Non-professional Users Research Articles

Talking Green on social media: An exploratory study of online environmental communication in Greece

Traditional news media have long been the primary source of information on environmental issues, influencing public perceptions and shaping discourse on ecological sustainability. Media representations play a central role in forming public opinion, especially on climate change, which has become a highly politicized and polarized topic, both offline and online. Political ideologies, economic interests, and government agendas significantly shape how the public discusses and responds to strategies for addressing climate change. This study focuses on the most engaging environmental content posted on social media and explores whether climate change is the dominant topic in these discussions. It also examines which actors (e.g., politicians, institutions, citizens) and sources (legacy media or alternative media) are featured in the most popular content. The aim is to identify the factors that drive user engagement on platforms like Facebook, Twitter (now X), and YouTube, and to investigate how social media users contribute to the plurality and diversity of voices shaping the discussions on environmental issues. The study considers whether these platforms support the democratic potential of public debate. Over a three-month period (Sept.–Nov. 2021), the study monitored social media and compared non-professional user’s posts with professional content from legacy media. The findings show that institutional actors dominate the online debate on environmental issues and climate change. Facebook engagement largely revolves around content from organizations and politicians, while Twitter users refer to organizational sources. On YouTube, however, local and regional media content is more prominent. This is surprising since surveys suggest that Greeks mistrust legacy media and prefer social media for information, viewing traditional outlets as corrupt and biased towards the dominant class.

Методологическое обоснование содержания, структуры и программной реализации базы геопространственных знаний территориальных образований

The article deals with substantiating the contents, structure and software implementation of the geospatial knowledge base. The importance and relevance of forming its infrastructure and the corresponding informational base on the territory in modern geoinformatics and cartography are noted. The requirements for the contents, formal and logical representation of the mentioned base, its structure and connections of the elements at the logical level are given. The authors consider the software implementation of the processes of searching and issuing geospatial knowledge in a visualized form at the request of untrained users in the field of geoinformatics and cartography. The requisitions and information on the special original software, which allows observing the contents of the geospatial knowledge base, are shown. The effectiveness of visualizing the geospatial knowledge base through interactive configuration of software elements is substantiated, based on the metadata principle proposed by the authors, two formalisms and an algorithm for creating corresponding services for geospatial knowledge and other related data. These formalisms of the client’s parameters and server software applications are described, a variant of the geospatial knowledge base structure is given, and the requirements for the necessary software for the effective solution of the task are formulated. The authors` solution is considered in the form of a complex of multicomponent software, used at working with geodata and creating their own interactive maps by poorly trained non-professional users

BIM and individual physical needs-trained model-enabled approach to spatial redesign and visualization

ABSTRACT This paper presents a new spatial visualization technique to redesign indoor spaces that reflect individual physical requirements. Designing safe and convenient environments necessitates differentiated facility installations based on factors such as age, gender, and disability status, emphasizing the need for customized spatial configurations. The study specifically focuses on elderly users, visualizing appropriate safety equipment placement to address physical aging challenges and proposing tailored space redesigns. High-Level of detail (LoD) Building Information Modeling (BIM) components were developed for the training model and utilized in the data processing workflow. The training data consists of images paired with explanatory text, and additional training was completed by optimizing the training hyperparameters. The resulting trained model generates high-quality images that accurately reflect the provided contextual prompts. Furthermore, this study showcases a practical application through the development of a user-friendly mobile web/application interface. This tool allows non-professional users to capture images using their smartphones and visualize potential redesign alternatives tailored to their specific physical requirements. By bridging the gap between spatial visualization techniques and users, this study contributes to making more inclusive and adaptable indoor environments for diverse users, particularly the elderly.

