User Sessions Research Articles

Hybrid session-aware recommendation with feature-based models

Session-based recommender systems model the interests of users based on their browsing behavior with the goal of making suitable item suggestions in an ongoing usage session. Most existing work in this growing research area make only use of the most recent observed interactions for each user, and they typically solely rely on user–item interaction data (e.g., click events) for interest modeling. Thus, they do not leverage important forms of other information which are commonly available in practical settings. In this work, we therefore propose a hybrid approach for personalized session-based (“session-aware”) recommendation, which (i) is able to take into account various types of side information as model features and which (ii) can be combined with existing session-based (or session-aware) recommendation models. Technically, our approach is based on stacking several session-based modeling approaches with efficient machine learning methods for tabular data, in our case using Gradient Boosting Machines (GBMs). We successfully evaluated our approach (named HySAR) on two public e-commerce datasets. Specifically, we also demonstrate the effectiveness of a number of novel model features that we engineered in the course of this research. These features, which were mostly unexplored in previous works, relate to various types of information related to the users, their actions, the items, as well as contextual session characteristics. Different existing recommendation approaches and further problem specific features can be easily added in our generic method to improve recommendations.

An intelligent scheduling for 5G user plane function placement and chaining reconfiguration

Services and use cases in 5G and beyond networks are characterized by strict requirements such as ultra-low latency, increased capacity, and high user mobility. Moreover, these networks must be capable of satisfying these ambitious demands as well as anticipating and adapting to dynamically changing conditions in a quick and feasible manner. This study deals with the problem of determining the best time to readjust the user plane function (UPF) placement and session mapping configuration to avoid quality of service (QoS) degradation in the system due to user mobility. To this aim, we rely on machine learning (ML) techniques to anticipate poor QoS events and decide whether a reconfiguration procedure is required based on a pre-established QoS tolerance threshold. Specifically, an ML-based framework, called intelligent scheduling of the reconfiguration (ISR), is proposed to automate the reconfiguration process. This framework applies supervised ML methods, either regressors or classifiers, to predict the QoS values/status at a given time horizon. The simulation experiments revealed the proposed mechanism’s superiority compared to the established scheduling baseline. The ISR solution could not only keep the system QoS under desired values most of the time but also reduce the number of readjustment events by at least 50% compared to the baselines.

A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic Performance

Due to usage of self-reported data which may contain biasness, the existing studies may not unveil the exact relation between academic grades and app categories such as Video. Additionally, the existing systems' requirement for data of prolonged period to predict grades may not facilitate early intervention to improve it. Thus, we presented an app that retrieves past 7 days' actual app usage data within a second (Mean=0.31s, SD=1.1s). Our analysis on 124 Bangladeshi students' real-time data demonstrates app usage sessions have a significant (p<0.05) negative association with CGPA. However, the Productivity and Books categories have a significant positive association whereas Video has a significant negative association. Moreover, the high and low CGPA holders have significantly different app usage behavior. Leveraging only the instantly accessed data, our machine learning model predicts CGPA within ±0.36 of the actual CGPA. We discuss the design implications that can be potential for students to improve grades.

VR Traffic Dataset on Broad Range of End-User Activities

With the emergence of new internet traffic types in modern transport networks, it has become critical for service providers to understand the structure of that traffic and predict peaks of that load for planning infrastructure expansion. Several studies have investigated traffic parameters for Virtual Reality (VR) applications. Still, most of them test only a partial range of user activities during a limited time interval. This work creates a dataset of captures from a broader spectrum of VR activities performed with a Meta Quest 2 headset, with the duration of each real residential user session recorded for at least half an hour. Newly collected data helped show that some gaming VR traffic activities have a high share of uplink traffic and require symmetric user links. Also, we have figured out that the gaming phase of the overall gameplay is more sensitive to the channel resources reduction than the higher bitrate game launch phase. Hence, we recommend it as a source of traffic distribution for channel sizing model creation. From the gaming phase, capture intervals of more than 100 s contain the most representative information for modeling activity.

