Information Space Research Articles

Data Protection in Healthcare Information Systems Using Cryptographic Algorithm with Base64 512 bits

One of the major roles of government is to curb crime. Despite the measures the government has taken to counteract criminal activity, the security situation in many urban centers has gotten worse. The goal of this study was to create and assess a machine learning model with the core function of forecasting crime categories and utilizing contextual features found in the datasets to visualize the locations in which they occur. This was achieved by combining time, space, and contextual information with machine learning to improve crime prediction and mapping. The datasets were collected from various sources were subjected to a number of machine learning algorithms to evaluate how well they performed. The random forest algorithm emerged as the best algorithm with a classification accuracy of 97% or 0.973301 using the confusion matrix. The longitude and latitude features were used to tag the specific locations of crime occurrences on a map.

Improving the planning and accounting of material costs for the repair and maintenance needs of an industrial enterprise

The optimal use of material resources is an essential factor in ensuring the profitability of the business of an enterprise in the real sector of the economy. An essential part of the system of material resources of an industrial enterprise is the material costs of repair and maintenance needs. The purpose of the research presented in this article is to structure the information space with a high level of transparency and improve the quality of tools for operational planning and accounting of these expenses of an industrial enterprise at all stages of production activity; The tasks include the development of a model for improving operational planning of the need for these resources for repairs, maintenance and operation of production equipment in the context of production orders and operational accounting, including the preparation of management reports on the implementation of planned plans. The research used methods of systematic and situationally oriented analysis; collection, accumulation and systematization of information about economic activity; economic analysis; classification; aggregation; reduction; generalization; graphical visualization of data; comparison; process approach.

Quantitative dispersion characterization of cement particles in hardened cement matrix

Optimizing the cement particle distribution in hardened cement matrix is one of the effective means to utilize cement efficiently, and obtaining information about cement distribution is a prerequisite for this. In view of this, this paper investigates the quantitative dispersion of cement particles in hardened cement paste from an experimental perspective. Cement pastes with different water-binder (W/B) ratios were prepared as the objects. BSE images of these samples were acquired for further analysis, including Delaunay triangulation algorithm, nearest neighbor distance function G(r) and fractal theory. Results showed that when the W/B ratio increases from 0.36 to 0.42, the average cement particle spacing rises from 32.7 μm to 34.9 μm and the proportion of particles with spacing between particle centroids <20 μm decreases from 41.96 % to 25.98 %. Similarly, the increasing W/B ratio causes the nearest neighbor distance function G(r) to shift towards larger distances. When the W/B ratio is 0.34, the median distance between particles (R50) exhibits the most significant change. Additionally, the intricate details of cement particle spacing information can be accurately described by fractal theory. Parameters such as R50 and the proportion of particles with spacing between centroids <20 μm demonstrate strong fits with various fractal parameters. Notably, they exhibit optimal fitting with the spectral width of the generalized fractal spectrum, with an R2 value of 0.99.

ВИКТИМОЛОГИЧЕСКАЯ ПРОФИЛАКТИКА ПРЕСТУПЛЕНИЙ, СОВЕРШЕННЫХ С ИСПОЛЬЗОВАНИЕМ ИНФОРМАЦИОННЫХ (ЦИФРОВЫХ) ТЕХНОЛОГИЙ

The article presents the general characteristics of victimological prevention of crimes committed using in-formation (digital) technologies. The authors emphasize that the study of criminal law and criminological features of digital crime is justified and necessary at the present stage of development of society and the state. The relevance of the research is due to the fact that the modern world is more focused on the transi-tion to the digital space. And since modern crime actively uses information (digital) technologies for its criminal purposes, a law enforcement officer engaged in combating digital crimes needs theoretically grounded research on this topic. The purpose of this work is to reveal the victimological features of the prevention of certain types of digital crimes, as well as the study of criminologically significant characteristics of modern digital crime and the development on this basis of key measures aimed at preventing crimes committed in the infor-mation space. In this study, the authors focus on the basics of victimological prevention of digital crime. The article discusses what basic measures should be taken by citizens to protect themselves from becoming a victim of a digital (cyber) crime. The prevalence of the use of modern digital technologies is beyond doubt, and a person using ITT can easily become a victim of cybercrime or simply suffer due to the lack of basic rules of digital security. In this regard, the basics of victimological prevention are an important component of the prevention of digital crime. This study is intended for researchers interested in the issues of criminal law counteraction to crimes committed using information (digital) technologies.

Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning

Vehicular edge computing (VEC), a promising paradigm for the development of emerging intelligent transportation systems, can provide lower service latency for vehicular applications. However, it is still a challenge to fulfill the requirements of such applications with stringent latency requirements in the VEC system with limited resources. In addition, existing methods focus on handling the offloading task in a certain time slot with statically allocated resources, but ignore the heterogeneous tasks’ different resource requirements, resulting in resource wastage. To solve the real-time task offloading and heterogeneous resource allocation problem in VEC system, we propose a decentralized solution based on the attention mechanism and recurrent neural networks (RNN) with a multi-agent distributed deep deterministic policy gradient (AR-MAD4PG). First, to address the partial observability of agents, we construct a shared agent graph and propose a periodic communication mechanism that enables edge nodes to aggregate information from other edge nodes. Second, to help agents better understand the current system state, we design an RNN-based feature extraction network to capture the historical state and resource allocation information of the VEC system. Thirdly, to tackle the challenges of excessive joint observation-action space and ineffective information interference, we adopt the multi-head attention mechanism to compress the dimension of the observation-action space of agents. Finally, we build a simulation model based on the actual vehicle trajectories, and the experimental results show that our proposed method outperforms the existing approaches.

Affordances of Technology for Sustainability-Oriented K–12 Informal Engineering Education

The need for sustainability-oriented K–12 engineering education that expands beyond the classroom and the increased accessibility of educational technologies create an opportunity for examining the affordances of educational technologies in low-stakes informal engineering education settings. In this paper, we share our experiences of using novel technologies to develop sustainability-oriented mental models in K–12 informal engineering education. Through the use of technologies including Augmented Reality (AR), Virtual Reality (VR), Minecraft video games, Tinkercad (browser-based application for computer-aided design (CAD)), and physical computing, we have designed and tested approaches to introduce students to engineering design and engineering habits of mind with an overarching theme of developing sustainability-oriented mental models among K–12 youth in informal engineering education spaces. In this paper, we share our approaches, and lessons learned, and outline directions for future research.

Explainable machine learning for high frequency trading dynamics discovery

High-frequency trading (HFT) plays an essential role in the financial market. However, discovering and revealing trading dynamics remains a challenge in Fintech. In this study, we propose a novel explainable machine learning approach: Feature-Interpolation-based Dimension Reduction SCAN (FIDR-SCAN) to address the challenge by creating a trading map. The trading map deciphers an HFT security’s trading dynamics by marking the status of each transaction, grouping transactions in clusters, and identifying the trading markers. The proposed method presents new feature interpolation techniques to build a more informative and explainable feature space, unveiling hidden trading behaviors. It mines HFT data in their low-dimensional embedding to seek exceptional trading markers and classify the statuses of transactions. We validate the meaningfulness and effectiveness of the trading markers discovered by FIDR-SCAN in trading as well as examining its special characteristics. Additionally, we apply the proposed algorithm to cryptocurrency data and achieve reliable performance. We design AI trading algorithms by reusing trading markers identified during explainable trading dynamics discovery, applying them to HFT stock and cryptocurrency markets, besides constructing trading machines using identified trading markers. To the best of our knowledge, this study is the first to use interpretable machine learning to reveal HFT trading dynamics.

The role and place of Ukraine in modern international information policy: modern challenges and strategic imperatives of cooperation

Introduction: the relevance of the study is determined by evident and paradigmatic change of global political landscape and corresponding international and nation-state information policy. At the same time, attention is drawn to the presence of theoretical gaps in defining the definition of “information policy”, its institutional structure, as well as the mechanisms for its formation and implementation in today realities, in particular against the backdrop of Russia-Ukraine war and challenges concerning the role and place of Ukraine in international information landscape of international relations. Objectives: the article aims to develop an array systematizing the processes taking place within Ukrainian media space during the war, which would allow comprehending them based on theoretical paradigms and today realities of information policy, information landscape, and information geopolitics.Method: the methodological basis for considering research problems is based on the methods of systemic, structural-functional and institutional analysis. The research combines the methodological achievements of several paradigms, constituting the basis of philosophical pluralism - the synergistic method and the paradigm of nonlinearity.Results: the political role of the information space in modern society and public policy is comprehended. This role lies in the ability of the information space to reflect and subsequently change social processes (including political processes), as well as the interests of the actors dominating the political process. The negative effects of populism in the information policy of Ukraine and the discrepancies between internal and external media narratives are noted. The increasing role of civil journalism and Telegram channels in the information field of Ukraine is shown and an understanding of the corresponding challenges for information policy is proposed

