User Behavior Patterns Research Articles

Reinforced Imitative Graph Learning for Mobile User Profiling

Mobile user profiling refers to the efforts of extracting users’ characteristics from mobile activities. In order to capture the dynamic varying of user characteristics for generating effective user profiling, we propose an imitation-based mobile user profiling framework. Considering the objective of teaching an autonomous agent to imitate user mobility based on the user’s profile, the user profile is the most accurate when the agent can perfectly mimic the user behavior patterns. The profiling framework is formulated into a reinforcement learning task, where an agent is a next-visit planner, an action is a POI that a user will visit next, and the state of the environment is a fused representation of a user and spatial entities. An event in which a user visits a POI will construct a new state, which helps the agent predict users’ mobility more accurately. In the framework, we introduce a spatial Knowledge Graph (KG) to characterize the semantics of user visits over connected spatial entities. Additionally, we develop a mutual-updating strategy to quantify the state that evolves over time. Along these lines, we develop a reinforcement imitative graph learning framework for mobile user profiling. Finally, we conduct extensive experiments to demonstrate the superiority of our approach.

Cultural similarity and guest-host interaction for virtual tourism

Online experience program is a novel virtual tourism product in which guests can experience interesting activities online. However, the mechanism behind guest-host interactions for online experience remains unexplored. From the perspective of cultural similarity, this study tries to discover how guests review differently to native hosts and foreign hosts, as well as how hosts reply differently to native guests and foreign guests. Based on the data collected from Airbnb, we conduct empirical analyses from the perspectives of guests and hosts separately. Our results show that guests tend to pay more efforts when they write online reviews for native hosts, whereas hosts prefer to pay more efforts to foreign guests while making managerial responses. Our work not only sheds light on the user behavioral patterns for virtual tourism products but also provides multiple practical implications for the platforms, businesses, and guests in the sharing economy.

Enhancing the Performance of XR Environments Using Fog and Cloud Computing

The extended reality (XR) environment demands high-performance computing and data processing capabilities, while requiring continuous technological development to enable a real-time integration between the physical and virtual worlds for user interactions. XR systems have traditionally been deployed in local environments primarily because of the need for the real-time collection of user behavioral patterns. On the other hand, these XR systems face limitations in local deployments, such as latency issues arising from factors, such as network bandwidth and GPU performance. Consequently, several studies have examined cloud-based XR solutions. While offering centralized management advantages, these solutions present bandwidth, data transmission, and real-time processing challenges. Addressing these challenges necessitates reconfiguring the XR environment and adopting new approaches and strategies focusing on network bandwidth and real-time processing optimization. This paper examines the computational complexities, latency issues, and real-time user interaction challenges of XR. A system architecture that leverages edge and fog computing is proposed to overcome these challenges and enhance the XR experience by efficiently processing input data, rendering output content, and minimizing latency for real-time user interactions.

Assessing Interactive Web-Based Systems Using Behavioral Measurement Techniques

Nowadays, e-commerce websites have become part of people’s daily lives; therefore, it has become necessary to seek help in assessing and improving the usability of the services of e-commerce websites. Essentially, usability studies offer significant information about users’ assessment and perceptions of satisfaction, effectiveness, and efficiency of online services. This research investigated the usability of two e-commerce web-sites in Saudi Arabia and compared the effectiveness of different behavioral measurement techniques, such as heuristic evaluation, usability testing, and eye-tracking. In particular, this research selected the Extra and Jarir e-commerce websites in Saudi Arabia based on a combined approach of criteria and ranking. This research followed an experimental approach in which both qualitative and quantitative approaches were employed to collect and analyze the data. Each of the behavioral measurement techniques identified usability issues ranging from cosmetic to catastrophic issues. It is worth mentioning that the heuristic evaluation by experts provided both the majority of the issues and identified the most severe usability issues compared to the number of issues identified by both usability testing and eye-tracking combined. Usability testing provided fewer problems, most of which had already been identified by the experts. Eye-tracking provided critical information regarding the page design and element placements and revealed certain user behavior patterns that indicated certain usability problems. Overall, the research findings appeared useful to user experience (UX) and user interface (UI) designers to consider the provided recommendations to enhance the usability of e-commerce websites.

