International Journal of Computational Science and Engineering (38 papers in press)

Regular Issues

• A multi-objective optimisation multicast routing algorithm with diversity rate in cognitive wireless mesh networks  
  by Zhufang Kuang 
  Abstract: Cognitive Wireless Mesh Networks (CWMNs) were developed to improve the usage ratio of the licensed spectrum. Since the spectrum opportunities for users vary over time and location, enhancing the spectrum effectiveness is a goal and also a challenge for CWMNs. Multimedia applications have recently generated much interest in CWMNs supporting Quality-Of-Service (QoS) communications. Multicast routing and spectrum allocation is an important challenge in CWMNs. In this paper, we design an effective multicast routing algorithm based on diversity rate with respect to load balancing and the number of transmissions for CWMNs. A Load Balancing wireless links weight computing function and computing algorithm based on Diversity Rate (LBDR) are proposed, and a load balancing Channel and Rate Allocating algorithm based on Diversity Rate (CRADR) is proposed. On this basis, a Load balancing joint Multicast Routing, channel and Rate allocation algorithm based on Diversity rate with QoS constraints for CWMNs (LMR2D) is proposed. Balancing the load of node and channel, and minimising the number of transmissions of multicast tree are the objectives of LMR2D. Firstly, LMR2D computes the weight of wireless links using LBDR and the Dijkstra algorithm for constructing the load balancing multicast tree step by step. Secondly, LMR2D uses CRADR to allocate channel and rate of its to links, which is based on the Wireless Broadcast Advantage (WBA). Simulation results show that LMR2D can achieve the expected goal. Not only can it balance the load of node and channel, but also it needs fewer transmissions for multicast tree.
  Keywords: cognitive wireless mesh networks; multicast routing; spectrum allocation; load balanced; diversity rate.
  
  
• Advances in the enumeration of foldable self-avoiding walks  
  by Christophe Guyeux, Jean-Claude Charr, Jacques Bou Abdo, Jacques Demerjian 
  Abstract: Self-Avoiding Walks (SAWs) have been studied for a long time owing to their intrinsic importance and the many application fields in which they operate. A new subset of SAWs, called foldable SAWs, has recently been discovered when investigating two different SAW manipulations embedded within existing Protein Structure Prediction (PSP) software. Since then, several attempts have been made to find out more about these walks, including counting them. However, calculating the number of foldable SAWs appeared as a tough work, and current supercomputers fail to count foldable SAWs of length exceeding ~30 steps. In this article, we present new progress in this enumeration, both theoretical (mathematics) and practical (computer science). A lower bound for the number of foldable SAWs is firstly proposed, by studying a special subset called prudent SAWs that is better known. The triangular and hexagonal lattices are then investigated for the first time, leading to new results about the enumeration of foldable SAWs on such lattices. Finally, a parallel genetic algorithm has been designed to discover new non-foldable SAWs of lengths ~100 steps, and the results obtained with this algorithm are promising.
  Keywords: self-avoiding walks; foldable SAWs; prudent SAWs; genetic algorithm.
  
  
• Distributed nested streamed models of tsunami waves  
  by Kensaku Hayashi, Alexander Vazhenin, Andrey Marchuk 
  Abstract: This research focuses on designing a high-speed scheme for tsunami modelling using nested computing. Computations are carried out on a sequence of grids composed of geographical areas with resolutions where each is embedded within another. This decreases the total number of calculations by excluding unimportant coastal areas from the process. The paper describes the distributed streaming computational scheme allowing for flexible reconfiguration of heterogeneous computing resources with a variable set of modelling zones. Computations are implemented by distributing these areas over modelling components and by synchronising the transitions of boundary data between them. Results of numerical modelling experiments are also presented.
  Keywords: tsunami modelling; nested grids; distributed systems; coarse-grained parallelisation; streaming computing; communicating processes; process synchronisation; task parallelism; programming model; component-based software engineering.
  
  
• E-commerce satisfaction based on synthetic evaluation theory and neural networks  
  by Jiayin Zhao, Yong Lu, H.A.O. Ban, Ying Chen 
  Abstract: The rapid development of e-commerce has led to the increasing role of satisfaction in more fields. Therefore, the customers opinion has become a necessary role for the success of related companies. E-commerce satisfaction, as the key factor affecting the performance of e-commerce enterprises, has become a research hotspot in academia. This paper proposes a synthetic evaluation model of satisfaction and logistics performance based on a fuzzy synthetic model and a dynamic weighted synthetic model, respectively. A modified ASCI analysis method based on the structured equation model is also proposed to compare with the synthetic method. Beyond this, we have also evaluated consumer satisfaction based review data. Corresponding suggestions are given for the operation of e-commerce enterprises.
  Keywords: E-commerce satisfaction; fuzzy synthetic model; structured equation model; neural networks.
  