Temperature retrieval of near space with the combined use of O2(a1Δg) and O2(b1∑+ g) dayglow emissions under self-absorption effect correction

Atmospheric temperature information in the near space is of great academic significance and engineering value to support the development and utilization of the near space. Based on the theory of O2 molecular dayglow spectroscopy and the mechanism of atmospheric radiative transfer, a method is proposed for the joint retrieval of temperature profiles in the near space using O2(a1Δg) and O2(b1∑+g) bands dayglow spectroscopy signal with the self-absorption effect. First, the temperature dependence of O2(a1Δg) and O2(b1∑+g) bands dayglow is investigated, and the influence of the self-absorption effect on the radiative transfer characteristics is analyzed in the limb-view mode. Then, with the use of the onion peeling algorithm, the dayglow emission spectra signals of the O2(a1Δg) and O2(b1∑+g) bands measured by the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) in the limb-viewing mode were processed, and combined with optimization algorithms, the temperature profiles from 35 km to 120 km is successfully retrieved. Finally, the accuracy and reliability of the self-absorption effect correction as well as the joint temperature retrieval were verified by comparing with temperature product data from remote sensing satellites such as Sounding of the Atmosphere using Broadband Emission Radiometry (SABER), Atmospheric Chemistry Experiment Fourier-Transform Spectrometer (ACE-FTS), and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS). The error analysis shows that the temperature retrieval error after correction for the self-absorption effect is about 3 K minimum and 20 K maximum.

Towards a climate of hope: learning in response to frames at an innovative museum exhibit

As trusted public institutions, museums could offer crucial opportunities for climate learning. Yet, empirical research on effective approaches is limited. One potential pathway is through strategic framing, given that much research in climate communication suggests the value of specific frames. We developed an innovative climate exhibit, A Climate of Hope (ACOH), guided by five strategic frames: hope, gain, local, community, and playful. To investigate visitor learning at a prototype of ACOH, we collected data from 31 groups as they interacted with a prototype of ACOH while wearing audio recorders. Integrating theories of knowledge and discourse, we investigated learners’ use of resources at ACOH through qualitative discourse analysis and mixed-methods analysis. We found that learners brought a diverse array of resources, some of which were related to incoming views of climate change. We interpret our results as speaking to the potential of framing climate messages through a gain frame. We provide implications for educational practice around climate change in informal spaces.

M2DCapsN: Multimodal, Multichannel, and Dual-Step Capsule Network for Natural Language Moment Localization.

Natural language moment localization aims to localize the target moment that matches a given natural language query in an untrimmed video. The key to this challenging task is to capture fine-grained video-language correlations to establish the alignment between the query and target moment. Most existing works establish a single-pass interaction schema to capture correlations between queries and moments. Considering the complex feature space of lengthy video and diverse information between frames, the weight distribution of information interaction flow is prone to dispersion or misalignment, which leads to redundant information flow affecting the final prediction. We address this issue by proposing a capsule-based approach to model the query-video interactions, termed the Multimodal, Multichannel, and Dual-step Capsule Network ( [Formula: see text]DCapsN), which is derived from the intuition that "multiple people viewing multiple times is better than one person viewing one time." First, we introduce a multimodal capsule network, replacing the single-pass interaction schema of "one person viewing one time" with the iterative interaction schema of "one person viewing multiple times," which cyclically updates cross-modal interactions and modifies potential redundant interactions via its routing-by-agreement. Then, considering that the conventional routing mechanism only learns a single iterative interaction schema, we further propose a multichannel dynamic routing mechanism to learn multiple iterative interaction schemas, where each channel performs independent routing iteration to collectively capture cross-modal correlations from multiple subspaces, that is, "multiple people viewing." Moreover, we design a dual-step capsule network structure based on the multimodal, multichannel capsule network, bringing together the query and query-guided key moments to jointly enhance the original video, so as to select the target moments according to the enhanced part. Experimental results on three public datasets demonstrate the superiority of our approach in comparison with state-of-the-art methods, and comprehensive ablation and visualization analysis validate the effectiveness of each component of the proposed model.

