Reviewerly/exHarmony huggingface dataset:
Financial intelligence (monitoring) of the transactions with virtual assets: new legislation and best practices of foreign countries

Purpose This paper aims to illustrate how virtual assets are used in such criminal offenses as money laundering and seeks to study the role of financial intelligence (monitoring) of transactions with virtual assets effectively in combating money laundering. Design/methodology/approach This research methodology includes system and structural methods that help analyze the theoretical, organizational and legal bases of the financial intelligence (monitoring) of transactions with virtual assets. The authors use the doctrinal legal research approach to analyze and describe the legislation connected to the financial intelligence (monitoring) operations with virtual assets. To identify critical issues of understanding the “virtual assets” and “cryptocurrency” essence, the peculiarities of the scientific community views on the given definitions, the authors use the method of terminological analysis and concepts operationalization. The authors use the extrapolation method to determine the possibility of implementing the analyzed best practices of foreign countries in the domestic practice of financial intelligence (monitoring) of transactions with virtual assets as an effective way in combating money laundering. Findings This study demonstrates the role of financial intelligence (monitoring) of transactions with virtual assets as an effective way to combat money laundering. Originality/value The article is devoted to comprehensively studying “virtual assets” and “cryptocurrency” concepts. The authors carried out a comparative analysis of these two concepts with the definition of their features and the main characteristics and features that separate them from each other. The authors also stressed the need for countries to strengthen the requirements for situations and activities with virtual assets, where there is a high level of risk in a risk-based approach.
TopUMS: Top-k Utility Mining in Stream Data

Top-k utility mining has attracted great attention in the past few years. Its goal is to discover a set of patterns that have the k highest utilities in a database. Many algorithms have been proposed to efficiently discover top-k high utility itemsets (HUIs), but most of them assume that the database is static. The data are mostly streaming data with continuous, high-speed, and unrestricted features. Thus, it is not possible to store the complete stream data in the same manner as static data; real-time storage and processing are required. In this study, we propose a novel algorithm called TopUMS to mine the top-k HUIs in stream data using the sliding window model. Additionally, we propose a data structure called uList, which is constructed in a horizontal method without ineffective comparison operations. Moreover, we utilize the common batch utility between two consecutive windows to raise minimum utility threshold for the next sliding window. The experimental results demonstrate that TopUMS outperform the state-of-the-art algorithm in terms of execution time and memory.
On the History of the Academic Series “Literary Heritage” (for the 90th Anniversary)

The article examines the history of the publication of the academic series Literary Heritage based on new archival materials. The publication was initiated in 1931, when archival and publishing activities were affected by political trials. The author of the draft used political rhetoric to get permission to publish it from the Central Committee of the CPSU(b). In 1932, following the decree “On the Restructuring of Literary and Artistic Organizations,” Literary Heritage became an exemplary academic publication and received international recognition. Literary Heritage developed a new method as it placed the author in the position of a student at the editorial board. In Soviet times, the Literary Heritage existed under the conditions of censorship and ideological control but still managed to publish a volume on Russian symbolism in 1937. The years 1947–1959 were difficult for Literary Heritage when the editorial office was accused of cosmopolitanism. In its publishing policy, Literary Heritage was ahead of time and above the reader’s dogmatism which led to the sequestration of several volumes. The history of Literary Heritage contributed to creating an intellectual and ideological platform that nurtured a new generation of literary historians.
Privacy preserving human activity recognition framework using an optimized prediction algorithm

Human activity recognition, in computer vision research, is the area of growing interest as it has plethora of real-world applications. Inferring actions from one or more persons captured through a live video has its immense utility in the contemporary era. Same time, protecting privacy of humans is to be given paramount importance. Many researchers contributed towards this end leading to privacy preserving action recognition systems. However, having an optimized model that can withstand any adversary models that strives to disclose privacy information. To address this problem, we proposed an algorithm known optimized prediction algorithm for privacy preserving activity recognition (OPA-PPAR) based on deep neural networks. It anonymizes video content to have adaptive privacy model that defeats attacks from adversaries. The privacy model enhances the privacy of humans while permitting highly accurate approach towards action recognition. The algorithm is implemented to realize privacy preserving human activity recognition framework (PPHARF). The visual recognition of human actions is made using an underlying adversarial learning process where the anonymization is optimized to have an adaptive privacy model. A dataset named human metabolome database (HMDB51) is used for empirical study. Our experiments with using Python data science platform reveal that the OPA-PPAR outperforms existing methods.
Image compression using singular value decomposition by extracting red, green, and blue channel colors

