Real-time User Research Articles

Monitoring Pemakaian Daya Listrik Rumah Berbasis IoT

The current prepaid and postpaid electrical payment systems still have limitation, in monitoring electrical powerconsumption. In postpaid electricity, recording through officers is still done manuallywhen monitoring electrical power usage, whereas for the prepaid system users cannot know electrical power usage in real time. Based on these conditions, a toolwas designed that capable of calculatingthe electrical power used by using home appliancesin real time and can be monitored remotely. This system is designed using the N. meanwhile, it can beode MCU ESP8266as the main controller and the PZEM-004T sensor to measure voltage (V), current(A), plectrical Powerower (W), and energy (kWh), as well as fees that must be paid (Rp) accordingly. Usage, whre the data will bedisplayed on a 20x4 LCD for on-site monitoring. Meanwhile, it canbe monitored remotely using Blynk IoT application whichis connected to the internet network. Generally this system can work with in error rate of <5%, where testing is carried out for 20 minutes for indescent lamp loads.

Inference Analysis of Video Quality of Experience in Relation with Face Emotion, Video Advertisement, and ITU-T P.1203

This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study’s aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T P.1203, by up to 37.1% in terms of accuracy and 21.74% after attribute selection. Our study contributes to the field in two ways. First, we offer a promising solution to enhance user satisfaction in video streaming services via real-time user emotion and user feedback integration, providing a more holistic understanding of user experience. Second, high-quality data collection and insights are offered by collecting real data from diverse regions to minimize any potential biases and provide advertisement placement suggestions.

Real-Time Ideation Analyzer and Information Recommender

The benefits of ideation for both industry and academia alike have been outlined by countless studies, leading to research into various approaches attempting to add new ideation methods or examine how the quality of the ideas and solutions created can be measured. Although AI-based approaches are being researched, there is no attempt to provide the ideation participants with information that inspire new ideas and solutions in real time. Our proposal presents a novel and intuitive approach that supports users in real time by providing them with relevant information as they conduct ideation. By analyzing their ideas within the respective ideation sessions, our approach recommends items of interest with high contextual similarity to the proposed ideas, allowing users to skim through, for example, publications and inspire new ideas quickly. The recommendations also evolve in real time. As more ideas are written during the ideation session, the recommendations become more precise. This real-time approach is instantiated with various ideation methods as a proof of concept, and various models are evaluated and compared to identify the best model for working with ideas.

A Fullstack Web Application to Provide On-demand Household Services

Abstract: A Fullstack Web Application to Provide On-demand Household Services is a paper focused on designing and creating a full stack web application. The application aims to help users find nearby household service providers, such as electricians, plumbers, gardeners, carpenters etc. Users can easily browse the available services, book the appointments, track the location of service professionals, and share feedback about completed work. For service professionals, the application offers tools to manage appointments and track earnings. The application incorporates advanced technologies like Blockchain for secure storage of customer ratings, reviews, and work history generated by service professionals. Additionally, Machine Learning is utilized to develop chatbots for real-time user assistance. Few technological domains which are covered in this application are Full Stack development, Blockchain technology and Machine Learning. In summary, the goal of this application is to improve the skilled workforce connection system, making it efficient, secure, and engaging for all users

Skeleton based tetrahedralization of surface meshes

We propose a new method for generating tetrahedralizations for 3D surface meshes. The method builds upon a segmentation of the mesh that forms a rooted skeleton structure. Each segment in the structure is fitted with a stamp - a predefined basic shape with a regular and well-defined topology. After molding each stamp to the shape of the segment it is assigned to, we connect the segments with a layer of tetrahedra using a new approach to stitching two triangulated surfaces with tetrahedra. Our method not only generates a tetrahedralization with regular topology mimicking a bone-like structure with tissue being grouped around it, but also achieves running times that would allow for real-time usages. The running time of the method is closely correlated with the density of the input mesh which allows for controlling the expected time by decreasing the vertex count while still preserving the general shape of the object.

