Cloud application programming interface (API) is a software intermediary that enables data exchange, business logic or functionality delivery between applications, infrastructures and IoT devices for supporting service oriented architecture. Currently, the number of cloud API in the Web is increasing and the number of cloud API with similar functionality is very large. Since quality of service (QoS)

An essential mesoscopic concept in network analysis is that of community structure. However, conventional nature-inspired optimization algorithms encounter serious challenges and difficulties when used directly to seek communities in networks, due to the large amount of data and the NP-hard combinatorial nature of the problem. Thus in this paper, we introduce a novel bacterial foraging optimization

In smart city, technologies such as blockchain, Internet of Things (IoT) and edge computing tend to be integrated that promotes horizontal information exchange and multi-party cooperation. Large amounts of superior services can be provided by analysing the information. However, lots of IoT devices generating the information do not have sufficient resources to resist malicious attacks so that the devices

The impact of IoT-ML in the healthcare sector is very significant and it has helped us to change our view at the traditional treatment methods. In IoT-ML–based healthcare applications, the sensing layer is responsible for collecting information from humans and transferring it to the storage layer through communication technology. ML is implemented to make intelligent decisions for healthcare applications

High-utility itemset mining has evolved as an essential and captivating research topic. It aims to extract the patterns/itemsets having high utility value; hence, they are called high utility itemsets (HUIs). From a business perspective, a utility can be the benefit associated with the sale of a particular item or the usefulness or satisfaction that a customer experiences from a product. The economic

Distributed scheduling problem, a novel model of intelligent manufacturing, urgently needs new scheduling methods to meet the dynamic market demand. The inverse scheduling in a distributed shop greatly impacts both its energy consumption and productivity. This paper proposes the energy-efficient distributed permutation flow-shop inverse scheduling problem to minimize adjustment and energy consumption

Fetal congenital heart disease (CHD) is a prevalent and highly complicated fetal deformity. Furthermore, the number of infants with CHD accounts for as high as 6%–8% among all the living newborns in China. For the early diagnosis and screening of the fetal CHD, echocardiography technology plays a vital role in assessing anatomical structures and functions of the fetal heart. However, the prenatal detection

An intelligent assistant (IA) is a computer system endowed with artificial intelligence and/or machine learning techniques capable of intelligently assisting people. IAs have gained in popularity over recent years due to their usefulness, significant commercial developments, and a myriad of scientific and technological advances resulting from research efforts by the computer science community. In particular

High-frequency water quality monitoring systems provide valuable measurements for predicting the trend of water quality, warning of abnormal activities or operating hydrological models. However, missing values are prevalent due to network miscommunication, device replacement or failure. Applying datasets with missing values can lead to biased results in statistical analysis or hydrological modelling

Automatically discovering message formats of unknown service or system protocols from network traces has become important for a variety of applications, such as emulating the behavior of an unknown protocol in service virtualization, or enabling deep packet inspection in network security. Among existing schemes, the keyword extraction based approaches have been shown to be effective. Inspired by the

Spotting fake news is a critical problem nowadays. Social media are responsible for propagating fake news. Fake news propagated over digital platforms generates confusion as well as induce biased perspectives in people. Detection of misinformation over the digital platform is essential to mitigate its adverse impact. Many approaches have been implemented in recent years. Despite the productive work

Corporate social responsibility (CSR) represents a form of international private business self-regulation that contributes to the social aims of a charitable, activist, or philanthropic nature through helping ethically based practices. The benefits from CSR like generating better public image, enhance in community support, enhance in market share, enhance in the satisfaction of the consumer, enhance

In this article, we address a fully fuzzy triangular linear fractional programming (FFLFP) problem under the condition that all the parameters and decision variables are characterized by triangular fuzzy numbers. Utilizing the computation of triangular fuzzy numbers and Lexicographic order (LO), the FFLFP problem is changed over to a multi-objective function. Consequently, the problem is changed into

