Query Evaluation in the Presence of Fine-grained Access Control

Access controls are mechanisms to enhance security by protecting data from unauthorized accesses. In contrast to traditional access controls that grant access rights at the granularity of the whole tables or views, fine-grained access controls specify access controls at finer granularity, e.g., individual nodes in XML databases and individual tuples in relational databases. While there is a voluminous literature on specifying and modeling fine-grained access controls, less work has been done to address the performance issues of database systems with fine-grained access controls. This thesis addresses the performance issues of fine-grained access controls and proposes corresponding solutions. In particular, the following issues are addressed: effective storage of massive access controls, efficient query planning for secure query evaluation, and accurate cardinality estimation for access controlled data. Because fine-grained access controls specify access rights from each user to each piece of data in the system, they are effectively a massive matrix of the size as the product of the number of users and the size of data. Therefore, fine-grained access controls require a very compact encoding to be feasible. The proposed storage system in this thesis achieves an unprecedented level of compactness by leveraging the high correlation of access controls found in real system data. This correlation comes from two sides: the structural similarity of access rights between data, and the similarity of access patterns from different users. This encoding can be embedded into a linearized representation of XML data such that a query evaluation framework is able to compute the answer to the access controlled query with minimal disk I/O to the access controls. Query optimization is a crucial component for database systems. This thesis proposes an intelligent query plan caching mechanism that has lower amortized cost for query planning in the presence of fine-grained access controls. The rationale behind this query plan caching mechanism is that the queries, customized by different access controls from different users, may share common upper-level join trees in their optimal query plans. Since join plan generation is an expensive step in query optimization, reusing the upper-level join trees will reduce query optimization significantly. The proposed caching mechanism is able to match efficient query plans to access controlled query plans with minimal runtime cost. In case of a query plan cache miss, the optimizer needs to optimize an access controlled query from scratch. This depends on accurate cardinality estimation on the size of the intermediate query results. This thesis proposes a novel sampling scheme that has better accuracy than traditional cardinality estimation techniques

Modeling of counter-current spontaneous imbibition in independent capillaries with unequal diameters

Spontaneous imbibition is a crucial process for oil recovery from fractured and unconventional reservoirs. Herein, with the assumption of capillaries being independent, a new mathematical model for spontaneous imbibition is proposed and solved using a numerical method. The simulated results show that the wetting phase preferentially enters smaller capillaries where the advancement velocity is higher than that in larger ones, while the non-wetting phase can be displaced out in the larger capillaries. In addition, the effect of fluid viscosity ratio on counter-current imbibition is analyzed. The results show that imbibition velocity becomes higher with the increase in the viscosity ratio. When the viscosity of the non-wetting phase is larger than that of the wetting phase, the end pressure gradually increases as the imbibition front advances. In contrast, when the viscosity of the non-wetting phase is less than that of the wetting phase, the end pressure decreases with the infiltration. With a higher viscosity ratio of non-wetting and wetting phase, the heterogeneity of the interface advancement among different capillaries increases.Cited as: Chen, K., Xu, H., Zhang, Z., Meng, Q., Zhang, T. Modeling of counter-current spontaneous imbibition in independent capillaries with unequal diameters. Capillarity, 2022, 5(6): 115-122. https://doi.org/10.46690/capi.2022.06.0

Computationally Efficient DOA Tracking Algorithm in Monostatic MIMO Radar with Automatic Association

We consider the problem of tracking the direction of arrivals (DOA) of multiple moving targets in monostatic multiple-input multiple-output (MIMO) radar. A low-complexity DOA tracking algorithm in monostatic MIMO radar is proposed. The proposed algorithm obtains DOA estimation via the difference between previous and current covariance matrix of the reduced-dimension transformation signal, and it reduces the computational complexity and realizes automatic association in DOA tracking. Error analysis and Cramér-Rao lower bound (CRLB) of DOA tracking are derived in the paper. The proposed algorithm not only can be regarded as an extension of array-signal-processing DOA tracking algorithm in (Zhang et al. (2008)), but also is an improved version of the DOA tracking algorithm in (Zhang et al. (2008)). Furthermore, the proposed algorithm has better DOA tracking performance than the DOA tracking algorithm in (Zhang et al. (2008)). The simulation results demonstrate effectiveness of the proposed algorithm. Our work provides the technical support for the practical application of MIMO radar

Structural and functional properties of OSA-starches made with wide-ranging hydrolysis approaches

Octenyl succinic anhydride modified starches (OSA-starches) are widely used as emulsifiers and stabilizers in the food industry. This study investigates the relationships between molecular structure and emulsifying and antioxidant properties of OSA-starches with a wide range of structures, formed by hydrolysis by α-amylase, β-amylase and HCl for various hydrolysis times. Structural parameters, namely molecular size distribution, chain-length distribution, degree of branching (DB) and degree of OSA substitution (DS) were characterized using size-exclusion chromatography and H nuclear magnetic resonance. These parameters were then correlated with viscosity, emulsification performance and antioxidant properties for OSA-stabilized oil emulsions, to gain improved understanding of structure-property relationships. The average chain length (DP) and DB respectively showed positive and negative correlations with the viscosity, total antioxidant activity (TAC), creaming extent and the emulsion z-average droplet size for all the hydrolyzed samples. The OSA-starches treated by α-amylase generally had the smallest average DP and largest DB, resulting in the lowest viscosity and the best droplet stability with the smallest creaming extent. The acid-hydrolyzed OSA-starch samples presented larger average DP than the enzyme-hydrolyzed samples, in agreement with their better TAC, while larger creaming extent. The β-amylase-hydrolyzed samples produced moderate structural degradation and emulsifying properties compared to the OSA-starches treated by α-amylase and HCl. The structure-property correlations indicate that the average chain length and DB are the two most important structural parameters in determination of the functional properties for the OSA-modified starches. These findings will help develop improved food additives with desired functions