SlowR50-SA: A Self-Attention Enhanced Dynamic Facial Expression Recognition Model for Tactile Internet Applications

Emotion recognition from facial expressions is a challenging task due to the subtle and nuanced nature of facial expressions. Within the framework of Tactile Internet (TI), the integration of this technology has the capacity to completely transform real-time user interactions, by delivering customized emotional input. The influence of this technology is far-reaching, as it may be used in immersive virtual reality interactions and remote tele-care applications to identify emotional states in patients. In this paper, a novel emotion recognition algorithm is presented that integrates a Self-Attention (SA) module into the SlowR50 backbone (SlowR50-SA). The experiments on the DFEW and FERV39K datasets demonstrate that the proposed model achieves good performance in terms of both Unweighted Average Recall (UAR) and Weighted Average Recall (WAR) metrics, achieving a UAR (WAR) of 57.09% (69.87%) on the DFEW dataset, and UAR (WAR) of 39.48% (49.34%) on the FERV39K dataset. Notably, SlowR50-SA operates with only eight frames of input at low temporal resolution, highlighting its efficiency. Furthermore, the algorithm has the potential to be integrated into Tactile Internet applications, where it can be used to enhance the user experience by providing real-time emotion feedback. SlowR50-SA can also be used to enhance virtual reality experiences by providing personalized haptic feedback based on the user’s emotional state. It can also be used in remote tele-care applications to detect signs of stress, anxiety, or depression in patients.

Research on the design and application of 3D scene animation game entertainment system based on user motion sensing participation

Developed a 3D indoor virtual scene assembly system based on dual touch screen. The system adopts a combination of 2D views and 3D views. The 2D and 3D views are displayed on the two touch screens. We have comprehensively designed various interactive operations of 2D view and 3D view from the perspectives of simplicity, easy learning and easy to use, and reduced the complex 3D interactive operations to 2D interactive operations. Users can conduct two-dimensional interactive operation and 3D interactive operation based on two display screens, which can realize the efficient assembly of 3D virtual scene. The results indicate significant differences in the number of modifications of mode A and B (t = 2.982, p = 0.006 < 0.01), effective design time (t = 4.990, p < 0.001), first design satisfaction (t = 5.074, p = <0.001) and final design satisfaction (t = 2.787, p = 0.010 < 0.05). Participants in mode A had fewer modifications and shorter effective design times compared to mode B. Study and design a new workbench supporting the collaborative design of 3D virtual scenes. The workbench can support a three-dimensional indoor virtual scene assembly system based on dual touch screen, and designers and users can assemble the virtual scene together. The workbench mainly consists of two touch displays and a holographic display device. The holographic display device displays the three-dimensional scene designed by the user in real time, and supports the designer and the user to operate the three-dimensional scene through the touch film attached to it. Touch display enables designers and users to assemble scenes on 2D views through simple touch operations such as drag and drop. The face-to-face collaborative design method provided by the workbench is more conducive to the communication between designers and users, so as to improve the design efficiency of 3D scenes.

An Integrated Smart Metering System Using LORA Technology

Precise meter readings are essential for effectively regulating residential energy systems in India's smart city context. However, due to the high number of users and the absence of daily consumption monitoring, standard meter reading methods are expensive and time-consuming, which causes consumer unhappiness. The suggested approach to address this issue is to put in place an integrated wireless smart energy system that makes use of smart metering technologies. The way utilities manage energy could be completely changed by this method. The integrated system is intended to take the place of the traditional digital energy meters, which are primarily used to record meter readings properly for payment purposes. This allows for automatic meter readings without the need for frequent travels to the location where the meters are located. Low Power Wide Area Networks (LPWAN) technology, which is renowned for its low power consumption, cost-effectiveness, long-range coverage, and efficient penetration, are used in this technology to enable the transfer of accurate and secure data on energy usage in real-time. The experiments that resulted from the trials allowed the energy to successfully communicate and documented reliable readings. By providing users with real-time usage data and guarding against overloaded scenarios, the system also benefits consumers. Additionally, the device reports electricity theft to the utility company. The shortcomings of the conventional systems and design difficulties are addressed by the suggested system.