Task space mapping approaches for bilateral teleoperation, namely object-centered ones, have yielded the most promising results. In this paper, we propose an invertible mapping approach to realize teleoperation through online motion mapping by taking into account the locations of objects or tools in manipulation skills. It is applied to bilateral teleoperation, with the goal of handling different

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This article mainly focuses on putting forward new fixed-time (FIXT) stability lemmas of delayed Filippov discontinuous systems (FDSs). By providing the new inequality conditions imposed on the Lyapunov-Krasovskii functions (LKF), novel FIXT stability lemmas are investigated with the help of inequality techniques. The new settling time is also given and its accuracy is improved in comparison with pioneer

Understanding the private car aggregation effect is conducive to a broad range of applications, from intelligent transportation management to urban planning. However, this work is challenging, especially on weekends, due to the inefficient representations of spatiotemporal features for such aggregation effect and the considerable randomness of private car mobility on weekends. In this article, we propose

The distributed, real-time algorithms for multiple pursuers cooperating to capture an evader are developed in an obstacle-free and an obstacle-cluttered environment, respectively. The developed algorithm is based on the idea of planning the control action within its safe, collision-free region for each robot. We initially present a greedy capturing strategy for an obstacle-free environment based on

The designers of a new coordination interface enacting complex workflows have to tackle a dichotomy: choosing a language-independent or language-dependent approach. Language-independent approaches decouple workflow models from the host code’s business logic and advocate portability. Language-dependent approaches foster flexibility and performance by adopting the same host language for business and

As cloud computing and Internet of Things (IoT) are becoming more and more deeply integrated, the new secure challenge for IoT data has attracted great attention. Privacy preserving keyword search, as one important secure technique, achieves search over encrypted cloud-based IoT data. Forward security and verifiability are two important security properties for privacy preserving keyword search for

This special issue was desired to foster the progress in research on the development of novel defense methods in information security, especially for sophisticated and networked/hyper-connected systems, including those within IoT and CPS scenarios.

Scientific workflows are a cornerstone of modern scientific computing. They are used to describe complex computational applications that require efficient and robust management of large volumes of data, which are typically stored/processed on heterogeneous, distributed resources. The workflow research and development community has employed a number of methods for the quantitative evaluation of existing

In 2015, the National Institute of Standards and Technology (NIST) announced Keccak as the new primitive to be used in SHA 3, not replacing but complementing SHA 2. The Keccak primitive, based on a sponge construction, has flexible parameters that can be controlled by the user to fit the needs of the application. However, the SHA 3 standard constrains and predefines the Keccak parameters to be used

In order to save energy consumption, improve network performance and prolong the life cycle of communication network, a reasonable relay selection algorithm for energy collection wireless sensor networks is proposed. Firstly, taking solar wireless sensor network as an example, the energy consumption and solar energy state of sensor nodes are mathematically analyzed. Then, the cooperation probability

Enterprises are striving to remain protected against malware-based cyber-attacks on their infrastructure, facilities, networks and systems. Static analysis is an effective approach to detect the malware, i.e., malicious Portable Executable (PE). It performs an in-depth analysis of PE files without executing, which is highly useful to minimize the risk of malicious PE contaminating the system. Yet,

Consortium blockchain has been widely used in different scenarios, where blockchain members demand that their uploaded data could be audited based on data range under their identities without exposing the data themselves. However, so far, current solutions only support simple (e.g., standard-range) privacy-preserving data auditing, and can not be used in multi-party scenarios. To address the problem

The process of integration through classification provides a unified representation of diverse data sources in Big data. The main challenges of big data analysis are due to the various granularities, irreconcilable data models, and multipart interdependencies between data content. Previously designed models were facing problems in integrating and analyzing big data due to highly complex and dynamic

Hate speech should be tackled and prosecuted based on how it is operationalized. However, the existing theoretical definitions of hate speech are not sufficiently fleshed out or easily operable. To overcome this inadequacy, and with the help of interdisciplinary experts, we propose an empirical definition of hate speech by providing a list of 10 hate speech indicators and the rationale behind them