This paper presents an image compression using singular value decomposition (SVD) by extracting the red, green, and blue (RGB) channel colors. Image compression is needed in the development of various multimedia computer services and applications for example in the telecommunications and storage technologies. Now a days, video technology, digital broadcast codec and teleconferencing become popular and always requires high image compression process for display. Hence, efficient image compression is compulsory to reduce the number of storage sizes and maintain the image quality. Therefore, this article proposes image compression using SVD, which this method is efficiently reducing the image storage size and at the same time maintaining the image quality. The SVD removes redundant pixel values based on RGB colors to make the storage image size decreased. Based on the experimental analysis on two different type of image extensions (i.e., jpg and png), the SVD is capable to reduce the image size and at the same time preserving the image quality.
Performance-Cost Trade-Off in Auto-Scaling Mechanisms for Cloud Computing

Cloud computing has been widely adopted over the years by practitioners and companies with a variety of requirements. With a strong economic appeal, cloud computing makes possible the idea of computing as a utility, in which computing resources can be consumed and paid for with the same convenience as electricity. One of the main characteristics of cloud as a service is elasticity supported by auto-scaling capabilities. The auto-scaling cloud mechanism allows adjusting resources to meet multiple demands dynamically. The elasticity service is best represented in critical web trading and transaction systems that must satisfy a certain service level agreement (SLA), such as maximum response time limits for different types of inbound requests. Nevertheless, existing cloud infrastructures maintained by different cloud enterprises often offer different cloud service costs for equivalent SLAs upon several factors. The factors might be contract types, VM types, auto-scaling configuration parameters, and incoming workload demand. Identifying a combination of parameters that results in SLA compliance directly in the system is often sophisticated, while the manual analysis is prone to errors due to the huge number of possibilities. This paper proposes the modeling of auto-scaling mechanisms in a typical cloud infrastructure using a stochastic Petri net (SPN) and the employment of a well-established adaptive search metaheuristic (GRASP) to discover critical trade-offs between performance and cost in cloud services.The proposed SPN models enable cloud designers to estimate the metrics of cloud services in accordance with each required SLA such as the best configuration, cost, system response time, and throughput.The auto-scaling SPN model was extensively validated with 95% confidence against a real test-bed scenario with 18.000 samples. A case-study of cloud services was used to investigate the viability of this method and to evaluate the adoptability of the proposed auto-scaling model in practice. On the other hand, the proposed optimization algorithm enables the identification of economic system configuration and parameterization to satisfy required SLA and budget constraints. The adoption of the metaheuristic GRASP approach and the modeling of auto-scaling mechanisms in this work can help search for the optimized-quality solution and operational management for cloud services in practice.
Focusing on shared areas for partial person re-identification

Person re-identification (Re-ID) can achieve ideal performance based on the prerequisite that the sampling image is complete. However, the whole body cannot be detected because pedestrians may be occluded or are at the edge of the surveillance range in real-world scenarios. Consequently, the image only contains part of the visible information of the pedestrian. When using the standard person re-identification to match the partial image with the complete one, we witness the problem of spatial misalignment and interference caused by missing areas. Hence, we propose a focused shared area model (FSA) for partial re-identification to solve such descriptive problems. We use self-supervised learning to locate the shared area and learn region-level features. In addition, we adopt self-attention mechanism to help the network visualize the important features of the image, thus reducing the influence of the background information. Finally, we verify the effectiveness of our method through experiments on two mainstream datasets: Market-1501, DukeMTMC-reID and two important partial datasets: Partial-REID and Partial-iLIDS.
PAC-Bayesian theory for stochastic LTI systems

In this paper we derive a PAC-Bayesian error bound for autonomous stochastic LTI state-space models. The motivation for deriving such error bounds is that they will allow deriving similar error bounds for more general dynamical systems, including recurrent neural networks. In turn, PAC-Bayesian error bounds are known to be useful for analyzing machine learning algorithms and for deriving new ones.
Adaptation of the CCSDS 123.0-B-2 Standard for RGB Video Compression

