Engagement With a Mobile Chat-Based Intervention for Smoking Cessation: A Secondary Analysis of a Randomized Clinical Trial.

Importance: Determining how individuals engage with digital health interventions over time is crucial to understand and optimize intervention outcomes.

Objective: To identify the engagement trajectories with a mobile chat-based smoking cessation intervention and examine its association with biochemically validated abstinence.

Design, Setting, And Participants: A secondary analysis of a pragmatic, cluster randomized clinical trial conducted in Hong Kong with 6-month follow-up.

DAFuzz: data-aware fuzzing of in-memory data stores.

Fuzzing has become an important method for finding vulnerabilities in software. For fuzzing programs expecting structural inputs, syntactic- and semantic-aware fuzzing approaches have been particularly proposed. However, they still cannot fuzz in-memory data stores sufficiently, since some code paths are only executed when the required data are available.

Factors associated with willingness and preferences to attend family services in Hong Kong: A population-based survey.

Objective: Family services are open to the community at large as well as vulnerable groups; however, little is known about the willingness of communities to attend such services. We investigated the willingness and preferences to attend family services and their associated factors (including sociodemographic characteristics, family wellbeing, and family communication quality) in Hong Kong.

Methods: A population-based survey was conducted on residents aged over 18 years from February to March 2021.

Associations of changes in smoking-related practices with quit attempt and smoking consumption during the COVID-19 pandemic: A mixed-methods study.

Introduction: How changes in smoking routine due to COVID-19 restrictions (e.g. refraining from smoking outdoors and stockpiling tobacco products) influence smoking behaviors remains understudied.

Multi-Step Attack Detection Based on Pre-Trained Hidden Markov Models.

Currently, hidden Markov-based multi-step attack detection models are mainly trained using the unsupervised Baum-Welch algorithm. The Baum-Welch algorithm is sensitive to the initial values of model parameters. However, its training uses random or average parameter initialization methods, which frequently results in the model training into a local optimum, thus, making the model unable to fit the alert logs well and thereby reducing the detection effectiveness of the model.

MultiFuzz: A Coverage-Based Multiparty-Protocol Fuzzer for IoT Publish/Subscribe Protocols.

The publish/subscribe model has gained prominence in the Internet of things (IoT) network, and both Message Queue Telemetry Transport (MQTT) and Constrained Application Protocol (CoAP) support it. However, existing coverage-based fuzzers may miss some paths when fuzzing such publish/subscribe protocols, because they implicitly assume that there are only two parties in a protocol, which is not true now since there are three parties, i.e.