User Location Research Articles

Aplikasi Absensi Siswa Berbasis Android

Students at SMA Negeri 1 Central Bengkulu are still dominated by the manual method, namely taking student absences by recording them on paper which will later be summarized from each student's attendance data. Because the manual recap of student attendance data also affects the information about attendance recaps which is still limited to the school, so parents/guardians cannot monitor student attendance levels. Inconsistency in attendance data can be detrimental to students, there is often a difference between the absences listed in the attendance register book/record into the system, which is one of the factors that makes the original data and data that have been verified into the system different. Utilizing the location of an Android smartphone to become an attendance system, which functions to provide location information based on the user's nearest location, assisting in searching for locations based on 6 existing parameters, namely education (high school/vocational school/university), hospital, hotel, restaurant, tourist attraction , and malls/traditional markets. It is hoped that this application will always be up to date so that this application follows developments in Android-based lecturer/teacher attendance information technology using the Flutter framework.

The Reality of the Situation: A Survey of Situated Analytics.

The advent of low-cost, accessible, and high-performance augmented reality (AR) has shed light on a situated form of analytics where in-situ visualizations embedded in the real world can facilitate sensemaking based on the user's physical location. In this work, we identify prior literature in this emerging field with a focus on situated analytics. After collecting 47 relevant situated analytics systems, we classify them using a taxonomy of three dimensions: situating triggers, view situatedness, and data depiction. We then identify four archetypical patterns in our classification using an ensemble cluster analysis. We also assess the level which these systems support the sensemaking process. Finally, we discuss insights and design guidelines that we learned from our analysis.

Application-based evaluation of multi-basin hydrological models

Hydrological models are generally calibrated and validated using a suite of well-known statistical metrics, which sometimes lack clear connection and tailoring to the local users’ need and therefore limits the evaluation, especially in the case of global climate services. Therefore, in this study, two types of application-based evaluation metrics are introduced, addressing (i) temporal matching of quantile extremes and (ii) relative bias in flow signatures, which supplements commonly used model performance assessment metrics. The introduced metrics are compared to conventional statistical metrics, at seven case study areas across the world, with three model settings representing different datasets and calibrations, generated from the global hydrological model World-Wide HYPE (WW HYPE). The results suggest that different performance results can occur when comparing application-based metrics to conventional ones. This implies that different evaluation metrics reveal models’ capability in various aspects, supporting their application under the corresponding circumstances. Finally, these metrics enable us to propose two model applicability scenarios: generally applicable models and conditionally applicable models. For instance, the WW HYPE with global dataset and local calibration can yield optimal estimates concerning the timing of quantile extremes and temporal variations in flow signatures, despite its suboptimal performance in conventional evaluation metrics. Therefore, it may be considered as a conditionally applicable model which can be used in regions with limited local datasets, supplying reliable information for decision-makers in formulating strategic plans for water resources management.

Implementation of 802.11ax and cell-free massive MIMO scenario for 6G wireless network analysis extending OMNeT++ simulator

In an era where ubiquitous connectivity and escalating data demands are altering the landscape of wireless communications, our paper proposes a pioneering enhancement for the OMNeT++ simulator to support the advanced features of IEEE 802.11ax high efficiency (HE) alongside cell-free massive multiple-input multiple-output (MIMO) systems. Traditional wireless networks face daunting challenges in sustaining elevated quality of service (QoS), primarily due to fluctuating user densities and signal quality. Cell-free massive MIMO serves as a compelling answer to this predicament by decentralizing the cellular architecture. It eradicates conventional cell boundaries, furnishing uniform QoS regardless of user locations. However, these advancements come at the expense of complex backhaul networks and articulated joint signal processing. The 802.11ax standard, touted for its robustness and efficiency, remains underexplored in this new paradigm. Our research not only dissects the architectural elements and constraints of both 802.11ax and cell-free massive MIMO but also elaborates on the adaptations required to extend OMNeT++ functionalities for these technologies. By doing so, we bridge a crucial gap, enabling the simulator to provide a more precise, detailed, and scalable evaluation of emerging 6G scenarios and directional communications also taking into account the impact of the most known routing protocols such as dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV), optimized link state routing (OLSR), and dynamic mobile ad hoc network on-demand (DYMO) that were selected for this comparative study. The proposed extensions promise to revolutionize network simulations and lay the foundation for in-depth analyses of wireless systems in complex and dynamic environments. Through extensive simulations, our study demonstrates that cell-free massive MIMO configurations significantly improve network throughput in high-density mobile ad hoc network (MANET) environments, with results indicating an average throughput gain of up to 30% compared with non-cell-free configurations. This improvement highlights the efficacy of cell-free massive MIMO to take advantage of the spatial and frequency multiplexing capabilities inherent in the 802.11ax standard, making it a promising solution for future wireless systems in densely populated areas.

