Spatial inverse query processing

Traditional spatial queries return, for a given query object q, all database objects that satisfy a given predicate, such as epsilon range and k-nearest neighbors. This paper defines and studies inverse spatial queries, which, given a subset of database objects Q and a query predicate, return all objects which, if used as query objects with the predicate, contain Q in their result. We first show a straightforward solution for answering inverse spatial queries for any query predicate. Then, we propose a filter-and-refinement framework that can be used to improve efficiency. We show how to apply this framework on a variety of inverse queries, using appropriate space pruning strategies. In particular, we propose solutions for inverse epsilon range queries, inverse k-nearest neighbor queries, and inverse skyline queries. Furthermore, we show how to relax the definition of inverse queries in order to ensure non-empty result sets. Our experiments show that our framework is significantly more efficient than naive approaches.     

Treatment of biologically treated leachate by oxidation and coagulation

This work aims to investigate removal efficiency of oxidation and coagulation/flocculation processes, to provide an effective method for the treatment of biologically pre-treated leachate. Leachate containing 985 mg L(-1) COD was treated by using three treatment schemes, i.e. oxidation, coagulation/flocculation and the combined process of coagulation/flocculation followed by oxidation. The application of single oxidation resulted in the effective removal of COD and color up to 80.4 and 83.2%, respectively. However, residual COD values lower than 200 mg L(-1) could only be achieved under intensive experimental conditions (high dosage of Ca(ClO)(2) and prolonged oxidation time). Coagulation/flocculation yielded residual COD values higher than 200 mg L(-1) even at the optimum coagulation conditions. The combined treatment by coagulation/flocculation followed by oxidation yielded final COD lower than 100 mg L(-1) at the following conditions: pre-coagulation with 250 mg L(-1) PFS (poly-ferric sulfate) and over 30-min post-oxidation, or pre-coagulation with 300 mg L(-1) PFS and over 20-min post-oxidation. Hence, pre-coagulation with PFS followed by oxidation with Ca(ClO)(2) was recommended for advanced treatment of biologically treated leachate.     

Skeleton-based intrinsic symmetry detection on point clouds

We present a skeleton-based algorithm for intrinsic symmetry detection on imperfect 3D point cloud data. The data imperfections such as noise and incompleteness make it difficult to reliably compute geodesic distances, which play essential roles in existing intrinsic symmetry detection algorithms. In this paper, we leverage recent advances in curve skeleton extraction from point clouds for symmetry detection. Our method exploits the properties of curve skeletons, such as homotopy to the input shape, approximate isometry-invariance, and skeleton-to-surface mapping, for the detection task. Starting from a curve skeleton extracted from an input point cloud, we first compute symmetry electors, each of which is composed of a set of skeleton node pairs pruned with a cascade of symmetry filters. The electors are used to vote for symmetric node pairs indicating the symmetry map on the skeleton. A symmetry correspondence matrix (SCM) is constructed for the input point cloud through transferring the symmetry map from skeleton to point cloud. The final symmetry regions on the point cloud are detected via spectral analysis over the SCM. Experiments on raw point clouds, captured by a 3D scanner or the Microsoft Kinect, demonstrate the robustness of our algorithm. We also apply our method to repair incomplete scans based on the detected intrinsic symmetries.     

Social commerce research: An integrated view

Social commerce has quickly emerged as a new area of inquiry for both practitioners and researchers, suggesting the potential impacts of social media and social networking technologies and services in shaping commercial channels on and off the Internet. This essay starts by providing a brief overview of social commerce research and practice in light of the wide attention it has drawn in the industry. Then, we propose a research framework with an integrated view of social commerce that consists of four key components: business, technology, people, and information. The framework helps us understand the development of social commerce research and practice to date. Subsequently, we report some preliminary findings from a bibliometric study of academic and industry publications in social commerce to reveal recent trends and research topics, as well as some verification of the research framework. Finally, we discuss five articles in this special issue and categorize them in terms of the proposed social commerce research framework.     

On Detectability and Observability of Discrete‐Time Stochastic Markov Jump Systems with State‐Dependent Noise

This paper mainly studies the notions of detectability and observability for discrete‐time stochastic Markov jump systems with state‐dependent noise. Two concepts, called “W‐detectability” and “W‐observability,” for such systems are introduced, and are shown to coincide with the other concepts on detectability and observability reported recently in literature. Moreover, some criteria and interesting properties for both W‐detectability and W‐observability are obtained.     

