One of the fundamental problems when building perception systems for robot is to be able to provide semantic information as well as positioning in three‐dimensional (3D) space. However, two‐dimensional (2D) object detectors only can provide the semantic information and pixel coordinate in 2D space. While, the depth image can reflect the relative distance, and the semantic description of the object

As demand for intelligent manufacturing continues to grow, tiny object quality assessment (TOQA) is becoming increasingly importance in industrial automation. Recently, visual‐based TOQA has attracted an increasing attention, since the physical appearance is the foremost assessment index for evaluating the tiny object quality. It is exhausted and challenging to determine the quality of tiny object

A distributed denial of service (DDoS) attack is a significant threat to web‐based applications and hindering legitimate traffic (denies access to benign users) by overwhelming the victim system or its infrastructure (service, bandwidth, networking devices, etc.) with a large volume of attack traffic. It leads to a delay in responses or sometimes a crash victim system. Even a few moments of pause in

With the development of information technology, the Internet has become an important channel of public opinion for expressing public interests, emotion, and ideas. Public emergency usually spreads via network. Due to the temporal and spatial flexibility and the information amplification of network, the opinions from different regions and background are easy to be represented as network public opinion

Early stage of diagnosis of eye diseases through automatic analysis in the retinal image is the emerging technology in the area of retinopathy. Glaucoma is the primary reason for the loss of visibility in people around the world. The separation of the disc and the cup in the optic region is the technique used to identify glaucoma in the human retinal image. In this paper, superpixel segmentation, followed

The single‐channel blind source separation of paired carrier multiple access (PCMA) signal is a key technology in satellite communications. Due to the high‐order complexity of existing separation methods and the uncertainty of channel parameters, the processing of noncooperative PCMA signals with long memory remains a great challenge. In this article, the blind separation was solved as the joint channel

There is a growing demand for correct parallel programs, mainly due to nowadays availability of parallel architectures. Structural testing allows identifying defects by analyzing the internal structures of a program. However, communication and synchronization in parallel programs bring new challenges to the testing activity, such as nondeterminism. Message‐passing parallel programs require structural

Deep learning practices have a great impact in many areas. Big data and significant hardware developments are the main reasons behind deep learning success. Recent advances in deep learning have led to significant improvements in text analysis and classification. Progress in the quality of word representation is an important factor among these improvements. In this study, we aimed to develop word2vec

Three‐dimensional Network‐on‐Chip (3D NoC) is a promising solution for solving 2D NoC problems while optimizing the system's performance. Mapping applications in 3D NoC is a crucial step as it has a significant impact on overall system performance. Moreover, multi‐task supported processing elements (PEs) are needed to run multiple applications and provide more scalability. Most of the existing 3D mapping

With the increase of the search for computational models where the expression of parallelism occurs naturally, some paradigms arise as options for the current generation of computers. In this context, dynamic dataflow and Gamma—General Abstract Model for Multiset mAnipulation—emerge as interesting computational model choices. In dynamic dataflow model, operations are performed as soon as their associated

In the big data era, for science gateways (SG) and cyberinfrastructure (CI) projects to have the greatest impacts, they need to be widely adopted in the scientific community. However, diffusion activities, or activities aimed to spread SG/CI in the science, technology, engineering, and mathematics community, are often an afterthought in projects. We warn against the fallacy of “If You Build It, They

In recent years, with the rapid development of mobile social networks and services, the research of mobile malicious webpage detection has become a hot topic. Most of the existing malicious webpage detection systems are deployed on desktop systems and servers. Due to the limitation of network transmission delay and computing resources, these existing solutions fail to provide the real‐time and lightweight

Cloud computing provides a set of resources and services for customers on the Internet on demand and based on a pay as you go model. Cloud providers are looking to decrease costs and increase profits. Therefore, resource management and provisioning are very important for cloud providers. Automated scaling can be used to provide resources for user requests. Auto‐scaling can decrease the total operational

Concurrent search trees (STs) are among the most widely used data structures to store and retrieve data in contemporary multithreaded applications. Despite the high amount of prior work, it still remains challenging to implement highly efficient concurrent STs. This is mainly due to the fact that both traditional synchronization methods (i.e., locks and atomic operations) and more novel ones (i.e.

