Most sensors have been taken up, which resulted in a massive data size, with the continuously growing IoT (Internet of Things) devices and communications infrastructure in development. The inspection of the manufacturer to identify product defects is one of the most common examples. In order to develop an effective inspection system with greater precision, this paper has been proposed a Fog Computing

Mobile Edge Computing (MEC) provides different storage and computing capabilities within the access range of mobile devices. This moderates the burden of offloading compute/storage-intensive processes of the mobile devices to the centralized cloud data centers. As a result, the network latency is reduced and the quality of service provided for the mobile end users is improved. Different applications

Search results of spatio-temporal data are often displayed on a map, but when the number of matching search results is large, it can be time-consuming to individually examine all results, even when using methods such as filtered search to narrow the content focus. This suggests the need to aggregate results via a clustering method. However, standard unsupervised clustering algorithms like K-means (i)

In this paper we study and implement real-time adaptation schemes for video encoding and channel selection that work in tandem to facilitate HD and 360° video streaming for secondary users in a dynamic spectrum access network. Out-of-band feedbacks on instantaneous pathloss of the signal between the transmitter and the receiver, the received signal strength indicator (RSSI) at the receiver, and the

Malaria is a life-threatening mosquito (Anopheles)-borne blood disease caused by the plasmodium parasite. Microscopic examination of peripheral blood smears by experts helps to identify parasites precisely. The manual assessment technique is a tedious and time-consuming process. The present study focuses on developing a hybrid screening algorithm for automated identification and classification of malaria

A fast and accurate algorithm is presented to register scans from an RGB-D camera, which rotates and scans the entire scene in an automated fashion on a pan-tilt platform. The proposed algorithm, Axis Bound Registration, exploits the movement of the camera that is bound by the two rotation axes of pan-tilt servos, so as to realize fast and accurate registration of acquired point clouds. The rotation

Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels

In recent years, the rapid development of fresh food e-commerce in China has brought about more development opportunities for the cold chain logistics industry but has also presented new challenges. With the development of cloud computing and big data technology, it is increasingly important to study the application of big data and cloud computing technology in cold chain logistics. The purpose of

Space filling curves are widely used in computer science. In particular, Hilbert curves and their generalizations to higher dimension are used as an indexing method because of their nice locality properties. This article generalizes this concept to the systematic construction of |$p$|-adic versions of Hilbert curves based on special affine transformations of the |$p$|-adic Gray code and develops a

Owing to the tremendous size of electronic biomedical documents, users encounter difficulties in seeking useful biomedical information. An efficient and smart access to the relevant biomedical information has become a fundamental need. In this research paper, we set forward a novel biomedical MeSH-based semantic indexing approach to enhance biomedical information retrieval. The proposed semantic indexing

The possibility of antibody-dependent enhancement (ADE) of disease is a general concern for the development of vaccines and antibody therapies because the mechanisms that underlie antibody protection have the theoretical potential to amplify viral infections or trigger immunopathology. Observations relevant to the risks of ADE of disease require careful review at this critical point in the SARS-CoV-2

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Contextuality in human cognition can be defined as the existence of thoughts that are locally consistent and globally inconsistent. Absence of contextuality in the representational theories that are used for modelling cognitive processes results in inconsistencies between expected and actual decisions. These inconsistencies are usually regarded as paradoxes. However, recent literature reports that

The topic of feature selection in high-dimensional data sets has attracted considerable attention. Feature selection can reduce the dimension of feature and improve the prediction accuracy of the classification model. Information-theoretical-based feature selection methods intend to obtain classification information regarding class labels from the already-selected feature subset as much as possible

Based on the analysis of multi-objective flexible job-shop scheduling problem (FJSP), a multi-objective low-carbon job-shop scheduling problem(MLFJSP) with variable processing speed constraint is proposed in this paper. The optimization objectives of MLFJSP include minimizing the makespan, total carbon emission and machine loading. Meanwhile, an improved artificial bee colony algorithm (IABC) is designed

Chaotic time series prediction is a research topic in both theoretical and real-life area. Its aim is to predict the future of the time series based on past observations. Reservoir computing (RC) is a promising tool widely used in time series prediction. Short-term memory (STM) is very important to model time-dependent time series by the RC approach. However, traditional RC hardly achieves sufficient

In this paper, a robust watermarking algorithm in integer wavelet domain using downsampling is proposed. The innovations of this paper can be summarized as follows. First, after downsampling both the host image and watermark, each sub-watermark is embedded into the corresponding sub-host image. Second, four sub-watermarks are encrypted by pixel scrambling and fractional random transform, and the right

For a graph G = (V, E) with no isolated vertices, a set \(D\subseteq V\) is called a semipaired dominating set of G if (i)D is a dominating set of G, and (ii)D can be partitioned into two element subsets such that the vertices in each two element set are at distance at most two. The minimum cardinality of a semipaired dominating set of G is called the semipaired domination number of G, and is denoted

