User Sessions Research Articles

A survey on different continuous authentication systems

There has been significant research in the provision of trustworthy initial login user authentication, however, there is still need for continuous authentication during a user session. Most mobile devices and computer systems authenticate a user only at the initial login session and do not take steps to recognise whether the present user is still the initial authorised user or an imposter pretending to be a valid user. Therefore, a system to check the identity of the user continuously throughout the whole session is necessary. To ensure the authenticity of the user during their whole login session, a continuous user authentication mechanism is required. In this paper, an overview of different continuous authentication methods is presented along with a discussion on the merits and demerits of the available approaches. This paper also discusses the understanding of the emerging necessities and open problems in continuous user authentication system.

A survey on different continuous authentication systems

There has been significant research in the provision of trustworthy initial login user authentication, however, there is still need for continuous authentication during a user session. Most mobile devices and computer systems authenticate a user only at the initial login session and do not take steps to recognise whether the present user is still the initial authorised user or an imposter pretending to be a valid user. Therefore, a system to check the identity of the user continuously throughout the whole session is necessary. To ensure the authenticity of the user during their whole login session, a continuous user authentication mechanism is required. In this paper, an overview of different continuous authentication methods is presented along with a discussion on the merits and demerits of the available approaches. This paper also discusses the understanding of the emerging necessities and open problems in continuous user authentication system.

GACOforRec: Session-Based Graph Convolutional Neural Networks Recommendation Model

The biggest challenge to recommendation systems based on user preferences is how to improve the ability of the recommendation system to mine and analyse user preferences and behaviours. In this process, we must not only consider the continuation of the user's long-term preference but also improve the system's ability to accommodate short-term preferences and discrete preferences. To this end, we focus on the performance of time factors of user preferences. However, the issue we are concerned about has not received much attention in the existing research. We propose a new recommendation model based on the perspective of user sessions, namely GACOforRec. This model can handle long-term and stable preferences at the same time and preserve the hierarchy of potential preferences. We conducted a large number of comparative experiments on two real datasets, and the results show that GACOforRec is significantly better than other state-of-the-art methods in the study of user sessions.

Energy-efficient prediction of smartphone unlocking

We investigate the predictability of the next unlock event on smartphones, using machine learning and smartphone contextual data. In a 2-week field study with 27 participants, we demonstrate that it is possible to predict when the next unlock event will occur. Additionally, we show how our approach can improve accuracy and energy efficiency by solely relying on software-related contextual data. Based on our findings, smartphone applications and operating systems can improve their energy efficiency by utilising short-term predictions to minimise unnecessary executions, or launch computation-intensive tasks, such as OS updates, in the locked state. For instance, by inferring the next unlock event, smartphones can pre-emptively collect sensor data or prepare timely content to improve the user experience during the subsequent phone usage session.

A Robust and Efficient ECC-based Mutual Authentication and Session Key Generation Scheme for Healthcare Applications.

Telecare medicine information system (TMIS) has provided an efficient and convenient way for communications of patients at home and medical staffs at clinical centers. To make these communications secure, user authentication by medical servers is considered as a crucial requirement. For this purpose, many user authentication and key agreement protocols have been put forwrad in order to fulfil this vital necessity. Recently, Arshad and Rasoolzadegan have revealed that not only the authentication and key agreement protocols suggested by Amin and Biswas and Giri et al. are defenseless against the replay attack and do not support the perfect forward secrecy, but also Amin and Biswas's protocol is susceptible to the offline password guessing attack. Nonetheless, in this paper, we demonstrate that Arshad and Rasoolzadegan's and the other existing schemes still fail to resist a well-known attack. Therefore, to cover this security gap, a new user authentication and session key agreement protocol is recommended that can be employed effectively for offering secure communication channels in TMIS. Our comparative security and performance analyses reveal that the proposed scheme can both solve the existing security drawback and, same as Arshad and Rasoolzadegan's scheme, has low communication and computational overheads.

Assessment of Secure OpenID-Based DAAA Protocol for Avoiding Session Hijacking in Web Applications

It is increasingly difficult to manage the user identities (IDs) of rapidly developing and numerous types of online web-based applications in the present era. An innovative ID management system is required for managing the user IDs. The OpenID lightweight protocol is a better solution to manage the user IDs. In an OpenID communication environment, OpenID URL is not secured in a session hijacking situation because in other existing OpenID communication methods such double factor authentication has more chances of valid user session hijacked. The proposed communication protocol secures the OpenID URL with the help of additional innovative parameters such as Special Alphanumeric String (SAS) and Special Security PIN (SSP). The anticipated triple authentication protocol authenticated client unique OpenID URL at OpenID Provider (OP) side once and SAS and SSP field at Relying Party (RP) side. The anticipated protocol provides unique Single-Sign-On (SSO) services to OpenID users. The experimental website is tested by experts of web developers for avoiding session hijacking situation in the presence of hackers. The findings demonstrated that Dense Authentication Authorization and Accounting (DAAA) protocol minimizes the risk of a session hijacking in OpenID communication environment.