A FRAMEWORK FOR CONVERTING SQL QUERIES FROM NATURAL LANGUAGE

The database interface, called the "natural language interface", uses natural language to allow users to access data without the need for SQL queries. Using query language to interact with databases is always a sophisticated issue. Due to this complexity, the client must use the clear reports that are included in some pre-executed programming projects when using information that is already present in database points of confinement. But, users can make it possible for each nonprofessional user to ask questions and provide requirements in natural language, which the computer can then process to produce the appropriate data. This study introduces a system developed using the "Natural Language Interface to Database" (NLIDB). This approach has led to the emergence of "experts" who can detect compound sentences in English and use them in lessons. After parsing the input sentences, the natural language is converted to SQL.

Investigation of the wear and friction profile of TPU-based polymers at different infill ratios

Additive manufacturing is a widely used method in industry and research areas. In particular, fused deposition modelling is the most prevalent technique used by many professional and nonprofessional users. Many polymers can be used with this system, including thermo polyurethanes (TPU). TPUs have excellent elastic properties and high endurance against corrosion, humidity, and oil, and they ex-hibit a great absorbance capability to noise and vibrations, biocompatibility, and chemical resistance. Thermoplastic polyurethane (TPU) is also preferred for use in 3D/4D printing applications due to its easy casting, injection, and extrusion capabilities and its shape memory features. In this study, flexible TPU and carbon-mixed TPU were used to produce specimens with fused deposition modelling tech-niques at different infill ratios with the same patterns. The effects of the infill ratio within the different and same materials were investigated in terms of wear and friction profiles. Additionally, thermal and worn surface images were taken using a digital microscope. The hardness and diameter value altera-tions were also investigated for different materials and infill ratios. As a result of the study, material al-teration is more effective than the infill ratios in all parameters.

Region-Aware Color Smudging.

Color smudge operations from digital painting software enable users to create natural shading effects in high-fidelity paintings by interactively mixing colors. To precisely control results in traditional painting software, users tend to organize flat-filled color regions in multiple layers and smudge them to generate different color gradients. However, the requirement to carefully deal with regions makes the smudging process time-consuming and laborious, especially for non-professional users. This motivates us to investigate how to infer user-desired smudging effects when users smudge over regions in a single layer. To investigate improving color smudge performance, we first conduct a formative study. Following the findings of this study, we design SmartSmudge, a novel smudge tool that offers users dynamical smudge brushes and real-time region selection for easily generating natural and efficient shading effects. We demonstrate the efficiency and effectiveness of the proposed tool via a user study and quantitative analysis.

ERTool: A Python Package for Efficient Implementation of the Evidential Reasoning Approach for Multi-Source Evidence Fusion.

Background: Multi-source evidence fusion aims to process and combine evidence from different sources to support rational and reliable decision-making. The evidential reasoning (ER) approach is a helpful method to deal with information from multiple sources with uncertainty. It has been widely used in business analytics, healthcare management, and other fields for optimal decision-making. However, computerized implementation of the ER approach usually requires much expertise and effort. At present, some ER-based computerized tools, such as the intelligent decision system (IDS), have been developed by professionals to provide decision support. Nevertheless, IDS is not open source, and the user interfaces are a bit complicated for non-professional users. The lack of a free-to-access and easy-to-use computerized tool limits the application of ER. Methods: We designed and developed a Python package that could efficiently implement the ER approach for multi-source evidence fusion. Further, based on it, we built an online web-based system, providing not only real-time evidence fusion but also visualized illustrations of combined results. Finally, a comparison study between the Python package and IDS was conducted. Results: A Python package, ERTool, was developed to implement the ER approach automatically and efficiently. The online version of the ERTool provides a more convenient way to handle evidence fusion tasks. Conclusions: ERTool, compatible with Python 3 and can be installed through the Python Package Index at https://pypi.org/project/ERTool/, was developed to implement the ER approach. The ERTool has advantages in easy accessibility, clean interfaces, and high computing efficiency, making it a key tool for researchers and practitioners in multiple evidence-based decision-making. It helps bridge the gap between the algorithmic ER and its practical application and facilitates its widespread adoption in general decision-making contexts.

