As a generalization of vertex connectivity, for connected graphs |$G$| and |$T$|⁠, the |$T$|-structure connectivity |$\kappa (G; T)$| (resp. |$T$|-substructure connectivity |$\kappa ^{s}(G; T)$|⁠) of |$G$| is the minimum cardinality of a set of subgraphs |$F$| of |$G$| that each is isomorphic to |$T$| (resp. to a connected subgraph of |$T$|⁠) so that |$G-F$| is disconnected. For |$n$|-dimensional hypercube

AbstractMany disciplines have adopted component-based principles to avail themselves of the many advantages they bring, especially component reusability. In a short time, the component-based architecture became a renown branch in the IT world and the center of interest of many researchers. Much work has been conducted in this context for the verification of component-based applications (CBAs). However

According to the World Alzheimer Report 2015, 46 million people are living with dementia in the world. The diagnosis of diseases helps doctors treating patients better. One of the signs of diseases is related to white matter, grey matter and cerebrospinal fluid. Therefore, the automatic segmentation of three tissues in brain imaging especially from magnetic resonance imaging (MRI) plays an important

AbstractEdge centrality has found wide applications in various aspects. Many edge centrality metrics have been proposed, but the crucial issue that how good the discriminating power of a metric is, with respect to other measures, is still open. In this paper, we address the question about the benchmark of the discriminating power of edge centrality metrics. We first use the automorphism concept to

Realizability is an important requirement for reactive system specifications. A reactive system interacts with an environment and responds to input events. Realizability ensures the reactive system behaves as specified, no matter how its environment provides input. Typically, reactive system specifications are given using linear temporal logic (LTL), and they can be tested to ascertain whether the

Twitter led a remarkable breakthrough in information sharing on online social media. The eminent technology can propagate a piece of rumor to a large community of people in a short period. The timely detection of rumor tweets in social media curtails panic among the public during critical situations. Traditional machine learning techniques are not capable of categorizing rumor information effectively

With the popularity of multimedia applications and social networks, various multimedia data (i.e., texts, images, and videos) on the internet have shown exponential growth [1, 5, 6]. By regarding the storage cost and the computation efficiency, it is becoming more and more popular for data owners to employ cloud services [2, 3]. However, the data owners afraid of cloud services to reveal their private

AbstractThis paper proposed a novel precise point set registration method based on feature fusion for three-dimensional data. Firstly, for the prominent foreground with dense and continuous cluster structure, we propose an automatic extraction method combining the principal component analysis projection and density-based clustering method. Secondly, for point sets containing noises, we introduce correntropy

AbstractAspect sentiment classification is an important research topic in natural language processing and computational linguistics, assisting in automatically review analysis and emotional tendency judgement. Different from extant methods that focus on text sequence representations, this paper presents a network framework to learn representation from concurrence-words relation graph (LRCWG), so as

Non-negative matrix factorization (NMF) is a powerful tool for data science researchers, and it has been successfully applied to data mining and machine learning community, due to its advantages such as simple form, good interpretability and less storage space. In this paper, we give a detailed survey on existing NMF methods, including a comprehensive analysis of their design principles, characteristics

AbstractThe task of question generation (QG) aims to create valid questions and correlated answers from the given text. Despite the neural QG approaches have achieved promising results, they are typically developed for languages with rich annotated training data. Because of the high annotation cost, it is difficult to deploy to other low-resource languages. Besides, different samples have their own

The use of fingerprint continues to increase constantly with the propagation of biometric authentication technologies. Fingerprint is today the most widely used biometric modality for human verification and identification, due to its practical use and its discriminative structure that allows to different identities to be easily distinguished. Unfortunately, this emergence has been accompanied by a

AbstractFlush+Reload is a powerful access-driven cache attack in which the attacker leverages a security weakness in the X86 processor architecture to extract the private data of the victim. This attack can be mounted in a cross-core setting, where the memory deduplication is enabled and several users are sharing the same physical machine. In this paper, for the first time, we demonstrate that SEED

AbstractModern 5G networks promise more bandwidth, less delay and more flexibility for an ever increasing number of users and applications, with Software Defined Networking, Network Function Virtualization and Network Slicing as key enablers. Within that context, efficiently provisioning the network and cloud resources of a wide variety of applications with dynamic user demand is a real challenge.

