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. From June 18 to September 30, 2017, 624 adult daily smokers were recruited from 34 community sites randomized to the intervention group. Data were analyzed from March 6 to October 30, 2023. Intervention: Chat-based cessation support delivered by a live counselor via a mobile instant messaging app for 3 months from baseline. Main Outcomes and Measures: Group-based trajectory modeling was used to identify engagement trajectories using the participants' weekly responses to the messages from the counselor over the 3-month intervention period. The outcome measures were biochemically validated tobacco abstinence at 3-month (end of treatment) and 6-month follow-ups. Covariates included sex, age, educational level, nicotine dependence, past quit attempt, and intention to quit at baseline. Results: Of 624 participants included in the analysis, 479 were male (76.8%), and the mean (SD) age was 42.1 (16.2) years. Four distinct engagement trajectories were identified: low engagement group (447 [71.6%]), where participants maintained very low engagement throughout; rapid-declining group (86 [13.8%]), where participants began with moderate engagement and rapidly decreased to a low level; gradual-declining group (58 [9.3%]), where participants had high initial engagement and gradually decreased to a moderate level; and high engagement group (58 [5.3%]), where participants maintained high engagement throughout. Compared with the low engagement group, the 6-month validated abstinence rates were significantly higher in the rapid-declining group (adjusted relative risk [ARR], 3.30; 95% CI, 1.39-7.81), gradual-declining group (ARR, 5.17; 95% CI, 2.21-12.11), and high engagement group (ARR, 4.98; 95% CI, 1.82-13.60). The corresponding ARRs (95% CI) of 3-month validated abstinence were 4.03 (95% CI, 1.53-10.59), 5.25 (95% CI, 1.98-13.88), and 9.23 (95% CI, 3.29-25.86). Conclusions and Relevance: The findings of this study suggest that higher levels of engagement with the chat-based smoking cessation intervention were associated with greater biochemically validated tobacco abstinence. Improving engagement with digital interventions may increase intervention benefits. Trial Registration: ClinicalTrials.gov Identifier: NCT03182790.

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. In this article, we propose a data-aware fuzzing method, DAFuzz, which is designed by considering the data used during fuzzing. Specifically, to ensure different data-sensitive code paths are exercised, DAFuzz first loads different kinds of data into the stores before feeding fuzzing inputs. Then, when generating inputs, DAFuzz ensures the generated inputs are not only syntactically and semantically valid but also use the data correctly. We implement a prototype of DAFuzz based on Superion and use it to fuzz Redis and Memcached. Experiments show that DAFuzz covers 13~95% more edges than AFL, Superion, AFL++, and AFLNet, and discovers vulnerabilities over 2.7× faster. In total, we discovered four new vulnerabilities in Redis and Memcached. All the vulnerabilities were reported to developers and have been acknowledged and fixed.

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. Data included sociodemographic characteristics (sex, age, education, housing type, monthly household income, and the number of cohabitants), willingness to attend family services to promote family relationships (yes/no), family service preferences (healthy living, emotion management, family communication promotion, stress management, parent-child activities, family relationship fostering, family life education, and social network building; each yes/no), family wellbeing, and family communication quality (both scores 0-10). Family wellbeing was assessed using the average scores of perceived family harmony, happiness and health (each score 0-10). Higher scores indicate better family wellbeing or family communication quality. Prevalence estimates were weighted by sex, age and educational level of the general population. Adjusted prevalence ratios (aPR) for the willingness and preferences to attend family services were calculated in relation to sociodemographic characteristics, family wellbeing, and family communication quality. Results: Overall, 22.1% (1,355/6,134) and 51.6% (996/1,930) of respondents were willing to attend family services to promote relationships or when facing problems, respectively. Older age (aPR = 1.37-2.30, P < 0.001-0.034) and having four or more cohabitants (aPR = 1.44-1.53, P = 0.002-0.003) were associated with increased aPR of willingness for both situations. Lower family wellbeing and communication quality were associated with lower aPR for such willingness (aPR = 0.43-0.86, P = 0.018-<0.001). Lower family wellbeing and communication quality were associated with preferences for emotion and stress management, family communication promotion, and social network building (aPR = 1.23-1.63, P = 0.017-<0.001). Conclusions: Lower levels of family wellbeing and communication quality were associated with unwillingness to attend family services and preferences for emotion and stress management, family communication promotion, and social network building.