Learning Dynamic App Usage Graph for Next Mobile App Recommendation

Next mobile app recommendation aims to recommend the next app that a user is most likely to use based on the user's app usage behaviors, which is beneficial for improving user experience, app pre-loading, and system optimization. However, existing works ignore the complex dependencies between apps in the app usage sessions. In addition, they do not consider the dynamics of user interests over time. To address these concerns, we propose a novel model named dynamic usage graph network (DUGN) to recommend the next app that a user is most likely to use. To model the complex dependencies among apps explicitly, we adopt the dynamic graph structure to learn the dynamics of user interests. Firstly, we extract user interests in each app usage graph by using the hierarchical graph attention mechanism. Secondly, we capture user interests evolving over time, and generate the dynamic user embeddings by modeling the temporal dependencies among multiple app usage graphs. Finally, we obtain the current user interests in the current app usage graph, fuse multiple user interests and generate comprehensive user embeddings for next mobile app recommendation. We conduct experiments on real-world datasets. The results show that our model outperforms the state-of-art recommendation methods.

Maximizing the Capacity of Edge Networks with Multicasting

Edge networks employ local computing and caching resources to process data, thus alleviating the bandwidth pressure on backbone networks and improve users’ quality of experience. System capacity is one of the key metrics to evaluate the performance of edge networks. However, maximizing system capacity in edge scenarios faces challenges due to the dynamical user sessions and the changing content popularity. This study reports on the influence of multicast communication on the capacity of edge caching networks. When large amounts of content are requested simultaneously or over a short period, one-to-one unicast transmission will consume copious network resources due to repetitious transmission. To solve this problem, this study used the one-to-many multicast scheme to realize cooperative transmission between edge servers. First, multiple copies of the content are distributed to multiple small base stations (SBSs) based on the content’s popularity and the SBSs’ cache sizes. Second, a multicast tree is constructed by using, as the root node, the SBS that stores the content. Third, the content is transmitted along the path of the multicast tree to each end-user. Finally, a simulation platform is constructed to analyze the performance of the two transmission schemes. The results of simulating on edge caching networks show that multicast communication responds well to users’ requests even when the requested content requires sudden transmission, is highly popular or is requested often within short time. This system’s capacity has been significantly improved compared to the classical methods.

Intelligent system for clustering users of social networks based on the message sentiment analysis

The main objective of this article is the analysis of the intelligent system for clustering users of social networks based on the messages sentiment analysis. The main goal of this intelligent system is to form a general image of the user of the system by analyzing the sentiment of the data of the user's social networks and their subsequent clustering. An intelligent system was designed, which, using the Identity and Access/Refresh JWT token algorithms, provides fast and maximally secure registration, authentication and processing of various system user sessions. The main approaches to the sentiment analysis of user messages and other data of various types are described, the principles of LSTM implementation of a recurrent neural network are described, which is very convenient for data analysis, because it works well and remembers the context of messages in the necessary time intervals, which increases the meaningfulness factor of the data analyzed according to the user of the intelligent system. General modern approaches to clustering and the most suitable clustering algorithm k-means is also described, since we will work with an undetermined amount of data each time, which can change significantly according to each individual user, the number of clusters and data processing will change because of this. Due to this, as a result of the work, the creation of a general image of the system user was described thanks to its comprehensive analysis, which made it possible to analyze users and display the corresponding results.

The EyeControl-Med device, an alternative tool for communication in ventilated critically ill patients: A pilot study examining communication capabilities and delirium