  
• On the build and application of bank customer churn warning model  
  by Wangdong Jiang, Yushan Luo, Ying Cao, Guang Sun, Chunhong Gong 
  Abstract: In view of the customer churn problem faced by banks, this paper will use the Python language to clean and select the original dataset based on real bank customer data, and gradually condense the 626 customer features in the original dataset to 77 customer features. Then, based on the pre-processed bank data, this paper uses logistic regression, decision tree and neural network to establish three bank customer churn warning models and compares them. The results show that the accuracy of the three models in predicting bank loss customers is above 92%. Finally, based on the logistic regression model with better evaluation results, this paper analyses the characteristics of the lost customers for the bank, and gives the bank management suggestions for the lost customers.
  Keywords: bank customer; churn warning model; logistic regression; customer churn.
  
  
• Efficient deep convolutional model compression with an active stepwise pruning approach  
  by Shengsheng Wang, Chunshang Xing, Dong Liu 
  Abstract: Deep models are structurally tremendous and complex, thus making them hard to deploy on embedded hardware with restricted memory and computing power. Although the existing compression methods have pruned the deep models effectively, some issues exist in those methods, such as multiple iterations needed in the fine-tuning phase, difficulty in pruning granularity control, and numerous hyperparameters needed to set. In this paper, we propose an active stepwise pruning method of a logarithmic function which only needs to set three hyperparameters and a few epochs. We also propose a recovery strategy to repair the incorrect pruning thus ensuring the prediction accuracy of model. Pruning and repairing alternately constitute a cyclic process along with updating the weights in layers. Our method can prune the parameters of MobileNet, AlexNet, VGG-16 and ZFNet by a factor of 5.6
  Keywords: deep convolutional model; model compression; active stepwise pruning; parameter repairing; pruning intensity; logarithmic function.
  
  
• Forecasting yield curve of Chinese corporate bonds  
  by Maojun Zhang 
  Abstract: Forecasting the yield curve of corporate bonds is an important issue about the corporate bond pricing and its risk management. In this paper, the dynamic Nelson-Siegel model is used to fit the yield of the corporate bonds in China, and the AR model is used to forecast the yield curve. It is found that the Nelson-Siegel model fitting the yield of the corporate bonds with different credit ratings is notonly very effective but also can indicate the long-term, medium-term and short-term dynamic features of the yield curve. Moreover, the linear AR (1) model might be more suitable than the nonlinear AR(1) model.
  Keywords: corporate bonds; yield curve; Nelson-Siegel model; AR(1) model.
  
  
• Review on blockchain technology and its application to the simple analysis of intellectual property protection  
  by Wei Chen, Kun Zhou, Weidong Fang, Ke Wang, Fangming Bi, Biruk Assefa 
  Abstract: Blockchain is a widely used decentralised infrastructure. Blockchain technology has the decentration of network, the unforgeability of block data, etc. Therefore, blockchain technology has developed rapidly in recent years, and many organisations are involved. Applications are generally optimistic. This paper systematically introduces the background development status of blockchain, and analyses the operation mechanism, characteristics and possible application scenarios of blockchain technology from a technical perspective. Finally, the blockchain technology is applied to the intellectual property protection method as an example to study domestic and foreign examples and analyse existing problems. The review article aims to provide assistance for the application of blockchain technology.
  Keywords: blockchain; bitcoin; operating mechanism; intellectual property.
  
  
• An improved Sudoku-based data hiding scheme using greedy method  
  by Chin-Chen Chang, Guo-Dong Su, Chia-Chen Lin 
  Abstract: Inspired by Chang et al.s scheme, an improved Sudoku-based data hiding scheme is proposed here. The major idea of our improved scheme is to find the approximate optimal solution of Sudoku using the greedy method instead of through a brute-force search for an optimal solution. Later, the found approximate optimal solution of Sudoku is used to offer satisfactory visual stego-image quality with a lower execution time during the embedding procedure. Simulation results confirmed that the average stego-image quality is enhanced by around 90.51% compared with Hong et al.s scheme, with relatively less execution time compared with a brute-force search method.
  Keywords: data hiding; Sudoku; greedy method; brute-force search method; approximate optimal solution.
  
  
• A novel domain adaption approach for neural machine translation  
  by Jin Liu, Xiaohu Tian, Jin Wang, Arun Kumar Sangaiah 
  Abstract: Neural machine translation has been widely adopted in modern machine translation as it brings state-of-the-art performance to large-scale parallel corpora. For real-world applications, high-quality translation for text in a specific domain is crucial. However, the performance of general neural machine models drops when they are applied in a specific domain. To alleviate this issue, this paper presents a novel method of machine translation, which explores both model fusion algorithm and logarithmic linear interpolation. The method we propose can improve the performance of the in-domain translation model, while preserving or even improving the performance of the out-domain translation model. This paper has carried out extensive experiments on the proposed translation model using the public United Nations corpus. The BLEU (Bilingual Evaluation Understudy) score of the in-domain corpus and the out-domain corpus reaches 30.27 and 43.17, respectively, which shows a certain improvement over existing methods.
  Keywords: neural machine translation; model fusion; domain adaption.
  
