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Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. There are many factors affecting the travel time of a driver on a given trajectory, including the distance, road type, driving habits, traffic congestion, etc. Existing works fail to model the complex relationships of these factors for TTE. To fill this gap, in this paper, we first analyze how these factors work together in determining the travel time. In particular, the travel time depends on the distance and driving speed on each road segment of the trajectory, where the driving speed depends on the driving habits and the environment, including the static factors like the road type (highway or byway) and speed limit and the dynamic factor like the time of the day and congestion. Among these factors, driving habits and traffic conditions (e.g., jam) are the most difficult ones to model. Second, we propose to learn the driving habits of each driver via meta-learning and estimate the conditions based on the current and historical traffic conditions (via recurrent neural networks) of this road and its connected road segments (via graph convolutional neural network). The experimental results on two real taxi trajectory datasets show that our approach outperforms three state-of-the-art methods significantly.

This paper revisits set containment join (SCJ) problem, which uses the subset relationship (i.e., ⊆ ) as condition to join set-valued attributes of two relations and has many fundamental applications in commercial and scientific fields. Existing in-memory algorithms for SCJ are either signature-based or prefix-tree-based. The former incurs high CPU cost because of the enumeration of signatures, while the latter incurs high space cost because of the storage of prefix trees. This paper proposes a new adaptive parameter-free in-memory algorithm, named as fre quency-ha sh join or FreshJoin in short, to evaluate SCJ efficiently. FreshJoin builds a flat index on-the-fly to record three kinds of signatures (i.e., two least frequent elements and a hash signature whose length is determined adaptively by the frequencies of elements in the universe set). The index consists of two sparse inverted indices and two arrays which record hash signatures of all sets in each relation. The index is well organized such that FreshJoin can avoid enumerating hash signatures. The rationality of this design is explained. And, the time and space cost of the proposed algorithm, which provide a rule to choose FreshJoin from existing algorithms, are analyzed. Experiments on 16 real-life datasets show that FreshJoin usually reduces more than 50% of space cost while remains as competitive as the state-of-the-art algorithms in running time.

Data transmissions suffer from TCP’s poor performance since the introduction of the first commercial wireless services in the 1990s. Recent years have witnessed a surge of academia and industry activities in the field of TCP performance optimization. For a TCP flow whose last hop is a wireless link, congestions in the last hop dominate its performance. We implement an integral data sampling, network monitoring, and rate control software-defined wireless networking (SDWN) system. By analysing our sampled data, we find that there exist strong relationships between congestion packet loss behaviors and the instant cross-layer network metric measurements (states). We utilize these qualitative relationships to predict future congestions in wireless links and enhance TCP performance by launch necessary rate control locally on the access points (AP) before the congestions. We also implement modeling and rate control modules on this platform. Our platform senses the instant wireless dynamic and takes actions promptly to avoid future congestions. We conduct real-world experiments to evaluate its performance. The experiment results show that our methods outperform the bottleneck bandwidth and RTT (BBR) protocol and a recently proposed protocol Vivace on throughput, delay, and jitter performance at least 16.5%, 25%, and 12.6%, respectively.

Recently, a query, called reverse top-k query, is proposed. The reverse top-k query takes an object as input and retrieves the users whose top-k query results include the object while the top-k query retrieves the top-k matching objects based on the user preference. In business analysis, reverse top-k queries are crucial for evaluating product impact and potential market. However, the reverse top-k query assumes that user’s preference is static. In practice, user preference may change with moods, seasons, economic conditions or other reasons. To overcome this disadvantage, this paper proposes a new reverse top-k query, named as durable reverse top-k query, without limitation of user’s preference being static. The durable reverse top-k query retrieves users who put a given object in the top-k favorite objects most of the time during a given time period. An efficient pruning-based algorithm for the queries with fixed k is proposed in this paper. For the case of k being variable, this paper proposes a pruning-based algorithm with an index to achieve a trade-off between time and space. Experiments on both real and synthetic datasets demonstrate that the proposed algorithms are very efficient.

Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. Even for a given trajectory, the travel time is affected by many spatial-temporal factors, including static ones such as distance, road type, and so on and dynamic ones such as speed, traffic condition, and so on. Challenges of accurate estimation lie in proper representation of these spatial-temporal factors and more importantly capturing the complex relationship among them for TTE. To tackle such challenges, we present a framework based on the fact that the travel time of each road segment is affected by its adjacent segments. It features a graph convolutional neural network and a recurrent neural network for basic TTE for each road segment and a graph attention network for the relation to estimations on the adjacent road segments. Finally, a multitask learning model is proposed for the travel time of the entire given path and that for each road segment. Experimental results on real taxi trajectory datasets of two cities show that the percentage estimation error of the new approach is well controlled at 13.91% and the proposed method outperforms three state-of-the-art methods significantly.

