User Model Research Articles

Topic based document modeling for information filtering

Information Filtering (IF), which has been popularly studied in recent years, is one of the areas that applies document retrieval techniques for dealing with the huge amount of information. In IF systems, modelling user’s interest and filtering relevant documents are major parts of the systems. Various approaches have been proposed for modelling the first component. In this study, we utilized a topic-modelling technique, Latent Dirichlet Topic Modelling, to model user’s interest for IFs. In particular, an extended model of it to represent user’s interest named Latent Dirichlet Topic Modelling with high Frequency Occurrences, shorted as LDA_HF, was proposed with the intention to enhance retrieving performance of IFs. The new model was then compared to the existing methods in modelling user’s interest such as BM25, pLSA, and LDA_IF over the big benchmark datasets, RCV1 and R8. The results of extensive experiments showed that the new proposed model outperformed all the state-of-the-art baseline models in user modelling such as BM25, pLSA and LDA_IF according to 4 major measurement metrics including Top20, B/P, MAP, and F1. Hence, the model LDA_HF promises one of the reliable methods of enhancing performance of IFs.

COVID-19 preventive behaviors and digital health communication media usage model

COVID-19 preventive behaviors and digital health communication media usage model

RETRACTED ARTICLE: Enhancing throughput using channel access priorities in frequency hopping network using federated learning

The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection against eavesdropping and anti-interference capabilities is provided. When using FHC in an environment, sharing time and frequency resources, avoiding collisions, and differentiating services are all made more complex as a result of this. A protocol for FHC that is based on dispersed wireless networks is presented by the authors of this research. It is a mechanism for multiple access control, which is prioritised and distributed. The ratio of empty channels metric can be found in the previous sentence. It is possible to provide priority in channel access by assigning different preset ratios of empty channel thresholds to the various traffic classes. Frames from frequency spread segments that have a partial collision are included as well. An analytical model is simulated for the analysis in terms of collision probability, transmission probability, and frame service time in order to carry out a theoretical examination of the performance of FHC. The objective of this inquiry is to determine how well FHC works. The analytical model has been proven correct by the exhaustive simulations as well as the theoretical findings. Cloud platforms are often used in the instruction of the most cutting-edge machine learning techniques of today, such as deep neural networks. This is done in order to take advantage of the cloud's capacity to scale elastically. In order to satisfy the criteria of these sorts of applications, federated learning, has been proposed as a distributed machine learning solution. This is done in order to fulfil the requirements of these kinds of applications. In federated learning (FL), even though everyone who uses the system works together to train a model, nobody ever shares their data with anybody else. Each user trains a local model with their own data, and then communicates the updated models with a FL server so that the data can be aggregated and a global model can be constructed. This process ensures that each user's model is unique. This process is repeated until a global model has been developed. This kind of training not only reduces the amount of network overhead that is necessary to transfer data to a centralised server, but it also safeguards the personal information of the users. Within the framework of this work, we looked at the feasibility of using the FL technique of learning on the many devices that are part of the dispersed network. On a centralised server, we conduct an analysis of the performance of the FL model by comparing its accuracy and the amount of time it takes to train using a range of various parameter value combinations. Additionally, the accuracy of these federated models may be made to reach a level that is comparable to that of the accuracy of central models.

Smartphone Apps-Based Health Communication Media Usage among Older Individuals During the COVID-19 Pandemic: The Role of System Quality Factors

