Categories Of Users Research Articles

A comprehensive approach to ensuring traffic safety by the police: modern European experience

The article is devoted to the problems of reducing the level of traffic injuries and ensuring road safety. The level of road traffic injuries on the roads of Ukraine indicates the need to improve the activities of the police and the state as a whole in this area, to make efforts in search of new strategies to increase the level of road safety and to implement effective and relevant technologies and measures, even in wartime conditions. The purpose of this article is to highlight and analyze modern European experience in ensuring traffic safety by the police. The article provides individual examples of modern approaches and measures to ensure road safety in the countries of the European Union (Germany, Poland, Sweden). As European experience shows, ensuring road traffic safety is not only measures of a regulatory and legal nature. The conditions for the effective influence of the police authorities on the state of safety in the field of road traffic are comprehensiveness in the combination of the mentioned measures with other modern informational and educational companies and the introduction of various modern technological innovations. It has been established that the achievement of significant progress in solving accident problems and increasing the effectiveness of police work in the specified area is due to a complex combination of a positivist (normative) approach with a multi-vector variety of social, organizational, technical measures and innovative methods of influencing the lawful behavior of various categories of road users . The study proves that the level of road deaths is lower not only in countries with stricter sanctioning systems, but also with more qualified police personnel and effective interaction between the police and other stakeholders in road safety. Taking into account the European experience of the police, promising directions for comprehensive solutions to road safety problems in our country have been outlined.

ICT for All: Where Do We Stand?

Although its importance is undeniable, designing in a more inclusive way is not yet fully adopted in the field of design and planning, whose reference continues to be the standard man. An approach which not only excludes people with disabilities, but also other categories that diverge from the physical and cognitive characteristics of the standard human model, such as women, the elderly, and children. This problem affects different contexts and can be observed especially in the area of Information and Communication Technologies (ICTs), which are often designed without taking into account the peculiarities that distinguish these categories of users. Referring to the categories affected by the digital divide, the article reflects on the need to promote specific methodologies, such as Universal Design and User-centered Design, so that attitudinal and psychological issues related to different categories of users are considered.

Freedom of contract: prerequisites for development in the conditions of digitalization of the economy

Digital technologies influenced the development of civil-law relations in the field of trade, financial, educational, consulting services, entertainment, etc., leading to the emergence of remote forms of offer and acceptance. The development of "business on the network" initiated the practice of applying the principle of freedom of contract by participants in civil-law relations in this renewed capacity. The acquired experience needs to be studied and understood in order to take into account in the legislation the needs of participants in civil-law relations in the world of Internet technologies. The purpose of the paper is to study the prerequisites for the development of the principle of freedom of contract in the digitalized economy. Based on the research materials, it was determined that the main prerequisites for the development of the principle of freedom of contract in the conditions of digitalization of the economy are: 1) increasing the availability of goods, works and services that are ordered (provided) through network technologies; 2) individualization of the provisions of the public offer by e-commerce entities in order to achieve more favorable conditions; 3) provision of timely and complete information to the counterparty before accepting the offer. The results of research into the activities of e-commerce entities show that there is a widespread practice of individualizing essential conditions for categories of unregistered users, which differ based on the frequency of visits to the entity's website, their geolocation, or other technological parameters. Considering the fact that the subjects act on the basis of a public offer (public contract), this practice contradicts the requirements of civil legislation. However, taking into account the requirements of reasonableness and justice, which are the basis of the principle of freedom of contract, we consider it appropriate to consider such a practice (if such an entity operate in a competitive market) as appropriate. We consider it justified to place on the side of the contract developer of the public offer an additional duty to assist the other party in conscious participation in contractual relations. Such an obligation as a legal means of ensuring freedom of will is necessary to ensure the good faith of counterparties in the digital economy.

Hierarchical Wi-Fi Trajectory Embedding for Indoor User Mobility Pattern Analysis

The recent advances in smart building technologies have enabled us to collect massive Wi-Fi network based trajectory data, which provide an unparalleled opportunity for understanding the indoor user mobility pattern and enabling a wide range of business applications. While some previous studies have explored the Wi-Fi positioning of users, there still lacks a systematic and effective solution for indoor user mobility pattern analysis based on Wi-Fi trajectory data. To this end, in this paper, we propose a unified framework for modeling Wi-Fi trajectory data, namely HWTE, which can empower various tasks of indoor user mobility pattern analysis, such as user classification, next location prediction and schedule estimation. Specifically, we first propose a session trajectory construction module to extract the spatio-temporal semantic information from the Wi-Fi trajectories of users. Then, we devise a pre-training module to learn the unified representation of Wi-Fi trajectories. In particular, a session position embedding technique and a position query task is introduced to enhance the representation ability of the whole trajectory. Moreover, we further propose a hierarchical Transformer-based fine-tuning module to support various application tasks with time and space efficiency. Finally, we validate our framework on a real-world dataset with all three kinds of downstream tasks.