Cloud computing (CC) allows on‐demand networks to access central computer resources, such as servers, databases, storage, and network services. While clouds can handle enormous amounts of data, they still encounter problems due to insufficient cloud resources. Therefore, another computing model, called fog computing, was introduced. However, the inefficient scheduling of user tasks in fog computing

In this article we present an application of the Usage Control paradigm to a Smart Home infrastructure, based on a model extension and structured use of obligations. In the proposed extended model obligations are exploited to enforce two different access revocation time, namely revoke and suspend. This increases the policy expressiveness and enable to optimize the resource usage. Furthermore, obligations

An emerging cloud‐Internet of Things (IoT) is a novel paradigm that brings potential benefits over a variety of applications. The pervasive use of IoT devices often struggle to meet resource requirements in cloud environment. The abundant wastage or inappropriate usage of resources leads to consume larger amount of energy and delayed response. Optimization algorithms are more popular for optimal selection

In order to solve the existing problems of time‐interleaved data acquisition system's poor scalability, limited acquisition channels, and complicated clock system based on System on Chip(SoC), this work presents a novel method of high‐speed data acquisition based on Network on Chip (NoC) communication architecture and time‐interleaved principle. Six analog‐to‐digital data acquisition resource nodes

This study proposes a method for analyzing the level of student engagement in a non‐face‐to‐face learning situation by processing facial expressions. In previous studies, the level of learning engagement was determined through biosignals, such as galvanic skin response feedback. The method proposed in this study assesses engagement levels in an individual education situation by processing emotions

A convolutional neural network‐regional long Short‐Term memory (CNN‐RLSTM) is proposed, which is a convolutional neural network‐regional long short‐term memory (CNN‐RLSTM) that combines CNN and regional LSTM. The model can effectively distinguish the affective polarity of different targets through a regional LSTM while reducing the training time of the model. In addition, the model can retain the sentiment

As the demand for cloud computing infrastructure increased, availability and reliability have become more important as they are major features in real‐time computing systems. The chances of failure in a cloud computing system increase compared to other computing systems. To achieve high reliability and availability for the cloud computing infrastructure fault tolerance is one of the most essential

Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In

Attribute‐based signature (ABS) is a widely used cryptographic primitive, that provides both data integrity and fine‐grained privacy protection. However, the signing overhead of ABS is usually too large, making it unsuitable for resource‐constrained devices. Using cloud computing technology, the notion of outsourced attribute‐based signatures (OABS) was introduced to overcome this drawback. In OABS

Distributed Stream Processing Systems (DSPS) are very popular to process unbounded data streams in real‐time. Low processing latency is a fundamental requirement for DSPS applications to maintain the real‐time response. This requirement of low processing latency for DSPS is badly affected due to inevitable failures in computing systems. Generally, DSPS grapple with these inevitable failures by triggering

Early and accurate assessment of plant disease severity is key to preventing disease attack. Traditional detection methods rely on manual vision to distinguish between types of disease infection, but this is time consuming, laborious and inaccurate. To address this problem, this paper proposes a deep learning‐based attentional network model (SEV‐Net) for plant disease severity identification and classification

Scientific publications from a group or consortium often form a coherent larger body of work with underlying threads and relationships. Rich social, structural, and topical networks between authors and organizations can be identified, and to convey these we have created the publicly available “Science Library” as a user‐centric, interactive portal. A key consideration in this endeavor is rapid and

Autonomous obstacle avoidance control of unmanned surface vessels (USVs) in complex marine environments is always fundamental for its scientific search and detection. Traditional methods usually model USV motion and environments in a mathematical way that needs perceptual information. Unfortunately, it is difficult to provide sufficient perceptual information due to complex marine environments, resulting

Witnessing broad energy‐critical applications of barrier coverage in mobile and wireless sensor networks, emerging practical applications have recently brought a new barrier coverage model which uses sink‐based mobile sensors for covering a given barrier with the aim of prolonging the lifespan of the coverage. In the model, a set of sink stations were distributed on the plane in which each sink can

In the new millennium, a myriad of large‐scale applications (e.g., social networks, ecommerce, internet of things, and scientific experiments) often generate large volumes of data. Given their volumes, their heterogeneous and distributed nature, the management of such data constitutes a challenge for distributed systems, particularly cloud computing. In this regard, data replication is a well‐known

Indoor smartphone positioning is one of the key information and cummunication technology techniques enabling new opportunities for indoor navigation and mobile location‐based services to enrich our everyday lives. Generally, the development of an indoor positioning system heavily relies on wireless sensor network. Since wireless sensors can estimate the probable distance between radio source and the