A Multiperspective Fraud Detection Method for Multiparticipant E-Commerce Transactions

Detection and prevention of fraudulent transactions in e-commerce platforms have always been the focus of transaction security systems. However, due to the concealment of e-commerce, it is not easy to capture attackers solely based on the historic order information. Many works try to develop technologies to prevent frauds, which have not considered the dynamic behaviors of users from multiple perspectives. This leads to an inefficient detection of fraudulent behaviors. To this end, this article proposes a novel fraud detection method that integrates machine learning and process mining models to monitor real-time user behaviors. First, we establish a process model concerning the business-to-customer (B2C) e-commerce platform, by incorporating the detection of user behaviors. Second, a method for analyzing abnormalities that can extract important features from event logs is presented. Then, we feed the extracted features to a support vector machine (SVM)-based classification model that can detect fraud behaviors. We demonstrate the effectiveness of our method in capturing dynamic fraudulent behaviors in e-commerce systems through the experiments.

Visual Design and Evaluation of Public Space Scene Based on Virtual Reality Technology

Public space scenes are being transformed by virtual reality (VR) technology, offering immersive and interactive experiences that redefine how people engage with their surroundings. Through VR simulations, users can explore and interact with virtual public spaces in ways that were previously impossible. From virtual parks and plazas to digital replicas of iconic landmarks, VR technology allows users to experience the ambiance and atmosphere of public spaces from the comfort of their own homes. Moreover, VR enables designers and planners to experiment with different layouts, amenities, and features, facilitating participatory design processes and community engagement. This paper presents a visual design and evaluation framework for public space scenes using virtual reality (VR) technology, enhanced by Virtual Parallel Edge Hashing Computing (VPE-HC). The framework aims to optimize the design and user experience of public spaces by leveraging VR technology to create immersive and interactive simulations. Through simulated experiments and empirical validations, the effectiveness of the VPE-HC-enhanced VR-based design and evaluation process is evaluated. Results demonstrate significant improvements in user satisfaction, engagement, and usability compared to traditional design methods. For instance, users interacting with public space scenes designed using VPE-HC reported a 40% increase in perceived safety and a 25% improvement in overall satisfaction ratings. Additionally, the framework enabled designers to optimize spatial layouts and amenities based on real-time user feedback and performance data, leading to more effective and user-centric design solutions. These findings underscore the potential of VPE-HC in enhancing the visual design and evaluation of public space scenes based on VR technology, facilitating more inclusive and user-friendly urban environments.

Design of Visual Communication Experience Based on Virtual Reality Technology

Visual communication experiences have been revolutionized by virtual reality (VR) technology, offering users immersive and captivating interactions with digital content. Through VR headsets and immersive environments, users can engage with visual content in unprecedented ways, feeling as though they are physically present within the digital space. Whether exploring virtual art galleries, immersive storytelling experiences, or interactive visualizations, VR technology provides users with a sense of presence and agency, enhancing their connection to the content. Furthermore, VR enables creators to design highly engaging and interactive visual experiences that transcend traditional media formats, allowing for innovative storytelling techniques and immersive narratives. This paper presents the design of a novel visual communication experience leveraging virtual reality (VR) technology enhanced by Multi-Spectral Multi-Point Co-Ordination (MSMPC). The proposed design aims to create immersive and engaging visual experiences that transcend traditional media formats. Through simulated experiments and empirical validations, the effectiveness of the MSMPC-enhanced VR-based visual communication experience is evaluated. Results demonstrate significant improvements in user engagement, immersion, and satisfaction compared to conventional approaches. The users interacting with the MSMPC-enhanced VR experience reported a 40% increase in immersion levels and a 25% improvement in overall satisfaction ratings. Additionally, the design enabled creators to optimize visual storytelling techniques based on real-time user feedback and performance data, leading to more impactful and effective communication. These findings highlight the potential of MSMPC in enhancing visual communication experiences based on VR technology, paving the way for innovative and immersive storytelling in various domains.