The evaluation of health, disability and aged care-sector engagement with resources designed to support optimisation of the allied health assistant workforce: a qualitative study

BackgroundAllied health assistants (AHAs) support allied health professionals (AHPs) to meet workforce demands in modern healthcare systems. In an Australian context historically, AHAs have been sub-optimally utilised. Prior research has identified that AHAs and AHPs working in health, disability and aged care sectors, and the Vocational Education and Training (VET) industry, may benefit from access to resources to support the optimisation of the AHA workforce. As a part of a Victorian department of health funded project, several resources were developed in line with workforce recommendations for use in each of the above sectors. Recommendations and resources covered the broad areas of pre-employment training, workforce planning and governance, consumer-centred therapy and supports, recruitment and induction and workplace training and development. This study aimed to evaluate the engagement with these newly designed resources to support optimisation of AHAs in the Victorian context.MethodsSemi-structured interviews were conducted to evaluate engagement with resources, from the perspective of AHAs, AHPs and allied health leaders (AHLs) in the health, aged care or disability sectors, and educators and managers of allied health assistance training. Thematic analysis was conducted using team-based framework analysis.ResultsThematic framework analysis of the interview data identified four themes; Why participants accessed the resources; How participants engaged with the resources; What (if any) changes in practice occurred as a result of engaging with the resources in a participant’s local context, How did participants envision the resources being utilised for AHA workforce optimisation in the future. Responses were mapped to the AHA workforce career pathway at the career preparation, career development and career trajectory tiers.ConclusionsAppetite for AHA workforce development and optimal utilisation is evident across Victoria, Australia. Readily accessible resources that inform AHA role and scope of practice, delegation practice, or improve the ability for an AHA to state their own development needs, were identified as useful by participants. The potential for these resources to assist in the optimal utilisation and development of AHA workforces across the career continuum differs according to the role, sector and geographical location of the resource user. Further study is needed to investigate the transferability of these resources to national and global contexts.

Modeling multi-factor user preferences based on Transformer for next point of interest recommendation

With the rapid development of the mobile network and the gradual popularization of mobile devices, more and more users try to find attractive places to visit through WeChat, Twitter applications. In this trend, personalized next point of interest(POI) recommendation in the Location-based Social Network (LBSN) has become the focus of research and practice. Most existing studies capture user interest changes between different days (i.e. weekend and weekday), however, they ignore seasonal factors in time transition and category factors and thus fail to capture seasonal-level and category-level movement patterns in users’ mobile trajectories. Besides, they neglect the relevance between POIs from all users’ trajectory and fail to generate expressive POI embedding representation without constructing trajectory graph, which will reduce the accuracy of the next POI recommendation. To address the issues above, a next POI recommendation method for modeling Multi-factor User Preferences based on Transformer (MUPT) is developed, which consists of a global POI relationship modeling, a local multi-factor user preference modeling and a prediction module. It first learns the collaborative information of users with similar behavior to generate expressive POI embedding representation. Then it captures the personalized movement patterns of users at the POI, category and time levels based on Transformer mechanism in the local module. Especially the seasonal and other fine-grained information on the time series are learned in the time preference modeling part. The prediction module designed tracks the relationship between multi-level motion pattern representation of user check-in behavior and the next POI accessed by the user, and it finally obtains user’s preference probability for next POIs. An extensive experiment has been conducted on four datasets, and the experimental results analysis demonstrates that our proposed MUPT method is superior to other methods in terms of accuracy(ACC), mean reciprocal rank(MRR) and normalized discounted cumulative gain(NDCG).

