Related Schemes Research Articles

Towards a Secure and Computational Framework for Internet of Drones Enabled Aerial Computing

Unmanned aerial vehicles (UAVs) are deployed in various mission-critical applications to collect sensitive data and disseminate it to the server placed on the ground station. Users mostly require real-time data from the drones, instead of the information stored by the server, in specific applications to perform timely actions. However, performing real-time communication with a drone through the public communication channel, which exposed to different security risks mandates an authenticated key exchange between users and drones in the Internet of Drones (IoD) networks for enabling users to communicate securely with the drone through the public communication infrastructure. This paper proffers a resource-efficient authentication scheme for the IoD networks, called REAS-IoD. The proposed REAS-IoD utilizes the lightweight hash function and authenticated encryption primitive, known as ACE to accomplish the AKE process securely. In REAS-IoD, a user's authentication is performed, followed by establishing a secure session key (SK) between a specific drone and the user, for future encrypted communication. The SK's security is established utilizing a random oracle model. A comparative study demonstrates that REAS-IoD requires fewer resource, without compromising security and privacy features than related eminent security schemes.

Time-Scale Separation Control for a Class of Current Distribution Strategy in AMBs-Rotor System With Bounded Bus Voltage

In active magnetic bearings (AMBs) systems, good dynamic characteristics and low power consumption are two important concerns, which are difficult to be obtained simultaneously if considering bounded bus voltage. This paper proposes a universal current distribution constraint format and a related time-scale separation control scheme to achieve improvements in both aspects. Considering the bounded bus voltage and symmetrical configuration in one AMB control axis, a double-input three-order nonlinear model is established to describe the AMBs-rotor system. Subsequently, a constraint format is propounded to cover and improve existing current distribution strategies. The power consumption and voltage response are compared between different instances under this constraint format. Then, the double-input model is converted to a single-input cascaded nonlinear system, and a time-scale separation control scheme is designed accordingly. The scheme contains a nominal controller and a dynamic inversion. The nominal controller determines the dynamic characteristics of the AMBs-rotor system. The dynamic inversion compensates for the nonlinearity of the system based on the characteristics of the amplifier. This scheme can preserve the closed-loop stability by only adjusting the time-scale coefficient. Finally, experimental results demonstrate the advantages of the proposed method in aspects of dynamic performance, voltage response, and power consumption.

A Three-Factor-Based Authentication Scheme of 5G Wireless Sensor Networks for IoT System

Internet of Things (IoT) is an expanding technology that facilitate physical devices to inter-connect each other over a public channel. Moreover, the security of the next-generation wireless mobile communication technology, namely 5G with IoT, has been a field of much interest among researchers in the last several years. Previously, Sharif et al. had suggested an IoTbased lightweight three-party authentication scheme proclaiming a secured scheme against different threats. However, it was found that the scheme could not achieve user anonymity and guarantee session key security. Additionally, the scheme fails to provide proper authentication in the login phase, and it s unable to update a new password in the password change phase. Thus, we propose an improved three-factor-based data transmission authentication scheme (TDTAS) to address the weaknesses. The formal security analysis has been proved using the Real-or-Random (RoR) model. The informal security analysis demonstrates that the scheme is secure against several known attacks and achieves more security features. In addition, the comparison of the work with other related schemes demonstrates the proposed scheme has less communicational and storage costs.

LPN-based Attacks in the White-box Setting

In white-box cryptography, early protection techniques have fallen to the automated Differential Computation Analysis attack (DCA), leading to new countermeasures and attacks. A standard side-channel countermeasure, Ishai-Sahai-Wagner’s masking scheme (ISW, CRYPTO 2003) prevents Differential Computation Analysis but was shown to be vulnerable in the white-box context to the Linear Decoding Analysis attack (LDA). However, recent quadratic and cubic masking schemes by Biryukov-Udovenko (ASIACRYPT 2018) and Seker-Eisenbarth-Liskiewicz (CHES 2021) prevent LDA and force to use its higher-degree generalizations with much higher complexity.In this work, we study the relationship between the security of these and related schemes to the Learning Parity with Noise (LPN) problem and propose a new automated attack by applying an LPN-solving algorithm to white-box implementations. The attack effectively exploits strong linear approximations of the masking scheme and thus can be seen as a combination of the DCA and LDA techniques. Different from previous attacks, the complexity of this algorithm depends on the approximation error, henceforth allowing new practical attacks on masking schemes which previously resisted automated analysis. We demonstrate it theoretically and experimentally, exposing multiple cases where the LPN-based method significantly outperforms LDA and DCA methods, including their higher-order variants.This work applies the LPN problem beyond its usual post-quantum cryptography boundary, strengthening its interest for the cryptographic community, while expanding the range of automated attacks by presenting a new direction for breaking masking schemes in the white-box model.

