The rising demand for AI-powered smart home solutions has produced a recent surge in the adoption of smart home devices (SHDs). SHDs are uniquely situated within private, personal environments, and understanding the impact of device design on users’ parasocial relationship, privacy perceptions, and overall adoption is crucial; however, the literature lacks a satisfactory exploration of this essential facet of integrating intelligent devices into everyday living. This study addresses this deficit by analysing the nuanced interplay between device design features and user adoption intentions. An online experiment was conducted using a 2 (communication style: task-oriented vs. social-oriented) × 3 (embodiment type: application voice vs. virtual animation vs. physical robot) between-subjects design (N = 297). The findings indicate that SHDs employing a social-oriented communication style, while promoting stronger parasocial interactions, are simultaneously correlated with increased perceptions of privacy risk when compared to those utilising a task-oriented communication style. The more embodied the SHDs, the stronger the perceived parasocial interaction. Furthermore, higher perceived privacy risks negatively affect purchase intention. The findings provide novel insights into the design of SHDs that not only address privacy concerns but also create positive user experiences in IoT-based smart homes, thereby fostering long-term adoption.

Although individual anxiety evaluation has been well studied, there is still not much work on evaluating public anxiety of groups, especially in the form of communities on social networks, which can be leveraged to detect mental healthness of a society. However, we cannot simply average individual anxiety scores to evaluate a community's public anxiety, because following factors should be considered: (1) impacts from interpersonal relations on each individual group member's anxiety levels (the${\tt Structural}$component); (2) topic-based discussions which reflect a community's anxiety status (the${\tt Topical}$component). In this paper, we initiate the study of evaluating public anxiety of Topic-based Social Network Communities ($\textsc {TSNC}$). We propose an evaluation framework to project the anxiety level of a$\textsc {TSNC}$into a score in the [0,1] range. We devise a cascading model to dynamically compute the individual anxiety scores using the${\tt Structural}$influence. We design a probabilistic model to measure anxiety score of social network messages using a generalized user, and compose a tree structure (${\tt MC}$-${\tt Tree}$) to effectively compute the anxiety score of a$\textsc {TSNC}$from the${\tt Topical}$aspect. For large communities, to avoid expensive real-time computing, we use a small sample to compute the public anxiety within given confidence interval. The effectiveness of our model are verified by precision and recall in an empirical study on real-world Weibo and Twitter data sets.

Ride-sharing (RS) has great values in saving energy and alleviating traffic pressure. In this paper, we propose a new ride-sharing model, where each driver requires that the shared route percentage (SRP, the ratio of the shared route's distance to the driver's total traveled distance) exceeds her expected rate (e.g., 0.8) when sharing with a rider. We consider two variants of this problem. The first considers multiple drivers and multiple riders, and aims to compute a set of driver-rider pairs to maximize the overall SRP. We model this problem as the maximum weighted bigraph matching problem. We propose an effective exact algorithm, and an efficient approximate solution with error-bound guarantee. The second considers multiple drivers and a single rider and aims to find the top-k drivers for the rider with the largest SRP. We devise pruning techniques and propose a best-first algorithm to progressively selects drivers with high probability to be in the top-k results.

Ride-sharing (RS) has great values in saving energy and alleviating traffic pressure. Existing studies can be improved for better efficiency. Therefore, we propose a new ride-sharing model, where each driver has a requirement that if the driver shares a ride with a rider, the shared route percentage (i.e., the ratio of the shared route's distance to the driver's total travel distance) exceeds an expectation rate of the driver, e.g., 0.8. We consider two variants of this problem. The first considers multiple drivers and multiple riders and aims to compute driver-rider pairs to maximize the overall shared route percentage (SRP). We model this problem as the maximum weighted bigraph matching problem, where the vertices are drivers and riders, edges are driver-rider pairs, and edge weights are driver-rider's SRP. However, it is rather expensive to compute the SRP values for large numbers of driver-rider pairs on road networks. To address this problem, we propose an efficient method to prune many unnecessary driver-rider pairs and avoid computing the SRP values for every pair. To improve the efficiency, we propose an approximate method with error bound guarantee. The basic idea is that we compute an upper bound and a lower bound for each driver-rider pair in constant time. Then, we estimate an upper bound and a lower bound of the graph matching. Next, we select some driver-rider pairs, compute their real shortest-route distance, and update the lower and upper bounds of the maximum graph matching. We repeat above steps until the ratio of the upper bound to the lower bound is not larger than a given approximate rate. The second considers multiple drivers and a single rider and aims to find the top-$k$drivers for the rider with the largest SRP. We first prune a large number of drivers that cannot meet the SRP requirements. Then, we propose a best-first algorithm that progressively selects the drivers with high probability to be in the top-$k$results and prunes the drivers that cannot be in the top-$k$results. Extensive experiments on real-world datasets demonstrate the superiority of our method.

