Effects of real-time traffic information systems on traffic performance under different network structures

The effects of real-time traffic information system(RTTIS)on traffic performance under parallel,grid and ring networks were investigated.The simulation results show that the effects of the proportion of RTTIS usage depend on the road network structures.For traffic on a parallel network,the performance of groups with and without RTTIS level is improved when the proportion of vehicles using RTTIS is greater than 0 and less than 30%,and a proportion of RTTIS usage higher than 90%would actually deteriorate the performance.For both grid and ring networks,a higher proportion of RTTIS usage always improves the performance of groups with and without RTTIS.For all three network structures,vehicles without RTTIS benefit from some proportion of RTTIS usage in a system.

Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

手术仿真中基于导纳控制的力触觉形变模型

为了在虚拟手术仿真中获得快速、准确的力触觉形变效果,提出一种基于导纳控制的力触觉形变模型.基于该模型,采用PHANTOM OMNI力触觉交互设备,以3DS MAX 2013,Microsoft Visual C++2012,Open GL函数库为基础搭建了实时柔性体力触觉再现系统,实现了虚拟双手对心脏双点的拉拽交互操作.感知实验和交互效率的结果表明,所提出的模型简单有效,形变效果逼真、视觉反馈流畅、力触觉反馈平稳,操作者对虚拟环境的感知和交互准确可靠,能够满足虚拟手术仿真系统的要求.

Contribution of South China Sea Tropical Cyclones to an Increase in Southern China Summer Rainfall Around 1993

The increase in southern China summer rainfall around 1993 was accompanied by an increase in tropical cyclones that formed in the South China Sea. This study documents the connection of these two features. Our analysis shows that the contribution of tropical cyclones that formed in the South China Sea to southern China summer rainfall experienced a significant increase around 1993, in particular, along the coast and in the heavy rain category. The number of tropical cyclones that formed in the western North Pacific and entered the South China Sea decreased, and their contribution to summer rainfall was reduced in eastern part of southern China （but statistically insignificant）. The increase in tropical cyclone-induced rainfall contributed up to -30＆ of the total rainfall increase along the coastal regions. The increase of tropical cyclones in the South China Sea appears to be related to an increase in local sea surface temperature.

Synergistic Contribution of Precipitation Anomalies over Northwestern India and the South China Sea to High Temperature over the Yangtze River Valley

This study explores the characteristics of high temperature anomalies over eastern China and associated influencing factors using observations and model outputs.Results show that more long-duration（over 8 days） high temperature events occur over the middle and lower reaches of the Yangtze River Valley（YRV） than over the surrounding regions,and control most of the interannual variation of summer mean temperature in situ.The synergistic effect of summer precipitation over the South China Sea（SCS） region（18°–27°N,115°–124°E） and the northwestern India and Arabian Sea（IAS） region（18°–27°N,60°–80°E） contributes more significantly to the variation of summer YRV temperature,relative to the respective SCS or IAS precipitation anomaly.More precipitation（enhanced condensational heating） over the SCS region strengthens the western Pacific subtropical high（WPSH） and simultaneously weakens the westerly trough over the east coast of Asia,and accordingly results in associated high temperature anomalies over the YRV region through stimulating an East Asia–Pacific（EAP） pattern.More precipitation over the IAS region further adjusts the variations of the WPSH and westerly trough,and eventually reinforces high temperature anomalies over the YRV region.Furthermore,the condensational heating related to more IAS precipitation can adjust upper-tropospheric easterly anomalies over the YRV region by exciting a circumglobal teleconnection,inducing cold horizontal temperature advection and related anomalous descent,which is also conducive to the YRV high temperature anomalies.The reproduction of the above association in the model results indicates that the above results can be explained both statistically and dynamically.

A Novel Auto-Annotation Technique for Aspect Level Sentiment Analysis

In machine learning,sentiment analysis is a technique to find and analyze the sentiments hidden in the text.For sentiment analysis,annotated data is a basic requirement.Generally,this data is manually annotated.Manual annotation is time consuming,costly and laborious process.To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis.Dataset is created from the reviews of ten most popular songs on YouTube.Reviews of five aspects—voice,video,music,lyrics and song,are extracted.An N-Gram based technique is proposed.Complete dataset consists of 369436 reviews that took 173.53 s to annotate using the proposed technique while this dataset might have taken approximately 2.07 million seconds(575 h)if it was annotated manually.For the validation of the proposed technique,a sub-dataset—Voice,is annotated manually as well as with the proposed technique.Cohen’s Kappa statistics is used to evaluate the degree of agreement between the two annotations.The high Kappa value(i.e.,0.9571%)shows the high level of agreement between the two.This validates that the quality of annotation of the proposed technique is as good as manual annotation even with far less computational cost.This research also contributes in consolidating the guidelines for the manual annotation process.