Comparison of scheduling algorithms for multiple mobile computing edge clouds

5G networks are anticipated to provide service for vertical industries. Mobile Edge Computing (MEC) framework is a physical infrastructure improvement to host mobile applications for the introduction of new service provisioning in 5G networks. Application Orchestration is responsible for scheduling user sessions to application instances. In this paper, we propose four scheduling algorithms to assign sessions in a system with multiple MEC clouds. Results show that scheduling based on the occupancy level of MECs is quite robust in various scenarios and can be applied with the fractional guard channel policy to provide service.

How Should Firms Manage Excessive Product Use? A Continuous-Time Demand Model to Test Reward Schedules, Notifications, and Time Limits

Several industries related to screen usage have recently been criticized by parents, think tanks, and governments for creating product environments that lead to excessive consumption. If firms do not properly manage product usage, demand may drop and public policy makers may intervene. We test alternative ways to manage the use of such products: re-designing the timing of rewards, introducing notifications to users, and imposing time limits. We propose a continuous-time demand model to be empirically estimated with high frequency data. The methodology is flexible enough to simultaneously explain multiple usage decisions that happen in quick succession, such as when to start and stop usage and how to respond to rewards or messages from the firm. The approach is implemented on a dataset from the online gaming industry, where we observe usage decisions of a large sample of individuals. We find that improving reward schedules and imposing time limits leads to shorter usage sessions and longer product subscriptions; a win-win outcome. Notifications are found not to be useful to manage product usage.

The Short-term User Modeling for Predictive Applications

One of the important purposes of data mining on the web is to reveal hidden characteristics of users including their behavior. These characteristics are often used to analyze previous user actions, his/her preferences, and also to predict the future behavior. An average user session consists of only few actions, which brings several complications for the user modeling and also for subsequent prediction tasks. Such tasks are usually researched from the long-term point of view (e.g., contract renewal or course quit). On the contrary, the short-term user modeling plays an important role in the context of web applications, where it helps to improve user experience. Its shortcoming is that it often requires rich data, which availability is rather rare. For this reason, we propose a novel user model focused on the capturing changes in the user’s behavior on the level of specific actions. The model idea is based on the enrichment of user actions by a comparison of actual user session data with previous sessions. As the model basis on generally available data sources, the approach is applicable to wide scale of existing systems. We evaluate our model by the task of session end intent prediction in the e-learning and news domain. Thanks to reflecting differences in user behavior we are able to predict the intent to end the session for particular user in the scale of his/her next couple of actions. Obtained results clearly show that the proposed model brings higher precision, accuracy and session hit ratio than baseline models.

Experimental Analysis of Web Browser Sessions Using Live Forensics Method

<span>In today's digital era almost every aspect of life requires the internet, one way to access the internet is through a web browser. For security reasons, one developed is private mode. Unfortunately, some users using this feature do it for cybercrime. The use of this feature is to minimize the discovery of digital evidence. The standard investigative techniques of NIST need to be developed to uncover an ever-varied cybercrime. Live Forensics is an investigative development model for obtaining evidence of computer usage. This research provides a solution in forensic investigation effectively and efficiently by using live forensics. This paper proposes a framework for web browser analysis. Live Forensics allows investigators to obtain data from RAM that contains computer usage sessions. </span>

Book review: Dan Wu and Shaobo Liang, Mobile Search Behavior: An In-depth Analysis Based on Contexts, APPs, and Devices

This review looks at a book that aims to provide further understanding of search behaviour on mobile devices, as people act through not only web browsers, but also specialist mobile apps, transition between those apps and also transition across devices. According to the authors the book addresses a number of perceived gaps in the mobile search behaviour literature. These include understanding mobile search behaviours from the perspective of user sessions, with tendencies for the current literature to focus on analysing patterns as a whole in large aggregated search logs of single search engines, rather than by individual sessions and multiple apps. The book also premises the increased importance of specialist search apps over and above traditional web browsers.