AMAKAS: Anonymous Mutual Authentication and Key Agreement Scheme for securing multi-server environments

The rapid growth of Internet users was the motivation of the emerge appearance of new computing models such as cloud computing, fog computing and edge computing. For this reason, the multi-server’s architecture has been introduced to extend scalability and accessibility. To ensure that these servers can only be accessed by the authorized users, many authentication and key agreement schemes have been introduced for multi–server environments. In this paper, we propose an anonymous mutual authentication and key agreement scheme for multi-server architecture based on elliptic curve cryptography to achieve the required security services and resist the well-known security attacks. Furthermore, formal and informal security analysis is conducted to prove the security of the proposed scheme. Moreover, we provide a performance comparison with related work in terms of computational cost, communication cost and the number of messages transferred on the public channel. This performance comparison clearly shows that the proposed scheme is highly efficient in terms of computation, communication cost and security analysis as compared to other related schemes which makes the proposed scheme more suitable and practical for multi-server environments than other related schemes.

A new paradigm for high-capacity reversible data hiding with pixel repetition and adaptive embedding

Reversible data hiding (RDH) offers a useful attribute of retaining the visual semantics with security properties, for the effective management and protection of digital images with their metadata. This paper introduces a Reversible Data Hiding (RDH) scheme with Pixel Repetition (PR) to selectively embed additional secret data. bits utilizing the image redundancy created with PR. A generalized framework of PR is first developed and the rate–distortion criterion is extended for adaptive embedding. An input image is defined to be scaled-up by replacing each original pixel (called seed) with a block of shape s×s containing the seed, where s is the scaling factor. For better utilizing this PR-based image redundancy, we then propose an RDH scheme with two-phase embedding for s=2. The first phase of the scheme embeds 4-bit data in each 2 × 2 image block using a mapping function, which is designed to modify the repeated pixels, keeping the seed intact in each block. Being invertible, the mapping function also allows another phase of embedding in the preserved seeds using an adaptive selection of their least significant bits. Compared to the prominent PR-based and other related schemes, an impressive rate–distortion performance of the proposed scheme is recorded with a greater potential for the applications that require a high and varying embedding rate with better image quality.

Diffusion and viscosity coefficient in Lennard-Jones fluids: excess entropy scaling law

We investigated the proportional relationship between the diffusion coefficient and the viscosity coefficient and excess entropy in Lennard-Jones fluids. Based on the theoretical basis of Dzugutov and Bell et al., we came up with a new expression. Using molecular dynamics simulation to calculate the radial distribution function (RDF), the reduced diffusion coefficient D* and the reduced shear viscosity coefficient η* in the state of reduced density ρ* = 0.5 and the reduced temperature range of 1 ≤ T* ≤ 2.8. The two body excess entropy S2 was calculated from RDF. The results showed that the reduced diffusion coefficient and the reduced viscosity coefficient and the excess entropy present the exponential scaling behaviour. Our results supported the notion put forth by Dzugutov and Bell et al. but had different adjust parameters. The dependence of the reduced diffusion coefficient on the reduced temperature are also explored, they qualitatively exhibited a 2/3 power function relationship. In addition, we have quantitatively calculated the relation schema of ln(η*)-ln(D*/T*) in Lennard-Jones fluids that with LJ potential (n = 12) without considering the influence of the packing fraction and found that the reduced diffusion coefficient and reduced viscosity satisfy the new Stokes–Einstein relationship proposed by Ohtori et al.

Transfer Learning across Graph Convolutional Networks: Methods, Theory, and Applications

Graph neural networks have been widely used for learning representations of nodes for many downstream tasks on graph data. Existing models were designed for the nodes on a single graph, which would not be able to utilize information across multiple graphs. The real world does have multiple graphs where the nodes are often partially aligned . For examples, knowledge graphs share a number of named entities though they may have different relation schema; collaboration networks on publications and awarded projects share some researcher nodes who are authors and investigators, respectively; people use multiple web services, shopping, tweeting, rating movies, and some may register the same email account across the platforms. In this paper, we propose partially aligned graph convolutional networks to learn node representations across the models. We provide multiple methods such as model sharing, regularization, and alignment reconstruction, as well as theoretical analysis to positively transfer knowledge across the set of partially aligned nodes. Extensive experiments on real-world knowledge graphs, collaboration networks, and bipartite rating graphs show the superior performance of our proposed methods on relation classification, link prediction, and item recommendation.

