Changes In User Behavior Research Articles

Keystroke dynamics obfuscation using key grouping

Keystroke dynamics is one of the most widely adopted identity verification techniques in remote systems. It is based on modeling users’ specific patterns of typing on the keyboard. When utilized in conjunction with the commonly used passwords, the use of keystroke dynamics can dramatically increase the level of security without interfering with the user experience. However, aspects of keystroke dynamics that applied on passwords, such as processing keystroke events and storing feature vectors or user models, can expose users to identity theft and a new set of privacy risks, thus questioning the added value of keystroke dynamics. In addition, common encryption techniques will be unable to mitigate these threats, since the user's behavior changes from one session to another. In this paper, we suggest key grouping as an obfuscation method to ensure keystroke dynamics privacy. When applied on the keystroke events, the key grouping dramatically reduces the possibility of password theft. To perform the key grouping optimally, we present a novel method which produces groups that can integrated with any keystroke dynamics algorithm. Our method divides the keys into groups using hierarchical clustering with dedicated statistical heuristics algorithm. We tested our method's key grouping output on five keystroke dynamics algorithms using a public dataset and managed to show a consistent improvement of up to 7% in the AUC over other, more intuitive key groupings and random key groupings.

Privacy & Market Concentration: Intended & Unintended Consequences of the GDPR

We show that the European Union's General Data Protection Regulation (GDPR) reduced data sharing online, but had the unintended consequence of increasing market concentration among technology vendors that provide support services to websites. We collect panel data on the web technology vendors selected by more than 27,000 top websites internationally. The week after the GDPR's enforcement, website use of web technology vendors for EU users falls by 15%. Websites that would face greater penalties under the GDPR drop more vendors. Websites are more likely to drop smaller vendors, which increases the relative concentration of the vendor market by 17%. Increased concentration predominantly arises among vendors that use personal data such as cookies, and from the increased relative shares of Facebook and Google-owned vendors, but not from website consent requests. This suggests that increases in concentration are driven by website vendor choices rather than changes in user behavior.

Understanding Health Behavior Technology Engagement: Pathway to Measuring Digital Behavior Change Interventions.

Researchers and practitioners of digital behavior change interventions (DBCI) use varying and, often, incongruent definitions of the term “engagement,” thus leading to a lack of precision in DBCI measurement and evaluation. The objective of this paper is to propose discrete definitions for various types of user engagement and to explain why precision in the measurement of these engagement types is integral to ensuring the intervention is effective for health behavior modulation. Additionally, this paper presents a framework and practical steps for how engagement can be measured in practice and used to inform DBCI design and evaluation. The key purpose of a DBCI is to influence change in a target health behavior of a user, which may ultimately improve a health outcome. Using available literature and practice-based knowledge of DBCI, the framework conceptualizes two primary categories of engagement that must be measured in DBCI. The categories are health behavior engagement, referred to as “Big E,” and DBCI engagement, referred to as “Little e.” DBCI engagement is further bifurcated into two subclasses: (1) user interactions with features of the intervention designed to encourage frequency of use (ie, simple login, games, and social interactions) and make the user experience appealing, and (2) user interactions with behavior change intervention components (ie, behavior change techniques), which influence determinants of health behavior and subsequently influence health behavior. Achievement of Big E in an intervention delivered via digital means is contingent upon Little e. If users do not interact with DBCI features and enjoy the user experience, exposure to behavior change intervention components will be limited and less likely to influence the behavioral determinants that lead to health behavior engagement (Big E). Big E is also dependent upon the quality and relevance of the behavior change intervention components within the solution. Therefore, the combination of user interactions and behavior change intervention components creates Little e, which is, in turn, designed to improve Big E. The proposed framework includes a model to support measurement of DBCI that describes categories of engagement and details how features of Little e produce Big E. This framework can be applied to DBCI to support various health behaviors and outcomes and can be utilized to identify gaps in intervention efficacy and effectiveness.