AbstractCounter-cryptanalysis uses cryptanalytic techniques to detect cryptanalytic attacks. It was introduced by Stevens with a collision detection algorithm that detects whether a message is one of a colliding message pair constructed using a collision attack. Later, Stevens and Shumow improved the collision detection against SHA-1 by using unavoidable conditions. However, there are no results improving

Traditional link prediction methods of social network are vulnerable to the influence of network structure and have poor generalization, and only on a small number of networks and evaluation indicators. To improve the stability and accuracy of link prediction, this paper assembles 15 similarity indexes, introduces the idea of stacking into the link prediction of complex networks, and presents a link

Recently, unmanned aerial vehicles (UAVs) have emerged to enhance data processing, network monitoring, disaster management and other useful applications in many different networks. Due to their flexibility, cost efficiency and powerful capabilities, combining these UAVs with the existing wireless sensor networks (WSNs) could improve network performance and enhance the network lifetime in such networks

Today, machine learning plays a major role in different branches of the healthcare industry, from prognosis and diagnosis to drug development providing a significant perspective on the medical landscape for disease prevention or treatment and the improvement of human life. Recently, the use of deep neural networks in different machine learning applications has shown a great contribution to the improvement

Crowdsourcing has become increasingly popular in recent years. In order to achieve the optimal task allocation, one of the most important issues is to select more suitable crowdworkers. By leveraging its pervasiveness, social network can be employed as a novel worker recruitment platform. A robust task allocation scheme over the social network could also consider the word-of-mouth (WoM) mode, in which

AbstractPlants’ need for water has become a topic of research for the agriculture industry. The fact that plant species are very diverse and each plant’s need for water varies makes it difficult to plan programs with conventional irrigation methods. Plants exhibit different stages from their seed time to harvest season. Each stage is defined within as days, and the amount of water needed by the plant

AbstractCollaborative filtering (CF) is a well-known and eminent recommendation technique to predict the preference of new users by revealing the structures of historical records of the examined users. Even though CF is effectively adapted in several commercial areas, many limitations still exist, particularly in the sparsity of rating data that raises many issues. This paper devises a novel deep learning

AbstractThere are several Contact Tracing solutions since the outbreak of SARS COVID-19. All these solutions are localized—specific to a country. The Apps supported by these solutions do not interwork with each other. There are no standards to the proximity data collected by these Apps. Once the international travel restrictions are relaxed, this will become an issue. This paper explores this issue

Autism is difficult to recognize in young ones before twenty-four months of age. The symptoms of autism usually occur between twelve to eighteen months. If autism is detected before eighteen months, suitable treatment could be offered to the children, by which normal functioning of the brain could be attained. However, till now, there were no advanced facilities for detecting autism in children on

The internet of things (IoT) is one of the most rapidly growing technologies. Therefore, the interest in industry and academia has been increasing. The published research data have evolved in IoT because of scientific advances in this field. Since science plays a vital role in decision-making, this study examines the thematic landscape of research on IoT, which may contribute to understanding the research

Data owners often encrypt their bulk data and upload it to cloud in order to save storage while protecting privacy of their data at the same time. A data owner can allow a third-party entity to decrypt and access her data. However, if that entity wants to modify the data and publish the same in an authenticated way, she has to ask the owner for a signature on the modified data. This incurs substantial

AbstractWith the advent of machine learning, numerous approaches have been proposed to forecast stock prices. Various models have been developed to date such as Recurrent Neural Networks, Long Short-Term Memory, Convolutional Neural Network sliding window, etc., but were not accurate enough. Here, the aim is to predict the price of a stock and compare the results obtained using three major algorithms

Feature selection, a combinatorial optimization problem, remains broadly applied in the area of Computational Learning with the aim to construct a model with reduced features so as to improve the performance of the model. Feature selection algorithm aims to identify admissible subgroup of features without sacrificing the accuracy of the model. This research works uses Improved Binary Particle Swarm

Pop-stacks are variants of stacks that were introduced by Avis and Newborn in 1981. Coincidentally, a 1982 result of Unger implies that every permutation of length |$n$| can be sorted by |$n-1$| passes through a deterministic pop-stack. We give a new proof of this result inspired by Knuth’s zero-one principle.