Consumer Social Connectedness and Persuasiveness of Collaborative-Filtering Recommender Systems: Evidence From an Online-to-Offline Recommendation App

Consumers often rely on their social connections or social technologies, such as (automated) system-generated recommender systems, to navigate the proliferation of diverse products and services offered in online and offline markets and cope with the corresponding choice overload. In this study, we investigate the relationship between the consumers’ social connectedness and the economic impact of recommender systems. Specifically, we examine whether the social connectedness levels of consumers moderate the effectiveness of online recommendations toward increasing product demand levels. We study this novel research question using a combination of datasets and a demand-estimation model. Interestingly, the empirical results show a positive moderating effect of social connectedness on the demand effect of online-to-offline recommendations. Further delving into the findings, we also provide empirical evidence that social identification might explain why denser social connectedness with local users accentuates the effects of collaborative-filtering online-to-offline recommendations. Our study enhances the understanding of community factors affecting the efficacy of social technologies in multichannel operations while also extending the social identity theory in operations in the digital realm. The results also have intriguing operational implications for operations managers and practitioners, while suggesting several interesting avenues for future research on social technologies and operations management.

Subgraph-level federated graph neural network for privacy-preserving recommendation with meta-learning

Graph neural networks (GNN) are widely used in recommendation systems, but traditional centralized methods raise privacy concerns. To address this, we introduce a federated framework for privacy-preserving GNN-based recommendations. This framework allows distributed training of GNN models using local user data. Each client trains a GNN using its own user–item graph and uploads gradients to a central server for aggregation. To overcome limited data, we propose expanding local graphs using Software Guard Extension (SGX) and Local Differential Privacy (LDP). SGX computes node intersections for subgraph exchange and expansion, while local differential privacy ensures privacy. Additionally, we introduce a personalized approach with Prototype Networks (PN) and Model-Agnostic Meta-Learning (MAML) to handle data heterogeneity. This enhances the encoding abilities of the federated meta-learner, enabling precise fine-tuning and quick adaptation to diverse client graph data. We leverage SGX and local differential privacy for secure parameter sharing and defense against malicious servers. Comprehensive experiments across six datasets demonstrate our method’s superiority over centralized GNN-based recommendations, while preserving user privacy.

Disparities in medical recommendations from AI-based chatbots across different countries/regions

This study explores disparities and opportunities in healthcare information provided by AI chatbots. We focused on recommendations for adjuvant therapy in endometrial cancer, analyzing responses across four regions (Indonesia, Nigeria, Taiwan, USA) and three platforms (Bard, Bing, ChatGPT-3.5). Utilizing previously published cases, we asked identical questions to chatbots from each location within a 24-h window. Responses were evaluated in a double-blinded manner on relevance, clarity, depth, focus, and coherence by ten experts in endometrial cancer. Our analysis revealed significant variations across different countries/regions (p < 0.001). Interestingly, Bing's responses in Nigeria consistently outperformed others (p < 0.05), excelling in all evaluation criteria (p < 0.001). Bard also performed better in Nigeria compared to other regions (p < 0.05), consistently surpassing them across all categories (p < 0.001, with relevance reaching p < 0.01). Notably, Bard's overall scores were significantly higher than those of ChatGPT-3.5 and Bing in all locations (p < 0.001). These findings highlight disparities and opportunities in the quality of AI-powered healthcare information based on user location and platform. This emphasizes the necessity for more research and development to guarantee equal access to trustworthy medical information through AI technologies.