The verification of contemporary distributed software systems is challenging, because they are heavily parameterised, containing components whose number and connections cannot be a priori fixed. In this work, we consider the multi-parameterised verification of safety properties by refinement checking in the context of labelled transition systems (LTSs). The LTSs are parameterised by using first-order

This research relies on nutation control strategy through state feedback with its application to a spin-stabilized autonomous system such as spacecraft. The key subject behind the investigation is to present a new formulation in the area of attitude control to be able to handle the rotational angles in connection with angular rates, while the system under control is in a predefined mission operation

Understanding the role of network for epidemic spread is a major concern in the study of complex network and disease spread. Localization of Principal EigenVector (PEV) of the network’s adjacency matrix has been explored in recent past by many researchers. Most of the previous works suggest the localization of PEV through maximization of inverse participation ratio (IPR). The network with maximum IPR

In this paper, we develop two novel hybrid models, namely N-soft expert set model and fuzzy N-soft expert set model. An N-soft expert set model is the combination of N-soft sets with soft expert sets, and a fuzzy N-soft expert model studies the fuzziness of N-soft expert sets. Further, we illustrate our hybrid models with daily life examples involving uncertainties in data. Moreover, we describe some

In this paper we study a dozen of rumors on Twitter to find new insights in user characteristics and macro patterns in the process of rumor spreading. The collection and curation of data has left us with 12 rumor datasets out of 56,852 tweets from 43,919 users. The analysis over data shows users with lower ratio of following-to-follower are more probable to spark the rumor diffusion while users with

With the increasing trend in softwarization and virtualization of network functions and systems, NFV management and orchestration (MANO) solutions are being developed to meet the agile and flexible management requirements of virtualized network services in the 5G era and beyond. In this regard, ETSI ISG NFV has specified a standard MANO system that is used as a reference by vendors as well as open-source

With innovation in persistent technologies, such as wearable sensor gadgets, sensor devices, and wireless ad-hoc communication networks connect everyday life things to the Internet, normally referred to as Internet of Things (IoT). IoT is observed as an active entity for design and development of smart and context awareness services and applications in the area of business, science and engineering

Machine-to-Machine (M2M) communication is a promising technology that may realize the Internet of Things (IoTs) in future networks. However, due to the features of massive devices and concurrent access requirement, it will cause performance degradation and enormous energy consumption. Energy Harvesting-Powered Cognitive M2M Networks (EH-CMNs) as an attractive solution is capable of alleviating the

Sixth generation (6G) communication environment is unfolded in the recent years in order to provide high throughput less latency services for the mobile users. This environment encloses a variety of heterogeneous resources and communication standards to ensure seamless availability of services. In this open environment, security is a concerning issue due to heterogeneous standard integration and access

Internet of Medical Things (IoMT) is now worth a billion dollar market. While offering enormous benefit, the prevalent and open environment of IoMT ecosystem can be a potential target of varied evolving cyber threats and attacks. Further, extensive connectivity of IoMT devices and their dynamic massive heterogeneous communication can create a new attack surface for sophisticated multivector malware

The proliferation of the Internet of Things (IoT) in conjunction with edge computing has recently opened up several possibilities for several new applications. Typical examples are Unmanned Aerial Vehicles (UAV) that are deployed for rapid disaster response, photogrammetry, surveillance, and environmental monitoring. To support the flourishing development of Machine Learning assisted applications across

In recent years, analyzing time-series data has become an ever important research topic due to an increased number of temporal datasets in science and engineering. Segmentation is widely used in time-series data analysis because it provides a more compact representation by dividing the series into segments. Segmentation approaches based on genetic algorithms have been proposed to extract segments and

Recent work has established the equivalence between deep neural networks and Gaussian processes (GPs), resulting in so-called neural network Gaussian processes (NNGPs). The behaviour of these models depends on the initialisation of the corresponding network. In this work, we consider the impact of noise regularisation (e.g. dropout) on NNGPs, and relate their behaviour to signal propagation theory

Accurate diagnosis of autism spectrum disorder (ASD), which is a neurodevelopmental disorder and often accompanied by abnormal social skills, communication skills, interests and behavior patterns, has always been a challenging task in clinical practice. Recent studies have shown great potential for using fMRI data to distinguish ASD from typical control (TC). However, it has always been a challenging

Recent studies show that the joint Chinese word segmentation and POS tagging can enhance the mutual interaction and yield better performances for two tasks. However, existing joint methods fail to effectively take the advantage of the multiple granularity of information, e.g., character, word and subword, which has been proven prominently useful. In this paper, we propose to improve the joint tasks

Pedestrian Attribute Recognition (PAR) plays an important role in intelligent video surveillance. This paper tackles two severe challenges in it i.e., complex relations between images and attributes, and imbalanced distribution of pedestrian attributes. Specifically, a new multiple time steps attention mechanism is proposed to boost the modeling of the relations. Different from existing attention approaches

