Cross-Technology Communication(CTC) enables that WiFi devices can talk to ZigBee devices directly without any hardware changes or gateway equipment, and WiFi occupies a much wider bandwidth (20MHz) than ZigBee (2MHz), which sheds the light on spoofing-jamming attack based on CTC, where a WiFi device, as a sophisticated attacker spoofs or jams an area in which multiple-channels sensor network operating

Privacy-Preserving Record Linkage (PPRL) intends to integrate private data from several data sources held by different parties. Due to recent laws and regulations (e.g, General Data Protection Regulation), PPRL approaches are increasingly demanded in real-world application areas such as health-care, credit analysis, public policy evaluation, and national security. However, the majority of the PPRL

The competitive facility location problem is the problem of determining facility locations involving multiple players to optimize their various gains. The Voronoi game is a competitive facility location problem on a given arena played by two players, the server and the adversary. The players alternately take turns, one or more times, to place their facilities in the arena with a predetermined set of

Federated learning is a deep learning optimization method that can solve user privacy leakage, and it has positive significance in applying industrial equipment fault diagnosis. However, edge nodes in industrial scenarios are resource-constrained, and it is challenging to meet the computational and communication resource consumption during federated training. The heterogeneity and autonomy of edge

The ongoing COVID-19 (novel coronavirus disease 2019) pandemic has triggered a global emergency, resulting in significant casualties and a negative effect on socioeconomic and healthcare systems around the world. Hence, automatic and fast screening of COVID-19 infections has become an urgent need of this pandemic. Real-time reverse transcription polymerase chain reaction (RT-PCR), a commonly used primary

The original version of this article unfortunately contained a mistake in equation 24.

Through virtualization and resource integration, cloud computing has expanded its service area and offers a better user experience than the traditional platforms, along with its business operation model bringing huge economic and social benefits. However, a large amount of evidence shows that cloud computing is facing with serious security and trust crisis, and building a trust-enabled transaction

We present a novel reduced order model (ROM) approach for parameterized time-dependent PDEs based on modern learning. The ROM is suitable for multi-query problems and is nonintrusive. It is divided into two distinct stages: A nonlinear dimensionality reduction stage that handles the spatially distributed degrees of freedom based on convolutional autoencoders, and a parameterized time-stepping stage

To better handle problems with non-preferential multi-outputs (NPMO), a new approach is proposed in this study by employing the belief rule base (BRB) to provide a superior nonlinearity modeling ability as well as good explainability. The new approach is thus called NPMO–BRB. First, a new optimization model is constructed where the optimization objective is the integration of multi-outputs and respective

This paper proposes a novel competition-guided multi-neighborhood local search (CMLS) algorithm for solving the curriculum-based course timetabling problem. In comparison with the classical metaheuristic methods in the literature, the proposed algorithm includes three main contributions. Firstly, a new way of combining multiple neighborhoods is presented, according to which, only one neighborhood is

The Whale Optimization Algorithm (WOA) is a recently developed algorithm that is based on the chasing mechanism of humpback whales. Benefiting from the unique structure, WOA has virtuous global search capability. One of the drawbacks of this algorithm is the slow convergence rate that limits its real-world application. In resolving complicated global optimization problems, without any exertion for

The development of optimisation algorithms heavily relies on comparing performance across benchmark problem suites. In continuous unconstrained multi-objective optimisation, the most popular suites are ZDT, DTLZ and WFG. For each problem in these suites, there are construction parameters that control characteristics such as the degree of multimodality or deceptiveness. Despite encouragement from the

The adaptability of multi-robot systems in complex environments is a hot topic. Aiming at static and dynamic obstacles in complex environments, this paper presents dynamic proximal meta policy optimization with covariance matrix adaptation evolutionary strategies (dynamic-PMPO-CMA) to avoid obstacles and realize autonomous navigation. Firstly, we propose dynamic proximal policy optimization with covariance

Intracity heavy truck freight trips are basic data in city freight system planning and management. In the big data era, massive heavy truck GPS trajectories can be acquired cost effectively in real-time. Identifying freight trip ends (origins and destinations) from heavy truck GPS trajectories is an outstanding problem. Although previous studies proposed a variety of trip end identification methods

Rapidly-emerging variants jeopardize antibody-based countermeasures against SARS-CoV-2. While cell culture experiments have demonstrated loss of potency of several anti-spike neutralizing antibodies against SARS-CoV-2 variant strains1–3, the in vivo significance of these results remains uncertain. Using a panel of monoclonal antibodies (mAbs) corresponding to many in advanced clinical development by

Though SARS-CoV-2 primarily targets the respiratory system, patients and survivors can suffer neurological symptoms1–3. Yet, an unbiased understanding of the cellular and molecular processes affected in the brains of COVID-19 patients is still missing. Here, we profile 65,309 single-nucleus transcriptomes from 30 frontal cortex and choroid plexus samples across 14 control (including 1 terminal influenza)

Bernardo Reyes-Tur aims to unravel the mating mysteries of Cuba’s imperilled Polymita.