Highly Efficient Hybrid Reconfigurable Intelligent Surface Approach for Power Loss Reduction and Coverage Area Enhancement in 6G Networks

This paper introduces a novel efficient hybrid reconfigurable intelligent surface (RIS) approach designed to significantly reduce power loss and enhance coverage area in 6G networks. The core innovation of this approach lies in an advanced iterative algorithm introduced as the Hybrid reconfigurable intelligent surface decision-making algorithm (HRIS-DMA) that integrates precise user location data into the RIS configuration process. By dynamically adjusting RIS elements to reflect and direct signals based on real-time user positions, this method minimises signal attenuation and optimises signal propagation. The mechanism driving the performance gains includes precise beamforming and intelligent reflection, continuously refined through iterative updates. This technique ensures robust signal strength and expanded coverage, addressing the challenges of dense and diverse deployment scenarios in 6G networks. The proposed scheme’s application in 6G networks demonstrates substantial improvements in signal quality and network reliability, paving the way for enhanced user experiences and efficient communication infrastructures. This novel approach was tested using MATLAB R2023a , and its performance was evaluated using three downlink scenarios: zero to few, few to moderate, and moderate to many obstacles. The three scenarios show higher coverages than conventional simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and base station (BS) handover. Based on the evaluation metrics, the analysis results of the novel HRIS-DMA show 70% less signal power loss, 0.17 μs less system delay, 25 dB and 12 dB channel gain compared with the conventional STAR-RIS and BS handover, respectively, and 95% improvement in the overall system’s efficiency compared to STAR-RIS and 13% compared to BS-BS handover.

A User Location Reset Method through Object Recognition in Indoor Navigation System Using Unity and a Smartphone (INSUS)

A User Location Reset Method through Object Recognition in Indoor Navigation System Using Unity and a Smartphone (INSUS)

Tourism Guide Application for Heritage Sites in Surabaya Based on Augmented Reality and GPS Gamification

One of the most important revenue streams for the nation's economy and development is tourism. The COVID-19 pandemic is, however, causing a reduction in tourism in Indonesia; Surabaya, the country's second-largest city, has seen a drop in visitors from 6.5 million to 500 thousand. To solve this problem, an innovation is required to boost the number of visitors to Indonesia's heritage sites who use contemporary technology, specifically smartphone with interactive media (with AR and GPS for instance). The author will develop an app that, depending on the user's location, will let them visit several Surabaya heritage sites and provide them with comprehensive details and instructions. Indonesia can stimulate its economy and return visitors to its heritage sites by employing this interactive gamification apps. However, on the examination to the user, we found a good implementation in detecting and measuring the nearest destination to the current user position, and how to parse into google map and guiding the destination to that heritage site. But we have some distraction when pointing the apps to the Augmented Reality (AR) marker on the site, since various historical places have various lighting, some devices are unable to take a clear picture of the marker due to its lack of lighting and abnormal size or form. Thus, it will be effective when the user points his smartphone camera to the AR marker, he/she supposed to use the smartphone lighting as well.

Mental health service user organizations in times of crises: adaptions, challenges and opportunities experienced by local associations during COVID-19

ABSTRACT Purpose This study examines how local user associations of mental health service-user organizations were affected by the pandemic, in order to provide guidance to user organizations and surrounding actors on future advancements. The pandemic is used as a case to explore organizational resilience and digitalization during crisis. Methods Data from focus group interviews and individual interviews with representatives of ten local associations were analysed using qualitative content analysis. A theoretical framework combining governance theory and organizational theory was applied. Results Typically, associations swiftly restructured activities to support members to meet the urgent needs that arose, not least in relation to the digital transition. Simultaneously, face-to-face interactions was valued and some members became isolated. Public sector actors often did not prioritize collaboration, and the associations had limited agency and influence in advocacy activities. Conclusions User organizations can play an important role in times of crisis. Surrounding social systems should provide resources to counteract resource dependencies and allow organizations to develop operating reserves. They should value collaboration and establish collaborative practices to ensure a readiness to utilize the organizations’ capacities when needed. User organizations should have control over future developments, both to harness the potential of digital connectivity and to prevent a digital divide.