  
• Formation path of customer engagement in virtual brand community based on back propagation neural network algorithm  
  by Lin Qiao, Mengmeng Song, Rob Law 
  Abstract: The formation path of customer engagement in a virtual brand community with customer engagement, which explores customers non-transactional behavior, has become increasingly popular in the marketing field. This paper introduces an approach that integrates structural equation modelling and back propagation artificial neural network to identify the motivating factors (interactivity, information quality, and convenience) that influence the perceived information value and social value of a virtual brand community and customer engagement. Our experiment shows that when perceived value plays a mediation role in the influence of interactivity and information quality on customer engagement, interactivity is positively associated with customer engagement in the virtual brand community. This study aims to provide meaningful implications to companies effective use of brand fan pages.
  Keywords: neural network algorithm; virtual brand community; brand fan page; perceived value.
  
  
• The mining method of trigger words for food nutrition matching  
  by Shunxiang Zhang 
  Abstract: The rational food nutrition matching plays a dual role in health and diet for humans. The trigger words related to food nutrition matching have an effect on classifying food nutrition matching into two types: reasonable nutrition matching and unreasonable nutrition matching. This paper proposes an aiming method of trigger words for food nutrition matching. First, food information frequency vector can be extracted by the number of food names, the number of nutrition ingredients and the number of matching effects in the sentence. By judging whether each component of food information frequency vector is 0 or not, the sentences unrelated to food nutrition matching can be filtered. Then, the two food verb-noun joint probability matrices can be constructed. The column of the first matrix is the food name, and the row is the verb. The column of the second matrix is the nutrition ingredient and matching effect, and the row is the verb. By comparing row mean value of the two matrices, whether the verb is a trigger word can be judged. Lastly, under the premise of commendatory and derogatory probabilities of the trigger word, the food nutrition matching can be classified as two types by naive Bayes. The experiments show that the proposed method effectively detects the trigger word related to food nutrition matching.
  Keywords: food nutrition matching; food information frequency vector; food verb-noun joint probability matrix.
  
  
• Estimating capacity-oriented availability in cloud systems  
  by Jamilson Dantas, Rubens Matos, Jean Teixeira, Eltton Túlio, Paulo Maciel 
  Abstract: Over the years, many companies have employed cloud computing to support their services and optimise their infrastructure usage. The provisioning of high availability and high processing capacity is a significant challenge when planning a cloud computing infrastructure. Even when the system is available, a part of the resources may not be offered owing to partial failures in just a few of the many components in an IaaS cloud. The dynamic behaviour of virtualised resources requires special attention to the effective amount of capacity that is available to users, so the system can be correctly sized. Therefore, the estimation of capacity-oriented availability (COA) is an important activity for cloud infrastructure providers to analyse the cost-benefit tradeoff among distinct architectures and deployment sizes. This paper presents a strategy to evaluate the COA of virtual machines in a private cloud infrastructure. The proposed strategy aims to provide an efficient and accurate computation of COA, by means of closed-form equations. We compare our approach with the use of models such as continuous time Markov chains, considering execution time and values of metrics obtained with both approaches.
  Keywords: capacity-oriented availability; closed-form equation; cloud computing; continuous time Markov chain.
  
  
• DDoS attack detection method based on network abnormal behaviour in big data environment  
  by Jing Chen, Xiangyan Tang, Jieren Cheng, Fengkai Wang, Ruomeng Xu 
  Abstract: Distributed denial of service (DDoS) attack is a rapidly growing problem with the fast development of the internet. The existing DDoS attack detection methods have time-delay and low detection rate. This paper presents a DDoS attack detection method based on network abnormal behaviour in a big data environment. Based on the characteristics of flood attack, the method filters the network flows to leave only the many-to-one network flows to reduce the interference from normal network flows and improve the detection accuracy. We define the network abnormal feature value (NAFV) to reflect the state changes of the old and new IP addresses of many-to-one network flows. Finally, the DDoS attack detection method based on NAFV real-time series is built to identify the abnormal network flow states caused by DDoS attacks. The experiments show that compared with similar methods, this method has higher detection rate, lower false alarm rate and lower missing rate.
  Keywords: DDoS; time series; ARIMA; big data; forecast.
  