Diffusion model based on shared friends-aware independent cascade

Due to the swift growth of social network and the large variety of its application, numerous research effort aims at predict or explain the information diffusion phenomenon and how the user behaviour changes according to the effect of social pressure. Applications like viral marketing, rumour controlling, and individual behaviour analysis are some areas where these researches are applied. Many models proposed to handle the problem of information diffusion. Such models mostly require disseminated probabilities to be assigned for each link in diffusion network. In this paper we address the problem of predicting the information diffusion cascade. The proposed model named “Shared Friends-Aware IC-Based Diffusion Model (SFA-ICBDM)” can be seen as extension to the well known diffusion model, Independent Cascade model(IC). (SFA-ICBDM) model make use of structural feature of diffusion network specifically the common friend and similarity in terms of behaviour among users as a probability of influencing among users, then predict the cascade of information dissemination. The experiment has been conducted using real world social network dataset (Meme Tracker). In sum, the proposed model has been compared with baseline model IC and related works, where it shows improvement over baseline and related works. In addition, some promising conclusions have been obtained.

Energy demand projection based on consumption habits in the residential sector

This paper analyses the behaviour of the demand curve in the residential sector from Bogota, Colombia, based on changes in the electric energy consumption behaviour of users. Initially, a survey divided in analysis units focused on obtaining the main characteristics of residential energy consumption was conducted. Using this information, a stochastic model was designed and developed in order to determine how changes in consumption habits during specific periods of the day influence the demand projection for a residential sector. A base scenario for the users of the selected population group was established from measurements in 18 houses and in the common point of a building, and the average energy consumed was 168.86 kWh/month. Through simulations using a system dynamics software, 12 scenarios were established. Consumption habits of users were modified in relation with periods and appliances, concluding that it is necessary to apply a set of strategies to encourage actions for changing the consumption habits of residential users, which has to be provided by government policies relevant to the energy area.

Promoting Sustainable Energy Consumption Behavior through Interactive Data Visualizations

With an increasing worldwide need for energy and the ever-decreasing availability of energy resources, a wide range of interactive visualizations are being developed to allow people to use energy more efficiently by monitoring their consumption patterns and changing their energy usage behavior. For these visualizations to achieve their aim, they must not only target people’s energy saving objectives but also support the necessary factors that help people to change their underlying energy consumption behavior. In this paper, we survey several categories of existing interactive energy visualizations and through a number of selected examples in each case, identify possible potentials for supporting any user behavior changes. For this survey, we have used the behavior change model originally proposed by B. J. Fogg, which defines three factors of motivation, trigger and ability. Our survey has shown that most existing interactive visualizations target the motivation factor, with some supporting trigger or ability and only a few dealing with all the three factors of the behavior change model.

Healthier Life with Digital Companions: Effects of Reflection-Level and Statement-Type of Messages on Behavior Change via a Perceived Companion

ABSTRACTQuantified-self tools track personal data as well as emotional and psychological scores, in real time, of people who use such tools. Such data have potential uses for initiating interactions to induce personal health-related behavior changes. However, notwithstanding this potential, not much benefit has been derived from the data tracked using various devices such as smartphones and fitness trackers. The main research goal of this study is to investigate how interactions of quantified-self tools should be designed for inducing user perception and behavior change. Particularly, this study uses two message representation formats (MRF) for users to perceive self-tracking tools as companion devices because the MRFs of smartphones and fitness trackers are important to interact with users in conversational interaction. This study developed a message expression algorithm, “Samantha,” to deliver personalized-messages automatically in real time about the values tracked by these devices to their users. The study studied the effect of the four message representation formats on the perception of companion and to induce behavior change.