The seed is an inevitable element for agricultural and industrial production. The non-destructive paddy seed variety identification is essential to assure paddy purity and quality. This research is aimed at developing a computer vision-based system to identify paddy varieties using multiple heterogeneous features, exploiting textural, external, and physical properties. We captured the paddy seed images

An elastic stream computing system is expected to process dynamic and volatile data streams with low latency and high throughput in timely manner. Effective management of stream application is considered one of the keys to achieve elastic computing by scaling in/out the workload of each computing node properly during runtime. Many existing work tried to build an elastic stream computing system from

Mobile edge computing, which enhances the computing power of mobile devices via computation offloading technology, has been proposed as a promising paradigm to alleviate the mismatch between limited computational resources and ever-increasing computational requirements. An appropriate offloading strategy must be identified to optimize the computation offloading performance. Moreover, a mobile user

In this article, an adaptive neural-network (NN) command-filtered output-feedback control strategy is proposed for a class of stochastic nonlinear systems (SNSs) with the actuator constraint. The problem of ``explosion of complexity'' existing in the conventional backstepping design procedure for SNSs is successfully resolved based on the command filter technique, and the error compensation mechanism

This article investigates the leader-following consensus problem of heterogeneous linear multiagent systems under switching and directed topologies. It is assumed that the communication between agents suffers from time-varying delays and only the neighboring agents of the leader are able to get access to the information of the leader agent, including its agent matrices. A key technical lemma on the

The exoskeleton is mainly used by subjects who suffer muscle injury to enhance motor ability in the daily life environment. Previous research seldom considers extending human collaboration skills to human-robot collaborations. In this article, two models, that is: 1) the following the better model and 2) the interpersonal goal integration model, are designed to facilitate the human-human collaborative

In this article, we focus on utilizing the idea of co-clustering algorithms to address the subspace clustering problem. In recent years, co-clustering methods have been developed greatly with many important applications, such as document clustering and gene expression analysis. Different from the traditional graph-based methods, co-clustering can utilize the bipartite graph to extract the duality relationship

Human-robot-collaboration requires robot to proactively and intelligently recognize the intention of human operator. Despite deep learning approaches have achieved certain results in performing feature learning and long-term temporal dependencies modeling, the motion prediction is still not desirable enough, which unavoidably compromises the accomplishment of tasks. Therefore, a hybrid recurrent neural

The study aims to conduct big data analysis (BDA) on the massive data generated in the smart city Internet of things (IoT), make the smart city change to the direction of fine governance and efficient and safe data processing. Aiming at the multi-source data collected in the smart city, the study introduces the deep learning (DL) algorithm while using BDA, and puts forward the distributed parallelism

To introduce blockchain technology to the mobile internet and Internet of Things, offloading proof-of-work tasks to edge computing nodes is considered an effective solution. Among these solutions, pricing edge computing resources through auctions has been widely accepted as a simple and efficient method. However, in these auctions, it is difficult to identify bid-rigging behavior by users, and the

Coflow scheduling can effectively improve the application performance and has been studied a lot in cooperative environments (e.g., private datacenter networks), where fairness is not the primary concern. In non-cooperative environments (e.g., multi-tenant datacenter networks), coflow scheduling should be strategy-proof; otherwise, some tenants could unfairly acquire more resources by cheating the

In digital forensics, file carving is the process of recovering files on a storage media in part or in whole without any file system information. An important problem in file carving is the identification of fragment types. Many fragment classification studies in the literature employ inflexible and indiscernible feature selection methods such as different statistics of byte frequency distributions

Privacy preserving methods supporting for data aggregating have attracted the attention of researchers in multidisciplinary fields. Among the advanced methods, differential privacy (DP) has become an influential privacy mechanism owing to its rigorous privacy guarantee and high data utility. But DP has no limitation on the bound of noise, leading to a low-level utility. Recently, researchers investigate