This survey aims to investigate, analyze, and compare the state‐of‐the‐art chatbots' feasibility and defects for psychotherapy. The survey points out a series of tasks necessary for future psychotherapy chatbots. We searched about 1200 related literature in public databases and selected five typical and state‐of‐the‐art psychotherapy chatbots. Most of the state‐of‐the‐art psychotherapy chatbots use

Event evolutionary graph (EEG) reflects sequential and causal relations between events, which is of great value for event prediction. However, lacking event context in the EEG raises the problems of direction uncertainty and low accuracy when making predictions. In this article, we propose a conditional event evolutionary graph (CEEG) to deal with these problems. CEEG extends EEG with an additional

Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic

Secret sharing schemes are being widely used to distribute a secret between various participants so that an authorized subset of participants belonging to appropriate access structures can reconstruct this secret. However, a dealer might get corrupted by adversaries and may influence this secret sharing or the reconstruction process. Verifiable secret sharing (VSS) overcomes this issue by adding a

With the advanced development of smart devices and network technique, Internet of Things has seen a large number of popular applications, among which, smart agriculture is a good example. The sensor nodes collect some parameters in the greenhouse, and send them to the control center. Then the control center can conduct some operations according to the analysis of the collected parameters. In this paper

Better duplicate elimination performance causes higher fragmentation which leads to degraded restore performance. As a result, restore performance needs to be optimized either through strengthening locality by selective rewriting and/or making the best use of the limited available memory through cache optimization. In this paper, we explore SACRO, SSD Assitsted Chunk Caching for Restore Optimization

This special issue of Concurrency and Computation: Practice and Experience contains revised and extended versions of selected papers presented at the 13th International Conference on Parallel Processing and Applied Mathematics, PPAM 2019, which was held on September 8–11, 2019 in Bialystok, Poland. PPAM 2019 was organized by the Department of Computer and Information Science of the Czestochowa University

Real‐time individuals' destination prediction is of great significance for real‐time user tracking, service recommendation and other related applications. Traditional technology mainly used statistical methods based on the travel patterns mined from personal history travel data. However, it is not clear how to predict the destinations of individuals with only limited personal historical data. In this

Face Recognition is a challenging task for recognizing and detecting the identity of an individual. Although, plethora of work has already been done in the field of pattern recognition still there has been lot which has not been addressed in any of the literature. In the current research, we have presented a comparative analysis using three popularly known techniques for face recognition namely, Principal

Around the new four‐wheel independent steering system, the adjustment principle of Akman angle is studied in this article. In order to improve steering stability and reduce tire wear, a new algorithm for optimal Ackerman angle allocation at full speed is proposed. Furthermore, combined with the minimum load rule, the four‐wheel rotation angle and driving force are optimized. In this process, Carsim

The classification of file fragments of various file formats is an essential task in various applications such as firewalls, intrusion detection systems, antiviruses, web content filtering, and digital forensics. However, the community lacks a suitable software tool that can integrate major methods for feature extraction from file fragments and classification among various file formats. In this article

Imaging systems with different imaging sensors are widely applied to surveillance field, military field, and medicine field. Particularly, infrared imaging sensors can acquire thermal radiations emitted by different objects but lack textural details, and visible imaging sensors can capture abundant textural information but suffer from loss of scene information under poor weather conditions. The fusion

The traditional support vector machine technique (SVM) has drawbacks in dealing with imbalanced data. To address this issue, in this paper we propose an algorithm of improved clustering with stratified sampling technique (CSS) to improve the classification performance of SVMs on imbalanced datasets. Instead of applying a single type of sampling method as used in the literature, our algorithm treats

The product modularity has an important influence on the whole product life cycle. In order to improve product recovery of a used product at the end‐of‐life stage, it is imperative to develop modular product architecture with remanufacturing strategies consideration. In this paper, an integrated modular design method for green remanufacturing considering hierarchical structure of the product and fuzzy

Reference architectures for big data and machine learning include not only interconnected building blocks but important considerations (among others) for scalability, manageability and usability issues as well. Leveraging on such reference architectures, the automated deployment of distributed toolsets and frameworks on various clouds is still challenging due to the diversity of technologies and protocols