The classification of risks facing society in connection with the creation and spread of virtual assets: institutional and scientific approaches

The article suggests that the emergence and proliferation of virtual assets has led to an increase in a number of negative events (risks) that society, States and investors might face. However, despite significant developments in identifying these risks, there is still no generalized classification of them. Given this, the purpose of the article is to analyze and classify the risks faced by society, States and investors in connection with the emergence and proliferation of virtual assets.
 The author proposes to classify the risks into six groups based on the study: economic, investment, criminological, technological, subjective, and environmental risks. Within each of the proposed classification groups, the author identifies the relevant risks. For example, the economic risks include the following: the possibility of affecting the foundations of financial stability and security of the state, inflation (possible impact on the monetary policy of the state due to unjustified and uncontrolled increase in the money supply), the possibility of creating pyramid schemes, the possibility of circumventing currency legislation (in particular, currency restrictions on the withdrawal of currency abroad).
 As to the subjective risks, the author identifies the following: the possibility of unfair behavior of entities providing services in the field of turnover of such assets, the possibility of losing a crypto wallet password and/or a secret personal PIN code by its owner, the possibility of an erroneous transaction during a transfer from one crypto wallet to another, the possibility of carrying out settlement operations and entering into smart contracts by persons under the appropriate age or with limited legal capacity, and the inability of some non-professional users to use virtual assets.
 It is emphasized that a comprehensive study of the risks that may arise in connection with the emergence and proliferation of virtual assets will form the basis for further development of legal support for their operation and implementation in all spheres of public life.

Accelerated naked-eye identification of cations using non-plasmonic amplification by functionalized GQDs: A novel photochemical microfluidic sensor for environmental/mineral analysis using Lab-on-paper technology

This study describes the synthesis of various types of thiolated-graphene quantum dots (GQDs) for the simultaneous multi-sensing of heavy metal ions using one-droplet microfluidic paper-based analytical devices (OD-µPADs). For the first time, functionalized GQDs were synthesized and stabilized on the surface of hydrophobic fiber-glass with simple manufacturing and assembly strategy towards colorimetric chemosensing of ions including iron, copper, cobalt, mercury, chrome, manganese, and nickel. For this purpose, GQDs are functionalized by various organic species including cysteamine, decanethiol, and mercaptopropionic acid (MPA) towards specific electrostatic interaction with metal ions of Fe (II), Cu (II), Co(II), Hg (II), Mn (II), and Ni (II). Therefore, an innovative GQDs-based miniaturized chemo-system was proposed for the environmental analysis. Additionally, the results were confirmed using fluorescence spectrometry as an optical detection method. Finally, this simple and efficient colorimetric platform was used for facile metal ions detection in human urine models, and environmental fluids, which can provide a commercial device for sensing contaminants by non-professional users in biomedical/environmental mixture. As an economical technique, this platform presents a “mix-and-sense” system without dye (as a side substance) or chemical treatments. The low instrumental requirements, the color uniformity, and the disposability of OD-μPADs approve the commercial application of the engineered ion-chemosensor.

DrawingInStyles: Portrait Image Generation and Editing With Spatially Conditioned StyleGAN.

The research topic of sketch-to-portrait generation has witnessed a boost of progress with deep learning techniques. The recently proposed StyleGAN architectures achieve state-of-the-art generation ability but the original StyleGAN is not friendly for sketch-based creation due to its unconditional generation nature. To address this issue, we propose a direct conditioning strategy to better preserve the spatial information under the StyleGAN framework. Specifically, we introduce Spatially Conditioned StyleGAN (SC-StyleGAN for short), which explicitly injects spatial constraints to the original StyleGAN generation process. We explore two input modalities, sketches and semantic maps, which together allow users to express desired generation results more precisely and easily. Based on SC-StyleGAN, we present DrawingInStyles, a novel drawing interface for non-professional users to easily produce high-quality, photo-realistic face images with precise control, either from scratch or editing existing ones. Qualitative and quantitative evaluations show the superior generation ability of our method to existing and alternative solutions. The usability and expressiveness of our system are confirmed by a user study.