A Novel Wrapper-Based Optimization Algorithm for the Feature Selection and Classification

Machine learning(ML)practices such as classification have played a very important role in classifying diseases in medical science.Since medical science is a sensitive field,the pre-processing of medical data requires careful handling to make quality clinical decisions.Generally,medical data is considered high-dimensional and complex data that contains many irrelevant and redundant features.These factors indirectly upset the disease prediction and classification accuracy of any ML model.To address this issue,various data pre-processing methods called Feature Selection(FS)techniques have been presented in the literature.However,the majority of such techniques frequently suffer from local minima issues due to large solution space.Thus,this study has proposed a novel wrapper-based Sand Cat SwarmOptimization(SCSO)technique as an FS approach to find optimum features from ten benchmark medical datasets.The SCSO algorithm replicates the hunting and searching strategies of the sand cat while having the advantage of avoiding local optima and finding the ideal solution with minimal control variables.Moreover,K-Nearest Neighbor(KNN)classifier was used to evaluate the effectiveness of the features identified by the proposed SCSO algorithm.The performance of the proposed SCSO algorithm was compared with six state-of-the-art and recent wrapper-based optimization algorithms using the validation metrics of classification accuracy,optimum feature size,and computational cost in seconds.The simulation results on the benchmark medical datasets revealed that the proposed SCSO-KNN approach has outperformed comparative algorithms with an average classification accuracy of 93.96%by selecting 14.2 features within 1.91 s.Additionally,the Wilcoxon rank test was used to perform the significance analysis between the proposed SCSOKNN method and six other algorithms for a p-value less than 5.00E-02.The findings revealed that the proposed algorithm produces better outcomes with an average p-value of 1.82E-02.Moreover,potential future directions are also suggested as a result of the study’s promising findings.

A Tri-Port MIMO Antenna Designed for Micro/Pico Cell Applications with Self-Decoupled Structure

A tri-port MIMO antenna designed for Micro/Pico-Cell application is proposed.It is based on printed elements with X-shaped arms,which are oriented to 0°,120° and240° in the azimuth plane.The arms of these elements are connected,with which a selfdecoupled structure is formed.The mutual coupling between adjacent elements is below-15 dB.Meanwhile,it size is compact and bidirectional radiation patterns with around 4dBi Gain and 92° 3dB beam width is achieved,which can provide good pattern diversity and full azimuth coverage in real applications.

Context and Machine Learning Based Trust Management Framework for Internet of Vehicles

Trust is one of the core components of any ad hoc network security system.Trust management(TM)has always been a challenging issue in a vehicular network.One such developing network is the Internet of vehicles(IoV),which is expected to be an essential part of smart cities.IoV originated from the merger of Vehicular ad hoc networks(VANET)and the Internet of things(IoT).Security is one of the main barriers in the on-road IoV implementation.Existing security standards are insufficient to meet the extremely dynamic and rapidly changing IoV requirements.Trust plays a vital role in ensuring security,especially during vehicle to vehicle communication.Vehicular networks,having a unique nature among other wireless ad hoc networks,require dedicated efforts to develop trust protocols.Current TM schemes are inflexible and static.Predefined scenarios and limited parameters are the basis for existing TM models that are not suitable for vehicle networks.The vehicular network requires agile and adaptive solutions to ensure security,especially when it comes to critical messages.The vehicle network’s wireless nature increases its attack surface and exposes the network to numerous security threats.Moreover,internet involvement makes it more vulnerable to cyberattacks.The proposed TM framework is based on context-based cognition and machine learning to be best suited to IoV dynamics.Machine learning is the best solution to utilize the big data produced by vehicle sensors.To handle the uncertainty Bayesian machine learning statistical model is used.The proposed framework can adapt scenarios dynamically and infer using the maximum possible parameter available.The results indicated better performance than existing TM methods.Furthermore,for future work,a high-level machine learning model is proposed.