Tackling the cold start issue in movie recommendations with a refined epsilon-greedy approach

With the rapid growth of the Internet and the consequent surge in data, the current era is characterized by information overload. As the domain of data processing and storage expands, recommendation systems have become pivotal tools in navigating this deluge, assisting users in filtering through vast information landscapes. A notable segment of this is movie recommendation systems. As living standards rise, so does the demand for cinematic experiences. Enhancing and refining the methodologies of these recommendation systems is, therefore, of significant value. However, a consistent challenge is the cold start problem encountered when new users join. Without prior viewing records or preferences, these users pose a dilemma for the system: how to offer relevant recommendations without historical data? Addressing this challenge, this paper proposes a unique method grounded in the N-armed bandit model, introducing an enhanced Epsilon-greedy algorithm specifically designed for movie recommendations for such users. By adjusting dynamically based on real-time user feedback, the algorithm aims to continuously hone its recommendation quality, ensuring a consistently better user experience.

Solar based Smart Sanitizer Dispenser System with Internet of Things (IoT) Technology

In response to the growing demand for efficient and sustainable hygiene solutions, this project introduces a Solar-Powered Smart Sanitizer Dispenser System leveraging IoT technology. The system aims to address the need for touchless and accessible hand sanitization in various environments, including public spaces, healthcare facilities, and commercial establishments. By integrating solar power as the primary energy source, the system offers autonomy and eco-friendliness, reducing dependency on conventional electricity grids and minimizing environmental impact. The core components of the system include a smart dispenser unit equipped with sensors for detecting hand proximity, a reservoir for storing sanitizer solution, and a microcontroller unit responsible for data processing and IoT connectivity. Through the IoT interface, users can remotely monitor sanitizer levels, receive real-time usage statistics, and manage dispenser settings, enhancing operational efficiency and maintenance. Furthermore, the incorporation of solar panels ensures continuous operation even in off-grid locations, making the system suitable for deployment in remote or outdoor settings where access to electricity may be limited. Maximizing the efficacy of solar power conversion and extending operational uptime.

Developing a New ANN Model to Estimate Daily Actual Evapotranspiration Using Limited Climatic Data and Remote Sensing Techniques for Sustainable Water Management

Accurate estimations of actual evapotranspiration (ETa) are essential to various environmental issues. Artificial intelligence-based models are a promising alternative to the most common direct ETa estimation techniques and indirect methods by remote sensing (RS)-based surface energy balance models. Artificial Neural Networks (ANNs) are proven to be suitable for predicting reference evapotranspiration (ETo) and ETa based on RS data. This study aims to develop a methodology based on ANNs for estimating daily ETa values using NDVI and land surface temperature, coupled with limited site-specific climatic variables in a large irrigation catchment. The ANN model has been applied to the two different scenarios. Data from only the 38 days of satellite overpass dates was selected in Scenario I, while in Scenario II all datasets, i.e., the 769-day data were used. An irrigation scheme, located in the Mediterranean region of Turkiye, was selected, and a total of 38 Landsat images and local climatic data collected in 2021 and 2022 were used in the ANN model. The ETa results by the ANN model for Scenarios I and II showed that the R2 values for training (0.79 and 0.86), testing (0.75 and 0.81), and the entire dataset (0.76 and 0.84) were all remarkably high. Moreover, the results of the new ANN model in two scenarios showed an acceptable agreement with ETa-METRIC values. The proposed ANN model demonstrated the potential for obtaining daily ETa using limited climatic data and RS imagery. As a result, the suggested ANN model for daily ETa computation offers a trustworthy way to determine crop water usage in real time for sustainable water management in agriculture. It may also be used to assess how crop evapotranspiration in drought-prone areas will be affected by climate change in the 21st century.