Industrial IoT (IIoT) era is evolving rapidly in parallel to the progress in Industry 4.0, which leads factories to increase the engagement with external parties through different communication infrastructures. This brings a larger attack surface and requires the development of new security solutions suitable for IIoT systems. Authentication is a key enabler to prevent the attacks that come from the

In this work, we investigate how the choice of the key-expansion algorithm and its interaction with the round function affect the resistance of Simon-like ciphers against rotational-XOR cryptanalysis. We observe that, among the key-expansion algorithms we consider, Simon is most resistant, while Simeck is much less so. Implications on lightweight ciphers design are discussed and open questions are

AbstractReliability evaluation of interconnection networks is of significant importance to the design and maintenance of interconnection networks. The extra connectivity is an important parameter for the reliability evaluation of interconnection networks and is a generalization of the traditional connectivity. Let $g\geq 0$ be an integer and $G$ be a connected graph; the $g$-extra connectivity of $G$

AbstractOpinion mining from medical forums such as health check-ups is sparking growing interest and a stimulating area for natural language processing. This allows for a better understanding of patient health status and drug reactions while generating new knowledge for health care professionals and drug manufacturers, which helps improve the quality of service and produce more effective treatments

Facial expression recognition (FER) is the process of identifying human expressions. People vary in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively important research area. Various works have been conducted on automating the recognition of facial expressions. The main intent of this paper is to plan for the FER model with

AbstractDue to the rapid increase in the exchange of text information via internet networks, the security and the reliability of digital content have become a major research issue. The main challenges faced by researchers are authentication, integrity verification, and tampering detection of the digital contents. In this paper, a Robust English Text Watermarking and Natural Language Processing Approach

AbstractVirtual network embedding (VNE) algorithms dominate the effectiveness of resource sharing in network virtualization. Heuristic embedding algorithms generally make embedding decisions by artificially specified strategies, in which the node importance is measured by simply summing or multiplying several node attributes. However, the contributions of different attributes may be combined through

Owing to scientific development, a variety of challenges present in the field of information retrieval. These challenges are because of the increased usage of large volumes of data. These huge amounts of data are presented from large-scale distributed networks. Centralization of these data to carry out analysis is tricky. There exists a requirement for novel text document clustering algorithms, which

The use of cloud computing and mobile devices is increasing in healthcare service delivery primarily because of the huge storage capacity of cloud, the heterogeneous structure of health data and the user-friendly interfaces on mobile devices. We propose a healthcare delivery scheme where a large knowledge base is stored in the cloud and user responses from mobile devices are input to this knowledge

AbstractThe release of millions of financial documents, which has been known as the ‘WikiLeaks’ of the financial world (a.k.a. ‘Panama Papers’), has dragged global attention in how highly structured means applied by some of the elite to conceal their financial assets. Consequently, significant financial corruption allegations were raised. We concentrate on a somewhat overlooked region, the Middle East

AbstractMany graph products have been applied to generate complex networks with striking properties observed in real-world systems. In this paper, we propose a simple generative model for simplicial networks by iteratively using edge corona product. We present a comprehensive analysis of the structural properties of the network model, including degree distribution, diameter, clustering coefficient

The enormous increase in the number of Internet of Things data sources (sensors, personal devices and embedded systems) has given rise to a huge amount of unnecessary and redundant data being sent to the cloud. This makes the task of processing and storing this volume of information a very hard one. Therefore, data pre-processing and filtering closer to data sources, such as in fog computing, is necessary

Parameterized complexity is an exemplary approach that extracts and exploits the power of the hidden structures of input instances to solve hard problems. The tree-width (⁠|$tw$|⁠), path-width (⁠|$pathw$|⁠), branch-width (⁠|$bw$|⁠), clique-width (⁠|$cw$|⁠), rank-width (⁠|$rw$|⁠) and boolean-width (⁠|$boolw$|⁠) are some width measures of graphs that are used as parameters. Applications of these width

AbstractA redactable consortium blockchain (RCB) can build a trust layer for industrial internet of things (IIoT) so as to enable IIoT to resist certain powerful attacks resulting in improper block content. The redactability is particularly important for blockchains applied in IIoT with valuable or sensitive activities such as financial IoT or energy-trading IoT. Huang et al. proposed a threshold chameleon

AbstractIdentifying influential nodes is a fundamental and open issue in analysis of the complex networks. The measurement of the spreading capabilities of nodes is an attractive challenge in this field. Node centrality is one of the most popular methods used to identify the influential nodes, which includes the degree centrality (DC), betweenness centrality (BC) and closeness centrality (CC). The

Since graph learning could preserve the structure information of the samples to improve the learning ability, it has been widely applied in both shallow learning and deep learning. However, the current graph learning methods still suffer from the issues such as outlier influence and model robustness. In this paper, we propose a new dynamic graph neural network (DGCN) method to conduct semi-supervised