The Global Positioning System(GPS) is used to find a specific point on the real earth although GPS positioning technology is becoming more and more mature, GPS always exists with equipment inherent errors or measurement methods errors. so map matching step is a very important preprocessing for lots of applications, such as traffic flow control, taxi mileage calculation, and finding some people. However, many current methods only deal with distance variables and do not handle angle variables between two segments. In this paper, we propose a new road network map matching algorithm, considering not only the distance between two sample points but also taking into account the angle between two candidate segments using the Hidden Markov Model (HMM) which is a popular solution for map matching. Subsequently, to solve the HMM problem, we make use of dynamic programming Viterbi algorithm to find the maximum probability road segments. The experiments are implemented on BEIJING CITY map real dataset and display that our map matching algorithm significantly improve the accuracy compared with ST-Matching global algorithm.

Integrity constraint (IC) based data repairing is typically an iterative process consisting of two parts: detecting and grouping errors that violate given ICs, and modifying values inside each group such that the modified database satisfies those ICs. However, most existing automatic solutions treat the process of detecting and grouping errors straightforwardly (e.g., violations of functional dependencies using string equality), while putting more attention on heuristics of modifying values within each group. In this paper, we propose a revised semantics of violations and data consistency w.r.t. a set of ICs. The revised semantics relies on string similarities, in contrast to traditional methods that use syntactic error detection using string equality. Along with the revised semantics, we also propose a new cost model to quantify the cost of data repairing by considering distances between strings. We show that the revised semantics provides a significant change for better detecting and grouping errors, which in turn improves both precision and recall of the following data repairing step. We prove that finding minimumcost repairs in the new model is NP-hard, even for a single FD. We devise efficient algorithms to find approximate repairs.

Integrity constraint based data repairing is an iterative process consisting of two parts: detect and group errors that violate given integrity constraints (ICs); and modify values inside each group such that the modified database satisfies those ICs. However, most existing automatic solutions treat the process of detecting and grouping errors straightforwardly (e.g., violations of functional dependencies using string equality), while putting more attention on heuristics of modifying values within each group. In this paper, we propose a revised semantics of violations and data consistency w.r.t. a set of ICs. The revised semantics relies on string similarities, in contrast to traditional methods that use syntactic error detection using string equality. Along with the revised semantics, we also propose a new cost model to quantify the cost of data repair by considering distances between strings. We show that the revised semantics provides a significant change for better detecting and grouping errors, which in turn improves both precision and recall of the following data repairing step. We prove that finding minimum-cost repairs in the new model is NP-hard, even for a single FD. We devise efficient algorithms to find approximate repairs. In addition, we develop indices and optimization techniques to improve the efficiency. Experiments show that our approach significantly outperforms existing automatic repair algorithms in both precision and recall.

Emerging vehicular trajectory data have opened up opportunities to benefit many real-world applications, e.g., frequent trajectory based navigation systems, road planning, car pooling, etc. The similarity join is a key operation to enable such applications, which finds similar trajectory pairs from two large collections of trajectories. Existing similarity metrics on trajectories rely on aligning sampling points of two trajectories. However, due to different sampling rates or different vehicular speeds, the sample points in similar trajectories may not be aligned. To address this problem, we propose a new bi-directional mapping similarity (BDS), which allows a sample point of a trajectory to align to the closest location (which may not be a sample point) on the other trajectory, and vice versa. Since it is expensive to enumerate every two trajectories and compute their similarity, we propose Strain-Join, a signature-based trajectory similarity join framework. Strain-Join first generates signatures for each trajectory such that if two trajectories do not share common signatures, they cannot be similar. In order to utilize this property to prune dissimilar pairs, we devise several techniques to generate high-quality signatures and propose an efficient filtering algorithm to prune dissimilar pairs. For the pairs not pruned by the filtering algorithm, we propose effective verification algorithms to verify whether they are similar. Experimental results on real datasets show that our algorithm outperforms state-of-the-art techniques in terms of both effectiveness and efficiency.

XML has become a popular method of data representation both on the web and in databases in recent years. One of the reasons for the popularity of XML has been its ability to encode structural information about data records. However, this structural characteristic of data sets also makes it a challenging problem for a variety of data mining problems. One such problem is that of clustering, in which the structural aspects of the data result in a high implicit dimensionality of the data representation. As a result, it becomes more difficult to cluster the data in a meaningful way. In this paper, we propose an effective clustering algorithm for XML data which uses substructures of the documents in order to gain insights about the important underlying structures. We propose new ways of using multiple sub-structuralinformation in XML documents to evaluate the quality of intermediate cluster solutions, and guide the algorithms to a final solution which reflects the true structural behavior in individual partitions. We test the algorithm on a variety of real and synthetic data sets.

In XML databases, materializing queries and their results into views in a semantic cache can improve the performance of query evaluation by reducing computational complexity and I/O cost. Although there are a number of proposals of semantic cache for XML queries, the issues of fast cache lookup and compensation query construction could be further studied. In this paper, based on sequential XPath queries, we propose fastCLU, a fast Cache LookUp algorithm and effiCQ, an efficient Compensation Query constructing algorithm to solve these two problems. Experimental results show that our algorithms outperform previous algorithms and can achieve good performance of query evaluation.