Mining Maximal Frequent Patterns in a Unidirectional FP-tree

Because mining complete set of frequent patterns from dense database could be impractical, an interesting alternative has been proposed recently. Instead of mining the complete set of frequent patterns, the new model only finds out the maximal frequent patterns, which can generate all frequent patterns. FP-growth algorithm is one of the most efficient frequent-pattern mining methods published so far. However, because FP-tree and conditional FP-trees must be two-way traversable, a great deal memory is needed in process of mining. This paper proposes an efficient algorithm Unid_FP-Max for mining maximal frequent patterns based on unidirectional FP-tree. Because of generation method of unidirectional FP-tree and conditional unidirectional FP-trees, the algorithm reduces the space consumption to the fullest extent. With the development of two techniques: single path pruning and header table pruning which can cut down many conditional unidirectional FP-trees generated recursively in mining process, Unid_FP-Max further lowers the expense of time and space.

Continuous-variable quantum teleportation of even and odd coherent states through varied gain channels

This paper has investigated quantum teleportation of even and odd coherent states in terms of the EPR entanglement states for continuous variables. It discusses the relationship between the fidelity and the entanglement of EPR states, which is characterized by the degree of squeezing and the gain of classical channels. It shows that the quality of teleporting quantum states also depends on the characteristics of the states themselves. The properties of teleporting even and odd coherent states at different intensities are investigated. The difference of teleporting two such kinds of quantum states are analysed based on the quantum distance function.

Escape from a Riddled-Like Basin

We investigate a system described by a conservative and a dissipative map concatenation. A fat fractal forbidden net, induced by interaction between discontinuous and noninvertible properties, introduces rippled-like attraction basins of two periodic attractors. Small areas, which serve as escaping holes of a new type of crisis, are dominated by conventional strong dissipation and are bounded by the forbidden region, but only in the vicinity of each periodic point. Based on this understanding, the scaling behaviour of the averaged lifetime of the crisis is analytically and numerically determined to be （τ） ∝ （b-b0）^γ, where b denotes the control parameter, bo denotes its critical threshold, and γ≌-1.5.

Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

Malaria is a lethal disease responsible for thousands of deaths worldwide every year.Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts.Computerbased automated diagnosis of diseases is progressively becoming popular.Although deep learning models show high performance in the medical field,it demands a large volume of data for training which is hard to acquire for medical problems.Similarly,labeling of medical images can be done with the help of medical experts only.Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system,which showed promising results.However,the most common problem with these models is that they need a large amount of data for training.This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning.The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models.Performance of the proposed model is evaluated on a publicly available dataset of blood smear images(with malariainfected and normal class)and achieved a classification accuracy of 96.6%.

A hybrid method for extraction of protein-protein interactions from literature

In this work, a hybrid method is proposed to eliminate the limitations of traditional protein-protein interactions （PPIs） extraction methods, such as pattern learning and machine learning. Each sentence from the biomedical literature containing a protein pair describes a PPI which is predicted by first learning syntax patterns typical of PPIs from training corpus and then using their presence as features, along with bag-of-word features in a maximum entropy model. Tested on the BioCreAtIve corpus, the PPIs extraction method, which achieved a precision rate of 64%, recall rate of 60%, improved the performance in terms of F1 value by 11% compared with the component pure pattern- based and bag-of-word methods. The results on this test set were also compared with other three extraction methods and found to improve the performance remarkably.

Generalized Model of Blood Flow in a Vertical Tube with Suspension of Gold Nanomaterials: Applications in the Cancer Therapy

Gold metallic nanoparticles are generally used within a lab as a tracer,to uncover on the presence of specific proteins or DNA in a sample,as well as for the recognition of various antibiotics.They are bio companionable and have properties to carry thermal energy to tumor cells by utilizing different clinical approaches.As the cancer cells are very smaller so for the infiltration,the properly sized nanoparticles have been injected in the blood.For this reason,gold nanoparticles are very effective.Keeping in mind the above applications,in the present work a generalized model of blood flow containing gold nanoparticles is considered in this work.The blood motion is considered in a cylindrical tube under the oscillating pressure gradient and magnetic field.The problem formulation is done using two types of fractional approaches namely CF(Caputo Fabrizio)and AB(Atangana-Baleanue)derivatives,whereas blood is considered as a counter-example of Casson fluid.Exact solutions of the problem are obtained using joint Laplace and Hankel transforms,and a comparative analysis is made between CF and AB.Results are computed in tables and shown in various plots for embedded parameters and discussed.It is found that adding 0.04-unit gold nanoparticles to blood,increase its heat transfer rate by 4 percent compared to regular blood.It is also noted that the heat transfer can be enhanced in the blood with memory.