Pedestrian Crossings as a Means of Reducing Conflicts between Cyclists and Pedestrians in Shared Spaces

One significant and simultaneously interesting problem in urban mobility has to do with the study of shared spaces where various categories of users coexist and act together. This paper aims to examine the behavior and preferences of pedestrians and cyclists, who both coexist in a shared space infrastructure along the seafront (which has a length of around 4.0 km) of the city of Thessaloniki, Greece. Furthermore, the problems caused by the coexistence, such as at the locations where there are pedestrian crossings on the bicycle lane, are recorded and evaluated. Traffic calming measures aimed at improving the existing situation in terms of safety and comfort for both pedestrians and cyclists are also explored. Data were collected through a web-based questionnaire survey, which was distributed via email to students and employees of Aristotle University of Thessaloniki. A total of 1194 questionnaires were collected in the framework of the survey during the year 2021, including responses from both pedestrians and cyclists. The questionnaires were analyzed through the use of descriptive and inferential statistics; the latter method suggested several significant differences in how each group of users (pedestrians or cyclists) perceived their behavior compared with the other. Latent variable and path models were estimated to investigate the behavior and attitude of users towards the crossings, examined as a function of their perception towards the other group; perception about the benefits of the infrastructure; preference for additional interventions; and overall opinion about the quality of the shared space area. The results suggest that forms of aggressive behavior, preference towards using the crossings, and the perceived safety are affected by the aforementioned factors. The results of this study can inform decision takers and decision makers in the area of land use regarding policy recommendations for facilitating interactions between pedestrians and cyclists in shared spaces.

Active and passive safety systems (not only) for senior citizens : Systems to increase road safety

Due to a constantly increasing life expectancy and the need to stay mobile at a high age, the proportion of over 65-year-olds in traffic accidents is increasing from year to year. In order to identify potentials for increasing the safety of seniors in road traffic, accident data were analyzed with respect to the categories of road users and accident types of this age group. Based on the accident data analysis, active and passive safety systems are described that could increase the road safety particular for senior citizens. It was found that older road users are frequently involved in accidents as car occupants, cyclists and pedestrians. In addition, car drivers and cyclists aged 65 years and older are frequently involved in driving, turning and crossing accidents. Lane departure warning and emergency braking assistants have ahigh potential for accident avoidance as they can defuse critical situations at the last moment. Restraint systems (airbags, seat belts) adapted to physical characteristics could minimize the severity of injuries to older car occupants.

Predicting Future Location Categories of Users in a Large Social Platform

Understanding the users' patterns of visiting various location categories can help online platforms improve content personalization and user experiences. Current literature on predicting future location categories of a user typically employs features that can be traced back to the user, such as spatial geo-coordinates and demographic identities. Moreover, existing approaches commonly suffer from cold-start and generalization problems, and often cannot specify when the user will visit the predicted location category. In a large social platform, it is desirable for prediction models to avoid using user-identifiable data, generalize to unseen and new users, and be able to make predictions for specific times in the future. In this work, we construct a neural model, LocHabits, using data from Snapchat. The model omits user-identifiable inputs, leverages temporal and sequential regularities in the location category histories of Snapchat users and their friends, and predicts the users' next-hour location categories. We evaluate our model on several real-life, large-scale datasets from Snapchat and FourSquare, and find that the model can outperform baselines by 14.94% accuracy. We confirm that the model can (1) generalize to unseen users from different areas and times, and (2) fall back on collective trends in the cold-start scenario. We also study the relative contributions of various factors in making the predictions and find that the users' visitation preferences and most-recent visitation sequences play more important roles than time contexts, same-hour sequences, and social influence features.