IntroductionCommunication with ventilated patients in the Intensive care unit (ICU) is challenging. This may lead to anxiety and frustration, potentially contributing to the development of delirium. Various technologies, such as eye-tracking devices, have been employed to facilitate communication with varying grades of success. The EyeControl-Med device is a novel technology that delivers audio content and allows patients to interact by eye movements and could potentially allow for better communication in this setting. The aim of this exploratory concept study was to assess communication capabilities and delirium incidence using the EyeControl-Med device in critically ill patients unable to generate speech. Material and methodsA single-arm pilot study of patients in a mixed ICU. Patients were approached for consent if they were invasively ventilated and/or tracheotomized, hence unable to generate speech, but had no severe cognitive or sensory impairment that could prevent proper usage. Patients underwent at least 3 sessions with the EyeControl-Med device administered by a speech-language pathologist. Communication and consciousness were assessed using the Loewenstein Communication Scale (LCS) tool during the first and last sessions. Delirium was assessed using a computerized CAM-ICU questionnaire. Results15 patients were included, 40% of whom were diagnosed with COVID-19. All patients completed three to seven usage sessions. The mean LCS score improved by 19.3 points (p < 0.0001), with each of its five components showing significant improvements as well. The mean number of errors on the CAM-ICU questionnaire decreased from 6.5 to 2.5 (p = 0.0006), indicating a lower incidence of delirium. No adverse effects were observed. ConclusionThe EyeControl-Med device may facilitate communication and reduce the manifestations and duration of delirium in ventilated critically ill patients. Controlled studies are required to establish this effect.

Railway Reservation System using PHP

The railway reservation system is a web-based application that allows users to search for trains, book tickets, and make reservations online. In this project, we have developed a railway reservation system using PHP and MySQL. The system provides a user-friendly interface for users to search for trains, view train schedules, and book tickets. The system is designed to automate the ticket booking process and reduce the workload of railway staff. Users can register and login to the system to search for trains, view train schedules, and book tickets. The system also allows users to cancel their reservations if necessary. The system is implemented using PHP as the server-side scripting language and MySQL as the database management system. The system is designed to be scalable and secure, with features such as user authentication, session management, and data encryption. Overall, the railway reservation system using PHP is a useful application that can help to streamline the ticket booking process and improve the efficiency of railway operations. It can also provide users with a convenient and reliable way to make reservations and travel by train.

BLUFADER: Blurred face detection & recognition for privacy-friendly continuous authentication

Authentication and de-authentication phases should occur at the beginning and end of secure user sessions, respectively. A secure session requires the user to pass the former, but the latter is often underestimated or ignored. Unattended or dangling sessions expose users to well-known Lunchtime Attacks. To mitigate this threat, researchers focused on automated de-authentication systems, either as a stand-alone mechanism or as a result of continuous authentication failures. Unfortunately, no single approach offers security, privacy, and usability. Face-recognition methods, for example, may be suitable for security and usability, but they violate user privacy by continuously recording their actions and surroundings.In this work, we propose BLUFADER, a novel continuous authentication system that takes advantage of blurred face detection and recognition to fast, secure, and transparent de-authenticate users, preserving their privacy. We obfuscate a webcam with a physical blur layer and use deep learning algorithms to perform face detection and recognition continuously. To evaluate BLUFADER’s practicality, we collected two datasets formed by 30 recruited subjects (users) and thousands of physically blurred celebrity photos. The de-authentication system was trained and evaluated using the former, while the latter was used to appraise the privacy and increase variance at training time. To guarantee the privacy-preserving effectiveness of the selected physical blurring filter, we show that state-of-the-art deblurring models are not able to revert our physical blur. Further, we demonstrate that our approach outperforms state-of-the-art methods in detecting blurred faces, achieving up to 95% accuracy. Moreover, BLUFADER effectively de-authenticates users up to 100% accuracy in under 3 seconds, while satisfying security, privacy, and usability requirements. Last, our continuous authentication face recognition module based on Siamese Neural Network preventively protect users from adversarial attacks, enhancing the overall system security.