In data center networks (DCNs), flows with different objectives coexist and compete for limited network resources (such as bandwidth and buffer space). Without harmonious resource planning, chaotic competition among these flows would lead to severe performance degradation. Furthermore, low latency is critical for many emerging applications such as augmented reality(AR), virtual reality(VR) and telepresence

Sparse large-scale multi-objective optimization problems (LSMOPs) widely exist in real-world applications, which have the properties of involving a large number of decision variables and sparse Pareto optimal solutions, i.e., most decision variables of these solutions are zero. In recent years, sparse LSMOPs have attracted increasing attentions in the evolutionary computation community. However, all

To obtain more semantic information with small samples for medical image segmentation, this paper proposes a simple and efficient dual-rotation network (DR-Net) that strengthens the quality of both local and global feature maps. The key steps of the DR-Net algorithm are as follows (as shown in Fig. 1). First, the number of channels in each layer is divided into four equal portions. Then, different

In recent times, advanced developments in healthcare sector result in the generation of massive amounts of electronic health records (EHRs). EHR system enables the data owner to control his/her data and share it with designated people. The vast volume of data in the healthcare system makes it difficult for data to ensure security and diagnostic processes. To resolve these issues, this paper develops

Content-addressable memory (CAM) is a massively parallel searching device that returns the address of a given search input in one clock cycle. Field-programmable gate array (FPGA)-based CAMs are becoming popular due to their applications in the latest networking systems, e.g., software-defined networks (SDNs) leading to upcoming 5G networks. Ternary CAM (TCAM) implements a routing table in a network

Non-orthogonal multiple access (NOMA) as an enabling technology can be integrated with millimeter-Wave (mmWave) communication to achieve massive connectivity and better spectral efficiency in future wireless cellular networks. In this work, to reduce the system overhead, random beamforming is employed in the mmWave-NOMA networks where we optimized the power allocation factors to improve the random

In shared-control teleoperation, rather than directly executing a user’s input, a robot system assists the user via part of autonomy to reduce user’s workload and improve efficiency. Effective assistance is challenging task as it requires correctly inferring the user intent, including predicting the user goal from all possible candidates as well as inferring the user preferred movement in the next

Blockchain is an inspiring emerging technology that takes much attention from various researchers and companies. The technology offers various benefits such as data security, autonomy, immutability, transparency, and auditability. Hence, blockchain is getting large adoptions for various applications besides cryptocurrencies. Despite these benefits, scalability is a big challenge to blockchain impeding

The Network Function Virtualization (NFV) virtualizes the network appliances — such as routers and firewalls — with software running on commercial off-the-shelf servers. In NFV specification, Network Services (NS) are composed of multiple Virtual Network Functions (VNF) enabling elastic and finer lifecycle management operations such as scaling. For adapting resources to the VNF-based network services

The Integrated Circuit Metric (ICMetric) technology is a novel trust basis that uses the system features to create an identification of a device. The ICMetric of the device is used for the provision of security services, thereby addressing the issue of trust associated with device identity. The ICMetric technology can be adapted to function with varying environments; however, the short length and low

To accelerate searching an enjoyable holiday resort from massive-scale tourism spots on social media, the TR-DNNMF tourism spots recommendation system combining neural network and matrix decomposition is developed in this work. The model is built upon the architecture of the neural collaborative filtering model. It leverages the generalized matrix decomposition model coupled with the multi-layer neural

The electroencephalogram (EEG) has been commonly used to identify epileptic seizures, but identification of seizures from EEG remains a challenging task that requires qualified neurophysiologists. It is important to detect seizures in real time, which can be achieved in an internet of things (IoT)-based cloud platform to alert patients of impending seizures. Therefore, in this study, we propose a new

With the widespread diffusion of Massive Online Open Courses (MOOCs), educational recommender systems have become central tools to support students in their learning process. While most of the literature has focused on students and the learning opportunities that are offered to them, the teachers behind the recommended courses get a certain exposure when they appear in the final ranking. Underexposed