Road User Detection on Polarimetric Pre-CFAR Radar Data Level

Perceiving the road environment as robust and complete as possible is a fundamental requirement on the way to fully automated driving. Crucial components of perception include detection and classification of road users as well as estimating their extensions and orientation. Using data from a newly available automotive polarimetric radar, this work presents a neural network model using pre-CFAR data as input to detect road users and additionally predict their oriented bounding box. The model is trained to detect static and dynamic road users in diverse urban and rural scenarios on a large data set. A major improvement in detection performance is shown when comparing against CFAR based detection approaches. Additionally, the benefit of polarimetric information is evaluated by optimizing the model on two representations of polarimetric information and comparing it to a model on data without polarimetric information. Results show further promising performance increases when polarimetric information is available.

More Games, More Playtime

The global electronic gaming market has grown significantly, with 3.198 billion players generating a revenue of $196.8 billion in 2022. Gaming companies must understand their users and keep them engaged to succeed in this market. This article examines whether Steam, one of the top gaming enterprises, truly understands its users. Using one year's worth of data from 12393 players who made 200,000 purchases and played games on Steam, we conducted a k-means clustering analysis to identify four distinct user categories. Based on our findings, we suggest personalized services that Steam can offer to improve user stickiness.

Unified Framework for Identity and Imagined Action Recognition From EEG Patterns

We present a unified deep learning framework for the recognition of user identity and the recognition of imagined actions, based on electroencephalography (EEG) signals, for application as a brain–computer interface. Our solution exploits a novel shifted subsampling preprocessing step as a form of data augmentation, and a matrix representation to encode the inherent local spatial relationships of multielectrode EEG signals. The resulting image-like data are then fed to a convolutional neural network to process the local spatial dependencies, and eventually analyzed through a bidirectional long-short term memory module to focus on temporal relationships. Our solution is compared against several methods in the state of the art, showing comparable or superior performance on different tasks. Specifically, we achieve accuracy levels above 90% both for action and user classification tasks. In terms of user identification, we reach 0.39% equal error rate in the case of known users and gestures, and 6.16% in the more challenging case of unknown users and gestures. Preliminary experiments are also conducted in order to direct future works toward everyday applications relying on a reduced set of EEG electrodes.

Evaluation of Different Methods and Models for Grass Cereals’ Production Estimation: Case Study in Wheat

Adequate management of agricultural crops requires, among other things, accessible and sufficiently accurate methods for assessing plant nutrition and crop vegetation status and for agricultural production estimation. Sustainable technologies are based on correct decisions, prompt interventions and appropriate works, and correct information in real time, and the obtaining information methods can be simple, accessible, and appropriate in relation to different user categories (e.g., farmers, researchers, decision makers). This study used mineral fertilization (NPK), with 11 experimental variants, to ensure a controlled differentiated nutrition of the wheat plants, “Alex” cultivar. Regression analysis was used to obtain models in estimating wheat production, by methods based on: (a) NPK fertilizers applied (F) in the 11 experimental variants; (b) physiological indices (PI), represented by the chlorophyll content (Chl), and plant nutrition status on the experimental variants, in terms of macroelement content in the leaves, evaluated by foliar diagnosis (Nfd, Pfd, Kfd); (c) imaging analysis (IA) based on digital images of the wheat experimental variants, and calculated indices. A set of models was obtained, with different precision levels and statistical safety: R2 = 0.763, p = 0.013 for the model based on applied fertilizers (NPKF); R2 = 0.883, p < 0.01 for the model based on foliar diagnosis (NPKfd); R2 = 0.857, p < 0.01 for the model based on chlorophyll content (Chl); R2 = 0.975, p < 0.01 for the model based on normalized rgb color parameters (RGB color system); R2 = 0.925, p < 0.01 for the model based on the DGCI calculated index. The model based on applied fertilizers (F model) was tested in relation to wheat production data, for a period of six years, communicated by other studies. Fit degree analysis between predicted yield based on the F model and real yield (six-year average) was confirmed by R2 = 0.717, compared to R2 = 0.763 for the F model in this study. The models obtained in this study, related to the “Alex” wheat cultivar, can be used for other studies, but with a certain margin of error, given the coefficient values, specific to the obtained equations. The approach concept, methods, and models presented can be opportunities for other studies to facilitate their comparative analysis, their adaptation, and/or development in the form of new models that are useful in different studies, research, or agricultural practices, for their integration into crop management strategies.