DESIRE ARCHITETURE

People can change their behavior and wants to change it rapidly to acquire the benefits. This generates a need for a behavior change theory connected with their desire and this can be mediated by a manager. Gamification is a process that guide a person to Flow experience througth intuitive identification of individual’s gameful profile. Hence, this paper formulates the Desire Architeture Theory – a gamify process - which aims to acceletate the user behavior change based a user desire activation

Love Unshackled: Identifying the Effect of Mobile App Adoption in Online Dating

The proliferation of smartphones and other mobile devices has led to numerous companies investing significant resources in developing mobile applications, in every imaginable domain. As apps proliferate, understanding the impact of app adoption on key outcomes of interest and linking this understanding to the underlying mechanisms that drive these results is imperative. In this paper, we explore the changes in user behavior induced by adoption of a mobile application, in terms of engagement and matching outcomes in the online dating context. We also identify three mechanisms that are somewhat unique to the mobile environment, but are hitherto unestablished in the literature, that drive this shift in behavior: ubiquity, impulsivity, and disinhibition. Our main identification strategy uses propensity score matching combined with difference-in-differences, coupled with a rigorous falsification test to confirm the validity of our identification strategy. Our results demonstrate that mobile app adoption induces users to become more socially engaged as measured by key engagement metrics such as visiting significantly more profiles, sending significantly more messages, and importantly, achieving more matches. We also discover various mechanisms facilitating this increased engagement: ubiquity of mobile use—users log in more, and login across a wider range of hours in the day. We find that men act more impulsively, in that they are less likely to check the profile of a user who messaged them before replying to them. This effect is not visible for women who continue to be deliberate in their checking before replying even after adoption of the mobile app. Finally, we find that both men and women exhibit disinhibition, in that users initiate actions to a more diverse set of potential partners than they did before on dimensions of race, education, and height.

Mobile App Initiative and Firm Equity Value

With the continuous growth of the mobile market, many firms are considering investing in mobile apps. Prior research and anecdotal evidence have shown that the success of a mobile app is associated with positive changes in user behavior. However, it is unclear whether the performance implications of mobile app investment can be generalized to a wide range of firms. To further our understanding of IT investments in the mobile domain, we utilized the event study methodology to analyze 761 US public firms that released their first mobile app between 2008 and 2017 on the two leading mobile app stores, Apple’s App Store and Google Play. Our results show that, on average, public firms experience a negative abnormal stock return on the day they release their first mobile app and that the value creation process of mobile app investment is highly contingent on a firm’s market position, IT capability, and industry competition. As the first empirical study to rigorously and systematically document the impact of IT investment in the mobile domain, our findings imply that firms should act cautiously when planning to enter the mobile app market.

Investigation of a multizone building with HVAC system using a coupled thermal and airflow model

In building energy simulations, the air infiltration and interzonal airflow are generally either not considered or calculated oversimplified. However, the effects of air infiltration and building airflow have an impact on the thermal comfort and the building’s energy load. The various zones in multi-zone buildings, the operation of windows, doors and mechanical ventilation make the system’s analysis complex and challenging. Building airflow affects pressure, temperature and moisture differences. Therefore, this study investigate the airflow inside a multizone building with changing user behavior, using a coupled building and system energy simulation. A decentralized air-only HVAC system provides the ventilation system with a control strategy, which variably adapts the airflow to the load in the individual zones. The effects of the air infiltration, interzonal airflow and mechanical ventilation in the building are investigated with a node and link network in TRNSYS using the airflow model TRNFLOW (COMIS). Investigating different variations of the ventilation rates and building’s airtightnesses, the results are shown by comparison with a reference model without airflow simulation. Finally, this study shows a comprehensive approach at low computational costs, determining the air quality, the thermal conditions and the airflow in a multizone building using an HVAC system.