Time of Day Preferences and Daily Temporal Consistency for Predicting the Sustained Use of a Commercial Meditation App: Longitudinal Observational Study

The intensive data typically collected by mobile health (mHealth) apps allows factors associated with persistent use to be investigated, which is an important objective given users' well-known struggles with sustaining healthy behavior. Data from a commercial meditation app (n=14,879; 899,071 total app uses) were analyzed to assess the validity of commonly given habit formation advice to meditate at the same time every day, preferably in the morning. First, the change in probability of meditating in 4 nonoverlapping time windows (morning, midday, evening, and late night) on a given day over the first 180 days after creating a meditation app account was calculated via generalized additive mixed models. Second, users' time of day preferences were calculated as the percentage of all meditation sessions that occurred within each of the 4 time windows. Additionally, the temporal consistency of daily meditation behavior was calculated as the entropy of the timing of app usage sessions. Linear regression was used to examine the effect of time of day preference and temporal consistency on two outcomes: (1) short-term engagement, defined as the number of meditation sessions completed within the sixth and seventh month of a user's account, and (2) long-term use, defined as the days until a user's last observed meditation session. Large reductions in the probability of meditation at any time of day were seen over the first 180 days after creating an account, but this effect was smallest for morning meditation sessions (63.4% reduction vs reductions ranging from 67.8% to 74.5% for other times). A greater proportion of meditation in the morning was also significantly associated with better short-term engagement (regression coefficient B=2.76, P<.001) and long-term use (B=50.6, P<.001). The opposite was true for late-night meditation sessions (short-term: B=-2.06, P<.001; long-term: B=-51.7, P=.001). Significant relationships were not found for midday sessions (any outcome) or for evening sessions when examining long-term use. Additionally, temporal consistency in the performance of morning meditation sessions was associated with better short-term engagement (B=-1.64, P<.001) but worse long-term use (B=55.8, P<.001). Similar-sized temporal consistency effects were found for all other time windows. Meditating in the morning was associated with higher rates of maintaining a meditation practice with the app. This is consistent with findings from other studies that have hypothesized that the strength of existing morning routines and circadian rhythms may make the morning an ideal time to build new habits. In the long term, less temporal consistency in meditation sessions was associated with more persistent app use, suggesting there are benefits from maintaining flexibility in behavior performance. These findings improve our understanding of how to promote enduring healthy lifestyles and can inform the design of mHealth strategies for maintaining behavior changes.

Examining User Heterogeneity in Digital Experiments

Digital experiments are routinely used to test the value of a treatment relative to a status quo control setting — for instance, a new search relevance algorithm for a website or a new results layout for a mobile app. As digital experiments have become increasingly pervasive in organizations and a wide variety of research areas, their growth has prompted a new set of challenges for experimentation platforms. One challenge is that experiments often focus on the average treatment effect (ATE) without explicitly considering differences across major sub-groups — heterogeneous treatment effect (HTE). This is especially problematic because ATEs have decreased in many organizations as the more obvious benefits have already been realized. However, questions abound regarding the pervasiveness of user HTEs and how best to detect them. We propose a framework for detecting and analyzing user HTEs in digital experiments. Our framework combines an array of user characteristics with double machine learning. Analysis of 27 real-world experiments spanning 1.76 billion sessions and simulated data demonstrates the effectiveness of our detection method relative to existing techniques. We also find that transaction, demographic, engagement, satisfaction, and lifecycle characteristics exhibit statistically significant HTEs in 10% to 20% of our real-world experiments, underscoring the importance of considering user heterogeneity when analyzing experiment results, otherwise personalized features and experiences cannot happen, thus reducing effectiveness. In terms of the number of experiments and user sessions, we are not aware of any study that has examined user HTEs at this scale. Our findings have important implications for information retrieval, user modeling, platforms, and digital experience contexts, in which online experiments are often used to evaluate the effectiveness of design artifacts.

Anesthesiology 2023: Ch-ch-ch-ch-changes.

Anesthesiology 2023: Ch-ch-ch-ch-changes.

