An Anonymous System Based on Random Virtual Proxy Mutation

Anonymous systems are usually used to protect users\u27 privacy in network communication. However, even in the low-latency Tor system, it is accompanied by network communication performance degradation, which makes users have to give up using the anonymity system in many applications. Therefore, we propose a novel anonymity system with rotated multi-path accompanying virtual proxy mutation for data transmission. Unlike onion routing, in our system the randomly generated virtual proxies take over the address isolation executing directly on the network layer and expand the anonymity space to all terminals in the network. With the optimal algorithm of selecting the path, the network communication performance improved significantly also. The verification experiments show that the anonymity system terminal sends and receives data at 500 kbps, and only a slight delay jitter occurs at the receiving end, and the other network performance is not significantly reduced

Design and Comprehensive Analysis of a Noise-Tolerant ZNN Model With Limited-Time Convergence for Time-Dependent Nonlinear Minimization

Zeroing neural network (ZNN) is a powerful tool to address the mathematical and optimization problems broadly arisen in the science and engineering areas. The convergence and robustness are always co-pursued in ZNN. However, there exists no related work on the ZNN for time-dependent nonlinear minimization that achieves simultaneously limited-time convergence and inherently noise suppression. In this article, for the purpose of satisfying such two requirements, a limited-time robust neural network (LTRNN) is devised and presented to solve time-dependent nonlinear minimization under various external disturbances. Different from the previous ZNN model for this problem either with limited-time convergence or with noise suppression, the proposed LTRNN model simultaneously possesses such two characteristics. Besides, rigorous theoretical analyses are given to prove the superior performance of the LTRNN model when adopted to solve time-dependent nonlinear minimization under external disturbances. Comparative results also substantiate the effectiveness and advantages of LTRNN via solving a time-dependent nonlinear minimization problem

Fog computing-based approximate spatial keyword queries with numeric attributes in IoV

Due to the popularity of on-board geographic devices, a large number of spatial-textual objects are generated in Internet of Vehicles (IoV). This development calls for Approximate Spatial Keyword Queries with numeric Attributes in IoV (ASKIV), which takes into account the locations, textual descriptions, and numeric attributes of spatial-textual objects. Considering huge amounts of objects involved in the query processing, this paper comes up with the ideal of utilizing vehicles as fog-computing resource, and proposes the network structure called FCV, and based on which the fog-based Top-k ASKIV query is explored and formulated. In order to effectively support network distance pruning, textual semantic pruning, and numerical attribute pruning simultaneously, a two-level spatial-textual hybrid index STAG-tree is designed. Based on STAG-tree, an efficient Top-k ASKIV query processing algorithm is presented. Simulation results show that, our STAG-based approach is about 1.87x (17.1x, resp.) faster in search time than the compared ILM (DBM, resp.) method, and our approach is scalable.University of Derb

Edge intelligence-enabled cyber-physical systems

With the advent of the Internet of everything era, people's demand for intelligent Internet of Things (IoT) devices is steadily increasing. A more intelligent cyber-physical system (CPS) is needed to meet the diverse business requirements of users, such as ultra-reliable low-latency communication, high quality of services (QoS), and quality of experience (QoE). Edge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for the CPS, which provides the ability to analyze data at the edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyperconnected smart sensing devices.As a distributed intelligent computing paradigm in which computation is largely or completely performed at distributed nodes, EI provides for the rapid development of artificial intelligence (AI) and edge computing resources to support real-time insight and analysis for applications in CPS, which brings memory, computing power and processing ability closer to the location where it is needed, reduces the volumes of data that must be moved, the consequent traffic, and the distance the data must travel. As an emerging intelligent computing paradigm, EI can accelerate content delivery and improve the QoS of applications, which is attracting more and more research attentions from academia and industry because of its advantages in throughput, delay, network scalability and intelligence in CPS.The guest editors would like to thank all the authors and the reviewers for their hard work and contributions in helping to organize this special issue. They also would like to express their heartfelt gratitude to the Editor-in-Chief, Prof. David W. Walker, for giving us this great opportunity, and the members of the Editorial Staff for their support during the process.Scopu

Gigahertz-rate-switchable wavefront shaping through integration of metasurfaces with photonic integrated circuit

Achieving spatiotemporal control of light at high-speeds presents immense possibilities for various applications in communication, computation, metrology, and sensing. The integration of subwavelength metasurfaces and optical waveguides offers a promising approach to manipulate light across multiple degrees of freedom at high-speed in compact photonic integrated circuit (PICs) devices. Here, we demonstrate a gigahertz-rate-switchable wavefront shaping by integrating metasurface, lithium niobite on insulator (LNOI) photonic waveguide and electrodes within a PIC device. As proofs of concept, we showcase the generation of a focus beam with reconfigurable arbitrary polarizations, switchable focusing with lateral focal positions and focal length, orbital angular momentum light beams (OAMs) as well as Bessel beams. Our measurements indicate modulation speeds of up to gigahertz rate. This integrated platform offers a versatile and efficient means of controlling light field at high-speed within a compact system, paving the way for potential applications in optical communication, computation, sensing, and imaging

Disruption of the Golgi protein Otg1 gene causes defective hormone secretion and aberrant glucose homeostasis in mice.