E-commerce consumption data prediction and privacy protection based on improved neural network algorithm

In today's shopping platform, some enterprises can use all commercial means to obtain users' personal information. How to protect the privacy of consumers has become an important issue. This paper discusses the shortcomings of the traditional neural network algorithm, and puts forward some countermeasures to improve it. Based on the basic principle of standard neural network, a new improved algorithm is developed according to the needs of this paper. The basic principle of the algorithm is based on the added momentum method. This paper mainly uses data models to collect and organize user data through user behavior patterns, so as to prove that the record of target user data cannot predict the possibility of user transportation and consumption, so as to better promote the improvement of transactions and services through these data models.

Movie recommendation system using taymon optimized deep learning network

The increased usage of the internet and social networks generates a large volume of information. Exploring through the large collection is time-consuming and hard to find the required one, so there is a serious need for a recommendation system. Based on this context several movie recommendation (MR) systems have been recently established. In addition, they have poor data analytics capability and cannot handle changing user preferences. As a result, there are many movies listed on the recommendation page, which provides for a poor user experience is the major issue. Therefore, in this work, a novel Taymon Optimized Deep Learning network (TODL net) for recommending top best movies based on their past choices, behaviour and movie contents. The deep neural network is a combination of Dilated CNN with Bi-directional LSTM. The DiCNN-BiLSTM model eliminates the functionality pooling operations and uses a dilated convolution layer to address the issue of information loss. The DiCNN is employed to learn the movie contents by mining user behavioral pattern attributes. The BiLSTM is applied to recommend the best movies on basis of the extracted features of the movie rating sequences of users in other social mediums. Moreover, for providing better results the DiCNN-BiLSTM is optimized with Taymon optimization algorithm to recommend best movies for the users. The proposed TODL net obtains the overall accuracy of 97.24% for best movies recommendation by using TMDB and MovieLens datasets.

TEMPAT SAMPAH PINTAR “MYSMARTRECYLEBIN” BERBASIS TEKNOLOGI INTERNET OF THINGS (IOT) DAN BIG DATA

This research aims to develop "MySmartRecyleBin," a smart trash bin that utilizes Internet of Things (IoT) technology and Big Data in many cities around the world that face serious challenges in waste management. This research was conducted to face the urgency of handling the increasingly pressing waste problem. The aim is to help governments and waste managers overcome waste management challenges in a more sustainable manner, reduce waste management costs, and strengthen waste management systems. This research involves researchers and developers of IoT and Big Data technology collaborating to create a smarter and more efficient waste management system. By utilizing IoT and Big Data technology, this research produces a system that allows real-time data collection from various devices connected to the internet, such as sensors on trash cans and waste transport fleets. This data is then analyzed to understand user behavior and waste collection patterns. Through this technology, the waste management system can become more effective and efficient. This technology uses a Microcontroller, Load Cell, RFID Tag, and RestFull API Technology as a communication medium to the Waste Management Information System (MySipah). The test results show the success of sending data over the last 1 year, namely January 2022 is 152 kg, February 2022 is 109 kg, March 2022 is 93 kg, April 2022 is 113 kg, May 2022 is 126 kg, June 2022 is 161 kg, July 2022 is 188 kg, August 2022 is 143 kg, September 2022 is 102 kg, October 2022 is 113 kg, November 2022 is 116 kg, December 2022 is 124 kg.

Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security

Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events.