A Novel Network user Behaviors and Profile Testing based on Anomaly Detection Techniques

The proliferation of smart devices and computer networks has led to a huge rise in internet traffic and network attacks that necessitate efficient network traffic monitoring. There have been many attempts to address these issues; however, agile detecting solutions are needed. This research work deals with the problem of malware infections or detection is one of the most challenging tasks in modern computer security. In recent years, anomaly detection has been the first detection approach followed by results from other classifiers. Anomaly detection methods are typically designed to new model normal user behaviors and then seek for deviations from this model. However, anomaly detection techniques may suffer from a variety of problems, including missing validations for verification and a large number of false positives. This work proposes and describes a new profile-based method for identifying anomalous changes in network user behaviors. Profiles describe user behaviors from different perspectives using different flags. Each profile is composed of information about what the user has done over a period of time. The symptoms extracted in the profile cover a wide range of user actions and try to analyze different actions. Compared to other symptom anomaly detectors, the profiles offer a higher level of user experience. It is assumed that it is possible to look for anomalies using high-level symptoms while producing less false positives while effectively finding real attacks. Also, the problem of obtaining truly tagged data for training anomaly detection algorithms has been addressed in this work. It has been designed and created datasets that contain real normal user actions while the user is infected with real malware. These datasets were used to train and evaluate anomaly detection algorithms. Among the investigated algorithms for example, local outlier factor (LOF) and one class support vector machine (SVM). The results show that the proposed anomaly-based and profile-based algorithm causes very few false positives and relatively high true positive detection. The two main contributions of this work are a new approaches based on network anomaly detection and datasets containing a combination of genuine malware and actual user traffic. Finally, the future directions will focus on applying the proposed approaches for protecting the internet of things (IOT) devices.

Substantial rebound effects in urban ridesharing: Simulating travel decisions in Paris, France

This paper investigates how and to what extent changes in user behavior may mitigate the environmental benefits of urban ridesharing, a phenomenon commonly referred to as “rebound effect”. Ridesharing reduces both the individual cost of car travel (through cost splitting) and road travel times (by decreasing congestion). This may trigger a number of behavioral changes among transportation users, including: making less detours to avoid congestion (route choice effect), switching from public transit and active modes to the car (modal shift effect), travelling longer distances (distance effect), and relocating further from the urban center (relocation effect). Taking Paris region as a case study, this research applies an integrated transportation/land-use model to evaluate several ridesharing scenarios and quantify the four rebound effects. The overall rebound effect is found to be substantial, cancelling out from 68 to 77% of CO2 emission reductions and from 52 to 73% of aggregated social benefits (including congestion, air quality, CO2 emissions, noise) expected from ridesharing. This is primarily the result of the modal shift effect, supplemented as ridesharing develops by the distance effect. Although the simplified representation of ridesharing in the baseline model calls for caution regarding these estimates, a sensitivity analysis corroborates the main findings and the prevalence of substantial rebound effects. The paper also investigates to what extent three complementary policies - improving public transit, reducing road capacity or increasing the cost of car travel – might limit the overall rebound effect and thereby maximize the benefits of urban ridesharing.

Nutritional Mobile Applications for CKD Patients: Systematic Review

IntroductionMobile health applications offer the potential to help people living with chronic kidney disease (CKD) manage diet-related challenges. This systematic review examined CKD dietary mobile app interventions; specifically, app characteristics, feasibility, and effectiveness in changing user behavior, as well as user satisfaction.MethodsThis review was reported in accordance with PRISMA guidelines. We searched scholarly databases, as well as the gray literature, for all randomized controlled trials, observational studies, needs assessments, and pilot testing/studies/trials focused on the development or evaluation of CKD dietary mobile app interventions. The characteristics, user satisfaction with, usability/feasibility, and effectiveness in changing dietary behavior of the mobile application were summarized using descriptive statistics and in a narrative manner.ResultsThirteen full-text studies were included, of which 11 were single center, with a mean sample size of 23. Of the 7 studies that measured usability/feasibility, all found at least some aspects of the application feasible/useful. Of the 5 studies that reported an evaluation of changes in behavior/diet related to self-management, all reported some positive change.ConclusionAccording to current studies, nutritional apps show promise in CKD self-management.

Current and future directions in Belgian medical and health sciences librarianship: a user-tailored approach.

