User Profiles Research Articles

An Analysis of Vehicle-to-Grid in Sweden Using MATLAB/Simulink

With more electric vehicles introduced in society, there is a need for the further implementation of charging infrastructure. Innovation in electromobility may result in new charging and discharging strategies, including concepts such as smart charging and vehicle-to-grid. This article provides an overview of vehicle charging and discharging innovations with a cable connection. A MATLAB/Simulink model is developed to show the difference between an electric vehicle with and without the vehicle-to-grid capabilities for electricity grid prices estimated for Sweden for three different electric vehicle user profiles and four different electric vehicle models. The result includes the state-of-charge values and price estimations for the different vehicles charged with or without a bidirectional power flow to and from the electric grid. The results show that there is a greater difference in state-of-charge values over the day investigated for the electric vehicles with vehicle-to-grid capabilities than for vehicles without vehicle-to-grid capabilities. The results indicate potential economic revenues from using vehicle-to-grid if there is a significant variation in electricity prices during different hours. Therefore, the vehicle owner can potentially receive money from selling electricity to the grid while also supporting the electric grid. The study provides insights into utilizing vehicle-to-grid in society and taking steps towards its implementation.

ENHANCING USER EXPERIENCE IN INTERIOR ARCHITECTURE THROUGH BIOPHILIC DESIGN: A CASE STUDY OF URBAN RESIDENTIAL SPACES

This study aims to investigate how applying biophilic design principles to interior architecture—especially in urban residential contexts—can improve the user experience while accounting for the influence of different user profiles. It has been demonstrated that using natural components and patterns in constructed settings has a good influence on people's health and sense of connection to the natural world. The impact of numerous biophilic design elements on occupant well-being, productivity, and emotional connection to their living environments will be thoroughly examined. These include natural light, vegetation, water features and natural materials. University faculty and their families are the sole residents of Kayseri Nuh Naci Yazgan University Faculty Residences in Turkey a unique urban living community. Located at the corner of the university, this space offers individualized accommodation alternatives, creates a close-knit community and provides a distinctive living environment that meets faculty members' academic needs by guaranteeing easy access to educational resources. 54 residential units are participating in the study. Respondents are academic staff members that reside and work in the residences of Nuh Naci Yazgan University. The study centers on their housing experiences and they are its primary subjects. Both qualitative and quantitative data were gathered using a combination of in-depth observations, questionnaires and interviews in addition to random sampling. The study closes a crucial knowledge gap regarding the application of biophilic design in urban settings, given the current state of growing urbanization. This study is significant because it may help design more environmentally friendly and psychologically stimulating urban living environments. The results of this study can help architects, designers and urban planners create healthier and more user-centered urban places, which will ultimately improve the quality of life in urban settings, by examining how biophilic design affects user experiences. Using a case study methodology, the study was focused on NNYU Residences as a particular urban living environments as its study subjects. Observations, surveys and interviews was used to gather both qualitative and quantitative data to investigate users' opinions about the integration of biophilic design elements in their surroundings. This study has multiple main objectives. It seeks to comprehend how consumers interpret biophilic design elements, evaluate how the design affects inhabitants' well-being, investigate sustainability issues and offer useful advice for designers and architects. According to this study, biophilic design enhances sustainability and user satisfaction in urban residential environments. This has significant ramifications for urban planners and architects. Nonetheless, the case-study methodology and possibility for participant bias are two of the study's shortcomings. Also, because interior architecture is the study's primary focus, related bioinspired fields are not included. To learn more about these topics and how they contribute to biophilic design in interior products, future studies should investigate them.

Enhancing User Profile Personalization in Enterprise Systems: A Conceptual Framework Leveraging Application Usage Mining Methods

Enhancing User Profile Personalization in Enterprise Systems: A Conceptual Framework Leveraging Application Usage Mining Methods

ANNAPURNA CONNECT

In today's increasingly digital landscape, the agricultural sector is primed for transformation through the development of a Full-Stack Website dedicated to a Farmers' Marketplace. This initiative aims to create an integrated online platform connecting farmers, producers, and consumers, streamlining the exchange of agricultural products. With a focus on both front-end and back-end development, the website will offer a user-friendly interface with features such as product listings, search functionality, user profiles, reviews, and a simple checkout process. Meanwhile, the robust back-end infrastructure ensures data management, security, and scalability. By leveraging technology to bridge the gap between farmers and consumers, the Farmers' Marketplace Full-Stack Website has the potential to revolutionize agricultural trade, fostering sustainability and efficiency in the process. Keyword: Digital Marketplace, Mern stack, Farmers and buyers.