A smart city is engineered to be a self-sustained ecosystem driven by Internet-of-Things (IoT) devices. Smooth functioning of smart cities is conditioned on seamless communication between users and devices. Smart devices equipped with biometric authentication can offer security as well as personalized experience to the end users. Currently, a number of smart devices employ face, fingerprint, and voice

The paper presents a method to learn the substitution, deletion and insertion costs on nodes and edges applied to the graph edit distance. We model the learning strategy as a general model and then we concretise it in two different architectures: the first architecture is based on a neural network and the second architecture is based on a multivariate normal distribution, which have been previously

Unsupervised learning of 3D shape feature is a challenging yet important problem for organizing a large collection of 3D shape models that do not have annotations. Recently proposed neural network-based approaches attempt to learn meaningful 3D shape feature by autoencoding a single 3D shape representation such as voxel, 3D point set, or multiview 2D images. However, using single shape representation

Few-Shot Learning (FSL) aims at recognizing the target classes that only a few samples are available for training. The current approaches mostly address FSL by learning a generalized class-level metric while neglect the intra-class distribution information. In this work, we propose Improved Prototypical Networks (IPN) to address this issue. Inspired by the observation that the intra-class samples differ

Anastylosis is an archaeological technique which focuses on the reconstruction of collapsed building and destroyed artworks, starting from the original pieces. Many digital approaches have been developed in the last decade, mainly based on 2D and 3D analysis of the structure of the fragments. These techniques aim at supporting the priceless work of the involved operators, mainly in the decision processes

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Diabetes is a metabolic disease characterized by persistent hyperglycemia, which results in serious complications such as cardiovascular disorder, neuropathy, renal failure, and retinopathy. Various anti-diabetic drugs have been developed, however, their therapeutic effect is far from satisfactory. Therefore, increasing attention has been paid to natural products, whose multi-targets characteristics

In this study, a machine learning approach based on the unsupervised version of wavelet neural networks (WNNs) is used to solve two-dimensional elliptic partial differential equations (PDEs). The design of the WNNs must be judiciously addressed, particularly, the adopted training algorithm, since it greatly influences the generalization performance and the convergence rate of WNNs. Although the gradient

A metaheuristic algorithm called Harris hawks optimization (HHO) is gaining its popularity among its clan and useful for optimization. In this algorithm, the prey gets completely exhausted when the escape energy is equal to zero, therefore it fails to explore further. The random operator chosen in the existing method is a wastage of search agents (Harris hawk). To overcome this issue, we propose an

Accurate prediction of tumor motion for motion adaptive radiotherapy has been a challenge as respiration-induced motion is non-stationary in nature and often subjected to irregularities. Despite having a plethora of works for predicting this motion, their tracking capabilities are usually prone to large prediction errors due to the time-varying irregularities and intra-trace variabilities. To overcome

Landslide displacement prediction is an important part of reducing landslide hazard losses. The existing methods to predict landslide displacement are too complicated to be applied to engineering practice. The landslide displacement evolution and the grey model prediction mechanism show a good consistency. However, the existing grey prediction model also has some shortcomings including neglecting time

The presence of outliers in the dependent and/or independent features distorts predictions with machine learning techniques and may lead to erroneous conclusions. It is important to implement methods that are robust against the outliers to make reliable predictions and to know the accuracy of the existing methods when data is contaminated with outliers. The focus of this study is to propose a robust

It is well known that the classic {\L}o\'s-Tarski preservation theorem fails in the finite: there are first-order definable classes of finite structures closed under extensions which are not definable (in the finite) in the existential fragment of first-order logic. We strengthen this by constructing for every $n$, first-order definable classes of finite structures closed under extensions which are

We show that reachability, repeated reachability, nontermination and unboundedness are NP-complete for Lossy Channel Machines that are flat, i.e., with no nested cycles in the control graph. The upper complexity bound relies on a fine analysis of iterations of lossy channel actions and uses compressed word techniques for efficiently reasoning with paths of exponential lengths. The lower bounds already

We study countably infinite MDPs with parity objectives. Unlike in finite MDPs, optimal strategies need not exist, and may require infinite memory if they do. We provide a complete picture of the exact strategy complexity of $\varepsilon$-optimal strategies (and optimal strategies, where they exist) for all subclasses of parity objectives in the Mostowski hierarchy. Either MD-strategies, Markov strategies

The Hidden Weighted Bit function plays an important role in the study of classical models of computation. A common belief is that this function is exponentially hard for the implementation by reversible ancilla-free circuits, even though introducing a small number of ancillae allows a very efficient implementation. In this paper, we refute the exponential hardness conjecture by developing a polynomial-size

Analog photonic solutions offer unique opportunities to address complex computational tasks with unprecedented performance in terms of energy dissipation and speeds, overcoming current limitations of modern computing architectures based on electron flows and digital approaches. The lack of modularization and lumped element reconfigurability in photonics has prevented the transition to an all-optical