Improving Spatio-Temporal Topic Modeling with Swarm Intelligence

This study introduces innovative methodologies for spatiotemporal topic modeling applied to the TripAdvisor forum of Morocco, leveraging the diverse and geographically tagged user-generated content. We develop and evaluate two schemas integrating Latent Dirichlet Allocation (LDA) with advanced clustering techniques, including a hybrid K-Means algorithm that incorporates Genetic Algorithms and the Artificial Bee Colony method. The first schema independently processes user threads, publication times, and locations using LDA, followed by clustering, while the second schema combines these dimensions into a unified vector for holistic LDA application, facilitating direct comparisons of clustering efficacy. Our findings demonstrate that swarm intelligence significantly boosts clustering performance, especially for larger clusters, and enhances the visualization of complex data relationships. These insights offer actionable intelligence for tourism stakeholders and underscore the practical benefits of advanced computational techniques in harnessing user-generated content for strategic decision-making.

Prediction of influential nodes in social networks based on local communities and users’ reaction information

Identifying influential nodes is one of the basic issues in managing large social networks. Identifying influence nodes in social networks and other networks, including transportation, can be effective in applications such as identifying the sources of spreading rumors, making advertisements more effective, predicting traffic, predicting diseases, etc. Therefore, it will be important to identify these people and nodes in social networks from different aspects. In this article, a new method is presented to identify influential nodes in the social network. The proposed method utilizes the combination of users’ social characteristics and their reaction information to identify influential users. Since the identification of these users in the large social network is a complex process and requires high processing power and time, clustering and identifying communities have been used in the proposed method to reduce the complexity of the problem. In the proposed method, the structure of the social network is divided into its constituent communities and thus the problem of identifying influential nodes (in the entire network) turns into several problems of identifying an influential node (in each community). The suggested method for predicting the nodes first predicts the links that may be created in the future and then identifies the influential nodes based on an iterative strategy. The proposed algorithm uses the criteria of centrality and influence domain to identify this category of users and performs the identification process both at the community and network levels. The efficiency of the method has been evaluated using real databases and the results have been compared with previous works. The results demonstrate that the proposed method provides a more suitable performance in detecting the influential nodes and is superior in terms of accuracy, recall and processing time.

Next location prediction using heterogeneous graph-based fusion network with physical and social awareness

Location prediction based on social media information is highly valuable in human mobility research and has multiple real-life applications. However, existing research methods often ignore social influences, largely ignoring implicit information regarding interactions between users and geographical locations. Additionally, they generally employ single modeling structures, which restricts the effective integration of complex spatiotemporal characteristics and factors influencing user mobility. In this context, we propose a novel network with physical and social awareness that expresses both physical and social influences of user mobility from a global perspective based on a heterogeneous graph constructed using users and spatial locations as nodes and relationships between them as edges. This graph enables the model to leverage information from connected nodes and edges to infer missing or unobserved data. The model predicts future locations of users by effectively integrating the temporal and spatial features of user trajectory series. The proposed model is validated using three social media datasets. The experimental results demonstrate that the proposed method outperforms the state-of-the-art baseline models. This indicates the importance of considering complex interactions between users and locations, as well as the various influences of physical and social spaces.

TUMDOT–MUC: Data Collection and Processing of Multimodal Trajectories Collected by Aerial Drones

Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behavior of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 m continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data, therefore, includes various interactions between different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and the supporting data are available open source for research purposes to ensure global accessibility.

Dynamic SNR, Spectral Efficiency, and Rate Characterization in 5G/6G mmWave/sub-THz Systems with Macro- and Micro-Mobilities