Easykin: a flexible and user-friendly online tool for forensic kinship testing and missing person identification.

In forensic kinship testing and missing person identification, it is a fundamental question to choose the most informative reference relatives, select appropriate genotyping systems, and evaluate the weight of evidence comprehensively. Despite that several useful tools have been developed, they have not addressed these questions satisfactorily. In this paper, we develop a flexible and user-friendly online tool, Easykin, to address the aforementioned issues. It has some promising features: (i) Pedigrees can be constructed easily and presented intuitively with just a few mouse clicks. (ii) System power can be estimated before testing based on certain set of markers and reference relatives. (iii) The pruning function of EasyKin enables users to choose appropriate subsets of available references. (iv) Parameters at a specific LR for a single case may ease evidence interpretation. (v) The user interface (UI) is an HTML-based dashboard, which is friendly to both professional and non-professional users and can be used anytime and anywhere. Here, we presented three common cases as examples to demonstrate how kinship testing and missing person identification can be improved with EasyKin. In conclusion, this tool provides a one-stop solution for forensic use, that is, instructing users to choose appropriate kits and reference relatives before testing, calculating LR in the testing, and providing parameters for data interpretation after testing. EasyKin is freely available at https://forensicsysu.shinyapps.io/EasyKin/ .

SkyroadAR: An Augmented Reality System for UAVs Low-Altitude Public Air Route Visualization

Augmented Reality (AR) technology visualizes virtual objects in the real environment, offering users an immersive experience that enhances their spatial perception of virtual objects. This makes AR an important tool for visualization in engineering, education, and gaming. The Unmanned Aerial Vehicles’ (UAVs’) low-altitude public air route (Skyroad) is a forward-looking virtual transportation infrastructure flying over complex terrain, presenting challenges for user perception due to its invisibility. In order to achieve a 3D and intuitive visualization of Skyroad, this paper proposes an AR visualization framework based on a physical sandbox. The framework consists of four processes: reconstructing and 3D-printing a sandbox model, producing virtual scenes for UAVs Skyroad, implementing a markerless registration and tracking method, and displaying Skyroad scenes on the sandbox with GPU-based occlusion handling. With the support of the framework, a mobile application called SkyroadAR was developed. System performance tests and user questionnaires were conducted on SkyroadAR; the results showed that our approachs to tracking and occlusion provided an efficient and stable AR effect for Skyroad. This intuitive visualization is recognized by both professional and non-professional users.

Law and Artificial Intelligence: Possibilities and Regulations on the Road to the Consummation of the Digital Verdict

Aim: The continuous growing influence of technologies based on artificial intelligence will continue to have an increasingly strong impact on various fields of society, which is evident in the generation of a great expectation in continuous evolution that revolutionises many activities, including those of the legal sector.
 
 Methodology: That is why this paper aims to shed light on those aspects of artificial intelligence in the legal field that are not very clear and on some of the most relevant implications that, despite not being well known, will have a considerable impact on society at various levels, such as the role of bots in law firms, algorithms to assist in the treatment of cases and dilemmas such as whether non-professional users can trust the decision(s) that such tools would recommend, among others.
 
 Results: It also examines the ethical criteria linked to the application and its development.
 
 Conclusion: It concludes with a critical exposition from an academic perspective, without neglecting the aspect of public access to artificial intelligence and how to bring together a process of globalisation of the regulatory framework of these technologies.
 
 Originality/Value: this study highlities technologies based on artificial intelligence.

