User Profiles Research Articles

Socio-demographic and clinical profile of opoid users

IntroductionOpioid use disorder is a pattern of problematic opioid use, leading to impaired functioning or clinically significant suffering. Morocco, a pioneer in the Arab world in the field of opiate substitution, is no exception to this rule, and has found itself confronted with a situation where opiate use is much more widespread in the north of the country. Morocco’s geographical proximity to Europe and the multiple interactions fostered by migratory population flows undoubtedly contribute not only to the spread of hard drug use, particularly heroin, but also to the diversification of consumption methods (injection drugs)Objectives The main objective of our work was to study socio-demographic and clinical profile of opoid users in morocco, but also their quality of life after treatment in Morocco, before concluding with recommendations for improving the overall management of the patient.MethodsWe conducted a cross-sectional, analytical study in the Addictology Department at Ar-razi Hospital in Salé, which provides oral methadone substitution therapy for around 80 patients.ResultsThe total number of patients responding to the questionnaire was 60 participants.The population of methadone-treated patients in our study was 83.33% (n = 50) male and 16.67% female (n = 10).The most common age group in our study was between 31 and 45 (71.67%). 36.67% were married (n=22), 80% (n=48) lived with their family, 83.34% (n=50) had a secondary school education or higher, while the vast majority 63.33% (n=38) had no fixed occupation.96.7% (n=58) of participants were of Moroccan nationality, against only 3.3% who were foreigners (n=2).The main indication for methadone withdrawal in our patients was heroin use (66.67%), followed by Codeine, then Tramadol. The daily doses of methadone delivered ranged from 04 to 200 mg/patient, with an average of 75 mg.The main adverse effects reported by our patients were libido disturbance, constipation, fatigue and sleep disturbance.63.33% (n=38) of patients continued to use other psychoactive substances on a regular basis, mainly tobacco, followed by cannabis.13.33% (n=8) reported persistent craving, and the vast majority claimed to be supported by a family member (70%, n=40).ConclusionsFor several years, quality of life has been a major preoccupation of healthcare professionals in a bio-psycho-social approach. In this vision of care, quality of life should now be part of the clinical criteria for monitoring patients on methadone.Disclosure of InterestNone Declared

Children’s behavior at metaverses: Interactions, digital identities, and parent’s perceptions

Children are considered one of the main user profiles of metaverses, since they get into those digital universes by playing sandbox video games as Fortnite, Minecraft, or Roblox. However, a lack of research focused on the how, why and outcomes of this vulnerable target in metaverses has been noted. This study aims to investigate the behavior of children in the metaverses through their use of video games and the perceptions of their parents, considering them an indirect target. A total of 31 semi-structured interviews were conducted with children aged between six and 12 years old and their parents. Also, a non-participatory observation of five children while they were playing video games in their domestic environments were carried out. Results show that children play sandbox video games to have both online and offline presence, interacting with familiar people and strangers to feel connected to the community. Also, they recognize brands in those universes and the importance of designing avatars. For its part, parents identify some negative outcomes of video games, but their perception is mainly positive, above all in terms of collaboration and participation. A dichotomy between observation findings and parents’ response has been noted, reflecting a need of digital literacy in terms of communication.

3-Methoxy-Phencyclidine Induced Psychotic Disorder: A Literature Review and an 18F-FDG PET/CT Case Report.