Role of Design for Disassembly in Educating Consumers for Circular Behavior

Promotion of durable materials and products is a common approach to enhance sustainability. However, the effectiveness of such efforts lies on shifts in user behavior and consumption patterns, and these patterns are influenced not only by material aspects but also by social and experiential dimensions. It has been observed that the consumers’ consumption pattern, i.e., post-consumption behavior, is as harmful as production. However, this area remains largely unexplored. The primary purpose of this study is to explore sustainable garment design strategies to enhance emotional durability of garments and reduce pre-consumer and most importantly, the post-consumer waste. For this purpose, 18 garments were produced using ZWPC for pre-consumption waste reduction and DFD for post-consumption waste minimization. Three hypotheses were developed. Quantitative and qualitative data were collected through questionnaires and wear trials on the practicality of DFD implementation in garments. The results demonstrated that the combination of these strategies has the potential to curb both pre-consumer and post-consumer waste by designing garments that can enter the biological as well as technical cycle of circular fashion (CF). Furthermore, DFD is a success in increasing the use-life of a garment.

Incorporating users’ adaptive behaviors into multi-objective optimization of shading devices: A case study of an office room in Qingdao

Users' adaptive behaviors significantly affect the indoor environment and energy use. Neglecting them would introduce considerable uncertainty in the computer simulation results and lead to suboptimal or even invalid energy-efficiency solutions in optimization. To address this issue, we abstracted and simplified three user behavior patterns, including adjustment of operable shading devices, switching on/off artificial lighting, and opening windows for natural ventilation by a brief literature review. We then developed a new simulation approach that incorporates these behavior patterns and applied it to optimize the exterior fixed solar shade design of a south-facing office room in Qingdao as an example. The baseline model was established and validated in both daylight and energy simulations. The results demonstrate that the addition of user behavior models has a significant impact on both illuminance and load simulation results. The evaluation of each shading solution under the traditional approach and the newly-developed approach shows considerable differences, leading to the selection of different optimal solutions. Ultimately, the 0.6-meter-long type 8 (horizontal sunshade combined with vertical triangular panels on both sides) and 0.7-meter-long type 4 (double-sided vertical shading) were selected as the optimal solution using the traditional approach and new approach, respectively. This research offers valuable insights into the user behavior pattern and uses a shading optimization example to demonstrate the significant impact of incorporating adaptive behavior mechanisms on simulation and optimization results. The approach established in this paper has the potential to be extended to other solutions, being conducive to the study of refined building efficiency strategies for different conditions.

User behaviour in public squares after dark

This research concerns the influence of electric lighting on user behaviour in public squares and whether differences in people’s use of the square can be observed between daylight and darkness. Previous research on pedestrians suggests that lighting can support human needs for reassurance, accessibility, comfort and pleasure. While these findings are also likely to be applicable to the use of public squares, there is little empirical evidence to verify that. A field study was conducted to explore user behaviour in two differently illuminated public squares. Observations of the movements and stationary activities of people in the squares were recorded at both squares for the same times of day in the weeks before and after the daylight savings clock change, enabling a comparison of activity in daylight and after dark. 5296 observations were recorded and lighting conditions were captured with HDR-photography and aerial photos. Kirseberg square, with asymmetric luminaires and metal halide lamps, revealed a decrease in stationary activity after dark. Lindeborg square, with omnidirectional luminaires and high-pressure sodium lamps, revealed an increase in stationary activity. In conclusion, the patterns of user behaviour in the two public squares after dark seem to be differently influenced by electric lighting, pointing to a need for further understanding of users’ experience of the squares after dark.