Globe glider

Globe glider Tours and Travels aims to revolutionize the travel industry by offering a comprehensive platform that integrates seamless booking experiences, personalized trip planning, and cutting-edge travel recommendations. With an emphasis on leveraging advanced AI algorithms and machine learning techniques, Globe glider seeks to redefine the way travelers discover, plan, and embark on their journeys. Our platform will provide users with a holistic travel solution, from browsing destinations to booking accommodations, transportation, and activities, all tailored to their preferences and interests. Globe glider’s mission is the commitment to enhancing the traveler's experience through innovative technology and curated content. By harnessing vast datasets and employing sophisticated algorithms, our platform will deliver personalized recommendations for destinations, activities, and experiences based on each user's unique profile and past travel behavior. Additionally, Globeglider will offer real-time assistance and support throughout the travel journey, ensuring a seamless and stress-free experience from start to finish. In an ever-evolving travel landscape, Globe glider stands poised to empower travelers with the tools and resources needed to make informed decisions and create unforgettable experiences. With a focus on user-centric design, technological innovation, and unparalleled customer service, Globeglider aspires to become the go-to platform for travelers worldwide, facilitating seamless exploration, discovery, and adventure across the globe. Globeglider sets itself apart by prioritizing sustainability and responsible tourism practices. In an era where environmental conservation and ethical travel are paramount concerns, our platform will actively promote eco-friendly travel options, support local communities, and advocate for the preservation of cultural heritage sites. Through partnerships with eco-conscious suppliers and community-based tourism initiatives, we aspire to minimize the carbon footprint of travel while maximizing positive socio-economic impacts on host destinations. At the heart of GlobeGlider is a commitment to fostering a sense of exploration and connection among travelers. The application incorporates a user-centric approach, allowing individuals to customize their journeys based on preferences, interests, and travel goals. GlobeGlider not only streamlines the logistics of travel but also enhances the overall experience by offering curated suggestions, insider tips, and interactive maps. By combining convenience with a touch of adventure, GlobeGlider aims to empower users to embark on journeys that are not just destinations but transformative moments in their lives.

A Self-Guided Web-Based App (MyDiaMate) for Enhancing Mental Health in Adults With Type 1 Diabetes: Insights From a Real-World Study in the Netherlands.

MyDiaMate is a web-based intervention specifically designed for adults with type 1 diabetes (T1D) that aims to help them improve and maintain their mental health. Prior pilot-testing of MyDiaMate verified its acceptability, feasibility, and usability. This study aimed to investigate the real-world uptake and usage of MyDiaMate in the Netherlands. Between March 2021 and December 2022, MyDiaMate was made freely available to Dutch adults with T1D. Usage (participation and completion rates of the modules) was tracked using log data. Users could volunteer to participate in the user profile study, which required filling out a set of baseline questionnaires. The usage of study participants was examined separately for participants scoring above and below the cutoffs of the "Problem Areas in Diabetes" (PAID-11) questionnaire (diabetes distress), the "World Health Organization Well-being Index" (WHO-5) questionnaire (emotional well-being), and the fatigue severity subscale of the "Checklist Individual Strength" (CIS) questionnaire (fatigue). Two months after creating an account, study participants received an evaluation questionnaire to provide us with feedback. In total, 1008 adults created a MyDiaMate account, of whom 343 (34%) participated in the user profile study. The mean age was 43 (SD 14.9; 18-76) years. Most participants were female (n=217, 63.3%) and higher educated (n=198, 57.6%). The majority had been living with T1D for over 5 years (n=241, 73.5%). Of the study participants, 59.1% (n=199) of them reported low emotional well-being (WHO-5 score≤50), 70.9% (n=239) of them reported elevated diabetes distress (PAID-11 score≥18), and 52.4% (n=178) of them reported severe fatigue (CIS score≥35). Participation rates varied between 9.5% (n=19) for social environment to 100% (n=726) for diabetes in balance, which opened by default. Completion rates ranged from 4.3% (n=1) for energy, an extensive cognitive behavioral therapy module, to 68.6% (n=24) for the shorter module on hypos. There were no differences in terms of participation and completion rates of the modules between study participants with a more severe profile, that is, lower emotional well-being, greater diabetes distress, or more fatigue symptoms, and those with a less severe profile. Further, no technical problems were reported, and various suggestions were made by study participants to improve the application, suggesting a need for more personalization. Data from this naturalistic study demonstrated the potential of MyDiaMate as a self-help tool for adults with T1D, supplementary to ongoing diabetes care, to improve healthy coping with diabetes and mental health. Future research is needed to explore engagement strategies and test the efficacy of MyDiaMate in a randomized controlled trial.

