Survey research is a vital tool for understanding human opinions, behaviors, and preferences, but traditional methods face challenges such as high operational costs, limited sample diversity, and privacy concerns. Large Language Model (LLM)-based agents offer a novel approach to simulate human responses, generating high-quality synthetic data that preserves privacy while enabling researchers to explore patterns in public opinions. Our study introduces a flexible LLM-based survey simulation platform, which personalizes responses using real-world social media data to enhance realism and relevance. We conducted a validation case study using data from Steemit users, comparing the personalized simulated responses generated by LLM agents with actual survey responses collected from these users. This comparative analysis revealed key differences and potential biases, providing insights for refining survey methodologies. Personalized LLM simulations were highly effective for factual questions but faced limitations with subjective topics, often demonstrating lower variance compared to human responses. The quality of social media data and careful model selection also significantly influenced the accuracy of simulations. Our platform enables social science researchers to explore new methodologies for survey design, pre-test the impact of framing, and interact dynamically with LLM agents at reduced cost. This work provides valuable insights into the use of LLM-based agents for enhancing survey research, supporting their application in social science.

Cross-platform recommendation aims to improve recommendation accuracy by gathering heterogeneous features from different platforms. However, such cross-silo collaborations between platforms are restricted by increasingly stringent privacy protection regulations, thus data cannot be aggregated for training. Federated learning (FL) is a practical solution to deal with the data silo problem in recommendation scenarios. Existing cross-silo FL methods transmit model information to collaboratively build a global model by leveraging the data of overlapped users. However, in reality, the number of overlapped users is often very small, thus largely limiting the performance of such approaches. Moreover, transmitting model information during training requires high communication costs and may cause serious privacy leakage. In this paper, we propose a novel privacy-preserving double distillation framework named FedPDD for cross-silo federated recommendation, which efficiently transfers knowledge when overlapped users are limited. Specifically, our double distillation strategy enables local models to learn not only explicit knowledge from the other party but also implicit knowledge from its past predictions. Moreover, to ensure privacy and high efficiency, we employ an offline training scheme to reduce communication needs and privacy leakage risk. In addition, we adopt differential privacy to further protect the transmitted information. The experiments on two real-world recommendation datasets, HetRec-MovieLens and Criteo, demonstrate the effectiveness of FedPDD compared to the state-of-the-art approaches.

Abstract Social networking tools and visible information about developer activities on open source software (OSS) development platforms can leverage developers' social influence to attract more participation from their peers. However, the differential impacts of such social influence on developers' initial and sustained participation behaviors were largely overlooked in previous research. We empirically studied the impacts of two social influence mechanisms—word‐of‐mouth (WOM) and observational learning (OL)—on these two types of participation, using data collected from a large OSS development platform called Open Hub. We found that action (OL) speaks louder than words (WOM) with regard to sustained participation. Moreover, project age positively moderates the impacts of social influence on both types of participation. For projects with a higher average workload, the impacts of OL are reduced on initial participation but are increased on sustained participation. Our study provides a better understanding of how social influence affects OSS developers' participation behaviors. It also offers important practical implications for designing software development platforms that can leverage social influence to attract more initial and sustained participation.

Although person Re-Identification (ReID) is widely applied in a variety of multimedia systems, most of its essentially multifaceted output is evaluated and visualized inflexibly only using a list of images ranked by the similarity of image content, while the correlations between samples of different IDs and the spatial-temporal features of the images are underinvestigated. As system operators need a comfortable access to these important elements, we introduce an interactive design of a person ReID system to visualize these quantities. We demonstrate that a system offering these visual representations can effectively expedite and improve a person re-identification analysis and make it a much user-friendly experience.

