Neuromonitoring and neuroclassification of crime events in distributed network

Systemic analysis of criminal events and situations in some distributed network is a pressing problem not only of law enforcement structures, but also of the authorities, the whole society. In the investigation of each the crime requires speed, as well as completeness, accuracy and low uncertainty of the event. Effective investigation requires the involvement of IT procedures based not only on criminology, but also on psychology, mathematics, system analysis, computer science and other fields of knowledge. Only in this way can we analyze the goals, situations and tasks, develop and take decisions to counter crime not only in Vietnam, but also in many countries, including Russia. It's important to have not only methodology, but also technology, methods and tools. The work separately explores the monitoring tools of crime events. A systematic analysis of the problem has been carried out, on the basis of which methods of transition from traditional monitoring of a specific (problem-oriented) criminal situation to intellectual, systemic monitoring of the entire criminal environment have been proposed. Criminological identification of an event is a complex and multidimensional problem. The system analysis carried out will make it possible to formulate assessment measures, for example, based on neural systems. Procedures of neuro-classification and neuro-clustering of crime events, including mathematical and neural, as well as evaluation of efficiency of the conducted criminal policy are proposed. Methods of system analysis have been used - analysis-synthesis, decomposition, aggregation, identification, classification and monitoring, as well as mathematical and neural network modeling. This will improve the quality of crime analysis, both theoretically (modelling) and application (forecasting of counter-crime results). It's noted that multilevel and fragmented monitoring system does not contribute to operational law enforcement practice, IT-oriented monitoring is proposed

BUILDING STANDARDS FOR PHYSICAL ASSESSMENT OF SECOND YEAR STUDENTS AT KHANH HOA UNIVERSITY, VIETNAM

Research to build physical assessment standards for second-year students of Khanh Hoa University helps us get the basic information and basis to evaluate the teaching process and choose solutions. suitable in the training process, improve students' physical condition to meet learning requirements in new training trends.  Article visualizations

Recent Advances in BiVO4- and Bi2Te3-Based Materials for High Efficiency-Energy Applications

This chapter provides recent progress in developments of BiVO4- and Bi2Te3-based materials for high efficiency photoelectrodes and thermoelectric applications. The self-assembling nanostructured BiVO4-based materials and their heterostructures (e.g., WO3/BiVO4) are developed and studied toward high efficiency photoelectrochemical (PEC) water splitting via engineering the crystal and band structures and charge transfer processes across the heteroconjunctions. In addition, crystal and electronic structures, optical properties, and strategies to enhance photoelectrochemical properties of BiVO4 are presented. The nanocrystalline, nanostructured Bi2Te3-based thin films with controlled structure, and morphology for enhanced thermoelectric properties are also reported and discussed in details. We demonstrate that BiVO4-based materials and Bi2Te3-based thin films play significant roles for the developing renewable energy

Harnessing graph state resources for robust quantum magnetometry under noise

Precise measurement of magnetic fields is essential for various applications, such as fundamental physics, space exploration, and biophysics. Although recent progress in quantum engineering has assisted in creating advanced quantum magnetometers, there are still ongoing challenges in improving their efficiency and noise resistance. This study focuses on using symmetric graph state resources for quantum magnetometry to enhance measurement precision by analyzing the estimation theory under Markovian and non-Markovian noise models. The results show a significant improvement in estimating both single and multiple Larmor frequencies. In single Larmor frequency estimation, the quantum Fisher information spans a spectrum from the standard quantum limit to the Heisenberg limit within a periodic range of the Larmor frequency, and in the case of multiple Larmor frequencies, it can exceed the standard quantum limit for both Markovian and non-Markovian noise. This study highlights the potential of graph state-based methods for improving magnetic field measurements under noisy environments.Comment: 10 pages, 7 figure

Child Sexual Exploitation Investigation in Vietnam and Recommendations

Child sexual exploitation has been a serious trouble in Vietnam for a long time. Since 2014, the law enforcement instituted about 1,200 to 1,900 cases annually. Statistics show about 2,000 children are victims of child sexual exploitation crime every year, in which girls account for approximately 80%. A worrying trend is there are more and more children under 6 years old become victims. Thanks to recent efforts from the government and other stakeholders, the situation of this crime has been improvd effectively. In the coming years, all stakeholders need to adopt several measures for preventing and suppressing this type of crime. This paper presents some main problem, giving causes and effects, the recommendation and solution of issue. If these solutions could be applied, they will help to deal with child sexual more feasible; therefore, raising and minimizing this type of violation in Vietnam in the coming time

Testing Western Media Icons Influence on Arab Women’s Body Size and Shape Ideals: An Experimental Approach

Western media globalization is implicated in the spread of the thin body ideal to traditional societies. Qatar—a small conservative Middle-Eastern country—has recently witnessed rapid Westernization, but the influence of Western media icons on women’s body image dissatisfaction has rarely been studied here. A 2 (celebrity or model) × 3 (thin, average, or heavy) plus a control condition between-subject experiment tested the primary hypothesis that exposure to images of thin Western models or celebrities promotes a thinner body ideal compared to neutral images. A sample of young women (n = 1145) was randomly assigned to experimental images as part of an online survey. After exposure to images, participants rated their current and desired body size and shape, reported celebrity liking, and evaluated their favorite celebrity’s body. We found little support for the desire of thinness. Viewing thin- and average-sized celebrities was significantly associated with desiring a heavier and a thinner look (respectively) among those favoring thin celebrities. Images of thin models induced the desire for a curvaceous body figure with hips especially among those favoring celebrities with hips. The findings highlight important nuances in the influence of Western media icons on body image among women in a non-Western culture

VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph

The availability of vast amounts of visual data with heterogeneous features is a key factor for developing, testing, and benchmarking of new computer vision (CV) algorithms and architectures. Most visual datasets are created and curated for specific tasks or with limited image data distribution for very specific situations, and there is no unified approach to manage and access them across diverse sources, tasks, and taxonomies. This not only creates unnecessary overheads when building robust visual recognition systems, but also introduces biases into learning systems and limits the capabilities of data-centric AI. To address these problems, we propose the Vision Knowledge Graph (VisionKG), a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies. It can serve as a unified framework facilitating simple access and querying of state-of-the-art visual datasets, regardless of their heterogeneous formats and taxonomies. One of the key differences between our approach and existing methods is that ours is knowledge-based rather than metadatabased. It enhances the enrichment of the semantics at both image and instance levels and offers various data retrieval and exploratory services via SPARQL. VisionKG currently contains 519 million RDF triples that describe approximately 40 million entities, and are accessible at https://vision.semkg.org and through APIs. With the integration of 30 datasets and four popular CV tasks, we demonstrate its usefulness across various scenarios when working with CV pipelines