This article is part of a new series in this regular feature. The series intend to serve as a road map by sharing expertise and drawing together trends that are relevant to both health science librarians and health informatics professionals. The present article is a collaboration of six medical and health sciences libraries in Belgium and the Flemish library and archive association (VVBAD, n.d., https://www.vvbad.be/). It aims to elucidate the extended, user-tailored approach provided by medical and health sciences libraries in Belgium motivated by the recent changes in user expectations and behaviour.

Designing Emails Aimed at Increasing Family Physicians’ Use of a Web-Based Audit and Feedback Tool to Improve Cancer Screening Rates: Cocreation Process

BackgroundProviding clinical performance data to health professionals, a process known as audit and feedback, can play an important role in health system improvement. However, audit and feedback tools can only be effective if the targeted health professionals access and actively review their data. Email is used by Cancer Care Ontario, a provincial cancer agency, to promote access to a Web-based audit and feedback tool called the Screening Activity Report (SAR); however, current emails that lack behavior change content have been ineffective at encouraging log-in to the SAR.ObjectiveThe objective of our study was to describe the process and experience of developing email content that incorporates user input and behavior change techniques (BCTs) to promote the use of the SAR among Ontario primary care providers.MethodsOur interdisciplinary research team first identified BCTs shown to be effective in other settings that could be adapted to promote use of the SAR. We then developed draft BCT-informed email content. Next, we conducted cocreation workshops with physicians who had logged in to the SAR more than once over the past year. Participants provided reactions to researcher-developed BCT-informed content and helped to develop an email that they believed would prompt their colleagues to use the SAR. Content from cocreation workshops was brought to focus groups with physicians who had not used the SAR in the past year. We analyzed notes from the cocreation workshops and focus groups to inform decisions about content. Finally, 8 emails were created to test BCT-informed content in a 2×2×2 factorial randomized experiment.ResultsWe identified 3 key tensions during the development of the email that required us to balance user input with scientific evidence, organizational policies, and our scientific objectives, which are as follows: conflict between user preference and scientific evidence, privacy constraints around personalizing unencrypted emails with performance data, and using cocreation methods in a study with the objective of developing an email that featured BCT-informed content.ConclusionsTeams tasked with developing content to promote health professional engagement with audit and feedback or other quality improvement tools might consider cocreation processes for developing communications that are informed by both users and BCTs. Teams should be cautious about making decisions solely based on user reactions because what users seem to prefer is not always the same as what works. Furthermore, implementing user recommendations may not always be feasible. Teams may face challenges when using cocreation methods to develop a product with the simultaneous goal of having clearly defined variables to test in later studies. The expected role of users, evidence, and the implementation context all warrant consideration to determine whether and how cocreation methods could help to achieve design and scientific objectives.

Scenario-Based Insider Threat Detection From Cyber Activities

An insider threat scenario refers to the outcome of a set of malicious activities caused by intentional or unintentional misuse of the organization’s systems, networks, data, and resources. Prevention of insider threat is difficult, since trusted partners of the organization are involved in it, who have authorized access to these confidential/sensitive resources. The state-of-the-art research on insider threat detection mostly focuses on developing unsupervised behavioral anomaly detection techniques with the objective of finding out anomalousness or abnormal changes in user behavior over time. However, an anomalous activity is not necessarily malicious that can lead to an insider threat scenario. As an improvement to the existing approaches, we propose a technique for insider threat detection from time-series classification of user activities. Initially, a set of single-day features is computed from the user activity logs. A time-series feature vector is next constructed from the statistics of each single-day feature over a period of time. The label of each time-series feature vector (whether malicious or nonmalicious) is extracted from the ground truth. To classify the imbalanced ground-truth insider threat data consisting of only a small number of malicious instances, we employ a cost-sensitive data adjustment technique that undersamples the nonmalicious class instances randomly. As a classifier, we employ a two-layered deep autoencoder neural network and compare its performance with other popularly used classifiers: random forest and multilayer perceptron. Encouraging results are obtained by evaluating our approach using the CMU Insider Threat Data, which is the only publicly available insider threat data set consisting of about 14-GB web-browsing logs, along with logon, device connection, file transfer, and e-mail log files. We observe that both deep autoencoder and random forest classifiers classify the data-adjusted time-series feature set with high precision, recall, and f-score. Although multilayer perceptron has a high recall, it suffers from a lower precision and f-score compared to the other two classifiers.