Тенденции развития сверхширокополосных прямохаотических средств связи

The report discusses the development trends in the development of ultra-wideband wire-less communications based on chaotic signals. For the first time, direct chaotic communications were proposed in 2000 as a way to use noise-like (chaotic) signals for communication systems. The idea was to generate information-carrying signals directly in the region of radio or microwave frequencies where transmission takes place, and to modulate and demodulate these signals there without resorting to frequency transfer operations. In the basic version of the scheme, chaotic radio pulses were used as an information carrier, which made it possible to use “on-off” modulation and modulation of the positions of chaotic radio pulses. An envelope detector matched with the transmitted signal was used as a receiver. Another variant of the direct chaotic system is the recently proposed relative transmission scheme based on chaotic radio pulses. The report examines the characteristics and specifics of both schemes when used in ultra-wideband wire-less communication in the centimeter and decimeter wavelength ranges.

PQ-Mist: Priority Queueing-Assisted Mist–Cloud–Fog System for Geospatial Web Services

The IoT and cloud environment renders enormous quantities of geospatial information. Fog and mist computing is the scaling technology that handles geospatial data and sends it to the cloud storage system through fog/mist nodes. Installing a mist–cloud–fog system reduces latency and throughput. This mist–cloud–fog system has processed different types of geospatial web services, i.e., web coverage service (WCS), web processing services (WPS), web feature services (WFS), and web map services (WMS). There is an urgent requirement to increase the number of computer devices tailored to deliver high-priority jobs for processing these geospatial web services. This paper proposes a priority-queueing assisted mist–cloud–fog system for efficient resource allocation for high- and low-priority tasks. In this study, WFS is treated as high-priority service, whereas WMS is treated as low-priority service. This system dynamically allocates mist nodes and is determined by the load on the system. In addition to that, the assignment of tasks is determined by priority. Not only does this classify high-priority tasks and low-priority tasks, which helps reduce the amount of delay experienced by high-priority jobs, but it also dynamically allocates mist devices within the network depending on the computation load, which helps reduce the amount of power that is consumed by the network. The findings indicate that the proposed system can achieve a significantly lower delay for higher-priority jobs for more significant rates of task arrival when compared with other related schemes. In addition to this, it offers a technique that is both mathematical and analytical for investigating and assessing the performance of the proposed system. The QoS requirements for each device demand are factored into calculating the number of mist nodes deployed to satisfy those requirements.

Traffic data security sharing scheme based on blockchain and traceable ring signature for VANETs

Vehicular ad hoc networks (VANETs) is the hotspot research field of wireless mobile ad hoc network, it provides a new opportunity to create a safe and efficient transportation environment. However, as an open network where information has to interact frequently, it is difficult to ensure the security of data transmitted in VANETs and protect the privacy of drivers. Many existing information-sharing schemes use complex encryption algorithms to enable secure traffic data sharing. Nevertheless, these schemes are not suitable for VANETs because of their high computational overhead and lack of corresponding tracking mechanisms for malicious vehicles. Therefore, a traffic data security sharing scheme is designed that combines blockchain technology and traceable ring signature algorithms to secure the transmitted messages. The traceable ring signature algorithm is formulated in combination with bilinear pairing, enabling conditional privacy protection instead of traditional ring signature. To improve the efficiency of VANETs, this scheme introduces edge computing technology to reduce the computational burden of Road Side Units (RSUs) by offloading most of the computational tasks to the servers via edge nodes. In addition, we use smart contract to track malicious vehicles. Security analysis and performance comparison show that our scheme is more efficient and secure for drivers than other existing related schemes.

Multifractal Characterization of Texts for Pattern Recognition: On the Complexity of Morphological Structures in Modern and Ancient Languages.

The study of languages' structure and their organization in a set of well-defined relation schemes is a delicate matter. In the last decades, the convergence of traditional conflicting views by linguists is supported by an interdisciplinary approach that involves not only genetics or bio-archelogy but nowadays even the science of complexity. In light of this new and useful approach, this study proposes an in-depth analysis of the complexity underlying the morphological organization, in terms of multifractality and long-range correlations, of several modern and ancient texts pertaining to various linguistic strains (including ancient Greek, Arabic, Coptic, Neo-Latin and Germanic languages). The methodology is grounded on the mapping procedure between lexical categories belonging to text excerpts and time series, which is based on the rank of the frequency occurrence. Through the well-known MFDFA technique and a specific multifractal formalism, several multifractal indexes are then extracted for characterizing texts and the multifractal signature has been adopted for characterizing several language families, such as Indo-European, Semitic and Hamito-Semitic. The regularities and differences in the linguistic strains are assessed within a multivariate statistical framework and corroborated with a Machine Learning approach that is dedicated, in turn, to investigate the predictive power of the multifractal signature pertinent to text excerpts. The obtained results show a strong presence of persistence, i.e., memory, in the morphological structure of analyzed texts and we claim that this property has a role in characterizing the studied linguistic families. In fact, for example, the proposed analysis framework - grounded on complexity indexes - is able to easily distinguish ancient Greek texts from Arabic ones, as they belong to different language strains, i.e., indo-European and Semitic, respectively. The proposed approach has been proven effective and can be adopted for further comparative studies and for designing new informetrics for further advances in the fields of information retrieval and Artificial Intelligence.