Reading Comprehension in Information Retrieval (RCIR) for Personalized Results

ABSTRACTRecent research on personalized retrieval technology has been actively conducted to meet the needs of users for seeking adequate information. To refine the retrieval, researchers are considering user behavior patterns in a variety of ways. In this study, we use eye‐tracking metadata to predict users' levels of comprehension as textual evidence for IR processes. Furthermore, we incorporated eye‐tracking metadata on the Automated Readability Index (ARI), a readability assessment tool of an English text. Our research is largely divided into two tasks: i) comprehension evaluation task (CET) and ii) comprehension‐based retrieval task (CRT). In the CET task, for predicting the comprehension level, we applied various regression models. Among them, the Voting regressor demonstrated the highest performance with a Spearman's 𝜌 of 0.68. In the CRT task, we incorporated the level of comprehension predicted in the CET task and ARI score into the ranking results. We derived a sentenceBERT to find the relevant text for a query and the Normalized Discounted Cumulative Gain (nDCG) for evaluating the CRT task. The nDCG score for Comprehension Level only and that with ARI together were 0.65 and 0.78, respectively. Thus, applying ARI resulted in a higher nDCG score compared to comprehension level only.

Учёт психофизиологических особенностей пользователей при проектирования мобильных интерфейсов

The patterns of user behavior on websites and mobile applications are considered from the viewpoint of previous experience. The term “previous experience” means people’s preferences formed under the influence of psychophysiological characteristics in understanding different elements of the interface of mobile and not only applications. The article presents various patterns of the interface perception and describes ways to overcome or support the perception of established concepts of the interface element location.

Patterns of User Behavior and Token Adoption on ERC20

Patterns of User Behavior and Token Adoption on ERC20

Glucose Variability Analysis in Two Large-Scale and Real-World Data Sets of Open-Source Automated Insulin Delivery Systems.

Open-source automated insulin delivery (OS-AID) systems combine commercially available insulin pumps and continuous glucose monitors with open-source algorithms to automate insulin dosing for people with insulin-requiring diabetes. Two data sets (OPEN and the OpenAPS Data Commons) contain anonymized OS-AID user data. We assessed glycemic variability (GV) outcomes in the OPEN data set and characterized it alongside a comparison to the n = 122 version of the OpenAPS Data Commons. Glucose data are analyzed using an unsupervised machine learning algorithm for clustering, and GV metrics are quantified using statistical tests for distribution comparison. Demographic data are also analyzed quantitatively. The n = 75 OPEN data set contains 36 827 days worth of data. Mean TIR is 82.08% (TOR < 70: 3.66%; TOR > 180: 14.3%). LBGI (P < .05) differs by gender whereas HBGI distributions are similar (P > .05). GV metrics (except TOR < 70, LBGI) show a statistically significant difference (P < .05) between data sets. Both the OPEN and OpenAPS Data Commons data sets show TOR < 70, TIR, and TOR > 180 within recommended goals, adding additional evidence of real-world efficacy of OS-AID. Future research should evaluate in more detail potential data set differences and relationships between individual patterns of user behaviors and GV outcomes.

Throughput maximization with channel access fairness model using game theory approach

The &lt;span&gt;adoption of cognitive radio (CR) technology into wireless sensor networks (WSNs) effectively addresses the spectrum scarcity problem of traditional unlicensed spectrum. Allocating and managing limited network channel to secondary user (SUs) considering dynamic behavior pattern of primary users (PUs) is a critical issue of CR-WSN. Recently, various channel access methodologies using statistical, reinforcement learning (RL), game theory (GT), and deep learning (DL) model have been presented for CR-WSN. However, the existing channel access methodologies has following two limitations: i) fails to assure balance between maximizing throughput of SUs with minimal interference to PUs considering multi-channel CR-WSNs environment; and ii) maximizing throughput with minimal collision assuring access fairness among SUs considering energy constraint CR-WSNs. In addressing the research issues, this paper present throughput maximization channel access fairness using game theory (TMCAF-GT) model. The TMCAF employ both shared and non-shared channel access mechanism employing GT model for assuring throughput maximization with minimal interference and access fairness. Experiment outcome shows the TMCAF-GT provides superior throughput with minimal collision.&lt;/span&gt;

Учёт психофизиологических особенностей пользователей при проектирования мобильных интерфейсов

The patterns of user behavior on websites and mobile applications are considered from the viewpoint of previous experience. The term “previous experience” means people’s preferences formed under the influence of psychophysiological characteristics in understanding different elements of the interface of mobile and not only applications. The article presents various patterns of the interface perception and describes ways to overcome or support the perception of established concepts of the interface element location.