  
• A parallel adaptive-resolution hydraulic flood inundation model for flood hazard mapping  
  by Wencong Lai, Abdul Khan 
  Abstract: There is a growing demand for improved high-resolution flood inundation modelling in large-scale watersheds for sustainable planning and management. In this work, a parallel adaptive-resolution hydraulic flood inundation model is proposed for large-scale unregulated rivers. This model used the public best available topographic data and streamflow statistics data from USGS. An adaptive triangular mesh is generated with fine resolution (~30 m) around streams and coarse resolution (~200 m) away from streams. The river flood-peak discharges are estimated using the regression equations from the National Streamflow Statistics (NSS) Program based on watershed and climate characteristics. The hydraulic simulation is performed using a discontinuous Galerkin solver for the 2D shallow-water flow equations. The hydraulic model is run in parallel with the global domain partitioned using the stream link and stream length. The proposed model is used to predict the flooding in the Muskingum River Basin and the Kentucky River Basin. The simulated inundation maps are compared with FEMA maps and evaluated using three statistical indices. The results demonstrated that the model is capable of predicting flooding maps for large-scale unregulated rivers with acceptable accuracy.
  Keywords: flood inundation; flood mapping; unregulated rivers.
  
  
• The clothing image classification algorithm based on the improved Xception model  
  by Zhuoyi Tan, Yuping Hu, Dongjun Luo, Man Hu, Kaihang Liu 
  Abstract: This paper proposes a clothing image classification algorithm based on the improved Xception model. Firstly, the last fully connected layer of the original network is replaced with another fully connected layer to recognise eight classes instead of 1000 classes. Secondly, the activation function we employ in our network adopts both Exponential Linear Unit (ELU) and Rectified Linear Unit (ReLU), which can improve the nonlinear and learning characteristics of the networks. Thirdly, in order to enhance the anti-disturbance capability of the network we employ the L2 regularisation method. Fourthly, we perform data augmentation on the training images to reduce over-fitting. Finally, the learning rate is set to zero in the layers of the first two modules of our network and the network is fine-tuned. The experimental results show that the top-1 accuracy by the algorithm proposed in this paper is 92.19%, which is better than the state-of-the-art models of Inception-v3, Inception-ResNet-v2 and Xception.
  Keywords: clothing image classification; transfer learning; deep convolutional neural network; Xception.
  
  
• A hidden Markov model to characterise motivation level in MOOCs learning  
  by Yuan Chen, Dongmei Han, Lihua Xia, Jin Xian 
  Abstract: A participants learning in massive open online courses (MOOCs) highly relies on motivation. However, how to characterise motivation level is an open question. This study establishes a hidden Markov model to characterise motivation level and examines the model on the data from MOOCs in China. The empirical results show that two motivation levels (high and low) are characterised. Based on the two motivation levels, further analysis reveals the differences in participants learning behaviours with respect to learning activity and continuous learning. The hidden Markov model proposed in this study contributes to the development of the theoretical ground of current MOOCs. It also has important operational implications for MOOCs.
  Keywords: massive open online courses; motivation Level; hidden Markov model; learning behaviours.
  
  
• A short text conversation generation model combining BERT and context attention mechanism  
  by Huan Zhao, Jian Lu, Jie Cao 
  Abstract: The standard Seq2Seq neural network model tends to generate general and safe responses (e.g., I dont know) regardless of the input in the field of short-text conversation generation. To address this problem, we propose a novel model that combines the standard Seq2Seq model with the BERT module (a pre-trained model) to improve the quality of responses. Specifically, the encoder of the model is divided into two parts: one is the standard seq2seq which generates a context attention vector; the other is the improved BERT module which encodes the input sentence into a semantic vector. Then through a fusion unit, the vectors generated by the two parts are fused to generate a new attention vector. Finally, the new attention vector is transmitted to the decoder. In particular, we describe two ways to acquire a new attention vector in the fusion unit. Empirical results from automatic and human evaluations demonstrate that our model improves the quality and diversity of the responses significantly.
  Keywords: Seq2Seq; short text conversation generation; BERT; attention mechanism; fusion unit.
  
  
• Implicit emotional tendency recognition based on disconnected recurrent neural networks  
  by Yiting Yan, Zhenghong Xiao, Zhenyu Xuan, Yangjia Ou 
  Abstract: Implicit sentiment orientation recognition classifies emotions. The development of the internet has diversified the information presented by text data. In most cases, text information is positive, negative, or neutral. However, the inaccurate participle, the lack of standard complete sentuation lexicon, and the negation of words bring difficulty in implicit emotional recognition. The text data also contain rich and fine-grained information and thus become a difficult research point in natural language processing. This study proposes a hierarchical disconnected recurrent neural network to overcome the problem of lack of emotional information in implicit sentiment sentence recognition. The network encodes the words and characters in the sentence by using the disconnected recurrent neural network and fuses the context information of the implicit sentiment sentence through the hierarchical structure. By using the context information, the capsule network is used to construct different fine-grained context information for extracting high-level feature information and provide additional semantic information for emotion recognition. This way improves the accuracy of implicit emotion recognition. Experimental results prove that the model is better than some current mainstream models. The F1 value reaches 81.5%, which is 2 to 3 percentage points higher than those of the current mainstream models.
  Keywords: hierarchical interrupted circulation network; implicit emotion; capsule network; sentiment orientation identification.
  
