Introduction to the Special Issue on ComputerModeling for Smart Cities Applications

A significant fraction of the world’s population is living in cities. With the rapid development ofinformation and computing technologies (ICT), cities may be made smarter by embedding ICT intotheir infrastructure. By smarter, we mean that the city operation will be more efficient, cost-effective,energy-saving, be more connected, more secure, and more environmentally friendly. As such, a smartcity is typically defined as a city that has a strong integration with ICT in all its components, includingits physical components, social components, and business components [1,2].

Effective short text classification via the fusion of hybrid features for IoT social data

Nowadays short texts can be widely found in various social data in relation to the 5G-enabled Internet of Things (IoT). Short text classification is a challenging task due to its sparsity and the lack of context. Previous studies mainly tackle these problems by enhancing the semantic information or the statistical information individually. However, the improvement achieved by a single type of information is limited, while fusing various information may help to improve the classification accuracy more effectively. To fuse various information for short text classification, this article proposes a feature fusion method that integrates the statistical feature and the comprehensive semantic feature together by using the weighting mechanism and deep learning models. In the proposed method, we apply Bidirectional Encoder Representations from Transformers (BERT) to generate word vectors on the sentence level automatically, and then obtain the statistical feature, the local semantic feature and the overall semantic feature using Term Frequency-Inverse Document Frequency (TF-IDF) weighting approach, Convolutional Neural Network (CNN) and Bidirectional Gate Recurrent Unit (BiGRU). Then, the fusion feature is accordingly obtained for classification. Experiments are conducted on five popular short text classification datasets and a 5G-enabled IoT social dataset and the results show that our proposed method effectively improves the classification performance.

Fine-Grained Binary Analysis Method for Privacy Leakage Detection on the Cloud Platform

Nowadays cloud architecture is widely applied on the internet.New malware aiming at the privacy data stealing or crypto currency mining is threatening the security of cloud platforms.In view of the problems with existing application behavior monitoring methods such as coarse-grained analysis,high performance overhead and lack of applicability,this paper proposes a new fine-grained binary program monitoring and analysis method based on multiple system level components,which is used to detect the possible privacy leakage of applications installed on cloud platforms.It can be used online in cloud platform environments for fine-grained automated analysis of target programs,ensuring the stability and continuity of program execution.We combine the external interception and internal instrumentation and design a variety of optimization schemes to further reduce the impact of fine-grained analysis on the performance of target programs,enabling it to be employed in actual environments.The experimental results show that the proposed method is feasible and can achieve the acceptable analysis performance while consuming a small amount of system resources.The optimization schemes can go beyond traditional dynamic instrumentation methods with better analytical performance and can be more applicable to online analysis on cloud platforms.

Towards improving detection performance for malware with a correntropy-based deep learning method

With the rapid development of Internet of Things(IoT)technologies,the detection and analysis of malware have become a matter of concern in the industrial application of Cyber-Physical System(CPS)that provides various services using the IoT paradigm.Currently,many advanced machine learning methods such as deep learning are popular in the research of malware detection and analysis,and some achievements have been made so far.However,there are also some problems.For example,considering the noise and outliers in the existing datasets of malware,some methods are not robust enough.Therefore,the accuracy of malware classification still needs to be improved.Aiming at this issue,we propose a novel method that combines the correntropy and the deep learning model.In our proposed method for malware detection and analysis,given the success of the mixture correntropy as an effective similarity measure in addressing complex datasets with noise,it is therefore incorporated into a popular deep learning model,i.e.,Convolutional Neural Network(CNN),to reconstruct its loss function,with the purpose of further detecting the features of outliers.We present the detailed design process of our method.Furthermore,the proposed method is tested both on a real-world malware dataset and a popular benchmark dataset to verify its learning performance.

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identification has been a research hotspot in the field of network management and security mon-itoring.However,as more network services use encryption technology,network traffic identification faces many challenges.Although classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and labor-intensive.Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples.However,in real scenarios,labeled samples are often difficult to obtain.This paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning.Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.