Analysis of User Session Data using the Map Reduce Classification with Big Data

Analysis of User Session Data using the Map Reduce Classification with Big Data

Fast Streaming Behavioural Pattern Mining

Identification of typical user behaviour within a web application is a crucial assumption for revealing user characteristics, preferences and habits. Typical and repeating features of user behaviour during his/her interaction with web application can be generalized through behavioural patterns. In this paper, we propose HyBPMine—a novel method, for behavioural pattern mining over a data stream. Our method combines global patterns with patterns specific to dynamically identified groups of similar users. In this way, the method finds and combines the general global patterns (typical for high number of users) with the specific patterns (typical for user groups). We represent the patterns as frequent closed itemsets of items visited by users in their sessions. The behavioural patterns are often used for personalization, prediction or recommendation. In this paper, we evaluated the performance of our method indirectly, by applying discovered patterns in personalized recommendations. In other words, we recommended next user actions within the actual user session. We performed several experiments over data from e-learning and news domains. Our results clearly show that proposed method reaches higher precision than its components used individually as well as the state-of-the-art approaches. In addition, a inclusion of group patterns brings only low and constant computational load, which does not significantly increase resource requirements.

Visual clutter reduction in zoomable proportional point symbol maps

ABSTRACTProportional point symbols are commonly used in map displays. However, when such displays are zoomed out, the available map space shrinks and consequently symbols overlap and the display suffers from visual clutter. Therefore, solutions are needed to reduce visual clutter in zoomable map displays that contain proportional point symbols. The authors add to the body of knowledge through the investigation of the performance of three solutions: aggregation of unit areas, the displacement of overlapping symbols, and modified symbolization; four variations of modified symbolization are examined in detail. The original study begins with a consideration of the strengths and weaknesses of the solutions, with an emphasis on information loss. Thereafter, user sessions with 165 participants are arranged to gain empirical evidence of the performance of the solutions. The results reveal that although participants zoom out the map to zoom levels that implement visual clutter reduction techniques, they tend to zoom in the map and pan it to examine specific areas. Moreover, they willingly use the map with aggregated unit areas in cases when the solution facilitates task accomplishment. A certain amount of skepticism attaches to the solution that displaces overlapping symbols.

SMASH! The Salford medication safety dashboard

BackgroundPatient safety is vital to well-functioning health systems. A key component is safe prescribing, particularly in primary care where most medications are prescribed. Previous research has demonstrated that the number...

Real-World Assessment of Sugar.IQ with Watson—A Cognitive Computing-Based Diabetes Management Solution

Background: Cognitive computing technology providing personalized insights based on correlations between behavior patterns and glycemic outcomes was leveraged to create an interactive mobile assistant for T1D patients. Method: Medtronic MiniMed™ Connect users (N=256) were invited to take part in a 90-day Sugar.IQ app pilot program that began April 11, 2017. The percentage of time in target range (TIR, 70-180 mg/dL), <70mg/dL, >180mg/dL, and excursions (periods >20 minutes and <70mg/dL or >180mg/dL) were collected 30 days before Sugar.IQ onboarding, and compared to those 90 days later (August 2017). Insights delivered to users and user feedback were also analyzed. Results: There were 11,356 sensor-wear days; 10,761 unique Sugar.IQ usage sessions collected; and 4,688 insights delivered (1 every 3 days). Insights included 655 and 699 identifying behaviors associated with more time <70mg/dL and >180mg/dL, respectively. The Sugar.IQ app was used 2.1 times/day. Compared to baseline, TIR was 33 minutes longer per day (P<0.15) and hypoglycemia events reduced by 1.0 per month (P<0.001). A week after receiving insights associated with hypoglycemia, 55% and 54% of users had fewer hypoglycemia and hyperglycemia events, respectively. Hyperglycemia events >2 hours reduced by 1.3 per month (P<0.001). After receiving insights about low glucose associated with boluses delivered at rapid rates of change, users tended to take smaller boluses and consume less carb in the following 7 days. Among insights that included user feedback, 86% of users rated them as “Helpful” vs. “Not helpful.” Conclusion: Timely and personalized insights, such as those provided during the Sugar.IQ pilot, may advance patient understanding of glucose trends, aid in behavioral change that improves therapy adherence, and lead to better outcomes. Disclosure Y. Zhong: None. S. Arunachalam: None. P. Agrawal: None. H. Neemuchwala: None. T.L. Cordero: Employee; Self; Medtronic. F.R. Kaufman: Employee; Self; Medtronic.

Taking Constructivism One Step Further: Post Hoc Analysis of a Student-Created Wiki.