New Psychoactive Substances (NPS) are modifying the drug scenario worldwide and have become a public health concern because of their toxicological profiles and their harmful physical/psychological effects. 3-Methoxy-Phencyclidine (3-MeO-PCP), a non-competitive antagonist of glutamate N-methyl-D-aspartate (NMDA) receptors, belongs to the phencyclidine-like subfamily of arylcyclohexylamines and has gained attention for its toxic, sometimes fatal, effects. Despite several cases of intoxication and death reported in the literature, little is known about substance-induced psychotic disorders (SIP) and potential cognitive impairment following 3-MeO-PCP intake. This literature review aimed to summarize available evidence about 3-MeO-PCP mechanisms of action and physical and psychotropic effects and to spread preliminary findings about persistent psychotic symptoms and impaired cognitive functioning. Additionally, the case of an SIP is reported in a 29-year-old man with small oral intakes of 3-MeO-PCP over two weeks until a high dose ingestion. Psychometric and neuropsychological assessment and brain [18F]-fluorodeoxyglucose positron emission tomography integrated with computed tomography were used to support clinical description. Identifying and addressing the characteristic clinical features and neural substrates of NPS-induced psychoses might help clinicians with a more precise differentiation from other psychotic disorders. Although further studies are required, phenotyping the cognitive profile of NPS users might provide targets for tailored therapeutic approaches.

Social Net Secure using Natural Language Processing and Machine Learning

Abstract: In our contemporary era, social networking platforms have seamlessly integrated into the daily lives of the majority of individuals. With each passing day, a multitude of users create profiles on these platforms, fostering connections with others irrespective of geographical barriers or time constraints. While these platforms offer invaluable benefits to users, they also present inherent security challenges, particularly concerning the safeguarding of users' personal data. Addressing the risks associated with social networking platforms necessitates a systematic analysis of user profiles to discern potential threats. Through classification, we aim to distinguish between authentic and fraudulent profiles within these networks. Although various classification methods exist for detecting fake profiles, achieving optimal precision rates remains a formidable task. To enhance the precision rate of fake profile detection within social systems, this paper proposes the utilization of advanced Machine Learning (ML) and Natural Language Processing (NLP) techniques. Leveraging algorithms such as Support Vector Machine (SVM) and Naïve Bayes, we endeavor to refine the accuracy of identifying fraudulent profiles.

Fake Social Media Profile Detection and Reporting Using Machine Learning

Our research focuses on utilizing machine learning techniques, encompassing natural language processing and computer vision, to create an automated system for the detection and reporting of fake social media profiles across various platforms. Our approach involves feature extraction from both textual and visual content, followed by the application of machine learning models to classify profiles as fake or genuine. This system operates in real-time, monitoring user activity and promptly flagging suspicious profiles for user- initiated reporting. By combining the power of machine learning with cross-platform compatibility and user feedback, our solution aims to enhance online safety by swiftly identifying and addressing fraudulent social media profiles, thus fostering more secure and trustworthy online communities.

Social Media and Social Networking

A Social networking service (also social networking site, SNS or social media) is an online platform that is used by people to build social networks or social relations with other people who share similar personal or career interests, activities, backgrounds or real-life connections. The variety of stand-alone and built-in social networking services currently available in the online space introduces challenges of definition; however, there are some common features: (1) social networking services are Web 2.0 internet-based applications, (2) user-generated content (UGC) is the lifeblood of SNS organisms, (3) users create service-specific profiles for the site or app that are designed and maintained by the SNS organization, and (4) social networking services facilitate the development of online social networks by connecting a user's profile with those of other individuals and/or groups. Most social network services are web-based and provide means for users to interact over the Internet, such as by e-mail and instant messaging and online forums. Social networking sites are varied and they incorporate a range of new information and communication tools such as availability on desktop and laptops, mobile devices such as tablet computers and smart phones, digital photo/video/sharing and "web logging" diary entries online (blogging). Online community services are sometimes considered a social network service, though in a broader sense, social network service usually means an individual-centered service whereas online community services are group-centered. Social networking sites allow users to share ideas, digital photos and videos, posts, and inform others about online or real world activities and events with people in their network. While in-person social networking, such as gathering in a village market to talk about events has existed since the earliest developments of towns, the Web enables people to connect with others who live in different locations, ranging from across a city to across the world. Depending on the social media platform, members may be able to contact any other member. Key words : Social Networking, e-mail, Web-based, Social Media, Face Book .