Improving colloquial case legal judgment prediction via abstractive text summarization

Most studies on Legal Judgment Prediction (LJP) use court verdicts or indictments as the training data source. Such models could assist judicial professionals who can use legal jargon to efficiently predict sentences. However, for ordinary and non-professional users, who can only provide a vague and incomplete description of the situation due to the lack of legal background, the predictive ability of the model will be greatly limited. To address this issue, we propose a colloquial case-based LJP framework called PekoNet, which incorporates Abstractive Text Summarization (ATS) into training for the LJP task to improve prediction accuracy for colloquial case description. We considered two approaches: independent training and joint training. The former train two separate model independently, while the latter jointly train both the ATS and LJP modules with either ATS-Freezing or ATS-Finetuning. The performance of these models is evaluated on two automated summarized testing datasets - BART and ChatGPT as well as human-provided case summaries. The results of the experiments demonstrate that the models developed with PekoNet outperform the typical LJP model for colloquial case description by up to 3.6%-10.8%.

KGNav: A Knowledge Graph Navigational Visual Query System

Visual query is a vital technique for comprehending and analyzing knowledge graphs, which provides an effective method to lower the barrier of querying knowledge graphs for non-professional users. Nevertheless, visual query techniques for knowledge graphs and ontologies that have emerged in recent years cannot bridge the gap between global information provided by the knowledge graph schema and underlying data of knowledge graph. Thus it cannot fully exploit the global information to navigate users for querying knowledge graphs. This demonstration showcases KGNav, a Knowledge Graph Navigational visual query system. KGNav (1) redefines the minimal unit of operation to abstract the conceptual hierarchy, i.e., Knowledge Graph Schema, in the domain from the original knowledge graph in an offline semi-automatic way through the equivalence relations between these units; it also (2) provides a series of operators and an interactive GUI to capture user query intentions, guiding users to explore the Knowledge Graph Schema to achieve in-depth analysis of knowledge graphs. We will demonstrate the capability of KGNav in reducing tedious queries, enabling users to swiftly grasp the structure of the knowledge graph, and performing queries through several fundamental scenarios.

The impact of life cycle assessment database selection on embodied carbon estimation of buildings

Reliable and accurate carbon assessment of a building is becoming increasingly important along with carbon neutrality goals. Although various life cycle assessment (LCA) databases have been adopted for assessing buildings' embodied carbon, the results displayed inconsistencies and the reasons behind the differences were not well revealed. This study aims to identify the critical factors contributing to the inconsistencies by analyzing 20 databases and explore their numerical influences at the three levels of buildings’ products, i.e., material, component and unit levels. Using a four-principle framework, it was found that ecoinvent, AusLCI, and Global Digital EPD performed preeminently in flexibility, comprehensiveness, transparency, and uniformity. The quantitative analysis of a case building indicated that the numerical influences by different databases would be higher when evaluating a material compared with a more comprehensive building product (e.g., component or unit). For a concrete building, the emission factors of steel affected the total carbon a lot. The results suggested the importance of cautiously selecting a proper database and clearly clarifying the background information at the initial stage of carbon assessment, under the guidance of the four-step LCA framework (i.e., goal and scope definition, inventory analysis, life cycle impact assessment, and interpretation). By developing a systematic construct including qualitative and quantitative approaches, this study clarified the underlying reasons leading to inconsistencies by LCA databases. It facilitated a better way for professional and non-professional users to select a more suitable carbon assessment database according to their purpose.

Validation of a multiple‑lead smartphone-based electrocardiograph with automated lead placement for layman use in patients with hypertrophic cardiomyopathy.