During covid-19 pandemic, health education was performed by using online media, such as smartphone apps-based health communication media. Furthermore, middle and older individuals, 45 to 60 years old, were categorized as vulnerable during the pandemic. Thus, it was important to study smartphone apps-based health communication media usage of 45 to 60 years old individuals during the pandemic. However, there was no previous research that developed and tested a model of smartphone apps-based health communication media usage of 45 to 60 years old individuals during the pandemic. Given this, this study aimed to develop and test a model of smartphone apps-based health communication media usage of 45 to 60 years old individuals during the COVID-19 pandemic. More specifically, this study aimed to test the effect of system quality factors (ease of use, security, and interactivity), information timeliness, information completeness, information sources trustworthiness, perceived threat of COVID-19, and social environment support on smartphone apps-based health communication media usage. The research was conducted using a cross-sectional design. The data were collected through a survey method in the district and city of Tangerang. The survey locations were chosen due to the location’s internet access and the COVID-19 pandemic problems. The number of samples was 105. The data were analyzed using multiple regressions analysis. The results of the research showed that the smartphone apps-based health communication media usage of the 45 to 60 years old individuals was affected by ease of use, information completeness, social environment support, and perceived threat of COVID-19. However, information timeliness, information source trustworthiness, security, and interactivity were not proven to have a significant impact on the media usage. The managerial and policy implications were identified based on the findings.

Empathetic Conversational Systems: A Review of Current Advances, Gaps, and Opportunities

Empathy is a vital factor that contributes to mutual understanding, and joint problem-solving. In recent years, a growing number of studies have recognized the benefits of empathy and started to incorporate empathy in conversational systems. We refer to this topic as empathetic conversational systems. To identify the critical gaps and future opportunities in this topic, this paper examines this rapidly growing field using five review dimensions: (i) conceptual empathy models and frameworks, (ii) adopted empathy-related concepts, (iii) datasets and algorithmic techniques developed, (iv) evaluation strategies, and (v) state-of-the-art approaches. The findings show that most studies have centered on the use of the EMPATHETICDIALOGUES dataset, and the text-based modality dominates research in this field. Studies mainly focused on extracting features from the messages of the users and the conversational systems, with minimal emphasis on user modeling and profiling. Notably, studies that have incorporated emotion causes, external knowledge, and affect matching in the response generation models, have obtained significantly better results. For implementation in diverse real-world settings, we recommend that future studies should address key gaps in areas of detecting and authenticating emotions at the entity level, handling multimodal inputs, displaying more nuanced empathetic behaviors, and encompassing additional dialogue system features.

3D Boundary Modeling and Handover Analysis of Aerial Users in Heterogeneous Networks

Aerial user equipments (AUEs) levitating in the air are growingly used in various applications for the agility, but leads to link fluctuation and deterioration of handover (HO) performance, which is marked in heterogeneous networks (HetNets). However, existing HO models have not investigated HO performance of cellular-connected unmanned aerial vehicles (UAVs) under the influence of shadow fading at different altitudes. This paper proposes a 3D boundary model derived by trigger conditions to analyze HO performance of aerial users considering the shadow fading effect in two-tier HetNets, where the 3D HO trigger boundary and handover failure (HF) boundary are both in the form of a hemispherical shell with typical small base station (SBS) as center, composed of the point sets corresponding to the above HO events, which are derived by the Received Signal Strength (RSS) ratio of target SBS to source macro base station (MBS). Especially, the calculation of HO events probabilities under trigger conditions are transformed to the relationship between time-to-trigger (TTT) and during time inside 3D boundary by exploring the positional relationship between straight line and hemispherical shell boundaries. Thus, the compact expressions of HO probability, HF probability and PP probability are obtained to quantify the effect of 3D mobility and HO parameters in various deployment of HetNets. Theoretical results show that HF and PP probability dramatically drop by 68.5% and 47.1% when <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H=20$</tex-math></inline-formula> m comparing to scenario of terrestrial users and HetNets deployment should be considered when choosing appropriate HO parameters.

Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference

Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based recommender systems. In most reinforcement learning applications, reward functions provide the critical guideline for optimization. However, current reinforcement learning-based methods rely on manually-defined reward functions, which cannot adapt to dynamic, noisy environments. Moreover, they generally use task-specific reward functions that sacrifice generalization ability. We propose a generative inverse reinforcement learning for user behavioral preference modeling to address the above issues. Instead of using predefined reward functions, our model can automatically learn the rewards from user&#x0027;s actions based on discriminative actor-critic network and Wasserstein GAN. Our model provides a general approach to characterizing and explaining underlying behavioral tendencies. Our experiments show our method outperforms state-of-the-art methods in several scenarios, namely traffic signal control, online recommender systems, and scanpath prediction.