  
• Greedy algorithm for image quality optimisation based on turtle-shell steganography  
  by Guo-Hua Qiu, Chin-Feng Lee, Chin-Chen Chang 
  Abstract: Information hiding, also known as data hiding, is an emerging field that combines multiple theories and technologies. In recent years, Chang and Liu et al. have proposed new data-hiding schemes based on Sudoku, a turtle-shell, etc. These proposed schemes have their own advantages in terms of visual quality and embedded capacity. However, the reference matrices used in these schemes are not optimal. Based on the characteristics of these schemes, Jin et al. (2017) employed particle swarm optimisation to select the reference matrix and achieved approximately optimal results in reducing the distortion of the stego-image. However, the complexity is high. In this paper, a turtle-shell matrix optimisation scheme is proposed using a greedy algorithm. The experimental results show that our proposed greedy algorithm is better than the particle swarm optimisation scheme at finding a near-optimal matrix and achieving better stego-image quality, and it outperforms the particle swarm optimisation scheme in terms of computational amount and efficiency.
  Keywords: data hiding; turtle-shell steganography; particle swarm optimisation; greedy algorithm.
  
  
• Client-side ciphertext deduplication scheme with flexible access control  
  by Ying Xie, Guohua Tian, Haoran Yuan, Chong Jiang, Jianfeng Wang 
  Abstract: Data deduplication with fine-grained access control has been applied in practice to realise data sharing and reduce the storage space. However, many existing schemes can only achieve server-side deduplication, which greatly wastes the network bandwidth even when the data transmitted is particularly large. Moreover, few existing schemes consider attribute revocation, in which the forward and backward secrecy cannot be guaranteed. To address the above problems, in this paper, we introduce a client-side ciphertext deduplication scheme with more flexible access control. Specifically, we divide the data owner into different domains and distribute corresponding domain keys to them through the secure channel, achieving PoW verification in client-side deduplication. Besides, we realise attribute revocation through the proxy re-encryption technology, which cannot preset the maximum number of clients in system initialisation. Security and performance analysis shows that our scheme can achieve desired security requirements while realising the efficient client-side deduplication and attribute revocation.
  Keywords: client-side deduplication; flexible access control; attribute revocation; random tag.
  
  
• An efficient memetic algorithm using approximation scheme for solving nonlinear integer bilevel programming problems  
  by Yuhui Liu, Hecheng Li, Huafei Chen, Jing Huang 
  Abstract: Nonlinear integer bilevel programming problems (NIBLPPs) are a kind of mathematical model with hierarchical structure, which are known as strongly NP-hard problems. In general, it is extremely hard to solve this kind of problem because they are always non-convex and non-differentiable, especially when integer constraints are involved. In this paper, based on a simplified branch and bound method as well as interpolation technique, a memetic algorithm is developed to solve NIBLPPs. Firstly, the leaders variable values are taken as individuals in populations, for each individual in the initial population, a simplified branch and bound method is adopted to obtain the followers optimal solutions. Then, in order to reduce the computation cost in frequently solving followers problems for lots of offsprings generated in evolution, the interpolation method is applied to approximate the solutions to the followers problem for each individual in the population. In addition, among these approximated points, only potential better points can be chosen to endure further optimisation procedure, so as to obtain precise optimal solutions to followers problems. The simulation results show that the proposed memetic algorithm is efficient in dealing with NIBLPPs.
  Keywords: nonlinear integer bilevel programming problem; memetic algorithm; branch and bound method; interpolation function; optimal solutions.
  
  
• LMA: label-based multi-head attentive model for long-tail web service classification  
  by Guobing Zou, Hao Wu, Song Yang, Ming Jiang, Bofeng Zhang, Yanglan Gan 
  Abstract: With the rapid growth of web services, service classification is widely used to facilitate service discovery, selection, composition and recommendation. Although there is much research in service classification, rarely does work focus on the long-tail problem to improve the accuracy of those categories that have fewer services. In this paper, we propose a novel label-based attentive model LMA with the multi-head structure for long-tail service classification. It can learn the various word-label subspace attention with a multi-head mechanism, and concatenate them to get the high-level feature of services. To demonstrate the effectiveness of LMA, extensive experiments are conducted on 14,616 real-world services with 80 categories crawled from the service repository ProgrammableWeb. The results prove that the LMA outperforms state-of-the-art approaches for long-tail service classification in terms of multiple evaluation metrics.
  Keywords: service classification; service feature extraction; long tail; label embedding; attention.
  