Purpose tendency-aware diversified strategy for effective session-based recommendation

Session-Based Recommender Systems (SBRSs) process time-aware user–item interactions to capture users’ dynamic preferences. Most of the existing SBRSs mainly strive to improve recommendation accuracy by exploiting different deep learning techniques to represent each user on the basis of the most recent session. However, they generally ignore to capture the diversity preference at the session level. In this view, we consider both recommendation diversity and accuracy when generating recommendations. Particularly, we treat that users’ preferences towards diversity might be varied across users (sessions). Thus, we propose an end-to-end neural network model, namely Purpose Tendency-aware Diversified Strategy for Session-based Recommendation (PTDS-SR), where we design a Purpose Tendency Probability (PTP) module matching with a two-channel decoder to guide whether to recommend similar or diversified items given a session. We thus obtain a relatively personalized strategy for each session to exploit the diversity to facilitate recommendation accuracy regarding short-term user preferences (within a session). We compare our approach with representative, state-of-the-art baselines on three real-world datasets, in terms of recommendation accuracy, diversity and a comprehensive metric (considering both accuracy and diversity). Experimental results demonstrate the effectiveness of our approach over the state-of-the-art approaches. Meanwhile, with PTDS-SR, more purposeless sessions (e.g., searching products of various categories) will obtain more diverse recommendation list, and vice versa.

Flutter Based Chatbot for Tourism

Kanyakumari, a popular tourist destination in India, attracts tourists from around the world with its scenic beauty, cultural heritage, and historical significance. However, tourists often face challenges in finding accurate information, making bookings, and planning their itinerary, leading to a less-than-optimal experience. In this technology intervention, we propose the development of a chatbot using JavaScript to address these challenges and provide a seamless and memorable tourism experience in Kanyakumari. The chatbot will be designed to engage with tourists in a conversational manner, providing information about tourist attractions answering frequently asked questions, assisting with bookings for accommodations and tours, and offering personalized recommendations based on user preferences. The chatbot will leverage JavaScript libraries for natural language processing (NLP) to understand user inputs, integrate with relevant APIs to fetch real-time data about Kanyakumari's tourist offerings, and implement backend functionality for managing user sessions and storing data. We anticipate that the development of a chatbot as a technology intervention will enhance the tourism experience in Kanyakumari making it hassle-free and memorable for tourists. The chatbot will provide accurate information, streamline bookings, and offer personalized recommendations resulting in improved tourist satisfaction and increased engagement with the destinatio

Magnetic resonance imaging based neurosurgical planning on hololens 2: A feasibility study in a paediatric hospital.

The goal of this work is to show how to implement a mixed reality application (app) for neurosurgery planning based on neuroimaging data, highlighting the strengths and weaknesses of its design. Our workflow explains how to handle neuroimaging data, including how to load morphological, functional and diffusion tensor imaging data into a mixed reality environment, thus creating a first guide of this kind. Brain magnetic resonance imaging data from a paediatric patient were acquired using a 3 T Siemens Magnetom Skyra scanner. Initially, this raw data underwent specific software pre-processing and were subsequently transformed to ensure seamless integration with the mixed reality app. After that, we created three-dimensional models of brain structures and the mixed reality environment using Unity™ engine together with Microsoft® HoloLens 2™ device. To get an evaluation of the app we submitted a questionnaire to four neurosurgeons. To collect data concerning the performance of a user session we used Unity Performance Profiler. The use of the interactive features, such as rotating, scaling and moving models and browsing through menus, provided by the app had high scores in the questionnaire, and their use can still be improved as suggested by the performance data collected. The questionnaire's average scores were high, so the overall experiences of using our mixed reality app were positive. We have successfully created a valuable and easy-to-use neuroimaging data mixed reality app, laying the foundation for more future clinical uses, as more models and data derived from various biomedical images can be imported.

Модели и методы вычисления задержек при предоставлении услуг пользователем на сервисных платформах сеансовых инфокоммуникационных услуг

The purpose of this article is to develop methods for calculating the qualitative characteristics of infocommunication services whose workloads consist of user sessions. The scope of application of these methods affects the development and maintenance of infocommunication multi-user services, in terms of ensuring a high quality of user service. The quality of service (QoS) parameters under consideration determine the characteristics of both the system as a whole and individual elements of its architecture, and for this type of service, the qualitative characteristics are the waiting time for the start of user session maintenance, the load factor of server equipment, the number of user sessions simultaneously supported by the system during its error-free operation. Multi-user services are considered from the point of view of queuing theory as a network of queuing systems, with the duration of the user session and the time of receipt of requests for services by gaming services distributed exponentially. To evaluate the parameters of the models of infocommunication multi-user services, Jackson networks were chosen as a mathematical apparatus, which allow us to consider the necessary systems of the type M/M/v. As an approbation of the results, a mathematical analysis of multi-user gaming services was proposed, since their popularity, and hence the flow of users to game servers, is increasing at a tremendous rate. As an additional tool for assessing the qualitative characteristics of the session models under consideration, simulation modeling was proposed, which allows us to evaluate the accuracy of the proposed analytical method and analyze the characteristics of complex systems with time distribution laws other than exponential, for example, long-tailed distributions (Weibull, Pareto, etc.)