Research and reflection on college physical education classroom teaching based on SSGAN model

Abstract Teaching behavior recognition has a wide range of applications in the smart classroom and is one of the important means to achieve educational intelligence. To improve the performance of indoor teaching behavior recognition using CSI in complex scenes, this paper proposes an indoor teaching behavior recognition algorithm based on multi-feature fusion MLSTM by eliminating background noise to circumvent the influence of the experimental environment on CSI. To address the problem, the model cannot generalize in recognizing new users, and the labeled samples of new users are difficult to obtain in large quantities in a short period of time. In this paper, a new user recognition algorithm based on the SSGAN model is constructed, and then the input and output of MLSTM are modified as the discriminator of SSGAN to improve the recognition performance of the model for new users by semi-supervised learning. The recognition accuracy of the M-LSTM model on the sports, daily, and dance datasets is 0.985, 0.966, and 0.944, respectively, and the recognition accuracy of the SSGAN model on the three datasets is also around 90%, as verified by different experiments. The p-value is less than 0.05, and the student’s interest in physical education in the experimental group is 2.6 times higher than that in the control group. Therefore, the model proposed in this paper has good practicality.

ICT를 활용한 문학의 미래 모델 연구

This study analyzed the main factors affecting literature and the literary ecosystem in the ICT environment and what kind of future model of literature will be formed. As a result of the analysis, the literary ecosystem forms a future model in which 'literary content as IP' is traded and distributed based on platforms in a rapidly changing technological and policy environment in which literary content creators and producers and literary content users interact through ICT factors such as artificial intelligence, VR and AR, blockchain, digital publishing, and platforms. In this model, authors and communities of authors, creative users, copyright agencies, and artificial intelligence agencies are the agents of creation and production. The areas of 'creation/production' and 'reading/use' will be converged based on platforms, and the phenomenon of mutual conversion will become commonplace. Literature will evolve into an IP-centered activity model that creates transmedia content that crosses media and genres, centered on technology-based platforms.

Back and Forth—On Automatic Exposure of Origin and Dissemination of Files on Windows

The number of Child Sexual Abuse Material (CSAM) cases has increased dramatically in recent years. This leads to the need to automate various steps in digital forensic processing, especially for CSAM investigations. For instance, if CSAM pictures are found on a device, the investigator aim at finding traces about the origin and possible further dissemination, respectively. In this article, we address this challenge with respect to the widespread Windows operating system. We model different common scenarios of system use by CSAM offenders in the scope of file inbound and outbound transfer channels. This gives us insights about digital traces in the Windows operating system and its applications to get knowledge about origin and possible destination of a file. We review available concepts and applications to support this issue. Furthermore, we develop a recursive-based approach and provide a prototype as plugin for the open source application Autopsy. We call our prototype AutoTrack . Our evaluation against the different models of Windows system usage reveals that Autotrack is superior to existing solutions and provides support for an investigator to find digital traces about the origin and possible further dissemination of files. We publish our AutoTrack plugin and thus provide full reproducibility of our approach.

Could the Technology for Adaptive Learning Systems Come out of GBL?

Games based learning (GBL) has a related field that it can draw upon: intelligent tutoring systems (ITS). Acommon concern in both fields is adaptivity, whereby the system can automatically adapt to the user. In order to supportthis adaptation, an ITS will generally include a user model and may also have a formal domain model. Work in this areastarted optimistically some years ago, but seems to have either lost some of that initial enthusiasm or been diverted intoother directions. We scan recent ITS literature to help consider why this might be, and suggest how GBL may be the fieldbest placed to take the work forward again. Learner models have long been seen as useful for adaptive learning systems.They include information about the learner which allows the system to adapt the course of learning materials andexercises to the learner's particular characteristics. In order to achieve a good quality of adaptation to the user, a detailedmodel of the required domain knowledge is typically added. The user and domain models then have to be broughttogether to lay out a course of exercises for the learner to do, and to track progress as the knowledge is learned. It's anattractive research programme, but recent work has moved to new issues, such as MOOCs. The reasons for that are partlyopportunistic and economical, but also suggest a deeper problem with the research programme. It is a costly task todevelop a domain model, and a suitable learner model that can take advantage of it. We suggest that GBL is in a goodposition to push through this cost barrier, because much of the effort is already implicitly involved in the game designprocess, which typically has to be more rigorously planned out in order to make the game a good one. One might thusexpect the next breakthroughs in adaptive learning systems to come from GBL. We further argue that the advantages toresearch, offered by the ITS framework, are also potentially beneficial to the way we teach the subject of GBL to ourstudents on game development courses.