  
• MESRG: multi-entity summarisation in RDF graph  
  by Ze Zheng, Xiangfeng Luo, Hao Wang 
  Abstract: Entity summarisation has drawn a lot attention in recent years. But there still exist some problems. Firstly, most of previous works focus on individual entity summarisation. Secondly, the external resources such as WordNet are frequently used to calculate the similarity between Resource Description Framework (RDF) triples. However, the neighbours with common properties may affect the summarisation of individual entity, and the external resources are not always available in practice. To solve above two problems, this paper focuses on multi-entity summarisation, which aims at selecting representative triples for entities in an RDF graph without external knowledge. A topic model based model Multi-Entity Summarisation in RDF Graph (MESRG) is proposed for multi-entity summarisation, which is capable of extracting informative and diverse summaries and involves a two-phase process: 1) to select more important RDF triples, we propose an improved topic model that ranks triples with probability values: this model takes account of the effects of the neighbour entities rather than individual entity by the probability distribution; 2) to select diverse RDF triples. We use a graph embedding method to calculate the similarity between triples and obtain top k distinctive triples. Experiments of our model with significant results on the benchmark datasets demonstrate the effectiveness.
  Keywords: multi-entity summarisation; RDF graph; data sharing; topic model; graph embedding.
  
  
• Synthetic data augmentation rules for maritime object detection  
  by Zeyu Chen, Xiangfeng Luo, Yan Sun 
  Abstract: The performance of deep neural networks for object detection depends on the amount of data. In the field of maritime object detection, the diversity of weather, target scale, position and orientation make real data acquisition hard and expensive. Recently, the generation of synthetic data is a new trend to enrich the training set. However, synthetic data might not improve the detection accuracy. Two problems remain unsolved: 1) what kind of data need to be augmented? 2) how to augment synthetic data? In this paper, we use knowledge-based rules to constrain the process of data augmentation and to seek effective synthetic samples. Herein, we propose two synthetic data augmentation rules: 1) what to augment depends on the gap between training and expiring data distribution; 2) the robustness and effectiveness of synthetic data depends on the proper proportion and domain randomisation. The experiments show that the average accuracy of boat classification increases 3% with our synthetic data in Pascal VOC test set.
  Keywords: data augmentation; synthetic data; object detection; synthetic data augmentation rules.
  
  
• A new transmission strategy to achieve energy balance and efficiency in wireless sensor network  
  by Yanli Wang, Yanyan Feng 
  Abstract: Energy balancing and energy efficiency are very important in prolonging network life. In wireless sensor networks, cooperative MIMO technology has become a research hotspot in recent years. The appropriate cooperative nodes can transmit data efficiently. Meanwhile, energy harvesting pays attention to the transmission process of network also. This paper presents a selection of cooperative nodes and cluster nodes, which offers energy balance. The node is not only the transmitter of information, but also the transmitter of energy. In addition, the paper focuses on how to obtain the optimal energy efficiency with proposed resource allocation algorithm. Simulation results show that cluster nodes and the cooperative nodes are more balanced, the selection algorithm realises the energy balance. The energy efficiency increases rapidly with the amount of transmitted power, and tends to be stable when it reaches 3 dBm. The cooperative MIMO technology is adopted to obtain higher network utility.
  Keywords: cooperative node; wireless sensor network; energy harvesting; energy balance; energy efficiency.
  
  
• Reversible data-hiding scheme based on the AMBTC compression technique and Huffman coding  
  by Ting-Ting Xia, Juan Lin, Chin-Chen Chang, Tzu-Chuen Lu 
  Abstract: This paper proposes a reversible data-hiding (RDH) method based on the absolute moment block truncation coding (AMBTC) compression technique and Huffman coding. First, AMBTC is used to compress the original grayscale image to obtain two quantisation levels and a bitmap of each block. Next, the bitmap of each block is converted into a decimal number to calculate the frequency of the decimal number. A user-defined threshold is used to classify the block as embeddable or not. If the frequency of the decimal number is larger than or equal to the threshold, the bitmap is embeddable and is then compressed by the Huffman coding technology. The scheme takes the redundancy of each block by using the Huffman code instead of the bitmap to embed secret information. Experimental results show that our proposed scheme has a better hiding payload than other methods, as well as an acceptable image visual quality.
  Keywords: reversible data hiding; AMBTC; Huffman coding; hiding capacity; image visual quality; PSNR; information security; compression domain.
  
  
• Image of plant disease segmentation model based on improved pulse-coupled neural network  
  by Xiaoyan Guo, Ming Zhang 
  Abstract: Image segmentation is a key step in feature extraction and disease recognition of plant disease images. To avoid subjectivity while using a pulse-coupled neural network(PCNN), which realises parameter configuration through artificial exploration to segment plant disease images, an improved image segmentation model called SFLA-PCNN is proposed in this paper. The shuffled frog-leaping algorithm (SFLA) is used to optimise the parameters (?, ?_?, and V_? ) of PCNN to improve PCNN performance. A series of plant disease images are taken as segmentation experiments, and the results reveal that SFLA-PCNN is more accurate than other methods mentioned in this paper and can extract lesion images from the background area effectively, providing a foundation for subsequent disease diagnosis.
  Keywords: shuffled frog leap algorithm; pulse-coupled neural network; plant disease.
  