Combining Machine Learning and Lifetime-Based Resource Management for Memory Allocation and Beyond

Memory management is fundamental to the performance of all applications. On modern server architectures, an application's memory allocator needs to balance memory utilization against the ability to use 2MB huge pages, which are crucial for achieving high performance. This paper shows that prior C++ memory allocators are fundamentally limited because optimizing this trade-off depends on the knowledge of object lifetimes, which is information allocators lack. We introduce a two-step approach to attain high memory utilization in huge pages. We first introduce a novel machine-learning approach that predicts the lifetime of freshly allocated objects using the stack trace at the time of allocation and treats stack traces as natural language. We then present a fundamentally new type of memory allocator that exploits (potentially incorrect) object lifetime predictions to achieve high memory utilization at full huge page usage. In contrast to prior memory allocators that organize their heap around size classes and free lists, our allocator organizes the heap based on predicted lifetime classes and adjusts to mispredictions on the fly. We demonstrate experimentally that this learned lifetime-aware memory allocator (LLAMA) reduces fragmentation with huge pages by up to 78%. Our approach gives rise to a new methodology for applying ML in computer systems. In addition, similar space-time bin packing problems abound in computer science and we discuss how this approach has applications beyond memory allocation to a wide range of problems.

Generation of Visual Representations for Multi-Modal Mathematical Knowledge

In this paper we introduce MaRE, a tool designed to generate representations in multiple modalities for a given mathematical problem while ensuring the correctness and interpretability of the transformations between different representations. The theoretical foundation for this tool is Representational Systems Theory (RST), a mathematical framework for studying the structure and transformations of representations. In MaRE’s web front-end user interface, a set of probability equations in Bayesian Notation can be rigorously transformed into Area Diagrams, Contingency Tables, and Probability Trees with just one click, utilising a back-end engine based on RST. A table of cognitive costs, based on the cognitive Representational Interpretive Structure Theory (RIST), that a representation places on a particular profile of user is produced at the same time. MaRE is general and domain independent, applicable to other representations encoded in RST. It may enhance mathematical education and research, facilitating multi-modal knowledge representation and discovery.

LLMRG: Improving Recommendations through Large Language Model Reasoning Graphs

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.

Planning shared mobility hubs in European cities: A methodological framework using MCDA and GIS applied to Barcelona

In the move towards sustainable urban mobility through seamless intermodality, European cities are faced with the possibility of implementing shared mobility systems. They constitute an opportunity to create new urban hubs, considered as nodes of intermodality and places of urban intensity. In order to effectively plan the future locations of shared mobility hubs, this paper outlines the methodological framework that sustains the design of a new decision-support tool, the so-called Mcda2MobilityHub (M2MHub),11Link to the M2MHub tool, which is available as an open plugin for QGIS: https://github.com/natrouk/Mcda2MobilityHub-desicion-support-tool_QGIS-plugin. using Multi-Criteria Decision Analysis (MCDA) and Geographic Information Systems (GIS). For this purpose, the analysis of existing literature and interviews with local authorities and transport operators, led to the identification of six main location criteria (mobility, compactness, diversity of uses, profile of potential users, spatial configuration and environment). The criteria weights were calculated using the Analytic Hierarchy Process (AHP) and the framework was applied to the case study of the city of Barcelona. The results obtained are presented in the form of heat maps, highlighting the flexibility of the decision-support tool to transform open data into appropriate indicators and detailed maps. This output can be used to support future policies for planning shared mobility hubs as places for convenient multimodal transfers, which also enable social activities and improve the quality of life in European cities.