The Consistency of Gamification User Types: A Study on the Change of Preferences over Time

In recent decades, several studies have suggested and validated user models (e.g., Bartle, and Hexad) to represent different user profiles in games and gamified environments. However, when applying these user models in practice (e.g., to personalize gamification), several studies reported contradictory outcomes. Recently, some studies outlined that one of the possible explanations for these contradictory findings is that people can present changes in their user profiles over time. In this study (N = 118), we present an analysis of the consistency of gamification user orientations after six months of the initial identification, by analyzing the association between user orientations in the first and second data collection. Overall, our results corroborate prior research demonstrating that user orientations can not be considered stable over time and also that the strongest tendency of the users might not be sufficient to determine how users change. Furthermore, we were able to identify that some user orientations can be more stable than others and model some relationships between their profiles after six months. Based on the results, we indicate a research agenda that can further the knowledge about the topic, as well as indicate a set of suggestions on how to model user profiles based on our results.

Enhanced Method for Emergency Scheduling of Natural Gas Pipeline Networks Based on Heuristic Optimization

Safety and disturbance issues in system engineering have garnered substantial attention. This study focuses on the analysis of the distinct characteristics of emergency dispatch problems in Natural Gas Pipeline Networks (NGPS). Graph theory serves as a tool to transform the NGPS topology and establish an optimization model for NGPS emergency dispatch. The model also integrates user weights, satisfaction, and reduction factors into the user modeling approach. Its objective is to maximize overall system satisfaction while considering factors such as demand-side requirements and operational constraints. To solve this optimization model, the Particle Swarm Optimization (PSO) method is employed. An in-depth exploration of four unique disturbance scenarios provides solid evidence of the effectiveness and practicality of the PSO method. Compared to other methods, the PSO method consistently boosts overall user satisfaction and aligns more fluidly with the real-time demands of emergency scheduling, regardless of reduced supply capacity, complete supply interruptions, sudden surges in user demand, or pipeline connection failures. The developed emergency scheduling optimization method presents two key advantages. Firstly, it proficiently mitigates potential losses stemming from decreased supply capacity at local or regional levels. By adeptly adjusting natural gas supply strategies, it minimizes economic and production losses while ensuring a steady supply to critical users. Secondly, the method is superior at swiftly reducing the affected area and managing the increased demand for natural gas, thus maintaining NGPS stability. This research underscores the importance of considering user characteristics and demands during emergencies and demonstrates the effectiveness of employing the PSO method to navigate emergency scheduling challenges. By strengthening the resilience of the pipeline network and ensuring a sustainable natural gas supply, this study constitutes a significant contribution to energy security, economic development, and the promotion of clean energy utilization, ultimately propelling the achievement of sustainable development goals.

HT2ML: An efficient hybrid framework for privacy-preserving Machine Learning using HE and TEE

Outsourcing Machine Learning (ML) tasks to cloud servers is a cost-effective solution when dealing with distributed data. However, outsourcing these tasks to cloud servers could lead to data breaches. Secure computing methods, such as Homomorphic Encryption (HE) and Trusted Execution Environments (TEE), have been used to protect outsourced data. Nevertheless, HE remains inefficient in processing complicated functions (e.g., non-linear functions) and TEE (e.g., Intel SGX) is not ideal for directly processing ML tasks due to side-channel attacks and parallel-unfriendly computation.In this paper, we propose a hybrid framework integrating SGX and HE, called HT2ML, to protect user's data and models. In HT2ML, HE-friendly functions are protected with HE and performed outside the enclave, while the remaining operations are performed inside the enclave obliviously. HT2ML leverages optimised HE matrix multiplications to accelerate HE computations outside the enclave while using oblivious blocks inside the enclave to prevent access-pattern-based attacks. We evaluate HT2ML using Linear Regression (LR) training and Convolutional Neural Network (CNN) inference as two instantiations. The performance results show that HT2ML is up to ∼11× faster than HE only baseline with 6-dimensional data in LR training. For CNN inference, HT2ML is ∼196× faster than the most recent approach (Xiao et al., ICDCS'21).

