User Annoyance Research Articles

“Run Forrest Run!”: Measuring the Impact of App-Enabled Performance and Social Feedback on Athletic and Usage Outcomes

Exercise-tracking apps are digital tools for delivering personalized behavioral interventions. Despite the growing usage of exercise applications, the efficacy of in-exercise app features in driving usage and athletic outcomes remains poorly understood. To remain competitive, sports organizations now need to leverage tracking tools to efficiently allocate resources and streamline training regimens and interventions for their core assets (i.e., athletes). In response to these operational needs, we examine two specific forms of such in-exercise interventions, namely performance feedback and social feedback. We conducted an 18-month-long field study with 1,037 uniformed group servicemen to assess the effect of these feedback types on running and usage outcomes. Results from the field study provided evidence that these two app features improved the servicemen’s running times and frequency of application usage, on average. Contrary to the common belief that more features are better, the joint usage of two feedback features does not produce additive effects. Tests at more granular levels suggest that users who received both feedback types in exercise episodes exhibit overconfidence behavior by participating in fewer subsequent exercises. The receipt of both feedback may be redundant and can cause user annoyance. Heterogeneity tests revealed that while performance feedback benefited most runners, social features were effective only for already stronger runners. Also, only positive social feedback had a significant impact on running performance. The results further indicate that performance feedback generated a slow but sustained increase in usage frequency, while social feedback spurred quick initial growth in usage but dwindled in effectiveness over time. Implications for theory and practice, as well as directions for further research, are discussed.

Pclf: Parallel cnn-lstm fusion model for sms spam filtering

Short Message Service (SMS) is widely used for its accessibility, simplicity, and cost-effectiveness in communication, bank notifications, and identity confirmation. The increase in spam text messages presents significant challenges, including time waste, potential financial scams, and annoyance for users and carriers. This paper proposes a novel deep learning model based on parallel structure in the feature extraction step to address this challenge, unlike the traditional models that only enhance the classifier. This parallel model fuses local and temporal features to enhance feature representation by combining convolutional neural networks (CNN) and long short-term memory networks (LSTM). The performance of this model has been evaluated on the UCI SMS Collection V.1 dataset, which comprises both spam and ham messages. The model achieves an accuracy of 99.28% on this dataset. Also, the model demonstrates good precision, recall, and F1 score. This paper aims to provide the best protection from unwanted messages for mobile phone users.

Implementation of Captcha Mechanisms using Deep Learning to Prevent Automated Bot Attacks

Online platforms' integrity and security are seriously threatened by the growth of automated bot attacks, necessitating the development of effective methods for telling harmful bots apart from legitimate users. In order to successfully combat automated bot attacks, this project investigates the creation and application of CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) methods utilising deep learning techniques. In this study, we employ deep learning to create and apply complex CAPTCHA-problems that help us distinguish between real users and automated bots. Convolutional neural networks and recurrent neural networks are employed to build dynamic, adjustable CAPTCHAs in order to keep up with the bots' evolving approaches. Our research focuses on creating CAPTCHAs that are simple to use for humans but difficult to understand by robots, which creates a favourable user experience for those who are actually human. Character skewing, background noise injection, and image obfuscation are some of the methods we use to safeguard our CAPTCHAs while maintaining their usability. Furthermore, we carry out exhaustive trials in the real world to assess the effectiveness of our methods based on in-depth CAPTCHA learning. We evaluate their resistance to a variety of attack methods, such as counterattacks and machine learning-based bot attacks, in order to confirm that they are resilient. The findings of our study show that utilising deep learning in CAPTCHA methods to thwart automated bot attacks is both feasible and effective. Our method makes the internet environment safer and more user-friendly while also enhancing security and reducing user annoyance. This work is an important step in the fight against the growing menace of automated bot attacks in the digital sphere.

An Evaluation of Smartphone Tracking for Travel Behavior Studies

The use of smartphone tracking is seen as the way forward in data collection for travel behavior studies. It overcomes some of the weaknesses of the classical approach (which uses paper trip diaries) in terms of accuracy and user annoyance. This article evaluates if these benefits hold in the practical application of smartphone tracking and compares the findings of a travel behavior survey using smartphone tracking to the findings of a previous paper survey. We compare three phases of the travel behavior study. In the recruitment phase, we expect smartphone tracking to make people more willing to participate in surveys, given the innovative nature and reduced burden to participants. However, we found the recruitment of participants equally challenging as for classical methods. In the data collection phase, however, we observe that participants entering the smartphone tracking survey are much more likely to complete the data collection period than when using paper trip diaries. Because of the limited burden, the risk of drop-out from the survey is significantly lower, making the actual data collection more efficient, even for longer survey periods. Finally, in the data analysis phase, the travel behavior indicators derived from smartphone tracking data result in higher average trip rates, shorter average trip lengths and a higher share of active modes (bike, walking) than the results from the paper survey. Although this is explained by more complete and more consistent trip registration, this finding is problematic for comparability between surveys based on different methods, both for longitudinal monitoring (comparability over consequent surveys) and for benchmarking (comparability over geographical areas). Therefore, it is crucial to clearly report the applied data collection methods when describing or comparing travel indicators. In surveys, a combined approach of both written trip diaries and smartphone tracking is advised, where each method can complement the shortcomings of the other.

