THE INFLUENCE OF EWOM AND EDITOR INFORMATION ON INFORMATION USEFULNESS IN VIRTUAL COMMUNITY

Information Usefulness, eWOM Information, Editor Information, Sense of Belonging

Passive Inference Attacks on Split Learning via Adversarial Regularization

Split Learning (SL) has emerged as a practical and efficient alternative to traditional federated learning. While previous attempts to attack SL have often relied on overly strong assumptions or targeted easily exploitable models, we seek to develop more practical attacks. We introduce SDAR, a novel attack framework against SL with an honest-but-curious server. SDAR leverages auxiliary data and adversarial regularization to learn a decodable simulator of the client's private model, which can effectively infer the client's private features under the vanilla SL, and both features and labels under the U-shaped SL. We perform extensive experiments in both configurations to validate the effectiveness of our proposed attacks. Notably, in challenging but practical scenarios where existing passive attacks struggle to reconstruct the client's private data effectively, SDAR consistently achieves attack performance comparable to active attacks. On CIFAR-10, at the deep split level of 7, SDAR achieves private feature reconstruction with less than 0.025 mean squared error in both the vanilla and the U-shaped SL, and attains a label inference accuracy of over 98% in the U-shaped setting, while existing attacks fail to produce non-trivial results.Comment: 19 pages, 20 figure

Potential of harnessing operational flexibility from public transport hubs to improve reliability and economic performance of urban multi-energy systems: A holistic assessment framework

Growing penetration of renewable energy sources (RES) and emerging electrified loads (EEL) are bringing about increased difficulties for the power balancing and efficient operation of energy system, due to the impact of remarkable volatilities introduced. Public transport hub (PTH), as a new-style infrastructure of traffic service carriers, is regarded to offer a cogent solution to this problem, in terms of their potential operational flexibilities permitted by energy regulation, vehicular dispatch, and vehicle-to-grid (V2G) programs. As such, this paper carries out a comprehensive study to investigate the implication of harnessing PTH-enabled flexibility in a context of urban multi-energy system (UMES). The proposed methodology is established on a holistic reliability/economic analysis framework which is designed to indicate how UMES’s performances would vary with different utilization of PTH resources. In order to portray the real-time controllability of PTH during operation, a PTH model that takes into account the impacts of both energy- and service-related aspects has been developed, with particular focus on the interdependencies between the energy and transportation sector. The operational simulation of UMES in presence of PTHs is implemented by using a multi-modal-based optimization model, which captures the effects of PTH flexibility under both normal and contingency scenarios integratedly. By embedding the above formulation into a sequential Monte Carlo simulation-based assessment framework, the contribution of PTH to the reliability and economy of UMES can be determined. Numerical studies are conducted based on an illustrative electricity-gas-heat test case and the real PTH datasets in Beijing. The simulation results confirm the significance of PTH-enabled flexibility in improving the performances of UMES. Also, it is demonstrated that the reserving strategy adopted, the composition of vehicle model, and the travel demand profile of passengers are the noteworthy factors that influence the profitability of PTH exploitation

Histopathologic Finding of Both Gastric and Respiratory Epithelia in a Lingual Foregut Cyst

Foregut cysts are uncommon, mucosa-lined congenital lesions that may occur anywhere along the gastrointestinal or respiratory tract and typically present within the first year of life. Although infrequent, these cysts may generate feeding or respiratory difficulties depending on the size and location of the lesion. Foregut cysts of the oral cavity are rarely seen and of those cases localized to the tongue are even more uncommon. We describe a 4-month-old girl with a foregut cyst involving the floor of mouth and anterior tongue. Subsequent histologic analysis demonstrated a cyst lined with both gastric and respiratory epithelia. This case represents an extremely rare finding of both gastric and respiratory epithelia lined within a single cystic structure in the tongue. Although a very rare finding, a foregut cyst should be on the differential diagnosis of any lesion involving the floor of mouth or tongue in an infant or child

Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems

This paper proposes a comprehensive methodological framework to investigate the potential role of the grid-connected battery swapping station (BSS) with vehicle-to-grid (V2G) capability in improving the reliability of supply in future distribution networks. For this aim, we first develop an empirical model for describing the energy demand of electric vehicles (EVs) and their resultant available generation capacity (AGC) that can be utilized for BSS operation. Then, on this basis, a quantitative method to quantify the effect of grid-connected BSS on distribution system reliability is proposed. In order to capture the uncertainties associated with EV users’ behaviors, Latin Hypercube Sampling (LHS) methods were utilized to obtain the time series of the BSS traffic flow and initial State of Charge (SOC) of each EV battery, according to the probability distribution of corresponding uncertain factors whose statistics are obtained from real-world historical data. Compared with existing works in this research field, the main contributions of this paper are threefold. (i) A comprehensive and efficient method to assess the reliability benefits of BSS with an explicit consideration of BSS characteristics (including physical structure, charging strategy, and swapping model) is proposed, which is in contrast to most of the extant studies that only focus on the EV fast-charging paradigm and thus provide a practical tool to analyze the potential value of BSS resources in future distribution systems. (ii) The randomness of EV user behaviors in BSS operation is explicitly modeled and considered. (iii) The LHS-based sequential simulation is used to improve the accuracy and convergence performance of the evaluation, as compared to the traditional Sequential Monte Carlo Simulation (SMCS) method. To verify the effectiveness of the proposed approach, numerical studies are conducted based on a modified IEEE 33-bus distribution network. The simulation results show that with V2G capabilities, BSS can improve reliability to a certain extent and reduce the adverse impact on the reliability of the distribution network. In addition, EV resources should be orderly managed and exploited; otherwise, uncoordinated charging activities could impose a negative impact on the reliability performance of distribution networks. Finally, it is also shown that under the same sampling time, LHS-based sequential simulation could be better than SMCS in the accuracy and convergence speed of the procedure