Paulownia spp.: a bibliometric trend analysis of a global multi-use tree

The research on Paulownia spp. has increased in the last twenty years thanks to the growing interest in the application modalities of this plant in various sectors such as wood, phytoremediation, environmental protection, paper, biofuel, chemistry and medicine. For the first time, this study analyzed the papers present in the Web of Science Core Collection on “Paulownia” to obtain a set of characteristics in the work carried out from 1971 to 2021. This analysis selected and took into account 820 articles and provided evidence of the scientific production of authors, institutions, and countries. This work showed that the most studied species was Paulownia tomentosa, followed by P. fortunei and P. elongate. The JCR category and research area with the most publications was plant science, with 20.4% of the total. The papers were published in 460 journals and in a book series. The journals with the most publications were Bioresources, Advanced Material Research, Agroforestry Systems, Journal of Wood Science and Industrial Crops and Products. The institutions with the most prolific affiliation with the field of Paulownia spp. research were Henan University, the US Department of Agriculture, Belgrade University, the Chinese Academy, and Georgia University. Finally, the 3842 keywords were divided into nine different clusters and the trends of interest in the last fifteen years were highlighted

Optimization of Forcemyography Sensor Placement for Arm Movement Recognition

How to design an optimal wearable device for human movement recognition is vital to reliable and accurate human-machine collaboration. Previous works mainly fabricate wearable devices heuristically. Instead, this paper raises an academic question: can we design an optimization algorithm to optimize the fabrication of wearable devices such as figuring out the best sensor arrangement automatically? Specifically, this work focuses on optimizing the placement of Forcemyography (FMG) sensors for FMG armbands in the application of arm movement recognition. Firstly, based on graph theory, the armband is modeled considering sensors' signals and connectivity. Then, a Graph-based Armband Modeling Network (GAM-Net) is introduced for arm movement recognition. Afterward, the sensor placement optimization for FMG armbands is formulated and an optimization algorithm with greedy local search is proposed. To study the effectiveness of our optimization algorithm, a dataset for mechanical maintenance tasks using FMG armbands with 16 sensors is collected. Our experiments show that using only 4 sensors optimized with our algorithm can help maintain a comparable recognition accuracy to using all sensors. Finally, the optimized sensor placement result is verified from a physiological view. This work would like to shed light on the automatic fabrication of wearable devices considering downstream tasks, such as human biological signal collection and movement recognition. Our code and dataset are available at https://github.com/JerryX1110/IROS22-FMG-Sensor-OptimizationComment: 6 pages, 10 figures, Accepted by IROS22 (The 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation

Unsupervised Video Object Segmentation (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. However, previous methods do not fully use spatial-temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view. Specifically, LSTA consists of two dominant modules, i.e., Long Temporal Memory and Short Temporal Attention. The former captures the long-term global pixel relations of the past frames and the current frame, which models constantly present objects by encoding appearance pattern. Meanwhile, the latter reveals the short-term local pixel relations of one nearby frame and the current frame, which models moving objects by encoding motion pattern. To speedup the inference, the efficient projection and the locality-based sliding window are adopted to achieve nearly linear time complexity for the two light modules, respectively. Extensive empirical studies on several benchmarks have demonstrated promising performances of the proposed method with high efficiency

HR-Pro: Point-supervised Temporal Action Localization via Hierarchical Reliability Propagation

Point-supervised Temporal Action Localization (PSTAL) is an emerging research direction for label-efficient learning. However, current methods mainly focus on optimizing the network either at the snippet-level or the instance-level, neglecting the inherent reliability of point annotations at both levels. In this paper, we propose a Hierarchical Reliability Propagation (HR-Pro) framework, which consists of two reliability-aware stages: Snippet-level Discrimination Learning and Instance-level Completeness Learning, both stages explore the efficient propagation of high-confidence cues in point annotations. For snippet-level learning, we introduce an online-updated memory to store reliable snippet prototypes for each class. We then employ a Reliability-aware Attention Block to capture both intra-video and inter-video dependencies of snippets, resulting in more discriminative and robust snippet representation. For instance-level learning, we propose a point-based proposal generation approach as a means of connecting snippets and instances, which produces high-confidence proposals for further optimization at the instance level. Through multi-level reliability-aware learning, we obtain more reliable confidence scores and more accurate temporal boundaries of predicted proposals. Our HR-Pro achieves state-of-the-art performance on multiple challenging benchmarks, including an impressive average mAP of 60.3% on THUMOS14. Notably, our HR-Pro largely surpasses all previous point-supervised methods, and even outperforms several competitive fully supervised methods. Code will be available at https://github.com/pipixin321/HR-Pro.Comment: 12 pages, 8 figure