Hyper-Parameter Optimization of Semi-Supervised GANs Based-Sine Cosine Algorithm for Multimedia Datasets

Generative Adversarial Networks(GANs)are neural networks that allow models to learn deep representations without requiring a large amount of training data.Semi-Supervised GAN Classifiers are a recent innovation in GANs,where GANs are used to classify generated images into real and fake and multiple classes,similar to a general multi-class classifier.However,GANs have a sophisticated design that can be challenging to train.This is because obtaining the proper set of parameters for all models-generator,discriminator,and classifier is complex.As a result,training a single GAN model for different datasets may not produce satisfactory results.Therefore,this study proposes an SGAN model(Semi-Supervised GAN Classifier).First,a baseline model was constructed.The model was then enhanced by leveraging the Sine-Cosine Algorithm and Synthetic Minority Oversampling Technique(SMOTE).SMOTE was used to address class imbalances in the dataset,while Sine Cosine Algorithm(SCA)was used to optimize the weights of the classifier models.The optimal set of hyperparameters(learning rate and batch size)were obtained using grid manual search.Four well-known benchmark datasets and a set of evaluation measures were used to validate the proposed model.The proposed method was then compared against existing models,and the results on each dataset were recorded and demonstrated the effectiveness of the proposed model.The proposed model successfully showed improved test accuracy scores of 1%,2%,15%,and 5%on benchmarking multimedia datasets;Modified National Institute of Standards and Technology(MNIST)digits,Fashion MNIST,Pneumonia Chest X-ray,and Facial Emotion Detection Dataset,respectively.

Energy Price Forecasting Through Novel Fuzzy Type-1 Membership Functions

Electricity price forecasting is a subset of energy and power forecasting that focuses on projecting commercial electricity market present and future prices.Electricity price forecasting have been a critical input to energy corporations’strategic decision-making systems over the last 15 years.Many strategies have been utilized for price forecasting in the past,however Artificial Intelligence Techniques(Fuzzy Logic and ANN)have proven to be more efficient than traditional techniques(Regression and Time Series).Fuzzy logic is an approach that uses membership functions(MF)and fuzzy inference model to forecast future electricity prices.Fuzzy c-means(FCM)is one of the popular clustering approach for generating fuzzy membership functions.However,the fuzzy c-means algorithm is limited to producing only one type of MFs,Gaussian MF.The generation of various fuzzy membership functions is critical since it allows for more efficient and optimal problem solutions.As a result,for the best and most improved results for electricity price forecasting,an approach to generate multiple type-1 fuzzy MFs using FCM algorithm is required.Therefore,the objective of this paper is to propose an approach for generating type-1 fuzzy triangular and trapezoidal MFs using FCM algorithm to overcome the limitations of the FCM algorithm.The approach is used to compute and improve forecasting accuracy for electricity prices,where Australian Energy Market Operator(AEMO)data is used.The results show that the proposed approach of using FCM to generate type-1 fuzzy MFs is effective and can be adopted.

Counting and Randomly Generating <i>k</i>-Ary Trees

k-ary trees are one of the most basic data structures in Computer Science. A new method is presented to determine how many there are with n nodes. This method gives additional insight into their structure and provides a new algo-rithm to efficiently generate such a tree randomly.

Towards Auction-Based HPC Computing in the Cloud

Cloud computing is expanding widely in the world of IT infrastructure. This is due partly to the cost-saving effect of economies of scale. Fair market conditions can in theory provide a healthy environment to reflect the most reasonable costs of computations. While fixed cloud pricing provides an attractive low entry barrier for compute-intensive applications, both the consumer and supplier of computing resources can see high efficiency for their investments by participating in auction-based exchanges. There are huge incentives for the cloud provider to offer auctioned resources. However, from the consumer perspective, using these resources is a sparsely discussed challenge. This paper reports a methodology and framework designed to address the challenges of using HPC （High Performance Computing） applications on auction-based cloud clusters. The authors focus on HPC applications and describe a method for determining bid-aware checkpointing intervals. They extend a theoretical model for determining checkpoint intervals using statistical analysis of pricing histories. Also the latest developments in the SpotHPC framework are introduced which aim at facilitating the managed execution of real MPI applications on auction-based cloud environments. The authors use their model to simulate a set of algorithms with different computing and communication densities. The results show the complex interactions between optimal bidding strategies and parallel applications performance.