Qualitative and quantitative enhancement of parameter estimation for model-based diagnostics using automatic differentiation with an application to inertial fusion

Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require O(103) parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.

Polybenzoxazine-Based Nitrogen-Containing Porous Carbon and Their Composites with NiCo Bimetallic Oxides for Supercapacitor Applications.

Supercapacitors (SCs) are considered as emerging energy storage devices that bridge the gap between electrolytic capacitors and rechargeable batteries. However, due to their low energy density, their real-time usage is restricted. Hence, to enhance the energy density of SCs, we prepared hetero-atom-doped carbon along with bimetallic oxides at different calcination temperatures, viz., HC/NiCo@600, HC/NiCo@700, HC/NiCo@800 and HC/NiCo@900. The material produced at 800 °C (HC/NiCo@800) exhibits a hierarchical 3D flower-like morphology. The electrochemical measurement of the prepared materials was performed in a three-electrode system showing an enhanced specific capacitance for HC/NiCo@600 (Cs = 1515 F g-1) in 1 M KOH, at a current density of 1 A g-1, among others. An asymmetric SC device was also fabricated using HC/NiCo@800 as anode and HC as cathode (HC/NiCo@600//HC). The fabricated device had the ability to operate at a high voltage window (~1.6 V), exhibiting a specific capacitance of 142 F g-1 at a current density of 1 A g-1; power density of 743.11 W kg-1 and energy density of 49.93 Wh kg-1. Altogether, a simple strategy of hetero-atom doping and bimetallic inclusion into the carbon framework enhances the energy density of SCs.

Thermal Comfort Model Established by Using Machine Learning Strategies Based on Physiological Parameters in Hot and Cold Environments

The air-conditioning systems have become an indispensable part of our daily life for keeping the quality of life. However, to improve the thermal comfort and reduce energy consumption is crucial to use the air conditioners effectively with rapid development of artificial intelligence technology. This study explored the correlation between the response of human physiological parameters and thermal sensation voting (TSV) to evaluate the comfort level among various cold and hot stimulations. The variations of the three physiological parameters, which were body surface temperature, skin blood flow (SBF), and sweat area on the skin surface, and TSV values were all positively correlated with the stimulation amount under the stimulation of cold wind, hot wind, and heat radiation, but the relationship was not completely linear. Among the three physiological parameters, the forehead skin temperature has the closest relationship with TSV, followed by the SBF and sweat. Among three stimulations, the cold wind stimulation causes the closest relationship between TSV and forehead temperature, followed by the radiation and hot wind stimulations. Through three different machine learning models, namely, random forest (RF) model, support vector machine (SVM) model, and neural network (NN) model, the stimulation of cold wind, hot wind, and heat radiation was applied to investigate the variation of the three physiological parameters as the input of the models. Moreover, the models were evaluated and verified by TSV. The results revealed that among the three different machine learning methods, RF had the best accuracy. The established thermal comfort models can predict the real-time user’s thermal comfort feeling, so that air-conditioning equipment’s performance can be optimized to create a healthy and energy-saving comfortable environment.

Efficient Bike-sharing Repositioning with Cooperative Multi-Agent Deep Reinforcement Learning