Deep Attentive Interest Collaborative Filtering for Recommender Systems

Collaborative filtering (CF) is a pivotal building block in commercial recommender systems due to its strength and utility in user interest modeling. Recently, many researchers have turned to deep learning as a way to capture richer collaborative signals from user-item feature interactions. However, most deep-based methods only consider nonlinear, high-order interactions while ignoring the explicit collaborative signals in low-order interactions. They also typically ignore the quality of the user and item profiles. These are cornerstones in item recommendation that, we argue, must be considered for high-quality recommendations. Hence, we propose Deep Attentive Interest Collaborative Filtering (DAICF) to overcome these limitations. DAICF profiles users based on their interactive items, i.e., user neighborhood information. Similarly, item profiles are based on users who had interacted with it, i.e., item neighborhood information. Given a user's profile varies over different items, DAICF accurately models his attentive interests based on the specific target item. Low-order collaborative signals are captured by a shallow component, and high-order collaborative signals are captured by a deep component. These two complementary collaborative signals are then fused to provide rich recommendations that cut through today's information overload. By designing a personalized feature extraction method based on bilateral neighborhood information to solve the data sparsity problem in recommender systems, DAICF can dynamically distinguish the importance of a user's historical interaction items for predicting user preferences for a specific target item. A set of experiments against four real-world datasets validate that DAICF outperforms the most recent state-of-the-art recommendation algorithms and justifies the effectiveness and interpretability of our method.

What can TikTok tell us about the food practices of the residents of tiny apartments?

With continuing population growth and increased urbanisation, the prevalence of apartment living will also increase. The food practices and diet of apartment residents may differ to those in lower density, detached or semi-detached housing. Food practices may be impacted by physical constraints of apartment kitchens (size, storage, cooking facilities) and the influence of the surrounding community food environment, particularly in smaller apartments. High-rise apartment residents spend a larger proportion of their weekly household budget on eating foods prepared out of the home 1. This is concerning given meals prepared outside the home tend to be of lower diet quality 2 and are associated with higher body weight 3. This study investigated how kitchens, residents’ food practices and hypothesised barriers to healthy eating were portrayed on popular TikTok videos associated with hashtags denoting small apartments. Using the keyword ‘microapartment’, the top four most viewed hashtags relevant to the topic were identified: #studioapartment (190.5 million views), #smallapartment (152.5 million views), #tinyapartment (62 million views), and #microapartment/ #microapartments (4.9 million views combined). The most liked videos (n = 50) from each of these four hashtags and #apartment (as a comparator) were selected for investigation. Using a REDCap survey, two researchers independently coded a random sample of 15 videos, with comparison and discussion to tighten codebook definitions and reduce ambiguity. Subsequently over half of all videos were coded by at least two researchers, achieving acceptable inter-rater reliability (Cohen’s kappa >0.8). Each video was coded for engagement characteristics, user profile, and characteristics of individuals appearing. Further coding included apartment size, kitchen features displayed or mentioned, and food practices (grocery shopping, cooking at home, and eating foods prepared out of the home). Videos were categorised as having positive sentiment if they depicted the apartment in an explicitly positive way, including promoting or encouraging an aspect of apartment living or indicating support for a behaviour. Conversely, videos were coded as being of negative sentiment where they depicted a clear negative position on an aspect of apartment living. Else, the video was coded as neutral sentiment. The majority of videos (87%) portrayed apartment living with a positive or neutral sentiment; with only 2% of videos portraying kitchen size or function negatively. The expected physical constraints of small apartment kitchens were not evident in the videos, nor were interactions with the food environment surrounding the apartment. Indeed, in the small number of videos portraying the food practices of cooking, shopping and eating, the videos highlighted the ability to undertake these practices despite limitations of size and facilities. As the food practices of residents of small apartments are not well researched, it is not known whether their portrayal on TikTok may indeed reflect reality, or may be a glamorisation.