Temporally enhanced graph convolutional network for hand tracking from an egocentric camera

We propose a robust 3D hand tracking system in various hand action environments, including hand-object interaction, which utilizes a single color image and a previous pose prediction as input. We observe that existing methods deterministically exploit temporal information in motion space, failing to address realistic diverse hand motions. Also, prior methods paid less attention to efficiency as well as robust performance, i.e., the balance issues between time and accuracy. The Temporally Enhanced Graph Convolutional Network (TE-GCN) utilizes a 2-stage framework to encode temporal information adaptively. The system establishes balance by adopting an adaptive GCN, which effectively learns the spatial dependency between hand mesh vertices. Furthermore, the system leverages the previous prediction by estimating the relevance across image features through the attention mechanism. The proposed method achieves state-of-the-art balanced performance on challenging benchmarks and demonstrates robust results on various hand motions in real scenes. Moreover, the hand tracking system is integrated into a recent HMD with an off-loading framework, achieving a real-time framerate while maintaining high performance. Our study improves the usability of a high-performance hand-tracking method, which can be generalized to other algorithms and contributes to the usage of HMD in everyday life. Our code with the HMD project will be available at https://github.com/UVR-WJCHO/TEGCN_on_Hololens2.

Disinterest, normalisation of gender violence and fear of being cancelled: Mediatised learning on antifeminist and anti-LGBTIQ+ discourses among teenagers in Barcelona

This article examines the proliferation of antifeminist and anti-LGBTIQ+ discourses among teenagers in Barcelona (Catalonia) and the role of media and the manosphere – online communities and spaces where discussions about masculinity, anti-feminism, gender relations and men's issues take place – on the pollination of these discourses. Through short-term ethnography conducted in three education centres, involving the collaboration of 59 teenagers between 14 and 18 years old, the study researches the discourses that young people embrace, what mediatised environments are referring to when they speak about gender, feminism and LGBTIQ+ topics, and the meaning young people give to these media content. Findings reveal the manosphere facilitates young people's access to masculinist cultures and point to the urge of addressing the affective dimension of it. To conclude, it highlights the need to challenge these informal learning spaces within contemporary youth culture to promote gender equality and social justice.

Specificity of modeling the actuator for the deployment of transformable large-scale space structures

Abstract The creation of large-scale structures will be necessary for the development of information and transport space systems. Transformable systems form a distinct category of such space structures, designed based on automatic configuration changes. The transport and deployed operating states of transformable space systems can vary in dimensions by tens of times, with force actuators carrying out the deployment process. The optimal force actuator suitable for use in transformable space structures is an actuator comprising of active elements made of a titanium nickelide material with a shape memory effect, which has the ability to generate significant force, reliability, compactness, light weight and low power consumption. In the pursuit of developing large-scale transformable space systems, tremendous importance is placed on conducting full-scale experiments, which require costly equipment to be simulated in ground conditions. Therefore, an alternative option to verify the functionality of the large-scale transformable space system in development is through conducting a mathematical experiment utilizing the identifiable parameters of the developed computational models. Experimental and theoretical analyses of the active elements of the force actuator made of titanium nickelide material (1.5 mm diameter wire) have confirmed the fundamental possibility of creating a force actuator for opening large-scale transformable space structures.

ЕФЕКТИВНЕ ДЕРЖАВНЕ УПРАВЛІННЯ І НАЦІОНАЛЬНА БЕЗПЕКА В КОНТЕКСТІ DEEP STATE

The article explores the concept of the «deep state» as a key element of ensuring sustainable public administration and national security in war and peace. The author examines global and regional development trends that affect the formation of national security and analyses the mechanisms of power and governance in different countries. Particular attention is paid to the role of informal and closed networks in influencing official policy, which allows for a deeper understanding of the sources of information wars and manipulation of public opinion. The relevance of the study is driven by the need to build a transparent, fair and democratic society, as well as to strengthen democratic institutions and protect the rights and freedoms of citizens. The analysis of the deep state allows us to identify the interconnections between state institutions, the military, intelligence services and big business, which often remain outside democratic control. The author notes that the study of the deep state is important for the protection of democratic values, increasing transparency, accountability and efficiency of governance. Particular attention is paid to the influence of the deep state on the information space and the mechanisms of manipulating public opinion through the media. Understanding these processes contributes to the development of critical thinking and the protection of citizens' rights and freedoms. In the context of globalisation, the concept of the deep state is seen as an important factor influencing international relations, trade policies and economic alliances. The study of this phenomenon helps to predict changes in global politics and the economy, which is important for the development of long-term national security strategies. The term «deep state» is used to describe a network of influential individuals or organisations that can exert significant influence on public policy beyond democratic control. This concept is actively discussed in political debates, causing controversy over its role and influence on political processes.