Is ownership of brands passe? A new model of temporary usage for durable goods

PurposeThe trend toward subscription economy accelerated the rise of access-based consumption models for durable consumer goods, replacing individual ownership with subscription contracts. At the same time, disruptive platform businesses have arisen in several consumer markets, bypassing traditional value chains while growing through network effects. In a conceptual approach, the authors address the future market for durable consumer goods in light of developments toward access-based consumption, subscription models and platform business models.Design/methodology/approachIn a conceptual approach, the authors apply a scenario analysis following the Framework Foresight method and address trends, constants, plans and projections shaping the future market of subscriptions for durable goods. The authors create a baseline scenario and two alternative scenarios for the future of consumer durables and thereby discuss platform growth stages and implications for manufacturer brands.FindingsThe rising market power of platform companies leads to a baseline scenario where these platforms enter the market of subscriptions for durable goods. Alternative scenario 1 addresses the successful market entry of new platform businesses. In contrast, alternative scenario 2 describes the rise of manufacturer brand platforms.Originality/valueThis conceptual research enriches the discussion of access-based business models by creating scenarios depicting possible future developments. Moreover, it adds to the increasing focus on platform business models and thereby addresses the role of traditional manufacturer brands in markets for durable consumer goods subscriptions.

Privacy protection of quantum BP neural network based on game theory

How to ensure privacy security and improve computing efficiency is a research hotspot in the field of machine learning. Among them, how to balance the interests of users, cloud servers and attackers on the premise of ensuring user privacy is a difficult problem in the field of machine learning privacy protection. The development of quantum computing breaks through the computational bottleneck of classical machine learning and has derived the research direction of quantum machine learning. At present, hybrid quantum–classical machine learning in NISQ era has become a research hotspot, but researchers rarely pay attention to the privacy protection in quantum machine learning. Therefore, this paper is the first to apply game theory to the privacy protection in quantum machine learning and proposes the privacy game model of user - server - attacker in Hybrid Classical Quantum BP Neural Network (HCQBPNN). Different from previous studies, this paper sets game strategies based on users’ privacy requirements in practical applications, and aims to maximize the interests of attackers, cloud servers and users. The experiment proves that users can use the privacy game model proposed in this paper to get the optimal privacy combination strategy, and at the same time make the cloud server and the attacker can obtain positive income.

Evaluation of hospital information system acceptance from the point of view of users of teaching hospitals of Ahvaz Jundishapur University of Medical Sciences according to the information technology usage model

Introduction: The hospital information system is an electronic tool that collects, classifies, stores and retrieves the financial, administrative and clinical information of patients using computer capabilities and provides them to decision makers at any place and time. The present study was conducted with the aim of evaluating the acceptance of the hospital information system from the point of view of the users of teaching hospitals of Ahvaz University of Medical Sciences. Method: The present study is descriptive observational. The samples included 270 users of teaching hospitals in Ahvaz. Sampling was done randomly and samples were available. The data collection tool was a standardized questionnaire.An average of less than 2.5 indicates a low acceptance level, an average of 2.5 to less than 3.75 is considered an average acceptance level, and an average greater than 3.75 to 5 indicates a favorable level of HIS acceptance by users Results: The average variables of the questionnaire in terms of task support dimension, user interface, support for interdisciplinary cooperation and support service dimension are 4.42, 4.05, 4.64, 4.25, respectively, which indicates the level of acceptable acceptance. And in the dimensions of adaptability to tasks, learning ability, and accessibility, respectively, 3.73, 3.07, and 3.59, which indicate the average level of acceptance Conclusion: Officials can increase user satisfaction by providing appropriate platforms in terms of accessibility and learning capabilities, and also by improving hospital information systems, they can significantly save time and increase the quality of services provided in the health care sector