Two‐parameter estimators have increasing usage in the linear regression model concerning mitigating the problem of multicollinearity. In this type of biased estimators, two different parameters contribute to the solution of two different problems. Previously defined two‐parameter ridge estimator (TPRE) assures considerable merits in this context. This estimator eliminates unfavorable effects of multicollinearity

Wireless sensor networks (WSNs) are important building blocks of the communication infrastructure in smart cities, intelligent transportation systems, Industry, Energy, and Agriculture 4.0, the Internet of Things, and other areas quickly adopting the concepts of fog and edge computing. Their cybernetic security is a major issue and efficient methods to improve their safety and reliability are required

Face images from different perspectives reduce the accuracy of face recognition, and the generation of frontal face images is an important research topic in the field of face recognition. To understand the development of frontal face generation models and grasp the current research hotspots and trends, existing methods based on 3D models, deep learning, and hybrid models are summarized, and the current

Blockchain technologies make agreement among untrusted parties possible, without the need for certification authorities. Proposed frameworks have been put forward in sector as diverse as finance, health care, notary, intellectual property management, identity, provenance, international cooperation, social good, and security to cite but a few. Smart contracts, that is, self‐enforcing agreements in terms

In this article, we propose a new criterion for a switching algorithm to be used in MIMO‐OFDM systems. The switching algorithm, which selects between spatial multiplexing and MIMO diversity using the Demmel condition number as selection metrics, is first analyzed. Then, we show the limitation of this criterion, when increasing the receive antenna number. As a solution, we propose multiantenna processing

According to the different situation electromagnetic environment, the multiplatform collaborative detection task is planned under the condition of meeting the requirements of multiplatform performance constraints and detection and positioning accuracy. In this article, the 6‐tuples of multiple‐platform detection attributes is defined, and then a multiple‐platform cooperative detection resource scheduling

As a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real‐world conditions, hence human perception is the key indicator

Web directories like Wikipedia and Open Directory Mozilla facilitate efficient information retrieval (IR) of web documents from a huge web corpus. Maintenance of these web directories is understandably a difficult task that requires manual curation by human editors or semi‐automated mechanisms. Research on parallel algorithms for the automated curation of these web directories will be beneficial to

Sparse multinomial logistic regression (SMLR) is widely used in image classification and text classification due to its feature selection and probabilistic output. However, the traditional SMLR algorithm cannot satisfy the memory and time needs of big data, which makes it necessary to propose a new distributed solution algorithm. The existing distributed SMLR algorithm has some shortcomings in network

In view of the stability and reliability of energy supply, distinct from the time‐varying and uncertainty of energy harvesting systems, adopting mobile vehicles to replenish energy of sensors has become a research hotspot. While some existing studies on the mobile recharging problem ignored the limited energy capacity carried by mobile vehicle and the difference in energy consumption rates of sensors

We investigate fault‐tolerant shortest path problem in the hypercube between two nodes where some nodes are faulty (or blocked) and thus cannot be used in routing. Previously, several similar problems were studied where proposed algorithms are distributed and local‐information‐based, that is, each node in the network knows only its neighbor's status (faulty or not) and they also look for optimal or

Word embedding models become an increasingly important method that embeds words into a high dimensional space. These models have been widely utilized to extract semantic and syntactic features for sentiment analysis. However, using word embedding models cannot be sufficient for sentiment analysis tasks because they do not contain sentiment features. Therefore, word embedding models do not adequately

Distributed online social networks (DOSNs) were first proposed to solve the problem of privacy, security, and scalability. A significant amount of research was undertaken to offer viable DOSN solutions that were capable of competing with the existing centralized OSN applications such as Facebook, LinkedIn, and Instagram. This research led to the emergence of the use of peer‐to‐peer (P2P) networks as

Community detection on high‐mixed networks has been a challenging problem for complex network researchers. In a Lancichinetti–Fortunato–Radicchi (LFR) network with a mixing parameter mu greater than or equal to 0.5, the quality of the communities partitioned by currently available algorithms will decrease rapidly with increasing mu. To address this issue, we propose a label propagation algorithm on

Wireless Body Area Networks (WBAN) is a network of sensor devices that are connected together located in the clothes, on the body or underneath a human's skin to monitor patient's health continuously and communicate with required resources. Recently, WBAN offers many applications like remote health monitoring, sports, military, healthcare, and so forth. In healthcare, it improves the patient's life