AbstractSince recording the first case in Wuhan in November 2020, COVID-19 is still spreading widely and rapidly affecting the health of millions all over the globe. For fighting against this pandemic, numerous strategies have been made, where the early isolation is considered among the most effective ones. Proposing useful methods to screen and diagnose the patient’s situation for the purpose of specifying

AbstractClustering is a widely used technique in data mining applications and various pattern recognition applications, in which data objects are divided into groups. K-means algorithm is one of the most classical clustering algorithms. In this algorithm, the initial clustering centers are randomly selected, this results in unstable clustering results. To solve this problem, an optimized algorithm

This paper aims to develop a novel deep learning concept to deal with video inpainting. Initially, motion tracking is performed, which is the process of determining motion vectors that describe the transformation from adjacent frames in a video sequence. Further, the regions or patches of each frame are categorized using the optimized recurrent neural network (RNN), in which the region is split into

NewHope cryptosystem is one of the second-round submissions of the National Institute of Standards and Technology post-quantum cryptography standardization process, which is a suite of two key encapsulation mechanisms based on the ring-learning with errors (LWE) problem. It has received much attention from the research community due to its small key size and high efficiency. Recently, three key mismatch

Nowadays, mobile applications have been widely used in various health and social domains. While there are large number of smart devices that connect and transmit their context data in free manner, these mobile applications have limitations regarding the increasing number of heterogeneous context data sent by devices, conducting the problem of identifying user-centric situations and providing services

Deoxys-BC is an internal tweakable block cipher of the authenticated encryption algorithm Deoxys, which is a third-round finalist in the CAESAR competition. In this paper, we study the property of Deoxys-BC, such as the subtweakey difference cancelation and the freedom of the tweak. Combining the differential enumeration technique with these properties, the authors achieve the key-recovery attacks

Lattice-based online/offline signature is attractive for the merit of resisting quantum attacks besides the short online response time. Prior to this work, the hash-sign-switch paradigm lattice-based online/offline signatures usually increase the length of each signature, and the Fiat–Shamir candidates are highly inefficient due to multiple aborts in online signing phase. In this work we mainly address

AbstractMany long-running applications (LRAs) are increasingly using containerization in shared production clusters. To achieve high resource efficiency and LRA performance, one of the key decisions made by existing cluster schedulers is the placement of LRA containers within a cluster. However, they fail to account for estimating the size and affinity of LRA containers before executing placement.

Public-key cryptographic primitives are time consuming for resource-constrained devices. A classical problem is to securely offload group exponentiations from a (comparatively) weak device—the client—to an untrusted more powerful device—the server. A delegation protocol must usually meet two security objectives: privacy—the exponent or the base should not be revealed to a passive adversary—and verifiability—a

With the help of a multi-signature scheme, we can reduce the cost of storage and bandwidth in case of many signers signing the same message. Therefore, multi-signature schemes can be used in bitcoin to reduce the size of a blockchain. In this paper, we propose a lattice-based multi-signature scheme with the following highlighted features. Our lattice-based multi-signature scheme supports signature

AbstractMillions of new domain names are registered every day, but a large proportion of them are malicious and usually discovered and blacklisted after the crime has been committed. In order to improve the security of domain name registration, this paper proposes a lightweight detection method based on the AdaBoost to identify malicious domain names, which focuses on proactively detecting malicious

AbstractTypically, underwater image processing is mainly concerned with balancing the color change distortion or light scattering. Various researches have been processed under these issues. This proposed model incorporates two phases, namely, contrast correction and color correction. Moreover, two processes are involved within the contrast correction model, namely: (i) global contrast correction and

Cognitive Radio network (CRN) depends on opportunistic spectrum access and spectrum sensing for improving the wireless networks’ spectrum efficiency. Since throughput maximization can result in high-energy consumption, the spectrum sensing technique should address the energy-throughput trade-off. The spectrum sensing time has to be determined by the considering the residual battery energies of each

The most famous wireless sensor networks is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose

AbstractBrain tumor classification is highly effective in identifying and diagnosing the exact location of the tumor in the brain. The medical imaging system reported that early diagnosis and classification of the tumor increases the life of the human. Among various imaging modalities, magnetic resonance imaging (MRI) is highly used by clinical experts, as it offers contrast information of brain tumors

AbstractDue to the rapid growth of Internet, E-commerce and Internet of Things, people use Web based services for most of their needs including buying items, reading books, watching online shows etc. Several companies are using recommender systems to influence people’s choices based on their likings, behaviours etc. Hence, people fear that their privacy is violated. Also, some of the online applications