Data aggregation is a useful technology that can decrease the communication bandwidth cost in the process of data gathering in VANETs. However, data aggregation may lose some data accuracy. Current data aggregation schemes in VANETs only consider saving bandwidth cost while ignoring the application requirement, which may result in the inaccuracy of aggregated data for dynamic routing application. Therefore, we propose a framework in which application demands are considered in the process of data aggregation to ensure the accuracy of aggregated data for dynamic routing application. The framework consists of three parts: extracting QoI constraints of aggregated data, distributing the QoI-based data gathering queries, routing and aggregating data with QoI constraints. First, we propose average allocation method to handle the demand of single user and utilize convex optimization to handle multiuser demands. Then, we distribute the QUERY message with QoI constraints in the interested area. Last, we propose QoI-DG protocol to do two kinds of data aggregation operation, namely, AVERAGE aggregation and HISTOGRAM aggregation. Simulation experiments show that our proposed method can increase about 20 percent in the collected data rate and save 15 percent communication bandwidth cost in the process of data gathering in VANETs.

this paper takes the piston rod as the research object and establishes the mechanical model of piston rod. In order to solve the problem that during the piston rod coarse processing, due to the cutting parameters, the cutting force produces radial bending deformation. This paper uses the quadratic orthogonal regression design to optimize the cutting parameters which affect the cutting force and uses the finite element software to optimize the calibration.

Graph is a generic model of various networks in real-world applications. And, graph embedding aims to represent nodes (edges or graphs) as low-dimensional vectors which can be fed into machine learning algorithms for downstream graph analysis tasks. However, existing random walk-based node embedding methods often map some nodes with (dis)similar local structures to (near) far vectors. To overcome this issue, this paper proposes to implement node embedding by constructing a context graph via a new defined ripple distance over ripple vectors, whose components are the hitting times of fully condensed neighborhoods and thus characterize their structures as pure quantities. The distance is able to capture the (dis)similarities of nodes’ local neighborhood structures and satisfies the triangular inequality. The neighbors of each node in the context graph are defined via the ripple distance, which makes the short random walks from a given node over the context graph only visit its similar nodes in the original graph. This property guarantees that the proposed method, named as ripple 2 vec , is able to map (dis)similar nodes to (far) near vectors. Experimental results on real datasets, where labels are mainly related to nodes’ local structures, show that the results of ripple 2 vec behave better than those of state-of-the-art methods, in node clustering and node classification, and are competitive to other methods in link prediction.

Social activities play an important role in people's daily life since they interact. For recommendations based on social activities, it is vital to have not only the activity information but also individuals' social relations. Thanks to the geo-social networks and widespread use of location-aware mobile devices, massive geo-social data is now readily available for exploitation by the recommendation system. In this paper, a novel group recommendation method, called attentive geo-social group recommendation, is proposed to recommend the target user with both activity locations and a group of users that may join the activities. We present an attention mechanism to model the influence of the target user \\(u_T\\) in candidate user groups that satisfy the social constraints. It helps to retrieve the optimal user group and activity topic candidates, as well as explains the group decision-making process. Once the user group and topics are retrieved, a novel efficient spatial query algorithm SPA-DF is employed to determine the activity location under the constraints of the given user group and activity topic candidates. The proposed method is evaluated in real-world datasets and the experimental results show that the proposed model significantly outperforms baseline methods.

Roots are important plant organs. Lateral root (LR) initiation (LRI) and development play a central role in environmental adaptation. The mechanism of LR development has been well investigated in . When we evaluated the distribution of auxin and abscisic acid (ABA) in maize, we found that the mechanism differed from that in . The distribution of ABA and auxin within the primary roots (PRs) and LRs was independent of each other. Auxin localization was observed below the quiescent center of the root tips, while ABA localized at the top of the quiescent center. Furthermore, NaCl inhibited LRI by increasing ABA accumulation, which mainly regulates auxin distribution, while auxin biosynthesis was inhibited by ABA in . The polar localization of ZmPIN1 in maize was disrupted by NaCl and exogenous ABA. An inhibitor of ABA biosynthesis, fluridone (FLU), and the ABA biosynthesis mutant rescued the phenotype under NaCl treatment. Together, all the evidence suggested that NaCl promoted ABA accumulation in LRs and that ABA altered the polar localization of ZmPIN1, disrupted the distribution of auxin and inhibited LRI and development.