A CPK-Based Identity Authentication Scheme for IoT

As the power Internet of Things(IoT)enters the security construction stage,the massive use of perception layer devices urgently requires an identity authentication scheme that considers both security and practicality.The existing public key infrastructure(PKI)-based security authentication scheme is currently difficult to apply in many terminals in IoT.Its key distribution and management costs are high,which hinders the development of power IoT security construction.Combined Public Key(CPK)technology uses a small number of seeds to generate unlimited public keys.It is very suitable for identity authentication in the power Internet of Things.In this paper,we propose a novel identity authentication scheme for power IoT.The scheme combines the physical unclonable function(PUF)with improved CPK technology to achieve mutual identity authentication between power IoT terminals and servers.The proposed scheme does not require third-party authentication and improves the security of identity authentication for power IoT.Moreover,the scheme reduces the resource consumption of power IoT devices.The improved CPK algorithm solves the key collision problem,and the third party only needs to save the private key and the public key matrix.Experimental results show that the amount of storage resources occupied in our scheme is small.The proposed scheme is more suitable for the power IoT.

In vitro Propagation of Codonopsis pilosula"Fengdang"

[Objectives]The paper was to establish in vitro propagation of an excellent germplasm of Codonopsis pilosula"Fengdang".[Methods]The effects of different plant growth regulators on callus induction and redifferentiation were tested by using sterile stem explants in vitro that germinated from seeds of superior"Fengdang"plant.[Results]The optimum medium for callus induction was MS+0.5 mg/L NAA+1.0 mg/L 16-BA.The optimal medium for bud differentiation was MS+0.1 mg/L NAA+0.5 mg/L 6-BA,and that for rooting was 1/2 MS+0.2 mg/L NAA+0.2 mg/L IBA.By using the media mentioned above,the rates of callus induction,bud differentiation and rooting reached 91%,100%and 97%,respectively.[Conclusions]The rapid propagation system of regenerated plants in vitro established in this study lays a foundation for the popularization and industrialized seedling of excellent germplasm of"Fengdang".

Solanine Interferes with AKT/p-AKT and PI3K/p-PI3K Pathway to Inhibit HIF and Destroy Cell Energy Metabolism

The purpose of this study was to explore the mechanism of Solanine disrupting energy metabolism in human renal cancer ACHN cells and to clarify its target. The specific method was to culture human renal cancer ACHN cell lines, and to intervene with Solanine of high, medium and low concentrations. The content of ATP in cells was measured by ELISA method. The expression of HIF-1α protein and the expression of PI3K, AKT, p-PI3K, p-AKT in PI3K/AKT pathway were detected by Western blotting. The results showed that compared with the control group, the relative expression of p-PI3K and p-AKT showed a downward trend with the increase of Solanine concentration (P < 0.05), while the relative expression of PI3K and AKT showed no significant change (P > 0.05). In addition, the relative expression of HIF-1α also showed a downward trend (P < 0.05). According to the above results, it is suggested that Solanine can significantly inhibit the energy metabolism of renal cancer cells, the main mechanism of which is the down-regulation of HI-1αf downstream of the PI3K/Akt pathway by inhibiting the phosphorylation process of PI3K/p-PI3K and Akt/p-Akt.

Towards Rehabilitation at Home After Total Knee Replacement

In this paper,we present the design and implementation of an avatar-based interactive system that facilitates rehabilitation for people who have received total knee replacement surgeries.The system empowers patients to carry out exercises prescribed by a clinician at the home settings more effectively.Our system helps improve accountability for both patients and clinicians.The primary sensing modality is the Microsoft Kinect sensor,which is a depth camera that comes with a Software Development Kit(SDK).The SDK provides access to 3-dimensional skeleton joint positions to software developers,which significantly reduces the challenges in developing accurate motion tracking systems,especially for use at home.However,the Kinect sensor is not wellequipped to track foot orientation and its subtle movements.To overcome this issue,we augment the system with a commercial off-the-shelf Inertial Measurement Unit(IMU).The two sensing modalities are integrated where the Kinect serves as the primary sensing modality and the IMU is used for exercises where Kinect fails to produce accurate measurement.In this pilot study,we experiment with four rehabilitation exercises,namely,quad set,side-lying hip abduction,straight raise leg,and ankle pump.The Kinect is used to assess the first three exercises,and the IMU is used to assess the ankle pump exercise.