Enhancing Job Recommendation through LLM-Based Generative Adversarial Networks

Recommending suitable jobs to users is a critical task in online recruitment platforms. While existing job recommendation methods encounter challenges such as the low quality of users' resumes, which hampers their accuracy and practical effectiveness.With the rapid development of large language models (LLMs), utilizing the rich external knowledge encapsulated within them, as well as their powerful reasoning capabilities, is a promising way to complete users' resumes for more accurate recommendations. However, directly leveraging LLMs to enhance recommendation results is not a one-size-fits-all solution, as LLMs may suffer from fabricated generation and few-shot problems, which degrade the quality of resume completion. In this paper, we propose a novel LLM-based approach for job recommendation. To alleviate the limitation of fabricated generation for LLMs, we extract accurate and valuable information beyond users' self-description, which helps the LLMs better profile users for resume completion. Specifically, we not only extract users' explicit properties (e.g., skills, interests) from their self-description but also infer users' implicit characteristics from their behaviors for more accurate and meaningful resume completion. Nevertheless, some users still suffer from few-shot problems, which arise due to scarce interaction records, leading to limited guidance for high-quality resume generation. To address this issue, we propose aligning unpaired low-quality with high-quality generated resumes by Generative Adversarial Networks (GANs), which can refine the resume representations for better recommendation results. Extensive experiments on three large real-world recruitment datasets demonstrate the effectiveness of our proposed method.

Domestic Cartographies: A Post-Occupancy Ethnographic Assessment of Barcelona’s Social Housing Strategies, 2015–2023

The lack of affordable housing remains a major problem in Spain. Following the decline in public and affordable housing production caused by the economic, political, and social crisis of 2008, efforts to produce public housing were reactivated in the mid-2010s, gaining increasing importance. In Barcelona, housing policies have played a central role in recent political discourse, particularly with the tenure of housing rights activist Ada Colau (2015–2023). With traditional approaches failing to address the housing emergency, the local government introduced five new procurement strategies to increase the affordable housing stock. These involve new forms of council housing, delegated developments, limited-profit investments, zero-equity housing cooperatives, and urban refurbishment. This article uses a mixed methods approach to analyse these strategies. The analysis spans all design phases, from inception to construction, and includes post-occupancy evaluations. Methods include typological analysis, expert interviews, and spatial performance analysis using ethnographic methods and inhabitant interviews. The results evidence the importance of diversifying procurement models, tailoring approaches to different user profiles, and enhancing emerging opportunities by including new stakeholders in the development process.

Preact to lower the risk of falling by customized rehabilitation across Europe: the feasibility study protocol of the PRECISE project in Italy.

Falls are a major worldwide health problem in older people. Several physical rehabilitation programs with home-based technologies, such as the online DigiRehab platform, have been successfully delivered. The PRECISE project combines personalized training delivered through the application with an artificial intelligence-based predictive model (AI-DSS platform) for fall risk assessment. This new system, called DigiRehab, will enable early identification of significant risk factors for falling and propose an individualized physical training plan to attend to these critical areas. The study will test the usability of the DigiRehab platform in generating personalized physical rehabilitation programs at home. Fifty older adults participants will be involved, 20 of them testing the beta version prototype, and 30 participants testing the updated version afterwards. The inclusion criteria will be age ≥65, independent ambulation, fall risk (Tinetti test), Mini Mental State Examination ≥24, home residents, familiarity with web applications, ability and willingness to sign informed consent. Exclusion criteria will be unstable clinical condition, severe visual and/or hearing impairment, severe impairment in Activities of Daily Living and absence of primary caregiver. The first part of the screening consists in a structured questionnaire of 10 questions regarding the user's limitations, including the risk of falling, while the second consists in 10 physical tests to assess the functional status. Based on the results, the program will help define the user's individual profile upon which the DSS platform will rate the risk of falling and design the personalized exercise program to be carried out at home. All measures from the initial screening will be repeated and the results will be used to optimize the predictive algorithms in order to prepare the tool in its final version. For the usability assessment, the System Usability Scale will be administered. The follow-up will take place after the 12-week intervention at home. A semi-structured satisfaction questionnaire will also be administered to verify whether the project will meet the needs of older adults and their family caregiver. We expect that personalized training prescribed by DigiRehab platform could help to reduce the need for care in older adults subjects and the care burden.Clinical trial registration: [https://clinicaltrials.gov/], identifier [NCT05846776].