DDHCN: Dual decoder Hyperformer convolutional network for Downstream-Adaptable user representation learning on app usage

In mobile scenarios, there is a need for general user representations to solve multiple target tasks. However, there are some challenges in the related research (e.g., difficulty in learning a representation that satisfies both great generalization and performance). To address these problems, we proposed a network for downstream-adaptable mobile user modeling, which employed a novel fine-tuning strategy for optimizing the performance of several downstream tasks. Additionally, we designed a time-difference module to eliminate the impact of low-frequency and non-uniform app usage behavior over time. A parallel decoder structure was developed to incorporate multi-type features by minimizing information loss. We evaluated our method on a real-world dataset of 100,000 mobile users and three downstream tasks (i.e., age prediction, gender prediction, and app recommendation). The experimental results showed that our method could outperform existing methods significantly. It achieved 96.5% ACC on gender prediction, 68.1% ACC on age prediction, and 64.2% Recall@5 on app recommendation. These results imply that our method performs well on both generalization and performance. It could be anticipated promising to the unseen tasks inference.

Imperfect Surrogate Users: Understanding Performance Implications of Augmentative and Alternative Communication Systems through Bounded Rationality, Human Error, and Interruption Modeling

Nonspeaking individuals with motor disabilities frequently rely on augmentative and alternative communication (AAC) systems that allow users to communicate through a text entry interface coupled with a speech synthesizer. Such systems are notoriously difficult to evaluate with end-users. However, recent research has proposed envelope analysis as a method to estimate text entry rates and keystroke savings by simulating the interaction of an expert surrogate user entering sentences on a conceptual word-predictive text entry system. While only a part of the evaluation process of an AAC system, this method enables AAC designers to benefit from quantitative insights early on in the design process. This paper extends prior work by (1) demonstrating how to incorporate natural language generation, such as sentence generation, in such analyses; (2) presenting a model of an imperfect surrogate user that incorporates bounded rationality, human error, and interruptions to provide a more realistic simulation of text entry behavior; and (3) demonstrating how to estimate model parameters by observing users' actual typing behavior. We validate the model with data collected from eight participants using an AAC system on a touchscreen.

Electric field and SAR distribution in the vicinity of dental implants exposed to the cell phone electromagnetic radiation

PurposeThe purpose of this paper is to determine the electric field and specific absorption rate (SAR) distribution within biological tissues in the vicinity of dental implants, exposed to the mobile phone radiation.Design/methodology/approachThis research was performed for the frequency of 2.6 GHz, which corresponds to 4G mobile network. The adequate 3D realistic numerical models of the mobile phone user’s head, dental implants and actual smartphone model are created using packages based on the finite integral technique numerical method.FindingsThe obtained results yield to a conclusion that the presence of dental implants affects the increase in electric field intensity and SAR values within biological tissues in its vicinity.Research limitations/implicationsThe presented procedure is limited to the 4G mobile network frequency of 2.6 MHz. The study should be extended to other mobile network frequencies to be more general.Practical implicationsThe criteria for selection of the materials used for dental implants production should be extended with the recommended material characteristics related to their influence on the electric field and SAR distribution, to keep their values in the limits prescribed by standards.Social implicationsThe obtained results provide the foundation for future research in mobile devices’ electromagnetic fields’ influence on human health.Originality/valueThe accurate determination of the electric field and SAR values within different biological tissues and organs in the vicinity of dental implants exposed to mobile phone electromagnetic radiation, demands highly realistic model of observed biological structures. For purposes of the current study, the procedure for modeling of highly nonhomogeneous structure with finite number of homogenous domains having known electromagnetic parameters is described in the paper. As a result, the 3D complex users’ head model formed of 16 homogeneous domains of different electromagnetic parameters is created.