The integration of video sensors on-board satellites is becoming a trend in the space industry, since they provide extra information in the temporal domain when compared with traditional remote sensing imaging acquisition equipment.The inclusion of the temporal dimension together with the constant increase in the sensor resolution supposes a challenge for on-board processing, taking into account the limited computational and storage resources on-board satellites and that it is unfeasible to directly transmit raw video to ground, due to downlink bandwidth limitations.For these reasons, on-board video compression is needed.However, the inherent complexity of the video encoders used on ground limits their implementation on environments with high constraints in terms of computational burden, area, and power consumption.This article proposes an extended compression chain that implements as compression core the CCSDS 123.0-B-2 standard, originally developed for near-lossless compression of multi-and hyperspectral images.In addition, some preprocessing stages are included to manage the temporal dimension of RGB videos efficiently.The proposed solution guarantees low complexity and flexibility to compress both multi-and hyperspectral images and panchromatic and RGB videos by using a single compression instance, which is adapted by adding or removing the appropriate stages.Results demonstrate the viability of this solution to be implemented on space payloads, since high compression ratios are achieved without incurring in a penalty in terms of video quality.
The Elements of Computational Thinking in Learning Geometry by Using Augmented Reality Application

Augmented Reality (AR) technology has now become a growing trend within the education field. It has the capability to combine both digital and reality world that subsequently leads to the emergence of new possibilities in im-proving the quality of teaching and learning activities. The objective of this research is mainly to analyse the Computational thinking elements in solving Geometry topic after the intervention of AR application. Qualitative data analysis was employed as the research methodology of this study that in-volved interview protocol with 10 pupils enrolled in Form 1 Mathematics class at one of the selected schools in Johor Bahru, Malaysia. Content analy-sis was then administered using the ATLAS.Ti Version 8 software to identify the codes, themes, and meaning conveyed by the pupils’ answers in the Computational thinking test. Based on the interview and Computational thinking assessment show the pupils’ ability to solve Geometrical problems using Computational thinking elements. The researcher found that Computa-tional thinking elements do exist in problem solving pertaining to the topic of Geometry. However, the order of usage of these Computational thinking el-ements differs among the pupils. This leads to the conclusion that pupils who utilise the AR application with Computational thinking successfully solve Geometrical problems through the help of the Geometrical thinking process that comprises the elements of Abstraction, Generalisation, Decomposition, Algorithmic, and Debugging.
A modified PSO Algorithm based on Cloud Theory for optimizing the Fuzzy PID controller

Abstract To obtain a more efficient intelligent controller, this paper presents a modified cloud theory-based particle swarm optimization (CTPSO) algorithm to improve fuzzy PID controller. Cloud evolution and mutation methods based on cloud theory are introduced to tune inertia weight of particle swarm optimization algorithm (PSO), which can improve the optimization accuracy and speed of PSO. The comparative experiments indicate that CTPSO performs better than PSO and other optimization algorithm recently proposed by other researchers. The CTPSO is used to set the initial PID control parameters and optimize control rules of fuzzy PID controller. According to an engineering case of the 180°C-die heater’s temperature control, the novel optimized fuzzy PID controller can suppress the oscillation and overshoot significantly. It also owns smaller adjustment time, static error and better comprehensive performance compared with the initial controller.
Detecting phishing webpages <i>via</i> homology analysis of webpage structure

Phishing webpages are often generated by phishing kits or evolved from existing kits. Therefore, the homology analysis of phishing webpages can help curb the proliferation of phishing webpages from the source. Based on the observation that phishing webpages belonging to the same family have similar page structures, a homology detection method based on webpage clustering according to structural similarity is proposed. The method consists of two stages. The first stage realizes model construction. Firstly, it extracts the structural features and style attributes of webpages through the document structure and vectorizes them, and then assigns different weights to different features, and measures the similarity of webpages and guides webpage clustering by webpage difference index. The second phase completes the detection of webpages to be tested. The fingerprint generation algorithm using double compressions generates fingerprints for the centres of the clusters and the webpages to be tested respectively and accelerates the detection process of the webpages to be tested through bitwise comparison. Experiments show that, compared with the existing methods, the proposed method can accurately locate the family of phishing webpages and can detect phishing webpages efficiently.
Study on the Light Field Regulation of UVC-LED Disinfection for Cold Chain Transportation

In this paper, the pain point that cold chain transportation urgently needs for an efficient disinfection method is pointed out. Thus, this work aims at solving the problems and improving the disinfection efficiency in cold chain transportation. While Ultraviolet-C (UVC) irradiation is an effective method by which to kill viruses, it is difficult to apply the commonly used UVC-LED disinfection light source to ice-covered cold chain transportation due to its uneven light field distribution. Thus, the light field regulation of UVC-LED disinfection for cold chain transportation is studied. A UVC-LED chip with a wavelength of 275 nm was used as a light source, and parallel light was obtained by collimating lenses. Then, microlens array homogenization technology was used to shape the UVC light into a uniform light spot, with an energy space uniformity rate of 96.4%. Moreover, a simulation was conducted to compare the effects of the ice layer on the absorption of UVC light. Finally, an experiment was carried out to verify that the disinfection efficiency can be increased nearly by 30% with the proposed system by disinfecting E. coli (Escherichia coli), and the results indicate that the proposed system is an effective disinfection solution during cold chain transportation.
Features of Coping Strategies of Students of a Technical University with Different Experience of Participation in Group Activities