As the first national network of early-career researchers marks its 21st birthday, the founders of Hungary’s describe how and why they set up theirs in 2019.

A look at notable research tools and projects that have rocketed to prominence reveals some common routes to success.

Scientists are asking why the third mRNA vaccine may have fallen at the last hurdle.

G7 leaders commit to donating one billion COVID-19 vaccination doses by the end of next year. Plus, three missions to the ‘forgotten planet’ Venus and how scientists are embracing NFTs.

This work proposes a new and efficient sparse representation-based spatial compression of video signals integrated with a hyper-chaotic DNA coding-based encryption module that provides higher security. The betterment of compression efficiency is achieved by introducing sparse coding on the video frames, and higher security is obtained by applying 5D hyper-chaotic DNA coding on the sparse coded frames

Empirical wavelet transform (EWT) can successfully decompose a smooth or noise-free simulated signal into several components. This method has encountered difficulties in processing simulated signals containing noise and in applications. In order to improve the shortcomings of EWT and expand its application, this paper proposes a variable spectral segmentation EWT (VEWT) associated with the trend of

In this paper, a novel approach is presented to estimate the metric scale (MSC) and roll and pitch angles of a platform by using distance sensors in a monocular visual odometry setup. A state-of-the-art visual odometry algorithm Semi-Direct Visual Odometry (SVO) [1] is used to obtain sparse three dimensional (3D) point cloud which is then matched with the measurements obtained from the distance sensors

Smart wearable devices have become more and more popular in our daily life due to their unique power of “wearing-while-using.” However, requirements of light weight and compact size lead to limited on-device resources in most wearable products, which hamper the development of wearable technology. Edge computing provides an opportunity for wearable devices to access more resources without violating

Parametric timed automata are a powerful formalism for reasoning on concurrent real-time systems with unknown or uncertain timing constants. In order to test the efficiency of new algorithms, a fair set of benchmarks is required. We present an extension of the IMITATOR benchmarks library, that accumulated over the years a number of case studies from academic and industrial contexts. We extend here

In Statistical Relational Artificial Intelligence, a branch of AI and machine learning which combines the logical and statistical schools of AI, one uses the concept {\em para\-metrized probabilistic graphical model (PPGM)} to model (conditional) dependencies between random variables and to make probabilistic inferences about events on a space of ``possible worlds''. The set of possible worlds with

In game theory, mechanism design is concerned with the design of incentives so that a desired outcome of the game can be achieved. In this paper, we study the design of incentives so that a desirable equilibrium is obtained, for instance, an equilibrium satisfying a given temporal logic property -- a problem that we call equilibrium design. We base our study on a framework where system specifications

Maze generating and solving are challenging problems in mathematics and computing. Here we generated simple 2X2 maze problems applying four chemicals and created a set of engineered bacteria, which in a mixed population worked as a computational solver for any such problem. The input-output matrices of a mathematical maze were mapped through a truth table, where the logic values of four chemical inputs

In game theory, mechanism design is concerned with the design of incentives so that a desired outcome of the game can be achieved. In this paper, we study the design of incentives so that a desirable equilibrium is obtained, for instance, an equilibrium satisfying a given temporal logic property -- a problem that we call equilibrium design. We base our study on a framework where system specifications

The spread of disinformation on social media platforms such as Facebook is harmful to society. This harm can take the form of a gradual degradation of public discourse; but it can also take the form of sudden dramatic events such as the recent insurrection on Capitol Hill. The platforms themselves are in the best position to prevent the spread of disinformation, as they have the best access to relevant

Traffic light management is a broad subject with various papers published that put forth algorithms to efficiently manage traffic using traffic lights. Two such algorithms are the OAF (oldest arrival first) and ITLC (intelligent traffic light controller) algorithms. However, many traffic light algorithms do not consider future traffic flow and therefore cannot mitigate traffic in such a way as to reduce

Multiple shapes must be obtained in the mechanical design process to satisfy the required design specifications. The inverse design problem has been analyzed in previous studies to obtain such shapes. However, finding multiple shapes in a short computation period is difficult while using the conventional methods. This paper proposes the use of the conditional variational autoencoders (CVAE) with normal

Financial technology (FinTech) has drawn much attention among investors and companies. While conventional stock analysis in FinTech targets at predicting stock prices, less effort is made for profitable stock recommendation. Besides, in existing approaches on modeling time series of stock prices, the relationships among stocks and sectors (i.e., categories of stocks) are either neglected or pre-defined

A moplex is a natural graph structure that arises when lifting Dirac's classical theorem from chordal graphs to general graphs. However, while every non-complete graph has at least two moplexes, little is known about structural properties of graphs with a bounded number of moplexes. The study of these graphs is motivated by the parallel between moplexes in general graphs and simplicial modules in chordal

We consider the problem of assigning agents to resources in the two-sided preference model with upper and lower-quota requirements on resources. This setting (known as the HRLQ setting) models real-world applications like assigning students to colleges or courses, resident doctors to hospitals and so on. In presence of lower-quotas, an instance may not admit a stable matching that fulfils the lower-quotas