  
• General process of big data analysis and visualisation  
  by HongZhang Lv, Guang Sun, WangDong Jiang, FengHua Li 
  Abstract: There are innumerable data generated on the internet every day, which is hardly effectively analysed by traditional means because of its capacities and complexities. Not only is this data huge, but it also has complex relationships between different kinds of datasets. In addition, if people want to know more information of the changes of data during the certain period of time, that means the time factor will be taken into consideration, which leads to the problem of analysing dynamic data. This kind of data is called big data. Under the circumstances of the big data era, a new process for dealing with this data should be conceived. This process contains five steps. Those are collecting data, cleaning data, storing data, analysing data and data's further analysing. The aim of this paper is to illustrate every step. The fourth and fifth steps will be introduced in detail.
  Keywords: big data; visualisation; analysis; process; graph.
  
  
• Dynamic multiple copies adaptive audit scheme based on DITS  
  by Xiaoxue Ma, Pengliang Shi 
  Abstract: This paper proposes a dynamic multi-copy adaptive audit scheme based on DITS. In order to achieve the correctness and completeness detection of multiple copies, the data blocks and the corresponding location index information are connected to generate a duplicate file. The audit process is divided into two parts, one is the third-party audits and the other is client audits. When auditing, in order to prevent collusion attacks as well to improve the audit accuracy, the third-party auditors apply the challenge-response mode to detect the data block labels, and the client audit applies the audit algorithm to retrieve the index information of the data blocks. Through theoretical analysis and experimental comparison, the scheme is more secure in verifying the integrity of dynamic data and the correctness of the multi-copy, which can effectively prevent the existing data threats.
  Keywords: cloud storage; dynamic auditing; multiple copies; integrity.
  
  
• Basins of attraction and critical curves for Newton-type methods in a phase equilibrium problem  
  by Gustavo Platt, Fran Lobato, Gustavo Libotte, Francisco Moura Neto 
  Abstract: Many engineering problems are described by systems of nonlinear equations, which may exhibit multiple solutions, in a challenging situation for root-finding algorithms. The existence of several solutions may give rise to complex basins of attraction for the solutions in the algorithms, with severe influence in their convergence behaviour. In this work, we explore the relationship of the basins of attractions with the critical curves (the locus of the singular points of the Jacobian of the system of equations) in a phase equilibrium problem in the plane with two solutions, namely the calculation of a double azeotrope in a binary mixture. The results indicate that the conjoint use of the basins of attraction and critical curves can be a useful tool to select the most suitable algorithm for a specific problem.
  Keywords: Newton's methods; basins of attraction; nonlinear systems; phase equilibrium.
  
  
• Design and implementation of food supply chain traceability system based on hyperledger fabric  
  by Kui Gao, Yang Liu, Heyang Xu, Tingting Han 
  Abstract: Food safety problems always cause widespread concerns and panic when food-related incidents occur around the globe. Establishing a credible food traceability system is an effective solution to this issue. Most existing blockchain-based traceability systems are not convincing because the traceability information stored on the chain is just coming from one single organisation. Without the upstream and downstream trading information of the supply chain, even blockchain-based systems with immutability and decentralised trustworthy advantages cannot guarantee accurate traceability for customers. In this paper, we establish a food supply chain traceability system called FSCTS which aggregates all the enterprises and organisations along the food supply chain to make deals and transactions on the blockchain. Through analysing the trading data associating the whole food circulation from production to consumption, reliable transaction-based traceability can be achieved to provide trusted food tracing. We implement the system on the base of hyperledger fabric and prove the effectiveness and superiority of FSCTS by conducting extensive comparison experiments with some similar traceability systems.
  Keywords: food safety; food traceability; food trading; food supply chain; blockchain; consortium blockchain; hyperledger fabric.
  
  
• Automatic recommendation of user interface examples for mobile app development  
  by Xiaohong Shi, Xiangping Chen, Rongsheng Rao, Kaiyuan Li, Zhensheng Xu, Jingzhong Zhang 
  Abstract: It is an efficient development practice for user interface (UI) developers to exploit some examples for their reference. We propose an approach for automatic recommendation of UI examples for mobile app development. We first introduce a search engine for UI components of mobile applications based on their descriptions, graphical views and source code. From the search results, an algorithm, density-based clustering with maximum intra-cluster distance (DBCMID), is proposed to automatically recommend examples. The comparison between the recommended examples using our approach and existing summarised examples shows that for 83.33% of summarised examples, there are completely/partly matched examples in our recommended results. In addition, 39 new valuable examples are found based on the search results of six queries.
  Keywords: user interface search; user interface development; example recommendation.
  