Customer Authentication in Mobile Banking-MLOps Practices and AI-Driven Biometric Authentication Systems

The intersection of customer demands, security, and innovative services in the diverse mobile banking space calls for continuous adaptation by financial institutions. Small challenges such as on-demand customization and scalability are addressed through merging technologies, which is important for smaller institutions undergoing IT modernization. Despite the slow pace of ML adoption in banking, those leveraging ML experience increased their success in this competitive landscape. This article looks at the role of MLOps in overcoming challenges posed by evolving data volumes and complexities in deploying and developing ML models within financial institutions. As online banking authentication holds an important role in securing financial transactions, a historical overview of authentication methods, from biometrics to tokens, creates a chance to delve into the transformative potential of AI-driven biometric authentication. With the increase in mobile banking fraud, the need to safeguard sensitive customer data is met with radical technology. The article examines the varied authentication methods employed in online banking applications and depicts the potential of biometrics, majorly behavioral biometrics, to improve security and user experience. The rise of online mobile banking systems introduces both convenience and security concerns, thus prompting a closer look at the adoption of biometrics to mitigate fraud risks and improve the seamless authentication process throughout the user session. Customers increasingly demand quick and easy mobile payments, so biometrics has become a key fraud prevention and detection solution. By running in the background and eliminating setup authentication and risk-based authentication, behavioral biometrics significantly reduces fraud, thus addressing the limitations of traditional authentication methods like email verification and passwords. The article navigates the evolving mobile banking security space, highlighting the important role of MLOps and the potential of AI-driven biometric authentication in meeting the dual objectives of improving customer experience and strengthening severity protocols.

A Defensive Framework for Reflected XSS in Client-Side Applications

Cross-site scripting attack (XSS) is a common vulnerability that is exploited in modern web applications by entering advanced HTML tags and Java Script functions. An attacker could potentially use this vulnerability to steal users’ sensitive information, hijack user sessions or rewrite whole website contents displaying fake login forms. This class of attacks affects the client-side of a web application and is a critical vulnerability that is difficult to both detect and remediate for websites, often leading to insufficient server-side protection, which is why the end-users need an extra layer of protection at the client-side. In this paper, we analyze the best-known client-side XSS filters, study their mechanisms, structures and mentioned the advantages and disadvantages of each filter. This paper presents a novel XSS filtering model based on filtering rules, XSSFilter, uses Regular Expression in Xpath to detect reflected content, which makes it more robust for web sites that employ custom input sanitizations. We provide a detailed experimental evaluation to compare the four filters with respect to their usability and protection.

WEB MINING: DATA MINING ON THE INTERNET

In the article“Web Mining: Data Mining on the Web” 3 different components of Web Mining have been described and mainly Web Usage Mining has been discussed in detail and analyzed with examples. The general theme of the article is clarified by giving such sub-topics: Difficulties in analyzing data from the Internet, Stages of Web-Mining, Analysis of the use of Web resources (Web Usage Mining), Web Server Log Files and etc. General Relationships Between Web Mining Categories and Data Mining Tasks are shown. At the end, in the Conclusion, it is shown that to solve such problems, Data Mining technology is used. This name refers to a set of methods that allow us to extract useful information according to certain rules from the amount of collected data in which this information is implicitly contained. In this case, both statistical and intellectual processing methods are used. Keywords: Web-Mining, Data Mining Methods, Data from the Internet, Web Usage Mining, User Session Identification.