A smartphone 12-Lead ECG that enables layman ECG screening is still lacking. We aimed to validate D-Heart ECG device, a smartphone 8/12 Lead electrocardiograph with an image processing algorithm to guide secure electrode placement by non-professional users. One-hundred-fourty-five patients with HCM were enrolled. Two uncovered chest images were acquired using the smartphone camera. An image with virtual electrodes placement by imaging processing algorithm software was compared to the 'gold standard' electrode placement by a doctor. D-Heart 8 and 12-Lead ECG were obtained, immediately followed by 12‑lead ECGs and were assessed by 2 independent observers. Burden of ECG abnormalities was defined by a score based on the sum of 9 criteria, identifying four classes of increasing severity. A total of 87(60%) patients presented a normal/mildly abnormal ECG, whereas 58(40%) had moderate or severe ECG alteration. Eight(6%) patients had ≥1 misplaced electrode. D-Heart 8-Lead and 12‑lead ECGs concordance according to Cohen's weighted kappa test was 0,948 (p<0,001, agreement of 97.93%). Concordance was high for the Romhilt-Estes score (kw=0,912; p<0.01). Concordance between D-Heart 12-Lead ECG and standard 12-Lead ECG was perfect (kw=1). PR and QRS intervals measurements comparison with Bland-Altman method showed good accuracy (95% limit of agreement ±18ms for PR and±9ms for QRS). D-Heart 8/12-Lead ECGs proved accurate, allowing an assessment of ECG abnormalities comparable to the standard 12‑lead ECG in patients with HCM. The image processing algorithm provided accurate electrode placement, standardizing exam quality, potentially opening perspectives for layman ECG screening campaigns.

Poster graphic design with your Eyes: An approach to automatic textual layout design based on visual perception

The layout of graphic design can betedious, especially for non-professional users who require additional time. Therefore, this paper proposes a graphic design layout method based on visual perception. First, a graphic design dataset is trained based on a fully convolutional network, and a visual saliency prediction model is established to predict the visual saliency of the background image. Second, a probability density map of the background image is obtained using a diffusion equation. Third, based on a generation algorithm for a text anchor box, anchor boxes of different positions, sizes, and lengths are obtained. The experimental results show that, compared with previous work, the text position and size obtained by this method have higher correlations with the baseline.

3D-CariGAN: An End-to-End Solution to 3D Caricature Generation from Normal Face Photos.

Caricature is a kind of artistic style of human faces that attracts considerable attention in entertainment industry. So far a few 3D caricature generation methods exist and all of them require some caricature information (e.g., a caricature sketch or 2D caricature) as input. This kind of input, however, is difficult to provide by non-professional users. In this paper, we propose an end-to-end deep neural network model that generates high-quality 3D caricature directly from a simple normal face photo. The most challenging issue in our system is that the source domain of face photos (characterized by 2D normal faces) is significantly different from the target domain of 3D caricatures (characterized by 3D exaggerated face shapes and texture). To address this challenge, we (1) build a large dataset of 6,100 3D caricature meshes and use it to establish a PCA model in the 3D caricature shape space, (2) reconstruct a 3D normal full head from the input face photo and use its PCA representation in the 3D caricature shape space to set up correspondence between the input photo and 3D caricature shape, and (3) propose a novel character loss and a novel caricature loss based on previous psychological studies on caricatures. Experiments including a novel two-level user study show that our system can generate high-quality 3D caricatures directly from normal face photos.

A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform

ABSTRACT Spatiotemporal residual noise in terrestrial earth observation products, often caused by unfavorable atmospheric conditions, impedes their broad applications. Most users prefer to use gap-filled remote sensing products with time series reconstruction (TSR) algorithms. Applying currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage resources. This study introduces a new open-source software package entitled ‘HANTS-GEE’ that implements a well-known and robust TSR algorithm, i.e. Harmonic ANalysis of Time Series (HANTS), on the Google Earth Engine (GEE) platform for scalable reconstruction of terrestrial earth observation data. Reconstruction tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on GEE. According to site-based and regional-based case evaluation, the new tool can effectively eliminate cloud contamination in the time series of earth observation data. Compared with traditional PC-based HANTS implementation, the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated sites. The HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume, promoting algorithm usage by much broader user communities. To our knowledge, the software package is the first tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.