Planar Segment Based Three‐dimensional Point Cloud Registration in Outdoor Environments

We present an odometry‐free three‐dimensional (3D) point cloud registration strategy for outdoor environments based on area attributed planar patches. The approach is split into three steps. The first step is to segment each point cloud into planar segments, utilizing a cached‐octree region growing algorithm, which does not require the 2.5D image‐like structure of organized point clouds. The second step is to calculate the area of each segment based on small local faces inspired by the idea of surface integrals. The third step is to find segment correspondences between overlapping point clouds using a search algorithm, and compute the transformation from determined correspondences. The transformation is searched globally so as to maximize a spherical correlation‐like metric by enumerating solutions derived from potential segment correspondences. The novelty of this step is that only the area and plane parameters of each segment are employed, and no prior pose estimation from other sensors is required. Four datasets have been used to evaluate the proposed approach, three of which are publicly available and one that stems from our custom‐built platform. Based on these datasets, the following evaluations have been done: segmentation speed benchmarking, segment area calculation accuracy and speed benchmarking, processing data acquired by scanners with different fields of view, comparison with the iterative closest point algorithm, robustness with respect to occlusions and partial observations, and registration accuracy compared to ground truth. Experimental results confirm that the approach offers an alternative to state‐of‐the‐art algorithms in plane‐rich environments.     

A Stochastic Vehicle Routing Problem with Travel Time Uncertainty: Trade-Off Between Cost and Customer Service

On-time shipment delivery is critical for just-in-time production and quick response logistics. Due to uncertainties in travel and service times, on-time arrival probability of vehicles at customer locations can not be ensured. Therefore, on-time shipment delivery is a challenging job for carriers in congested road networks. In this paper, such on-time shipment delivery problems are formulated as a stochastic vehicle routing problem with soft time windows under travel and service time uncertainties. A new stochastic programming model is proposed to minimize carrier’s total cost, while guaranteeing a minimum on-time arrival probability at each customer location. The aim of this model is to find a good trade-off between carrier’s total cost and customer service level. To solve the proposed model, an iterated tabu search heuristic algorithm was developed, incorporating a route reduction mechanism. A discrete approximation method is proposed for generating arrival time distributions of vehicles in the presence of time windows. Several numerical examples were conducted to demonstrate the applicability of the proposed model and solution algorithm.     

Improving trust modeling through the limit of advisor network size and use of referrals

This paper explores potential improvements to the trust modeling of agents in multi-agent systems when a social network of advisors is employed as part of the trust modeling, and in particular, examines means of optimizing the number of advisors that should be maintained in the social network. We propose three such improvements, two directly relating to the limit of advisor network size by either setting a maximum size for a buyer’s advisor network or setting a minimum trustworthiness threshold for agents to be accepted into that advisor network, and a third which uses an advisor referral system in combination with one of the first two network-limiting techniques. We provide experimental results in defence of our approach for two distinct trust modeling systems, and show how these optimizations can improve, sometimes significantly, the accuracy of different trust models (in the context of electronic marketplaces). We believe that the proposed techniques will be very useful for trust researchers seeking to improve the accuracy of their own trust models by setting the size and composition of advisor networks.     

Trust fraud: A crucial challenge for China’s e-commerce market

Currently, China’s e-commerce market is growing at an unprecedented pace, however, it is faced with many challenges, among which the trust fraud problem is the biggest issue. In this article, we use Taobao as an example and conduct a thorough investigation of the trust fraud phenomenon in China’s e-commerce market. We present the development history of trust fraud, summarize its unique characteristics, and explore the reasons why so many sellers commit fraud. We further propose a dynamic time decay trust model that aims to deter trust fraud by raising its cost and promote the growth of small and medium-sized sellers. The model utilizes detailed seller ratings as the data source, and incorporates a transaction amount weight, a time decay coefficient, and three trust factors in the calculation of trust. We test the model on real transaction data from Taobao, and the experimental results verify its effectiveness. Our proposed trust model yields a practical approach to online trust management not only in the Taobao market but also for other e-commerce platforms.     

Robust preparation of four-qubit decoherence-free states for superconducting quantum interference devices against collective amplitude damping

Based on the quantum Zeno dynamics, we present an approach for deterministic preparation of arbitrary four-qubit decoherence-free state of superconducting quantum interference devices with respective to collective amplitude damping in a decoherence-free way, namely, not only the form of the target state is free of decoherence, but also the whole process for preparation. The operation is fast and convenient since we only need to manipulate three weak laser pulses sequentially. Other decoherence effects such as cavity decay and the spontaneous emission of qubits are also taken into account in virtue of master equation, and the strictly numerical simulation signifies the final fidelity is high corresponding to the current experimental technology.