Exploring usability problems of mHealth applications developed for cervical cancer: An empirical study.

Nowadays, mobile health applications are developed to raise awareness and facilitate screening and treatment of cervical cancer, while a very few studies have been conducted focusing on the measurement and assurance of usability and exploring the acceptable user experience of such applications. Usability issues become a crucial concern for such cervical-cancer-related applications because users with diverse backgrounds in terms of education, information technology literacy, and geographic reasons are required to access those applications. The objective of this research is to evaluate the usability of mobile health applications developed for cervical cancer patients. Two evaluation studies were conducted following the expert evaluation and a questionnaire-based user study. A total of four cervical-cancer-related applications that are focusing on the Awareness and Diagnosis theme were selected and each of the applications was evaluated by four usability experts. Then, a user study (n = 80) based on the Goal Question Metric was conducted to reveal the usability problems of four selected applications. Finally, findings of both evaluations were aggregated and analyzed. Both approaches showed that all applications suffer from several usability problems while "Cervical Cancer Guide" performs better and "Cervical Cancer Tracker" showed the least in performance from the usability perspective. Again, the Goal Question Metric performs noticeably better in assessing the learnability of the applications, while the analytical heuristic evaluation performs better in identifying the issues that cause user annoyance. The methodology adopted and the usability problems revealed through this study can be well utilized by the information technology professionals or user interface designers for designing, evaluating, and developing the cervical-cancer-related applications with enhanced usability and user experience.

DALock: Password Distribution-Aware Throttling

Large-scale online password guessing attacks are widespread and pose a persistant privacy and security threat to users. The common method for mitigating the risk of online cracking is to lock out the user after a fixed number (K) of consecutive incorrect login attempts. Selecting the value of K induces a classic security-usability trade-off. When K is too large, a hacker can (quickly) break into a significant fraction of user accounts, but when K is too low, we will start to annoy honest users by locking them out after a few mistakes. Motivated by the observation that honest user mistakes typically look quite different from an online attacker’s password guesses, we introduce DALock, a distribution-aware password lockout mechanism to reduce user annoyance while minimizing user risk. As the name suggests, DALock is designed to be aware of the frequency and popularity of the password used for login attacks. At the same time, standard throttling mechanisms (e.g., K-strikes) are oblivious to the password distribution. In particular, DALock maintains an extra “hit count" in addition to “strike count" for each user, which is based on (estimates of) the cumulative probability of all login attempts for that particular account. We empirically evaluate DALock with an extensive battery of simulations using real-world password datasets. In comparison with the traditional K-strikes mechanism, our simulations indicate that DALock offers a superior simulated security/usability trade-off. For example, in one of our simulations, we are able to reduce the success rate of an attacker to 0.05% (compared to 1% for the 3strikes mechanism) whilst simultaneously reducing the unwanted lockout rate for accounts that are not under attack to just 0.08% (compared to 4% for the 3-strikes mechanism).

Characterized Bioelectric Signals by Means of Neural Networks and Wavelets to Remotely Control a Human-Machine Interface.

Everyday, people interact with different types of human machine interfaces, and the use of them is increasing, thus, it is necessary to design interfaces which are capable of responding in an intelligent, natural, inexpensive, and accessible way, regardless of social, cultural, economic, or physical features of a user. In this sense, it has been sought out the development of small interfaces to avoid any type of user annoyance. In this paper, bioelectric signals have been analyzed and characterized in order to propose a more natural human-machine interaction system. The proposed scheme is controlled by electromyographic signals that a person can create through arm movements. Such arm signals have been analyzed and characterized by a back-propagation neural network, and by a wavelet analysis, in this way control commands were obtained from such arm electromyographic signals. The developed interface, uses Extensible Messaging and Presence Protocol (XMPP) to send control commands remotely. In the experiment, it manipulated a vehicle that was approximately 52 km away from the user, with which it can be showed that a characterized electromyographic signal can be sufficient for controlling embedded devices such as a Raspberri Pi, and in this way we can use the neural network and the wavelet analysis to generate control words which can be used inside the Internet of Things too. A Tiva-C board has been used to acquire data instead of more popular development boards, with an adequate response. One of the most important aspects related to the proposed interface is that it can be used by almost anyone, including people with different abilities and even illiterate people. Due to the existence of individual efforts to characterize different types of bioelectric signals, we propose the generation of free access Bioelectric Control Dictionary, to define and consult each characterized biosignal.

Detecting pop‐up advertisement browser windows using support vector machines

New browser windows automatically appear without clicking them when certain web sites are browsed by a user. The user does not desire to open such browser windows but is forced to look at them. Such automatic displays of browser windows sometimes contain important announcements, system notices, web guidance, other related enclosure description, pop‐up advertisements and so on. Moreover, pop‐up advertising browser windows are efficient tools for advertisers. However, huge usage of such advertising has become excessive and caused annoyance for users. This study is to analyze and characterize whole web page syntax to create a trained model from Support Vector Machines to effectively discriminate between pop‐up advertisement browser windows and desirable browser windows. The experimental results show that the overall accuracy of the proposed pop‐up detector is up to 92.11%. Hence, it can really reduce annoyance for users.