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. To solve this issue, we propose a pre-training method for multi-step attack detection models based on the high semantic similarity of alerts in the same attack phase. The method first clusters the alerts based on their semantic information and pre-classifies the attack phase to which each alert belongs. Then, the distance of the alert vector to each attack stage is converted into the probability of generating alerts in each attack stage, replacing the initial value of Baum-Welch. The effectiveness of the proposed method is evaluated using the DARPA 2000 dataset, DEFCON21 CTF dataset, and ISCXIDS 2012 dataset. The experimental results show that the hidden Markov multi-step attack detection method based on pre-training of the proposed model parameters had higher detection accuracy than the Baum-Welch-based, K-means-based, and transfer learning differential evolution-based hidden Markov multi-step attack detection methods.

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. We examined the associations of changes in smoking-related practices with quit attempts and smoking consumption in current smokers using a mixed-methods design. METHODS: In a community-based telephone survey conducted between the second and third wave of the COVID-19 pandemic in Hong Kong, 659 smokers (87.1% male; 45.2% aged 40-59 years) were asked about quit attempts and changes in cigarette consumption and five smoking-related practices since the COVID-19 outbreak. Logistic regression was used to calculate adjusted odds ratio (AOR), adjusting for sex, age, education level, chronic disease status, heaviness of smoking (HSI), psychological distress (PHQ-4) and perceived danger of COVID-19. A subsample of 34 smokers provided qualitative data through semi-structured interviews for thematic analyses. RESULTS: Favorable changes in smoking-related practices, including having avoided smoking on the street (prevalence: 58.9%) and reduced going out to buy cigarettes (33.5%), were associated with a quit attempt (AOR: 2.09 to 2.26; p<0.01) and smoking reduction (AOR: 1.76 to 4.97; p<0.05). Avoiding smoking with other smokers (50.5%) was associated with smoking reduction (AOR=1.76; p<0.05) but not quit attempt (AOR=1.26; p>0.05). Unfavorable changes, including having increased smoking at home (25.0%) and stockpiled tobacco products (19.6%), were associated with increased smoking (AOR: 2.84 to 6.20; p<0.05). Low HSI (0-2) was associated with favorable changes (p<0.01), while high HSI score (3-6) was associated with unfavorable changes (p<0.01). Qualitative interviews revealed a double-edged effect of staying at home on smoking consumption and that pandemic precautionary measures (e.g. mask-wearing) reduced outdoor smoking. CONCLUSIONS: Amid the pandemic, favorable changes in smoking-related practices in smokers were mostly associated with quit attempts and smoking reduction, while unfavorable changes were associated with increased smoking. Smokers with higher nicotine dependence were more negatively impacted.

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., the publisher, the subscriber and the broker. In this paper, we propose MultiFuzz, a new coverage-based multiparty-protocol fuzzer. First, it embeds multiple-connection information in a single input. Second, it uses a message mutation algorithm to stimulate protocol state transitions, without the need of protocol specifications. Third, it uses a new desockmulti module to feed the network messages into the program under test. desockmulti is similar to desock (Preeny), a tool widely used by the community, but it is specially designed for fuzzing and is 10x faster. We implement MultiFuzz based on AFL, and use it to fuzz two popular projects Eclipse Mosquitto and libCoAP. We reported discovered problems to the projects. In addition, we compare MultiFuzz with AFL and two state-of-the-art fuzzers, MOPT and AFLNET, and find it discovering more paths and crashes.