Space use in Central Business District of emerging economies: Regulation or rationale?

The Central Business District (CBD)[1] mimics a jungle where businesses and people compete for the best space and use because of its socioeconomic and political roles in a city. This zone attracts many users and uses, making it a focus of regulative planning, which is perceived as a useful behavioural control tool. However, whether a CBD's space user behaviour and use patterns are attributable solely to zoning guidelines and planning standards is not established. Using a case study design with Kumasi's CBD as the geographic scope, the study explored the patterns in space use within the lens of Ghana's Zoning Guidelines and Planning Standards and the factors informing user behaviour. The study found that space use in the CBD conforms extensively to the permissible activities enshrined in the guidelines, though open spaces and recreational parks are going extinct. However, some use prohibitions outlined in the Zoning Guidelines disconnect from reality and user rationality amid space use competitions. The study suggests that profit maximisation is the critical factor informing use patterns and user behaviour, aside factors like location, statutory demand, inheritance, social influence, and family decisions. The paper concludes that users’ subjective profit motives inform the user behaviour in CBDs of developing economies as against the perceived behavioural control embedded in guidelines and planning standards. It is recommended that attaining and maintaining inclusive and operative space use in the CBDs should integrate routine updating of regulatory frameworks and guidelines underpinned by rational choice models.[1] Central Business District.

CP-Link: Exploiting Continuous Spatio-Temporal Check-In Patterns for User Identity Linkage

Driven by the large amount of spatio-temporal data obtained from location-based social networks, the implementation of cross-domain user linkage, also known as the User Identity Linkage (UIL), has attracted increasing research attentions. While most of the existing UIL works discretize the spatio-temporal sparse data when identifying encountering or co-located events for UIL, user's distinctive behavior patterns implicit in the ‘`check-in’' spatio-temporal data with continuous nature pave the way for enhancing UIL performance. In this paper, we propose an approach dubbed {\it CP-Link} that exploits user behavior patterns in a continuous way. In CP-Link, the continuous space is divided into irregularly shaped stay regions, and a continuous time-based improved dynamic time warping (IDTW) method is proposed to calculate the similarity. {\color{blue}To bridge the gap between the ideal scenario with ample records and the reality with sparse data}, we adopt the user-associated location frequent pattern (LFP) model to compensate for the sparse deficiency. Extensive experiments conducted on real-world datasets demonstrate the effectiveness and superiority of CP-Link, which outperforms the state of the arts by more than 20% in terms of the AUC.

Quantification of User Driving Behavior in Motorized Mobility Scooters using CNN-based Head Pose Estimation Algorithm.

There is a growing concern regarding the driving safety of Motorized Mobility Scooters (MMSs) for the elderly and mobility-impaired individuals. Although various research has made progress in sensor-based driving assistance systems to identify environmental hazards, few studies focus on investigating the impact of user behavior on MMS driving. In this paper, a driving status logging (DSL) system is developed to measure the user's behavior while driving. A cross-correlation analysis is implemented to quantify the temporal relationship between the head movement and steering operation in the driving of MMSs. The preliminary results suggest that the head movement can be used as an appropriate index to predict the intended steering operation in the driving of MMSs. Moreover, the quantified head-steering lag time provides the possibility to identify the hazardous driving pattern of MMS users.Clinical Relevance- The investigation of user behavior has the potential to improve the safety of MMSs. In this study, the user behavior in the driving of MMSs was quantitatively measured using the developed DSL system. Consequently, the temporal relationship between head movement and steering operation was first quantified in MMS-related research. These outcomes provide valuable insights into developing behavioral interventions to address the user's risky behavior patterns, thereby promoting the driving safety of MMSs.