BackgroundWiki platform use has potential to improve student learning by improving engagement with course material. A student-created wiki was established to serve as a repository of study tools for students in a medical school curriculum. There is a scarcity of information describing student-led creation of wikis in medical education.ObjectiveThe aim is to characterize website traffic of a student-created wiki and evaluate student perceptions of usage via a short anonymous online survey.MethodsWebsite analytics were used to track visitation statistics to the wiki and a survey was distributed to assess ease of use, interest in contributing to the wiki, and suggestions for improvement.ResultsSite traffic data indicated high usage, with a mean of 315 (SD 241) pageviews per day from July 2011 to March 2013 and 74,317 total user sessions. The mean session duration was 1.94 (SD 1.39) minutes. Comparing Fall 2011 to Fall 2012 sessions revealed a large increase in returning visitors (from 12,397 to 20,544, 65.7%) and sessions via mobile devices (831 to 1560, 87.7%). The survey received 164 responses; 88.0% (162/184) were aware of the wiki at the time of the survey. On average, respondents felt that the wiki was more useful in the preclinical years (mean 2.73, SD 1.25) than in the clinical years (mean 1.88, SD 1.12; P<.001). Perceived usefulness correlated with the percent of studying for which the respondent used electronic resources (Spearman ρ=.414, P<.001).ConclusionsOverall, the wiki was a highly utilized, although informal, part of the curriculum with much room for improvement and future exploration.

The Impact of a Custom Electromyograph (EMG) Controller on Player Enjoyment of Games Designed to Teach the Use of Prosthetic Arms

Using electromyography (EMG) for physical therapy is not a new field, but applying it to the game based training for kids who need prosthetic arms to train both use and muscle strength is. The ability to bring fun training games to this demographic of disability gamers is potentially life changing. In an effort to support the training of these children a number of training games were developed. These initial games replace traditional button presses with flex controls using a custom game controller developed specifically for this task. Due to cost, and other factors, kids are often left without prosthetics until they are adults and the rejection rates for adults can be quite high, so the need for these games to not only train but to also be fun and engaging is paramount. This research explores the impact of using a custom EMG controller in place of a keyboard on the usability and user experience of an entertaining training game. Initial user sessions with child users of prosthetics indicate usability scores in a range of 75–85 for most games and moderately high user experience scores (GUESS scores of 40–55). Further comparisons conducted with undergraduate students suggest significantly higher GUESS scores for games where the flex controller is used to control the arms of the player avatar. While mixed, the results of this study indicate a mostly positive impact from the novel nature of using a custom flex controller, while there are indications that sound game design and supportive narrative still matters when developing custom controllers for training or entertainment purposes. Future games and game design for these controllers will utilize the successful design strategies applied by the games tested in this study.

Application of the Information Bottleneck method to discover user profiles in a Web store

ABSTRACTThe paper deals with the problem of discovering groups of Web users with similar behavioral patterns on an e-commerce site. We introduce a novel approach to the unsupervised classification of user sessions, based on session attributes related to the user click-stream behavior, to gain insight into characteristics of various user profiles. The approach uses the agglomerative Information Bottleneck (IB) algorithm. Based on log data for a real online store, efficiency of the approach in terms of its ability to differentiate between buying and non-buying sessions was validated, indicating some possible practical applications of the our method. Experiments performed for a number of session samples showed that the method is capable of separating both types of sessions to a large extent. A detailed analysis was performed for the number of clusters ranging from two to seven, and the results were compared to those achieved by applying the most common clustering algorithm, k-means. Increasing the number of clusters generally leads to better results for both algorithms. However, IB demonstrated much higher average efficiency than k-means for the corresponding number of clusters, and this superiority was especially clear for lower number of clusters. The IB-based division of user sessions into seven clusters gives the mean entropy value of 0.28, which means the 95% separation of sessions of both types. Furthermore, a big advantage of our approach is that it gives a possibility to analyze the probability distribution of session attributes in individual clusters, which allows one to discover hidden knowledge about common characteristics of various user profiles and use this knowledge to support managerial decisions.

Achieving End-to-End Reliability of Mission-Critical Traffic in Softwarized 5G Networks

Network softwarization is a major paradigm shift, which enables programmable and flexible system operation in challenging use cases. In the fifth-generation (5G) mobile networks, the more advanced scenarios envision transfer of high-rate mission-critical traffic. Achieving end-to-end reliability of these stringent sessions requires support from multiple radio access technologies and calls for dynamic orchestration of resources across both radio access and core network segments. Emerging 5G systems can already offer network slicing, multi-connectivity, and end-to-end quality provisioning mechanisms for critical data transfers within a single software-controlled network. Whereas these individual enablers are already in active development, a holistic perspective on how to construct a unified, service-ready system as well as understand the implications of critical traffic on serving other user sessions is not yet available. Against this background, this paper first introduces a softwarized 5G architecture for end-to-end reliability of the mission-critical traffic. Then, a mathematical framework is contributed to model the process of critical session transfers in a softwarized 5G access network, and the corresponding impact on other user sessions is quantified. Finally, a prototype hardware implementation is completed to investigate the practical effects of supporting mission-critical data in a softwarized 5G core network, as well as substantiate the key system design choices.