BACKGROUND: Concerted hormone secretion is essential for glucose homeostasis and growth. The oocyte testis gene 1 (Otg1) has limited information in mammals before. Human OTG1 has been identified as an antigen associated with cutaneous T cell lymphoma, while worm Otg1 is recently reported to be a vesicle trafficking regulator in neurons. To understand the physiological role of Otg1 and its potential relation to hormone secretion, we characterized a mutation caused by the piggyBac transposon (PB) insertion in mice. RESULTS: Oocyte testis gene 1 encodes a Golgi localized protein that is expressed with a broad tissue distribution in mice. The PB insertion effectively blocks Otg1 expression, which results in postnatal lethality, growth retardation, hypoglycemia and improved insulin sensitivity in mice. Otg1 mutants exhibit decreased levels of insulin, leptin and growth hormone in the circulation and reduced hepatic IGF-1 expression. Decreased expression of Otg1 in pituitary GH3 cells causes reduced grow hormone expression and secretion, as well as the traffic of the VSVG protein marker. CONCLUSIONS: Our data support the hypothesis that Otg1 impacts hormone secretion by regulating vesicle trafficking. These results revealed a previously unknown and important role of Otg1 in hormone secretion and glucose homeostasis in mammals

A cascade learning approach for automated detection of locomotive speed sensor using imbalanced data in ITS

Automatic and intelligent railway locomotive inspection and maintenance are fundamental issues in high-speed rail applications and intelligent transportation system (ITS). Traditional locomotive equipment inspection is carried out manually on-site by workers, and the task is exhausting, cumbersome, and unsafe. Based on computer vision and machine learning, this paper presents an approach to the automatic detection of the locomotive speed sensor equipment, an important device in locomotives. Challenges to the detection of speed sensor mainly concerns complex background, motion blur, muddy noise, and variable shapes. In this paper, a cascade learning framework is proposed, which includes two learning stages: target localization and speed sensor detection, to reduce the complexity of the research object and solve the imbalance of samples. In the first stage, histogram of oriented gradient feature and support vector machine (HOG-SVM) model is used for multi-scale detection. Then, an improved LeNet-5 model is adopted in the second stage. To solve the problem of the imbalance of positive and negative samples of speed sensor, a combination strategy which draws on four individual classifiers is designed to construct an ensemble of classifier for recognition, and the results of three different algorithms are compared. The experimental results demonstrate that our approach is effective and robust with respect to changes in speed sensor patterns for robust equipment identification.N/

Comparative Evaluation of the LAMP Assay and PCR-Based Assays for the Rapid Detection of Alternaria solani

Early blight (EB), caused by the pathogen Alternaria solani, is a major threat to global potato and tomato production. Early and accurate diagnosis of this disease is therefore important. In this study, we conducted a loop-mediated isothermal amplification (LAMP) assay, as well as conventional polymerase chain reaction (PCR), nested PCR, and quantitative real-time PCR (RT-qPCR) assays to determine which of these techniques was less time consuming, more sensitive, and more accurate. We based our assays on sequence-characterized amplified regions of the histidine kinase gene with an accession number (FJ424058). The LAMP assay provided more rapid and accurate results, amplifying the target pathogen in less than 60 min at 63°C, with 10-fold greater sensitivity than conventional PCR. Nested PCR was 100-fold more sensitive than the LAMP assay and 1000-fold more sensitive than conventional PCR. qPCR was the most sensitive among the assays evaluated, being 10-fold more sensitive than nested PCR for the least detectable genomic DNA concentration (100 fg). The LAMP assay was more sensitive than conventional PCR, but less sensitive than nested PCR and qPCR; however, it was simpler and faster than the other assays evaluated. Despite of the sensitivity, LAMP assay provided higher specificity than qPCR. The LAMP assay amplified A. solani artificially, allowing us to detect naturally infect young potato leaves, which produced early symptoms of EB. The LAMP assay also achieved positive amplification using diluted pure A. solani culture instead of genomic DNA. Hence, this technique has greater potential for developing quick and sensitive visual detection methods than do other conventional PCR strategies for detecting A. solani in infected plants and culture, permitting early prediction of disease and reducing the risk of epidemics