Building digital resilience in the training of national security professionals and teachers of the «Defence of Ukraine»

The article reveals the peculiarities of forming digital resilience of national security specialists and teachers of the «Defense of Ukraine» as an important factor in countering modern challenges and threats, ensuring stable development of society and protecting its digital future. It is noted that specialists should identify sources of threats to the country's information space and apply mechanisms to discredit them, as well as take into account the focus of the enemy's destructive actions on vulnerable groups through social networks and targeted advertising. It is emphasized that the formation of digital resilience can be quite effective when studying the educational component «Strategic Communications»”, during which future national security specialists and teachers of the «Defense of Ukraine» study such key elements of this type of communication as informing, influencing and persuading; information efficiency; coordination and deconfliction; «communication by action” and reducing the gap in the «say-do» construct. Building digital resilience in national security professionals and teachers of the Defence of Ukraine involves mastering the skills of creating their own and using specialized critical platforms and tools necessary to identify and counter hybrid threats. Attention is focused on the mechanisms of digital resilience formation as a concept of «Society 5.0», which has become a kind of theoretical challenge to the socio-economic and socio-cultural transformations that occur as a result of the total introduction of digital technologies. It is noted that in the context of the problem under study, there is a risk of uneven development of digital media by people, which actualizes the need to introduce mechanisms to intensify adaptive processes at the state level.

Mass Appeal: Designing Open Learning STEM Environments to Promote Participation for Black Boys

This paper presents a theory for the design of informal STEM spaces, aimed particularly at nurturing STEM engagement, creativity, play, and penalty-free ideation among Black boys. It introduces a framework for the intentional creation of open learning STEM environments that enrich Black boys’ experiences within STEM disciplines through cultural STEM mapping. The ILLEST Lab serves as key evidence of effective design implementation, aligning student interests with technical skill enhancement and a sense of belonging in STEM.

A Critical Look at Digital Media, the Intellectual Property Regime and Free Open Source Software

This article examines how the intellectual property regime works in digital media and how free open source software is pushing the boundaries of the regime on the digital realm. The main purpose of this article is to highlight the differences that models of knowledge production can make in the process of building a participatory society. This article uses Michael Gibbons' Mode 1 and Mode 2 theories as a method. In the light of this theory, power relations in the production and distribution of knowledge are discussed. The Mode 2 model points to a commons-based model of knowledge production that has a democratic potential. The intellectual property regime is struggling to control the flow of information on the internet. It is quite difficult for the intellectual property rights regime to control the circulation of knowledge on the Internet, as digital media play a key role in the free flow of knowledge. Free open source software is an effective model for those who support the free flow of information in the digital space.

Cross-effects of cultural and gay capitals in access to refugee status on the grounds of SOGI in France: Study of the formal and informal preparations of West African men for asylum trials

Abstract According to literature, ethnocentric representations of homosexuality structure the decision to grant asylum, or not, on the ground of sexual orientation. This ethnocentric criticism has recently led to a technicalization of evaluation practices by asylum judges, which are now based on a narrative and discursive mastery, a field in which respondents are unequal depending on their cultural and gay capitals. While the assessment as such appears to be more equitable, Western norms of gender and sexuality, and practicing self-narrative through their prism, continue to impose themselves in different ways, particularly in formal associative accompaniment or in informal preparation for the refugee status determination process. A 2-fold ethnographic approach, attentive to associative backing and the daily experience of exile as formal and informal spaces of asylum preparation and identity formation, is used to analyse the manner in which cultural and gay capitals influence identity formation, preparation for asylum trials, and chances of obtaining refugee status.

Understanding Higher-Order Interactions in Information Space.

Methods used in topological data analysis naturally capture higher-order interactions in point cloud data embedded in a metric space. This methodology was recently extended to data living in an information space, by which we mean a space measured with an information theoretical distance. One such setting is a finite collection of discrete probability distributions embedded in the probability simplex measured with the relative entropy (Kullback-Leibler divergence). More generally, one can work with a Bregman divergence parameterized by a different notion of entropy. While theoretical algorithms exist for this setup, there is a paucity of implementations for exploring and comparing geometric-topological properties of various information spaces. The interest of this work is therefore twofold. First, we propose the first robust algorithms and software for geometric and topological data analysis in information space. Perhaps surprisingly, despite working with Bregman divergences, our design reuses robust libraries for the Euclidean case. Second, using the new software, we take the first steps towards understanding the geometric-topological structure of these spaces. In particular, we compare them with the more familiar spaces equipped with the Euclidean and Fisher metrics.