Muffin: Testing Deep Learning Libraries via Neural Architecture Fuzzing

Deep learning (DL) techniques are proven effective in many challenging tasks, and become widely-adopted in practice. However, previous work has shown that DL libraries, the basis of building and executing DL models, contain bugs and can cause severe consequences. Unfortunately, existing testing approaches still cannot comprehensively exercise DL libraries. They utilize existing trained models and only detect bugs in model inference phase. In this work we propose Muffin to address these issues. To this end, Muffin applies a specifically-designed model fuzzing approach, which allows it to generate diverse DL models to explore the target library, instead of relying only on existing trained models. Muffin makes differential testing feasible in the model training phase by tailoring a set of metrics to measure the inconsistencies between different DL libraries. In this way, Muffin can best exercise the library code to detect more bugs. To evaluate the effectiveness of Muffin, we conduct experiments on three widely-used DL libraries. The results demonstrate that Muffin can detect 39 new bugs in the latest release versions of popular DL libraries, including Tensorflow, CNTK, and Theano.Comment: Accepted to ICSE'2

THE INFLUENCE OF EWOM AND EDITOR INFORMATION ON INFORMATION USEFULNESS IN VIRTUAL COMMUNITY

Abstract This study proposes that editor information strength and completeness, as well as electronic word-ofmouth (eWOM

Prototype Network for Predicting Occluded Picking Position Based on Lychee Phenotypic Features

The automated harvesting of clustered fruits relies on fast and accurate visual perception. However, the obscured stem diameters via leaf occlusion lack any discernible texture patterns. Nevertheless, our human visual system can often judge the position of harvesting points. Inspired by this, the aim of this paper is to address this issue by leveraging the morphology and the distribution of fruit contour gradient directions. Firstly, this paper proposes the calculation of fruit normal vectors using edge computation and gradient direction distribution. The research results demonstrate a significant mathematical relationship between the contour edge gradient and its inclination angle, but the experiments show that the standard error projected onto the Y-axis is smaller, which is evidently more conducive to distinguishing the gradient distribution. Secondly, for the front view of occluded lychee clusters, a fully convolutional, feature prototype-based one-stage instance segmentation network is proposed, named the lychee picking point prediction network (LP3Net). This network can achieve high accuracy and real-time instance segmentation, as well as for occluded and overlapping fruits. Finally, the experimental results show that the LP3Net based on this study, along with lychee phenotypic features, achieves an average location accuracy reaching 82%, significantly improving the precision of harvesting point localization for lychee clusters

Recent progress of geophysical exploration in Earth's impact craters

Geophysical exploration plays an important role in detecting and studying impact structures. This article reviews the common geophysical features of Earth's impact craters, including their gravity, magnetic, electrical, and seismic characteristics. The most obvious geophysical feature of impact craters is the circular or ring-shaped negative gravity anomaly, which is mainly caused by rock fracture and brecciation resulting in lower rock density. Low magnetic anomalies with complex details are mainly due to impact melting reducing the magnetic susceptibility of rocks inside the crater and post-impact modification resulting in complex detailed features. High electrical conductivity is found in simple craters, while more complex craters have gradually increasing electrical conductivity from the central uplift to the marginal rim. The conductivity is dominated by the fracture extent and water content. Low seismic velocity is mainly due to the lower velocity of fractured breccia and fractures relative to the original rock. In addition, seismic reflection profiling has found that impact structures have distinct concave shapes.Internationally, there are abundant research on the geophysical exploration of impact craters. However, in China, confirmed impact craters are rare in number and lack related geophysical exploration. Summarizing the common geophysical characteristics of impact craters provides a basis for geophysical exploration of potential impact crater regions in China and offers material for popular science and public engagement purposes.There are two confirmed impact craters in China, the Xiuyan crater in Liaoning Province and the Yilan crater in Heilongjiang Province. Active seismic investigations had been conducted in Xiuyan crater, and revealed its relative velocity and attenuation structure. However, although several geological studies have been conducted, a comprehensive geophysical study of the newly discovered Yilan crater is still lacking. Recently, our group has conducted dense seismic nodes and distributed acoustic sensing in Yilan crater, the results of which will be reported in the near future