Realizing Video Analytic Service in the Fog-Based Infrastructure-Less Environments

Deep learning has unleashed the great potential in many fields and now is the most significant facilitator for video analytics owing to its capability to providing more intelligent services in a complex scenario. Meanwhile, the emergence of fog computing has brought unprecedented opportunities to provision intelligence services in infrastructure-less environments like remote national parks and rural farms. However, most of the deep learning algorithms are computationally intensive and impossible to be executed in such environments due to the needed supports from the cloud. In this paper, we develop a video analytic framework, which is tailored particularly for the fog devices to realize video analytic service in a rapid manner. Also, the convolution neural networks are used as the core processing unit in the framework to facilitate the image analysing process

A Genetic and Simulated Annealing Combined Algorithm for Optimization of Wideband Antenna Matching Networks

A genetic and simulated annealing combined algorithm is presented and applied to optimize broadband matching networks for antennas. As a result, advantages of both the genetic algorithm (GA) and simulated annealing (SA) are taken. Effectiveness and efficiency of the presented combined algorithm are demonstrated by optimization of a wideband matching network for a VHF/UHF discone-based antenna. The optimized parameters provide significant improvements of VSWR and transducer power gain for the antenna

DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities

Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded by designated rule-based detectors that heavily rely on empirical expertise, requiring tremendous effort from software experts to generate rule repositories for large code corpus. Recent advances in deep learning, especially Graph Neural Networks (GNN), have uncovered the feasibility of automatic detection of a wide range of software vulnerabilities. However, prior learning-based works only break programs down into a sequence of word tokens for extracting contextual features of codes, or apply GNN largely on homogeneous graph representation (e.g., AST) without discerning complex types of underlying program entities (e.g., methods, variables). In this work, we are one of the first to explore heterogeneous graph representation in the form of Code Property Graph and adapt a well-known heterogeneous graph network with a dual-supervisor structure for the corresponding graph learning task. Using the prototype built, we have conducted extensive experiments on both synthetic datasets and real-world projects. Compared with the state-of-the-art baselines, the results demonstrate promising effectiveness in this research direction in terms of vulnerability detection performance (average F1 improvements over 10\% in real-world projects) and transferability from C/C++ to other programming languages (average F1 improvements over 11%)

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods

Exploiting Spatial-temporal Data for Sleep Stage Classification via Hypergraph Learning

Sleep stage classification is crucial for detecting patients' health conditions. Existing models, which mainly use Convolutional Neural Networks (CNN) for modelling Euclidean data and Graph Convolution Networks (GNN) for modelling non-Euclidean data, are unable to consider the heterogeneity and interactivity of multimodal data as well as the spatial-temporal correlation simultaneously, which hinders a further improvement of classification performance. In this paper, we propose a dynamic learning framework STHL, which introduces hypergraph to encode spatial-temporal data for sleep stage classification. Hypergraphs can construct multi-modal/multi-type data instead of using simple pairwise between two subjects. STHL creates spatial and temporal hyperedges separately to build node correlations, then it conducts type-specific hypergraph learning process to encode the attributes into the embedding space. Extensive experiments show that our proposed STHL outperforms the state-of-the-art models in sleep stage classification tasks

APACHE IV Is Superior to MELD Scoring System in Predicting Prognosis in Patients after Orthotopic Liver Transplantation

This study aims to compare the efficiency of APACHE IV with that of MELD scoring system for prediction of the risk of mortality risk after orthotopic liver transplantation (OLT). A retrospective cohort study was performed based on a total of 195 patients admitted to the ICU after orthotopic liver transplantation (OLT) between February 2006 and July 2009 in Guangzhou, China. APACHE IV and MELD scoring systems were used to predict the postoperative mortality after OLT. The area under the receiver operating characteristic curve (AUC) and the Hosmer-Lemeshow C statistic were used to assess the discrimination and calibration of APACHE IV and MELD, respectively. Twenty-seven patients died during hospitalization with a mortality rate of 13.8%. The mean scores of APACHE IV and MELD were 42.32 ± 21.95 and 18.09 ± 10.55, respectively, and APACHE IV showed better discrimination than MELD; the areas under the receiver operating characteristic curve for APACHE IV and MELD were 0.937 and 0.694 ( for both models), which indicated that the prognostic value of APACHE IV was relatively high. Both models were well-calibrated (The Hosmer-Lemeshow C statistics were 1.568 and 6.818 for APACHE IV and MELD, resp.; for both). The respective Youden indexes of APACHE IV, MELD, and combination of APACHE IV with MELD were 0.763, 0.430, and 0.545. The prognostic value of APACHE IV is high but still underestimates the overall hospital mortality, while the prognostic value of MELD is poor. The function of the APACHE IV is, thus, better than that of the MELD

Ground Simulation Test of 2D Dynamic Overload Environment of Fuze Launching

The fuze launch process is subjected to backseat and spin overloads. To address this issue, a loading method of a 2D dynamic acceleration environment was developed in this study for testing fuze antioverload performance on ground. The techniques of flywheel energy storage, high-speed impact, and centrifugal rotation in the track are combined in a dynamic analysis and simulation. First, the flywheel is rotated at a constant speed by a variable-frequency motor to obtain high kinetic energy. Second, an impact hammer is instantaneously released on the specimen at a high speed, loading the backseat acceleration environment. Finally, the impact hammer is retracted, and the specimen is rotated in the track instead of spinning around its axis, thereby loading the centrifugal acceleration environment. The peak value and pulse width of the 2D overload acceleration can be adjusted by changing the speed of the flywheel and buffers in the abovementioned process. The experimental and simulation results observed that the peak value of backseat acceleration could reach 34,559 g, the pulse width was approximately 400 μs, and the peak value of the centrifugal acceleration was 1,020 g. The study results showed that the proposed approach fulfills the requirements of the 2D overload simulation test of the micro-electromechanical system (MEMS) fuze safety and arming mechanism. The proposed loading method has been successfully applied to ground simulation tests of the MEMS fuze safety and arming mechanism

Improved Gel Properties of Whey Protein-Stabilized Emulsions by Ultrasound and Enzymatic Cross-Linking

This study investigated the effects of high-intensity ultrasound (HUS) and transglutaminase pretreatment on the gelation behavior of whey protein soluble aggregate (WPISA) emulsions. HUS pretreatment and TGase-mediated cross-linking delayed the onset of gelation but significantly increased (p < 0.05) the gel firmness (G′) both after gel formation at 25 °C and during storage at 4 °C. The frequency sweep test indicated that all gels had a similar frequency dependence at 4 and 25 °C, and the elasticity and viscosity of the WPISA-stabilized emulsion gel were significantly enhanced by HUS pretreatment and TGase-mediated cross-linking (p < 0.05). HUS and TGase-mediated cross-linking greatly improved the textural properties of WPISA-stabilized emulsion gels, as revealed by their increases in gel hardness, cohesiveness, resilience, and chewiness. HUS pretreatment and TGase-mediated cross-linking significantly increased the water-holding capacity but decreased the swelling ratios of the gels (p < 0.05). Interactive force analysis confirmed that noncovalent interactions, disulfide bonds, and TGase-induced covalent cross-links were all involved in the formation of gel networks. In conclusion, the combination of HUS and TGase-mediated cross-linking were beneficial for improving the gelation properties of WPISA-stabilized emulsion as a controlled release vehicle for potential food industrial applications