As an emerging mobility-on-demand service, bike-sharing system (BSS) has spread all over the world by providing a flexible, cost-efficient, and environment-friendly transportation mode for citizens. Demand-supply unbalance is one of the main challenges in BSS because of the inefficiency of the existing bike repositioning strategy, which reallocates bikes according to a pre-defined periodic schedule without considering the highly dynamic user demands. While reinforcement learning has been used in some repositioning problems for mitigating demand-supply unbalance, there are significant barriers when extending it to BSS due to the dimension curse of action space resulting from the dynamic number of workers and bikes in the city. In this paper, we study these barriers and address them by proposing a novel bike repositioning system, namely BikeBrain, which consists of a demand prediction model and a spatio-temporal bike repositioning algorithm. Specifically, to obtain accurate and real-time usage demand for efficient bike repositioning, we first present a prediction model ST-NetPre, which directly predicts user demand considering the highly dynamic spatio-temporal characteristics. Furthermore, we propose a spatio-temporal cooperative multi-agent reinforcement learning method (ST-CBR) for learning the worker-based bike repositioning strategy in which each worker in BSS is considered an agent. Especially, ST-CBR adopts the centralized learning and decentralized execution way to achieve effective cooperation among large-scale dynamic agents based on Mean Field Reinforcement Learning (MFRL), while avoiding the huge dimension of action space. For dynamic action space, ST-CBR utilizes a SoftMax selector to select the specific action. Meanwhile, for the benefits and costs of agents’ operation, an efficient reward function is designed to seek an optimal control policy considering both immediate and future rewards. Extensive experiments are conducted based on large-scale real-world datasets, and the results have shown significant improvements of our proposed method over several state-of-the-art baselines on the demand-supply gap and operation cost measures.

GLULA: Linear attention-based model for efficient human activity recognition from wearable sensors.

Body-worn sensor data is used in monitoring patient activity during rehabilitation and also can be extended to controlling rehabilitation devices based on the activity of the person. The primary focus of research has been on effectively capturing the spatiotemporal dependencies in the data collected by these sensors and efficiently classifying human activities. With the increasing complexity and size of models, there is a growing emphasis on optimizing their efficiency in terms of memory usage and inference time for real-time usage and mobile computers. While hybrid models combining convolutional and recurrent neural networks have shown strong performance compared to traditional approaches, self-attention-based networks have demonstrated even superior results. However, instead of relying on the same transformer architecture, there is an opportunity to develop a novel framework that incorporates recent advancements to enhance speed and memory efficiency, specifically tailored for human activity recognition (HAR) tasks. In line with this approach, we present GLULA, a unique architecture for HAR. GLULA combines gated convolutional networks, branched convolutions, and linear self-attention to achieve efficient and powerful solutions. To enhance the performance of our proposed architecture, we employed manifold mixup as an augmentation variant which proved beneficial in limited data settings. Extensive experiments were conducted on five benchmark datasets: PAMAP2, SKODA, OPPORTUNITY, DAPHNET, and USC-HAD. Our findings demonstrate that GLULA outperforms recent models in the literature on the latter four datasets but also exhibits the lowest parameter count and close to the fastest inference time among state-of-the-art models.

PLATFORMS, POWER &amp; ADVERTISING: ANALYSING RELATIONS OF DEPENDENCY IN THE DIGITAL ADVERTISING ECOSYSTEM

This paper examines how dominant institutional actors exercise power and control over the digital advertising ecosystem. It pursues this inquiry through a case study on the 2021 introduction of Apple’s App Tracking Transparency (ATT) feature—a privacy setting newly integrated in the operating system of iOS mobile devices. Developing this case study, we ask: How do dominant market actors exercise control over the infrastructural layers of the ‘mobile ad stack’ and how do they gain access to end-user data? These questions are addressed through a mix-methods approach that involves (A) analysis of developer documentation provided by Apple, (B) a review of ongoing litigation, and (C) analysis of financial disclosure forms of two ad-driven platforms Meta and Snapchat. This inquiry shows, first, how and why Facebook and Google, each in their own way, have been highly successful in their ability to aggregate both ad inventory and accurate, real-time user data. Second, it demonstrates how ATT blocked the access of advertising platforms to a key part of this real-time user data, while, simultaneously, enabling Apple to gain control over end-users’ mobile data. Thus, the rollout of ATT and its subsequent shifts in revenue and data demonstrate the relational and constantly evolving nature of institutional power in the mobile advertising ecosystem.