Dynamic user profile construction and its application to smart product-service system design: A maternity-oriented case study

The human-centric philosophy is considered a crucial development direction for smart Product-Service Systems (smart PSS). Smart PSS has effectively advanced the circular economy by enhancing user experience, incorporating servitization and digital servitization, and extending product lifecycle, among other key aspects of sustainable development. However, there is currently a lack of a reasonable, accurate, and actionable framework and methodology to express the human element, namely user profiling, within this philosophy. To address this gap, this paper utilizes statistical methods such as factor analysis, cluster analysis, and regression analysis, building upon the concept of traditional user profiling. The aim is to integrate the three prominent approaches of goal-oriented, scenario-based, and data-driven user profiling, with the goal of complementing each other and designing a top-level user profiling framework for smart PSS. Furthermore, Industry 4.0 technologies and text mining techniques are employed to collect data on users' product and service usage. As these data contain real-time information about user needs, behaviors and goals at different time periods, they can be used to construct dynamic features of user profiling, and ultimately achieve the construction of dynamic user profiling for smart PSS. To validate the proposed user profiling framework and dynamic user features of smart PSS, this paper presents a case study focusing on the user group of maternal women. This case study promotes the in-depth exploration of smart PSS research in expressing the human element.

Knowledge-driven profile dynamics

In the last decades, user profiles have been used in several areas of information technology. In the literature, most research works, and systems focus on the creation of profiles (using Data Mining techniques based on user's navigation or interaction history). In general, the dynamics of profiles are made by means of a systematic recreation of the profiles, without using the previous profiles. In this paper we propose to formalize the creation, representation, and dynamics of profiles from a Knowledge-Driven perspective. We introduce and axiomatically characterize four operators for changing profiles using a belief change inspired approach.

Empowering Active Users: A Case Study with Economic Analysis of the Electric Energy Cost Calculation Post-Net-Metering Abolition in Slovenia

This paper addresses the issue of the abolition of annual net metering in Slovenia and compares the electric energy costs for the studied active user after the abolition. The article also provides an exploration of the role played by an aggregator, which serves as a central entity that enables individuals to participate in the electric energy market. An analysis of the case study of an active user was made, where an analysis was made of the measurements of household consumption and photovoltaic plant production for the year 2022. This article presents an economic analysis with and without net metering and an analysis of the aggregator involvement strategies. In addition, a battery energy storage system was also considered in the analysis. An important part of the article is the identification of the flexibility potential for shiftable loads, which enable an aggregator to acquire insight into the energy consumption profile and energy production profile of active users. The following indicators were used to compare the strategies: annual electric energy cost and the indicators including self-sufficiency, self-consumption, and grid dependency. The findings indicate that, even in the absence of annual net metering, the active user can lower their costs for electric energy with the help of an aggregator.

Unsupervised contaminated user profile identification against shilling attack in recommender system

A recommender system is susceptible to manipulation through the injection of carefully crafted profiles. Some recent profile identification methods only perform well in specific attack scenarios. A general attack detection method is usually complicated or requires label samples. Such methods are prone to overtraining easily, and the process of annotation incurs high expenses. This study proposes an unsupervised divide-and-conquer method aiming to identify attack profiles, utilizing a specifically designed model for each kind of shilling attack. Initially, our method categorizes the profile set into two attack types, namely Standard and Obfuscated Behavior Attacks. Subsequently, profiles are separated into clusters within the extracted feature space based on the identified attack type. The selection of attack profiles is then determined through target item analysis within the suspected cluster. Notably, our method offers the advantage of requiring no prior knowledge or annotation. Furthermore, the precision is heightened as the identification method is designed to a specific attack type, employing a less complicated model. The outstanding performance of our model, validated through experimental results on MovieLens-100K and Netflix under various attack settings, demonstrates superior accuracy and reduced running time compared to current detection methods in identifying Standard and Obfuscated Behavior Attacks.