  
• A DDoS attack detection method based on SVM and K-nearest neighbour in SDN environment  
  by Zhaohui Ma, Bohong Li 
  Abstract: This paper presents a detection method for DDos attack in SDN based on k-nearest neighbour algorithm (KNN) and support vector machine (SVM). This method makes use of the characteristics of SDN centralised control, collects the flow characteristic information efficiently, classifies the flow, screens out the attack flow, and determines whether the system is attacked or not. Experiments show that the method has high accuracy.
  Keywords: software define network; controller; detecting method; DDos attack; KNN; SVM.
  
  
• The sentiments of open financial information, public mood and stock returns: an empirical study on Chinese Growth Enterprise Market  
  by Qingqing Chang 
  Abstract: This study links public mood to stock performance and examines the moderating role of co-occurring sentiments as expressed at open financial information platforms in this relationship. Drawing on the agenda-setting and source credibility theories, we developed hypotheses with the use of 345 stocks listed on the Chinese Growth Enterprise Market and data on public mood, and open financial information sentiments collected between 1 October, 2012 and 30 September, 2015. Our findings suggest that public mood has a significant, positive impact on stock returns; more interestingly, we found that public mood has a stronger positive impact on stock performance than open financial information sentiments. Furthermore, the study finds a positive interactive effect between public mood and open financial information sentiments, and determined that variation in public mood is a driving force with a market reaction, while the co-occurring open financial information sentiments amplifies the effect of public mood on stock returns.
  Keywords: sentiment analysis; public mood; open financial information sentiments.
  
  
• Digital watermarking for health-care: a survey of ECG watermarking methods in telemedicine  
  by Maria Rizzi, Matteo D'Aloia, Annalisa Longo 
  Abstract: Innovations in healthcare have introduced a radical change in the medical environment, including patient diagnostic data and patient biological signal facilities and processing. The adoption of telemedicine services usually leads to an incremental trend in transmission of electronic sensitive data over insecure infrastructures. Since integrity, authenticity and confidentiality are mandatory features in telemedicine, the need to guarantee these requirements with end-to-end control arises. Among the various techniques implemented for data security, digital watermarking has gained considerable popularity in healthcare oriented applications. The challenge the watermarking insertion has to overcome is to avoid changes in health and medical history of a patient to a level where a decision maker can make a misdiagnosis. This paper presents a survey of different applications of electrocardiogram watermarking for telemedicine. The most recent and significant electrocardiogram watermarking schemes are reviewed, various issues related to each approach are discussed, and some aspects of the adopted techniques, including classification and performance measures, are analysed.
  Keywords: watermarking; electrocardiogram; telemedicine; data security; healthcare; integrity verification; authentication; patient record hiding; smart health.
  
  
• Web services classification via combining Doc2vec and LINE model  
  by Hongfan Ye, Buqing Cao, Jinkun Geng, Yiping Wen 
  Abstract: With the rapid increasing of the number of web services, web service discovery is becoming a challenging task. Classifying web services with similar functionality from a tremendous amount of web services can improve the efficiency of service discovery significantly. The current web services classification researches mainly focus on the independent mining of the hidden content semantic information or network structure information in the web service characterisation documents, but few of them integrate the two sets of information comprehensively to achieve better classification performance. To this end, we propose a web service classification method that combines content semantic information and network construction information.
  Keywords: web services classification; content semantic; network structure; LINE; Doc2Vec.
  
  

Special Issue on: Cyberspace Security Protection, Risk Assessment and Management

• Intrusion detection approach for cloud computing based on improved fuzzy c-means clustering algorithm  
  by Xuchong Liu, Jiuchuan Lin, Xin Su, Yi Zheng 
  Abstract: Recently, cloud computing has become more and more important on the internet. Meanwhile, network attackers aim at this platform, and launch various of attacks to threaten the security of cloud computing. Some researchers have proposed fuzzy C-means clustering algorithm (FCM) to detect such attack. However, FCM contains some limitations, such as low detection accuracy, low precision, and slow convergence speed when detecting intrusions under the cloud computing scenario. In this paper, we propose an intrusion detection approach based on an objective function optimisation FCM algorithm. This approach uses kernel function to improve optimisation ability of FCM algorithm. Then, the proposed approach uses Lagrange multiplier approach to calculate cluster centre and membership matrix, which is able to optimise the objective function of the FCM algorithm and reduce algorithm complexity. The simulation experiment shows that our approach can achieve higher detection accuracy and precision in detecting intrusion into a cloud computing network, and has great advantages in performance of convergence.
  Keywords: cloud computing; intrusion detection; network attack; objective function optimization; Lagrange multiplier approach.