSD and NWBD, for classifying mastery skills and knowledge bugs were proposed, respectively. In both, the variances of items with respect to the ideal responses were considered for computing the weighted Hamming distance, and the inverse distances between the observed and ideal responses were used as weights to obtain the probabilities of the mastering attributes of a student. Conversely, NWBD can classify students’ “bugs”, so teachers can provide suitable examples for precision assistance before teaching non-mastery skills. According to the experimental results on simulated and real datasets, the proposed methods outperform some standard methods in a small-class situation. The results also demonstrate that a remedial course with NWSD and NWBD is better than one with traditional group remedial teaching.
Dimension-wise scaled normal mixtures with application to finance and biometry

We propose the family of dimension-wise scaled normal mixtures (DSNMs) to model the joint distribution of a d-variate random variable with real-valued components. Each member of the family generalizes the multivariate normal (MN) distribution in two directions. Firstly, the DSNM has a more general type of symmetry with respect to the elliptical symmetry of the MN distribution. Secondly, the univariate marginals have similar heavy-tailed normal scale mixture distributions with (possibly) different tailedness parameters; as a consequence of practical interest, the DSNM allows for a different excess kurtosis on each dimension. These peculiarities are obtained in an MN scale mixture framework by introducing a d-variate mixing random variable with independent and similar components acting separately for each dimension. We examine several properties of DSNMs, such as the joint density function, hierarchical and stochastic representations, relations with other families of symmetric distributions, type of symmetry, marginal and conditional distributions, no correlation implying independence, moment generating function, and moments of practical interest. For illustrative purposes, we describe two members of the DSNM family obtained in the case of components of the mixing random vector being either uniform or shifted exponential; these are examples of mixing distributions that guarantee a closed-form expression for the joint density of the DSNM. For the two DSNMs analyzed in detail, we introduce parsimony by allowing the d tailedness parameters to be tied across dimensions, and describe algorithms, based on the expectation–maximization (EM) principle, to estimate the parameters by maximum likelihood. We use real data from the financial and biometrical fields to appreciate the advantages of our DSNMs over other symmetric heavy-tailed distributions available in the literature.
Implementation of an environmental laboratory workshop in a distance learning environment

Virtual laboratory facilities and methodological materials for laboratory work have been developed, aimed at distance learning of students in the direction of &#x201C;Technosphere Safety&#x201D;. The works reflect all the features of experiments and research on real devices. Methodological materials were prepared, including video lectures for laboratory work. The developed virtual laboratory works were tested when teaching students of Saint-Petersburg State Electrotechnical University (ETU &#x201C;LETI&#x201D;).
Infinite Lingos: A Straightforward Methodology for Speech Translation

Now a days people face a number of problems while communicating with people from other countries, who do not understand their languages. The communication barrier gets worsened when people do not have any common languages. Presently, many systems exist that record the interactions and through manual transactions, translate them into another language. However, in this paper, we propose a system that deploys three APIs, namely Speech to Text, Text to Text Translation and Text to Speech, in a native mobile application to work together to provide the user with real-time translation. The proposed method record's real time Urdu speech and converts it into the Urdu text then translates it into English text or speech and vice versa. To perform the operations, we used 300 different audios that were converted according to the methodology. The results show successful conversion of language into text and speech with minimum delay. The sentences were processed by the application in less time. The aim of this work is to break the communication barrier between the visitors and the natives. It will assist both the tourist and the locals in communications.
Blockchain‐based access control with k $k$‐times tamper resistance in cloud environment

While cloud computing services such as cloud storage are fairly mature, it remains challenging to design efficient and secure cryptographic schemes to facilitate fine-grained access control and achieve other features. For example, existing ciphertext-policy attribute-based encryption schemes do not generally have in-place limits on the number of access or access duration, which can consequently be exploited to perform economic denial of sustainability and other attacks. In addition, improving system efficiency can be challenging in large-scale operations. Thus, in this paper, we present a blockchain-based access control with k $k$ -times tamper resistance. Our proposed approach allows one to set and enforce quota/limits on the number of accesses allowed for each user, whose integrity is ensured using blockchain. In addition, our proposed approach also allows multiple attribute authorities to coexist and work together with a central authority to facilitate secret key distribution and improve system efficiency. We then evaluate the security and the efficiency of our proposed approach to demonstrate its utility.
Performance Analysis of the Database of Telecommunications

In this article, we will explain and describe the different steps of implementing a database, the generation of the data, and the operations we did on them. Furthermore, this project has for objective to highlight the different performance fluctuations and what they are linked to.
Knowledge transfer for labeling unknown fuzzy sets using Grammar-Guided Genetic Algorithms

Fuzzy Logic Systems have been popular in fields such as control, knowledge representation, and modeling. They involve linguistic information to represent a human perception in the form of Fuzzy Sets. Those systems are advantageous in cases where it is desired to have some interpretability. However, there are some processes, such as optimization, where the membership function related to a fuzzy set is modified. Therefore the linguistic label associated with them is unattached to the original meaning. This proposal aims to make linguistically-interpretable fuzzy sets from previously established ones but with modified parameters, thus maintaining the context of the knowledge of the domain application and the knowledge designer. The presented approach relies in the use of linguistic modifiers, which are operations linked to linguistic terms that apply on fuzzy sets, such as very, above, more-or-less, etc. A methodology using Grammar-Guided Genetic Algorithms is proposed to find the best linguistic modifier candidates to approximate the fuzzy set with modified parameters to guarantee a coherent and readable grammar structure at diverse levels of interpretability and accuracy. The obtained results support the feasibility of the proposed methodology with convex membership functions such as Gaussian, Triangular, and Trapezoidal.