The article is devoted to the results of a study of the peculiarities of coping strategies of students of a technical university with different experience of participation in group activities. It was found that coping strategies preferred by future engineers are associated with their experience of participation in group activities of different content and form. Purposeful inclusion of students in interactive team interaction was organized, which contributed to the expansion of the repertoire of constructive coping strategies in uncertain educational, professional and work situations. In the future, it is planned to form a stable cognitive model of constructive coping behavior in each subject, included in interactive team interaction through academic subjects and practices.
Games for Science Education: is this technique effective for developing students' creativity and scientific competence?

The authors examine the pros and cons of games in the context of increasing students' creativity and scientific competence. Research materials and methods consisted of two tests. The first included the use of 7 pedagogical methods that are valid for the mental diagnosis of students: speed of thinking; method of thinking flexibility; method of determining the level of imagination and others. After the first testing, the training program included techniques for playing chess (in the classroom and online) and sand animation (in the classroom). Re-testing was carried out to assess the effectiveness of the programs. The study results showed that play activities improve the quality of student learning both in the classroom and online and enhance the functioning of many processes, including the speed and flexibility of thinking, memory capacity and attentiveness. This study demonstrates that play activities used in the educational process can play a positive role in teaching students. Further research needs to focus on studying the role of age, gender and race in play activities and determine how these factors affect performance.&#x0D; &#x0D; Keywords: chess, cognitive processes, natural science education, pedagogical methods, performance, play activity, thinking
Hybrid neural coded modulation: Design and training methods

We propose a hybrid coded modulation scheme which composes of inner and outer codes. The outer-code can be any standard binary linear code with efficient soft decoding capability (e.g. low-density parity-check (LDPC) codes). The inner code is designed using a deep neural network (DNN) which takes the channel coded bits and outputs modulated symbols. For training the DNN, we propose to use a loss function that is inspired by the generalized mutual information. The resulting constellations are shown to outperform the conventional quadrature amplitude modulation (QAM) based coding scheme for modulation order 16 and 64 with 5G standard LDPC codes.
Segmentation of Tumour Regions for Tubule Formation Assessment on Breast Cancer Histopathology Images

Breast cancer is the second leading cause of cancer death in women worldwide. In Malaysia, one out of 20 women will develop breast cancer once in their lifetime. Nottingham Histological Grading (NHG) system is widely accepted worldwide as the gold standard in providing the overall grade to breast cancer. Tubule formation is an important breast cancer feature used in the NHG system. Assessment of tubule formation requires pathologist to identify tumour regions. However, colour variation on breast histopathology images could provide nonstandard imaging and influence tumour regions detection. Manual identification of tumour regions using microscope may also vary between pathologists. Thus, automatic segmentation is crucial to detect tumour regions. By eliminating the irrelevant regions on breast histopathology images using automatic segmentation method, pathologist can focus on the tumour regions for further investigation. This study proposed a simple segmentation using global thresholding method to segment tumour regions on breast histopathology images. Partial contrast stretching and median filter were applied as pre-processing to provide better image quality for further segmentation processing. Post-processing using morphological operation and boundary padding were applied to enhance the segmentation results. The proposed method was able to segment the tumour regions on breast histopathology images and obtained accuracy, sensitivity and specificity of 84.22%, 85.83% and 84.89% respectively.
A rotary vision system for high-precision measurement over large size

This paper introduced a rotary binocular vision measurement system (RBVMS) consisting of two cameras and two theodolites, with the latter employed as a two-dimensional rotation platform. The camera was driven by mounting it on the theodolite to cover different areas, effectively increasing the measuring range. In the camera calibration stage, virtual 3D points were generated by accurately moving the planar calibration board. A two-order radial and two-order tangential distortion model was considered. The experimental results showed that the intrinsic parameters of the camera had high stability, and the mean reprojection error was about 0.035 pixels. A unit quaternion method was applied to calibrate the transformation matrix between the camera and the theodolite. By solving the transformation matrix between the two theodolites, the measurement data of each position was unified under a fixed coordinate system. At 500 mm in front of the camera, the camera's field of view was about 200 mm. Two targets of different sizes were measured to verify the effectiveness and accuracy of the method. The measurement accuracy of 300 mm and 600 mm targets was about 0.014 mm and 0.027 mm, respectively.