Ising Model of User Behavior Decision in Network Rumor Propagation

The continuous breeding and rapid spread of rumors in social networks poses a severe challenge to the effective utilization and scientific management of social media. Therefore, it is of great theoretical significance and application value to study the decision-making behavior of users in rumors spread in social networks and to reveal the rumor transmission rules. Based on the Ising model, this paper constructs a social network rumor propagation dynamics model and then reveals the rumor transmission rules. In the model, the Monte Carlo method is used to simulate the interaction between the user’s self-identity attribute (micropart), the user-user interaction (the middle part), and the social environment’s influence (the macroscopic part) to study user decision behavior of rumor spread in a social network system. The results show that, in the rumor propagation system, von Neumann entropy can quantify well the phase transition of the system and is consistent with the phase transition information obtained by measuring the spontaneous magnetization and magnetic susceptibility of the system. In addition, the introduction of the self-identity characteristics of individual users into the Ising model has greatly changed the users’ decision-making behavior of rumor spreading and changed the internal structure of the system, thus changing the type of phase transition. As the degree of self-identification increases, the need for lower temperatures can change the orderly state of the system, and user behavior changes more rapidly. For the rumor transmission system, the state of close order is conducive to the blocking of rumors, thus maintaining social stability.

АНАЛІЗ ІСНУЮЧИХ ПІДХОДІВ ПРОТИДІЇ НАЙПОШИРЕНІШИМ КІБЕРНЕТИЧНИМ ВТРУЧАННЯМ В ІНФОРМАЦІЙНО–ТЕЛЕКОМУНІКАЦІЙНІ МЕРЕЖІ

The rapid development of information and communication technologies and the global Internet opens up many new opportunities in all spheres of human life and in the country as a whole. On the other hand, the use of computers, mobile gadgets and other digital devices has contributed to the development of a new threat - cybernetic interventions, which are understood as actions in cyberspace directed against the information and telecommunications network with the aim of influencing it by disrupting its functioning, gaining control over the network, copy, delete, damage, insert or destroy data, create conditions for changing user behavior. In Ukraine, from 2014 to 2017, a number of cybernetic interventions were carried out, which were widely publicized both in the country and in the world. In the context of the globalization of information processes and their integration into various spheres of public life, the leadership of the leading states of the world pays special attention to the creation and improvement of effective systems for protecting critical infrastructure against external and internal cyber threats. Therefore, it will be expedient to analyze the widespread cybernetic interventions that have been carried out on the territory of Ukraine and to consider existing methods and methods of preventing cybernetic interventions, as well as to offer a method and scheme for protection from cybernetic interventions.

Known and unknown requirements in healthcare

We report experience in requirements elicitation of domain knowledge from experts in clinical and cognitive neurosciences. The elicitation target was a causal model for early signs of dementia indicated by changes in user behaviour and errors apparent in logs of computer activity. A Delphi-style process consisting of workshops with experts followed by a questionnaire was adopted. The paper describes how the elicitation process had to be adapted to deal with problems encountered in terminology and limited consensus among the experts. In spite of the difficulties encountered, a partial causal model of user behavioural pathologies and errors was elicited. This informed requirements for configuring data- and text-mining tools to search for the specific data patterns. Lessons learned for elicitation from experts are presented, and the implications for requirements are discussed as “unknown unknowns”, as well as configuration requirements for directing data-/text-mining tools towards refining awareness requirements in healthcare applications.