Mobile business is becoming a reality due to ubiquitous Internet connectivity, popular mobile devices, and widely available cloud services. However, characteristics of the mobile environment, such as mobility, unpredictability, and variation of mobile network's signal strength, present challenges in selecting optimal services for composition. Traditional QoS-aware methods that select individual services with the best QoS may not always result in the best composite service because constant mobility makes the performance of service invocation unpredictable and location-based. This paper discusses the challenges of this problem and defines it in a formal way. To solve this new research problem, we propose a mobility model, a mobility-aware QoS computation rule, and a mobility-enabled selection algorithm with teaching-learning-based optimization. The experimental simulation results demonstrate that our approach can obtain better solutions than current standard composition methods in mobile environments. The approach can obtain near-optimal solutions and has a nearly linear algorithmic complexity with respect to the problem size.

QoS as an important criterion has attracted more and more attention in the service selection process. Various QoS-aware service selection methods have been proposed in recent years. However, few of them take into account of the QoS correlations between services, causing several performance issues. QoS correlations can be defined as that some QoS attributes of a service are not only dependent on the service itself but are also correlated to other services. Since such correlations will affect QoS values, it is important to study how to select appropriate candidate services while taking into account of QoS correlations when generating composite services with optimal QoS values. To this end, we propose a novel method of service selection, called the correlation-aware service pruning (CASP) method. It manages QoS correlations by accounting for all services that may be integrated into optimal composite services and prunes services that are not the optimal candidate services. Our experiments show that this method can manage complicated correlations between services and significantly improve the QoS values of the generated composite services.

Smog disaster is a type of air pollution event that negatively affects people's life and health. Forecasting smog disasters may largely reduce potential loss that they may cause. However, it is a great challenge since smog disasters are often caused by many complex factors. With the availability of huge amounts of data from the social web and physical sensors, covering information of air quality, meteorology, social event, human mobility, people's opinion, etc., it becomes possible to utilize such big data to forecast smog disasters. Especially, we can investigate the effect of social activities in smog disaster forecasting with the help of social web, which is ignored in traditional studies. In this paper, we propose a big data approach named B-Smog for smog disaster forecasting. It mainly has two components: 1) features extraction from multiple data sources to model the factors that indicate the appearance or disappearance of a smog disaster like traffic condition, human mobility, weather condition and air pollution transportation; 2) learning and predicting with heterogeneous features in multiple views. For the second component, we propose a prediction model based on an ensemble learning framework and artificial neural networks (ANNs), which achieves high accuracy in this application and can also be applied to other similar problems. We present the effectiveness of B-Smog through two cases studies in Beijing and Shanghai, and evaluate the accuracy of the prediction model through comparing it with some baselines. Moreover, the empirical findings of our study can also support decision making in smog disaster management.

Abstract Social networks evolve over time with the addition and removal of nodes and links to survive and thrive in their environments. Previous studies have shown that the link‐formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the important facilitators. In a research partnership with law enforcement agencies, we used dynamic social‐network analysis methods to examine several plausible facilitators of co‐offending relationships in a large‐scale narcotics network consisting of individuals and vehicles. Multivariate Cox regression and a two‐proportion z‐test on cyclic and focal closures of the network showed that mutual acquaintance and vehicle affiliations were significant facilitators for the network under study. We also found that homophily with respect to age, race, and gender were not good predictors of future link formation in these networks. Moreover, we examined the social causes and policy implications for the significance and insignificance of various facilitators including common jails on future co‐offending. These findings provide important insights into the link‐formation processes and the resilience of social networks. In addition, they can be used to aid in the prediction of future links. The methods described can also help in understanding the driving forces behind the formation and evolution of social networks facilitated by mobile and Web technologies.

"Dark Networks" refer to various illegal and covert social networks like criminal and terrorist networks. These networks evolve over time with the formation and dissolution of links to survive control efforts by authorities. Previous studies have shown that the link formation process in such networks is influenced by a set of facilitators. However, there have been few empirical evaluations to determine the significant facilitators. In this study, we used dynamic social network analysis methods to examine several plausible link formation facilitators in a large-scale real-world narcotics network. Multivariate Cox regression showed that mutual acquaintance and vehicle affiliations were significant facilitators in the network under study. These findings provide insights into the link formation processes and the resilience of dark networks. They also can be used to help authorities predict co-offending in future crimes.