We give an exposition of the hidden subgroup problem for dihedral groups from the point of view of the standard hidden subgroup quantum algorithm for finite groups. In particular, we recall the obstructions for strong Fourier sampling to succeed, but at the same time, show how the standard algorithm can be modified to establish polynomial quantum query complexity. Finally, we explain a new connection

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

AbstractSince 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

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

Anomaly detection in process mining aims to recognize outlying or unexpected behavior in event logs for purposes such as the removal of noise and identification of conformance violations. Existing techniques for this task are primarily frequency-based, arguing that behavior is anomalous because it is uncommon. However, such techniques ignore the semantics of recorded events and, therefore, do not take

In Papadakis et al. (2020), we presented the latest release of JedAI, an open-source Entity Resolution (ER) system that allows for building a large variety of end-to-end ER pipelines. Through a thorough experimental evaluation, we compared a schema-agnostic ER pipeline based on blocks with another schema-based ER pipeline based on similarity joins. We applied them to 10 established, real-world datasets

Accurate estimation of error covariances (both background and observation) is crucial for efficient observation compression approaches in data assimilation of large-scale dynamical problems. We propose a new combination of a covariance tuning algorithm with existing PCA-type data compression approaches, either observation- or information-based, with the aim of reducing the computational cost of real-time

Recent advances in Next Generation Gene Sequencing (NGS) technologies brought an abundance of bioinformatics and phylogenetics data. The available datasets create new opportunities for studies about the genetic relationships among organisms, which previously relied mainly on manual observations. The state-of-the-art shows that the software employed in this area is based on technologically outdated

The essential component of the Big Data system is the processing frameworks and engines responsible for crunching the data. To cope with the growing computing demands of real-time Big Data applications, researchers have proposed several computing frameworks. The core of the computing frameworks i.e., the scheduling mechanisms for real-time stream processing need to accommodate several important aspects

Signaling in the control charts is usually followed by a substantial amount of delay, in which precise identification of the time when a change has occurred in a process simplifies the removal of change causes. This problem is referred to as change point (CP) estimation in the literature. This paper proposes a novel ensemble model to estimate CP under different processes’ changes in the phase II applications

The development of IoT techniques and the automation enabled by trigger-action platforms have brought great convenience to human life. However, with time the proliferating IoT devices will eventually overwhelm such platforms. Specifically, the ever-increasing huge amount of linkage rules signifificantly challenge the response rate as well as the effificiency of conflict rule detection, which will lead

The industry-wide adoption of graph databases has been hindered due to the fragmentation in syntax and semantics of available graph query languages. As a result, several projects have been proposed by industry and academia to develop a standard query language by integrating features from existing practical graph query languages. A significant factor that can impact query language integration is the

Business process management has been widely adopted by many organisations, resulting in the accumulation of large collections of process models. The majority of these models are rather large and complex. Even though such models constitute a great source of knowledge, they cannot be easily understood by all process stakeholders. Process model abstraction techniques have been proven effective in generating

We study the NP-hard graph problem Collapsed k-Core where, given an undirected graph G and integers b, x, and k, we are asked to remove b vertices such that the k-core of remaining graph, that is, the (uniquely determined) largest induced subgraph with minimum degree k, has size at most x. Collapsed k-Core was introduced by Zhang et al. (2017) and it is motivated by the study of engagement behavior

We study the reachability problem for queue automata and lossy queue automata. Concretely, we consider the set of queue contents which are forwards resp. backwards reachable from a given set of queue contents. Here, we prove the preservation of regularity if the queue automaton loops through some special sets of transformation sequences. This is a generalization of the results by Boigelot et al. and

Wireless Sensor Network (WSN) applications working on TinyOS operating system is widely used in various areas. However, the requirement of managing the power constraints makes TinyOS different from ordinary systems and hence building WSNs with TinyOS can be a challenging and time-consuming task. As successfully applied in many other domains, model-driven engineering (MDE) can facilitate the design

In the era of modern technology, the demand for seamless and fast communication service is gradually increasing. Moreover, as time goes on, more devices are getting connected to the network. In the vast network, for quick responses to these devices, the delay becomes one of the important factors to concern about. Among different types, Queuing delay causes more impact on a network compared to other

The main aim of this paper is to establish the hidden incidences that can improve specific aspects of beekeeper Small and Medium Enterprises (SME) innovation capabilities in Boyacá-Colombia. The methodology is focused on the use of the expertons model, adequacy coefficient and forgotten effects theory. A questionnaire of 58 items about different innovation management areas is distributed to 14 beekeeper

In this paper, we introduce a simple interactive agent mechanism, where the distribution of returns generated from the mechanism match stylized facts in financial markets. We introduce one more key factor, the length of time horizon on performance evaluations between strategies, which also has a significant influence on price fluctuations. To investigate the transitions among states, we introduce a