Diagnosing the socio-technical refrigeration system with Radical Innovation Design methodology to target innovations

ABSTRACT This research aims to address the increasing regulatory and environmental challenges associated with refrigeration systems by fostering innovations. Refrigeration systems, as socio-technical entities, present a challenge due to their complexity, diverse stakeholders, and evolving regulatory constraints. Identifying opportunities for innovation in such intricate systems is further complicated by various performance-contributing factors. The Radical Innovation Design (RID) methodology, designed for aligning design solutions with activities, proves effective in pinpointing and prioritizing innovation targets across the lifecycle of a complex system. In this study, data from interviews and practices in the retail refrigeration sector inform the RID methodology. Two sets of results are presented. Firstly, a knowledge book of the socio-technical system is categorized into four RID dimensions: usage situations, user profiles, current problems, and existing solutions. The second set includes outcomes from two RID decision-support tools: the RID Comparator evaluates the effectiveness of existing solutions, while the RID Compass identifies three improvement areas – poor adaptation of new technical clusters, disrupted stakeholder interaction processes, and a lack of common tools. These results underwent validation through expert iterations, leading to a recommended hybrid solution involving the development of an integrated model-based platform.

Patients torn to pieces.

Dissecting bodies is a common practice in many cultures. But in "big data medicine", the art of dissecting the human body has become an obsession. Indeed, modern biotechnology allows us to see and measure the molecular components of every single cell. But how can we put this immense number of bits and pieces back together again and see the patient as a whole? The first turning point is that proposed by René Descartes, who, inspired by dreams and visions, conceived the idea of unifying all scientific disciplines through the pervasive application of mathematics. Descartes formulates four basic rules, the second (top-down method) and third (bottom-up method) of which become crucial in modern data analysis. An instructive case study considered here is that of pulmonary tuberculosis, where the Cartesian approach of decomposing problems into smaller and smaller "pieces" - from organism to organ and from cellular lesion to the microscopic level - has led to the cure of the disease through antibiotics. This success story inspired Paul Ehrlich who, with the concept of the "magic bullet", defined modern pharmacology. However, this paradigm is being challenged today by multifactorial diseases and big data medicine, where the enormous availability of clinical and molecular data must be integrated to arrive at a therapeutic decision. The Cartesian approach shows its limitations today, as witnessed by the similar difficulty in fields other than medicine, illustrated here by the case of choosing to produce a successful television series based on user profiling. The take-home message is that the amount of data collected does not automatically guarantee success but that, instead of being data-driven, a collective "human" overview and assessment is inevitable. That is, close collaboration between clinicians and data analysts, integrating expertise, is needed to address challenges in the diagnosis and treatment of complex diseases through imagination and not mere extrapolation.

Profile of cannabis users among a population of Tunisian young adults residing abroad

IntroductionTunisian emigrants may consume psychoactive substances in other countries. This community is exposed to sociocultural and legal contexts different from those in Tunisia and the degree of acculturation would tend to increase over time. However, data on the use of psychoactive substances, particularly cannabis, in this particular population are scarce.ObjectivesThis study aims to determine the profile of Tunisian young adults residing abroad who use cannabis.Methods We conducted a cross-sectional, descriptive and analytic study. It was carried out in the form of an online survey. We focused on young Tunisians people who have completed their secondary studies at the pilot high school of Sfax and currently residing abroad. Data collection was through Google Forms administred questionnaire.ResultsThirty-five participants were included in our study. Cannabis use behavior affected 48.6% of them (N=17)) and it was done with friends in a festive setting in 88% of cases (N=15). Cannabis use was more common among people who were single (p=0.001), living alone (p=0.047), had a psychiatric history (p=0.032) and hanging out with friends who also smoked cannabis (p=0.032).Cannabis use was also more common among cigarette smokers (p=0.000) and alcohol consumers (p=0.000). It was significantly more common among people who shared erroneous beliefs about cannabis, that it is a mild drug (p= 0.024) and that it does not cause dependence (p= p=0.042).ConclusionsCannabis use among Tunisian young adults residing abroad seems to be a form of poly-consumption, more common among singles, living alone and having a psychiatric vulnerability. These findings underscore the need for targeted interventions and educational initiatives to address cannabis use within this specific population.Disclosure of InterestNone Declared

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.