Expecting the unpredictable: Categorisation of children and youth during driver training

This paper presents an analysis of how the particular categories of children and youth are used within the instructional work of learning to drive. Using Membership Categorisation Analysis (MCA) and multimodal Conversation Analysis (CA) of a collection of cases drawn from 85 video-recorded driving lessons, we demonstrate how the participants treat children and youth as a category of traffic users whose main category predicate appears to be their expected unpredictability and carelessness, placing particularly high demands on drivers’ awareness and caution. This is evident in in-event and post-event interactions about traffic encounters with children and youth, as well as in traffic contexts where they have not (yet) been spotted but their sudden appearance is anticipated. The results suggest that the institutional constructions of children and youth as a potential source of trouble prepare trainee drivers for unforeseen events and contingencies and shape their social stock of knowledge as future motorists.

Investigating the potential of groundwater recycling as an alternative to groundwater trading in terms of resource efficiency

Water and in particular groundwater is undoubtedly a natural resource of fundamental importance to humanity. However, their limited availability and the large increase in demand observed in recent years call for its optimal management. In this context, tradable water rights systems and the circular economy of water, which promotes water recycling, are two concepts on which management policies towards sustainable and efficient water management can be based. This paper attempts to highlight the potential that a management policy based on the circular economy of water can have against a policy based on tradable water rights in terms of economic efficiency of the resource. Thus, the research hypothesis of this paper is that under certain conditions water recycling can lead to higher economic efficiency for the resource than water trading can. For this purpose, two time-dynamic models are formulated, one for each different management policy, concerning the pumping of water from an aquifer by two different categories of users, and the corresponding optimal control problems are solved. The results of the simulations based on hydro-economic data of a region in Northern Greece show that there is a high dependence of the efficiency of the water recycling system on the recycled water tariff. Thus, it is concluded that for charging prices up to and beyond 0.20€/m3 a management policy of an aquifer based on the concept of circular economy can lead to a higher economic efficiency of water.

Cash rich to cashless market: Segmentation and profiling of Fintech-led-Mobile payment users

Considering the growing prominence of the Fintech-led-Mobile-Payment (FMP) platform, it has become imperative for practitioners and academicians to look beyond technology adoption to understand causal relationships. Building on the premise, previous literature primarily examined the causal relationships to describe the phenomenon of evolving digital payment technology in a varied context. However, limited studies explored user classification and profiling with some exceptions, principally stressing upon barely online and mobile-based banking segmentation contexts.Grounded in analyzing user taxonomy based on benefits-trust-behavioral linkage model from the purview of an emerging mobile-based digital payment market, this research aims to identify and test an a-priori approach to market segmentation of FMP platform users and profiling them based on perceived benefits, behavioral, and socio-demographic characterization. To analyze the model of users' classification, 550 valid online responses were received and employed a blended analytical approach of multivariate techniques (CFA, cluster analysis, discriminant function analysis) along with the bivariate technique of the Chi-square test for clustering procedures followed by resulting clusters' validation and their profiling. The three distinct segments are identified as ‘The Fintech Dubious,’ ‘The Cash Conservatives, ‘and ‘The Fintech Enthusiasts.’ The study also advances the current theoretical understanding of the trust based modified version of UTAUT with the associative framework of ‘perceived benefits-attitude, and intention’ by adding two additional cognitive constructs of perceived trust and perceived price value along with added socio-demographics (usage frequency and geographical area). Lastly, this timely study offers several unflinching implications for the practitioners of strategic marketing and policymakers for encouraging FMP usage.

The Visual Behaviour of the Cyclist: Comparison between Simulated and Real Scenarios

Cyclists are one of the main categories of road users particularly exposed to accident risk. The increasing use of this ecological means of transport requires a specific assessment of cyclist safety in terms of traffic flow and human factors. In this study, a particular visual tracking tool has been used to highlight not only the main critical points of the infrastructure, where a high level of distraction is recorded, but also the various interactions with different road users (pedestrians, vehicles, buses, wheelchairs, cyclists). To confirm the critical aspects of the infrastructure and the trend of workload, a similar circuit was reproduced in a bicycle simulator, which also allowed a meaningful comparison of cycling behaviour. The innovative component of this paper is a comparison between a real test, held in Stockholm, and a simulator where the same scenario has been represented, in order to highlight the objective differences in behaviour. The cycling performance was also evaluated both from an objective point of view, with the count of frames related to each category of visualization, and from a subjective one, through the questionnaires. The results show the crossing as a critical aspect because only 4/3% fixation is required for both simulated and real tests to confirm the significance of the comparison between the two experiments. The high attention rate, resulting from frame-by-frame analysis, also points to a clear difference in the perception of users, who feel with a low workload.