The performance of 5G/6G cellular systems operating in millimeter wave (mmWave, 30–100 GHz) and sub-terahertz (sub-THz, 100–300 GHz) bands is conventionally assessed by utilizing the static distributions of user locations. The rationale is that the use of the beam tracking procedure allows for keeping the beams of a base station (BS) and user equipment (UE) aligned at all times. However, by introducing 3GPP Reduced Capability (RedCap) UEs utilizing the Radio Resource Management (RRM) Relaxation procedure, this may no longer be the case, as UEs are allowed to skip synchronization signal blocks (SSB) to improve energy efficiency. Thus, to characterize the performance of such UEs, methods explicitly accounting for UE mobility are needed. In this paper, we will utilize the tools of the stochastic geometry and random walk theory to derive signal-to-noise ratio (SNR), spectral efficiency, and rate as an explicit function of time by accounting for mmWave/sub-THZ specifics, including realistic directional antenna radiation patterns and micro- and macro-mobilities causing dynamic antenna misalignment. Different from other studies in the field that consider time-averaged performance measures, these metrics are obtained as an explicit function of time. Our numerical results illustrate that the macro-mobility specifies the overall trend of the time-dependent spectral efficiency, while local dynamics at 1–3 s scales are mainly governed by micro-mobility. The difference between spectral efficiency corresponding to perfectly synchronized UE and BS antennas and time-dependent spectral efficiency in a completely desynchronized system is rather negligible for realistic cell coverages and stays within approximately 5–10% for a wide range of system parameters. These conclusions are not affected by the utilized antenna array at the BS side. However, accounting for realistic radiation patterns is critical for a time-dependent performance analysis of 5G/6G mmWave/sub-THz systems.

Negotiating Hegemonic Masculinities and Mutual Exoticization within Grindr Tourism Interactions: “Everyone in Tel Aviv is a Muscle God”

ABSTRACT Dating apps are spaces where masculinities are communicated digitally, affecting inequalities around gender expressions. This study aims to identify the masculinities communicated within Grindr tourism interactions in Tel Aviv and analyze how masculinities shape relations among international tourists and locals. Methods consist of interviews and audio diaries with nineteen tourist, local, and immigrant Grindr users in Tel Aviv. Theories of affordances, hegemonic masculinities, and relational sociology are used to analyze the data. In discussion of the results, the concept of mutual exoticization is put forth to interpret the social dynamics valued within Grindr tourism interactions, thereby revealing oppositional fantasies tied to nationalities and masculinities. The study also explores ethnicity’s association with social and erotic capital in digital circulations of a localized Mizrahi masculinity. Findings reveal that while hegemonic masculinities are circulated on Grindr, resistance is also a part of Grindr tourism relations around masculinities. This research concludes by shedding light on masculinity inequalities within digital spaces where different nationalities co-congregate, revealing everyday negotiations of gender regimes. Grindr is the site of both reproduction of and resistance to social inequalities around masculinities.

IoT Privacy Risks Revealed.

A user's devices such as their phone and computer are constantly bombarded by IoT devices and associated applications seeking connection to the user's devices. These IoT devices may or may not seek explicit user consent, thus leaving the users completely unaware the IoT device is collecting, using, and/or sharing their personal data or, only marginal informed, if the user consented to the connecting IoT device but did not read the associated privacy policies. Privacy policies are intended to inform users of what personally identifiable information (PII) data will be collected about them and the policies about how those PII data will be used and shared. This paper presents novel tools and the underlying algorithms employed by the Personal Privacy Assistant app (UTCID PPA) developed by the University of Texas at Austin Center for Identity to inform users of IoT devices seeking to connect to their devices and to notify those users of potential privacy risks posed by the respective IoT device. The assessment of these privacy risks must deal with the uncertainty associated with sharing the user's personal data. If privacy risk (R) equals the consequences (C) of an incident (i.e., personal data exposure) multiplied by the probability (P) of those consequences occurring (C × P), then efforts to control risks must seek to reduce the possible consequences of an incident as well as reduce the uncertainty of the incident and its consequences occurring. This research classifies risk according to two parameters: expected value of the incident's consequences and uncertainty (entropy) of those consequences. This research calculates the entropy of the privacy incident consequences by evaluating: (1) the data sharing policies governing the IoT resource and (2) the type of personal data exposed. The data sharing policies of an IoT resource are scored by the UTCID PrivacyCheck™, which uses machine learning to read and score the IoT resource privacy policies against metrics set forth by best practices and international regulations. The UTCID Identity Ecosystem uses empirical identity theft and fraud cases to assess the entropy of privacy incident consequences involving a specific type of personal data, such as name, address, Social Security number, fingerprint, and user location. By understanding the entropy of a privacy incident posed by a given IoT resource seeking to connect to a user's device, UTCID PPA offers actionable recommendations enhancing the user's control over IoT connections, interactions, their personal data, and, ultimately, user-centric privacy control.