Using Digital Forensics for Android Smartphone Devices to aid Criminal Investigations

In the past decade, there has been an exponential adoption and ownership of smartphones by billions of users worldwide. However, as smartphone usage increases, criminals have taken advantage of them for illicit or criminal purposes. In criminal investigations, smartphone data has become an invaluable source of information. This study focuses on constructing a snapshot of Android-operated smartphone data to assist investigators in answering critical investigative questions. A thorough review of the literature with regard to the use of smartphone evidence in criminal cases, with the goal of emphasising the investigative phase and the supportive role of mobile data evidence in guiding investigations will be performed. In order to identify additional persons of interest and develop a thorough understanding of the case, the methodology will entail analysing user profiles, smartphone usage patterns, communication logs, application usage, geographic lookups, and device interactions. By conducting a thorough examination of relevant literature, designing a suitable model, and executing a case study, the study intends to offer valuable perspectives on the creation of timelines or visual representations derived from smartphone data. The results of this study will contribute to improving the efficacy of mobile forensics in assisting investigators and facilitating the use of Android-operated smartphone data as supporting evidence in criminal investigations.

Professionals' digital competences and user profiles in social agencies and their impact on professional practice, family autonomy and wellbeing.

The health crisis of the last 3 years has revealed the weaknesses of the child and family support system based on the professional use of digital resources in social agencies. This study addresses three aims: to examine the level of professional digital competence; to analyze the user profiles in a variety of digital resources; and to test the impact of level of competences and user profiles on four aspects: professional practice, family satisfaction with the services, child and family wellbeing, and family autonomy in the exercise of the parenting role. Participants were 148 practitioners from social agencies who voluntarily responded to an online survey with 47 questions. Results showed that professionals perceive themselves as more competent in areas of information / data management and communication / collaboration than in the creation of digital content, security measures, and technical problem solving. Websites, email, and instant messaging were the sources more frequently used and with higher satisfaction, than structured programs, social networks and multimedia content. Variability in the user profiles showed three clusters: Cluster 1 Social network user (n = 13), Cluster 2 Diversified user (n = 75) and Cluster 3 Communicative instant user (n = 60). Participants in Cluster 2 compared to those in the other clusters were the most proficient on their digital competences and acknowledge the positive impact of digital resources on their professional practice and the psychological and social wellbeing of families. This study points the need for improvement in professionals' digital competences in some of the measured areas and the user profile of digital resources since both provide benefits on professional practice and family autonomy and wellbeing.

Evolution of the profiles of new psychotropic drug users before and during the COVID-19 crisis: an original longitudinal approach through multichannel sequence analysis using the French health-care database.

The COVID-19 pandemic has had a substantial impact on mental health. An increase in the use of anxiolytic, hypnotic, and antidepressant drugs has been highlighted in France, but with no information at the individual level (trajectories) or concerning patient characteristics. The objective of this study was to describe the profile of new psychotropic drug users since the beginning of the pandemic. We formed two historical cohorts using the Pays-de-la-Loire regional component of the National Health Data System (SNDS): a "COVID-19 crisis cohort" (2020-2021) and a "control cohort" (2018-2019). We analyzed reimbursements for psychotropic medications (anxiolytics, antidepressants, hypnotics, mood stabilizers, and antipsychotics) using a multichannel sequence analysis and performed clustering analysis of sequences. The proportion of new consumers of psychotropic drugs was higher in the COVID-19 crisis cohort (18.0%) than that in the control cohort (16.0%). In the COVID-19 cohort, three clusters of psychotropic drug users were identified, whereas four clusters were identified in the control cohort. A time lag in treatment initiation was observed in the COVID-19 crisis cohort (September) compared with the control cohort (July). This study is one of the first to analyze the profile of psychotropic treatment users during the COVID-19 crisis. Our analysis sheds light on changes in patterns of psychotropic drug use during the COVID-19 pandemic, possibly associated with changes in prescribing conditions and mental health conditions during the crisis. This study also provides an example of the application of an innovative longitudinal analysis methodology in the field of pharmacoepidemiology.