Status, Challenges and Trends in Videoconferencing Platforms

Although videoconferencing platforms were a growing market even before the Covid-19 pandemic, they became very popular and widely used within weeks of the beginning of the pandemic. The compulsion of social distancing, lockdown, the need to work from home have led to an exponential demand for videoconferencing applications and an adaptation of the way we carry out our current activities such as work, decision-making, communicating with family and colleagues, or education. With the diversification of the categories of users of videoconferencing systems and the contexts in which they were used, many other needs have emerged and the demand to adapt these platforms, both in terms of ease of use and functionality, as well as security. Videoconferencing systems are no longer meant only for decision-makers or trainers, but also for general public. This paper reviews various data regarding the use of videoconferencing systems in recent years, a number of problems encountered by users, and expected trends in the future.

A Machine Learning Based Methodology for Load Profiles Clustering and Non-Residential Buildings Benchmarking

Buildings benchmarking based on their electric profiles is a fundamental step to identify, evaluate and then possibly implement energy efficiency oriented actions. Indeed, benchmarking enables comparison among peer buildings or industrial sites and the identification of reference cases, either efficient and inefficient ones. In this regard, temporal data clustering is an effective and widely applicable benchmarking tool. In this work, we propose a novel Machine Learning based methodology, taking advantage of two fundamental tools, namely a decomposition algorithm and a clustering one. Several clustering algorithms have been tested to identify k-Means as the most suitable one. The proposed methodology includes the evaluation of energy Key Performance Indicators for effective analysis and comparison of buildings. The proposed framework has been tested on a real-world case study including around 2000 non-residential buildings. The classification of buildings based on K-Means achieved an accuracy of 99.7% with respect to their usage category. Furthermore, reference Key Performance Indicator values for each cluster are obtained and discussed to understand buildings' energy behaviour and possible reasons for inefficiencies.

User Classification Based On Mouse Dynamic Authentication Using K-Nearest Neighbor

User Classification Based On Mouse Dynamic Authentication Using K-Nearest Neighbor

An Efficient Approach to Manage Natural Noises in Recommender Systems

Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates that natural noises may exist in the historical ratings and mislead the recommendation results. To deal with natural noises, different methods have been proposed, such as directly removing noises, correcting noise by re-predicting, or using additional information. However, these methods introduce some new problems, such as data sparsity and introducing new sources of noise. To address the problems, we present a new approach to managing natural noises in recommendation systems. Firstly, we provide the detection criteria for natural noises based on the classifications of users and items. After the noises are detected, we correct them with threshold values weighted by probabilities. Experimental results show that the proposed method can effectively correct natural noise and greatly improve the quality of recommendations.

24 h mortality and its predictors among road traffic accident victims in a resource limited setting; a multicenter cohort study

IntroductionThe incidence of road traffic accidents (RTAs) is on the rise contributing to the global burden of mortality as a major global health threat. It has been estimated that 93% of RTAs and more than 90% of the resulting deaths occur in low and middle income countries. Though death due to RTAs has been occurring at an alarming rate, there is paucity of data relating to incidence and predictors of early mortality. This study was aimed at determining the 24 h mortality and its predictors among RTA patients attending selected hospitals in western Uganda.MethodsThis was a prospective cohort that consecutively enrolled 211 RTA victims admitted and managed in emergency units of 6 hospitals in western Uganda. All patients who presented with a history of trauma were managed according to the advanced trauma life support protocol (ATLS). The outcome regarding death was documented at 24 h from injury. Data was analyzed using SPSS version 22 for windows.ResultsMajority of the participants were male (85.8%) aged 15–45 years (76.3%). The most common road user category was motorcyclists (48.8%). The 24 h mortality was 14.69%. At multivariate analysis, it was observed that a motorcyclist was 5.917 times more likely to die compared to a pedestrian (P = 0.016). It was also observed that a patient with severe injury was 15.625 times more likely to die compared to one with a moderate injury (P < 0.001).ConclusionThe incidence of 24 h mortality among road traffic accident victims was high. Being motorcycle rider and severity of injury according to Kampala trauma score II predicted mortality. Motorcyclists should be reminded to be more careful while using the road. Trauma patients should be assessed for severity, and the findings used to guide management since severity predicted mortality.