Optimized Controller Provisioning in Software-Defined LEO Satellite Networks

The controller provisioning, which adjusts the number, locations, and members of satellite controllers adaptive to the dynamic network load and topology, fundamentally impacts the performance of software-defined satellite networks (SDSNs). An ideal provisioning strategy should achieve a low total control overhead throughout the entire satellite operation period, which is extremely challenging since the network load can only be predicted in a short time scale. Existing methods can hardly achieve this goal for they greedily configure controllers in each time slot, where switches have to frequently migrate from one controller to another. In this paper, we focus on achieving globally optimized strategies with only current network load information. We firstly propose a comprehensive control overhead model and formulate the Controller Provisioning Problem (CPP) in SDSNs as a non-convex integer programming problem. To solve the problem, we propose an approximate algorithm named AROA by introducing a regularization framework and based on randomized rounding. We theoretically derive its competitive ratio. To produce strategies in time for future large satellite constellations, we further propose a more efficient heuristic algorithm HROA. Evaluations on our built simulation system show that our proposed methods significantly outperform related schemes in control overhead, latency, and scalability.

A blockchain-based privacy-preserving auditable authentication scheme with hierarchical access control for mobile cloud computing

Blockchain-based authentication mode, a fundamental solution to prevent unauthorized access behavior, gradually becomes a focus in future distributed mobile cloud computing (MCC) services. However, due to the transparent and immutable characteristics of blockchain, users’ access behaviors are facing huge security and privacy threats. Storing the encrypted data on chain is an effective way to address these issues, but access permission confirmation and update in the form of ciphertext is the main bottleneck. To this end, this paper proposes a blockchain-based unified authentication and hierarchical access control scheme for the MCC environment, which provides both privacy protection and auditability. In the proposed scheme, users can access multiple MCC services with different access permissions using a single credential. To protect the privacy of both users and service providers, while still supporting auditability, the data on the public ledger is blinded using Pedersen commitments. Besides, the proposed scheme provides flexible dynamic updating in encrypted form. Theoretical analysis indicates that the proposed scheme can meet various security and privacy requirements in the MCC environment. Compared with related schemes, it has better communication efficiency. Therefore, the proposed scheme is more suitable for the actual MCC environment.

Authentication Healthcare Scheme in WBAN

A wireless body area network (WBAN) connects separate sensors in many places of the human body, such as clothes, under the skin. WBAN can be used in many domains such as health care, sports, and control system. In this paper, a scheme focused on managing a patient’s health care is presented based on building a WBAN that consists of three components, biometric sensors, mobile applications related to the patient, and a remote server. An excellent scheme is proposed for the patient’s device, such as a mobile phone or a smartwatch, which can classify the signal coming from a biometric sensor into two types, normal and abnormal. In an abnormal signal, the device can carry out appropriate activities for the patient without requiring a doctor as a first case. The patient does not respond to the warning message in a critical case sometimes, and the personal device sends an alert to the patient’s family, including his/her location. The proposed scheme can preserve the privacy of the sensitive data of the patient in a protected way and can support several security features such as mutual authentication, key management, anonymous password, and resistance to malicious attacks. These features have been proven depending on the Automated Validation of Internet Security Protocols and Applications. Moreover, the computation and communication costs are efficient compared with other related schemes.

Rematching on-the-fly: Sequential matched randomization and a case for covariate-adjusted randomization.

Covariate-adjusted randomization (CAR) can reduce the risk of covariate imbalance and, when accounted for in analysis, increase the power of a trial. Despite CAR advances, stratified randomization remains the most common CAR method. Matched randomization (MR) randomizes treatment assignment within optimally identified matched pairs based on covariates and a distance matrix. When participants enroll sequentially, sequentially matched randomization (SMR) randomizes within matches found "on-the-fly" to meet a pre-specified matching threshold. However, pre-specifying the ideal threshold can be challenging and SMR yields less-optimal matches than MR. We extend SMR to allow multiple participants to be randomized simultaneously, to use a dynamic threshold, and to allow matches to break and rematch if a better match later enrolls (sequential rematched randomization; SRR). In simplified settings and a real-world application, we assess whether these extensions improve covariate balance, estimator/study efficiency, and optimality of matches. We investigate whether adjusting for more covariates can be detrimental upon covariate balance and efficiency as is the case of traditional stratified randomization. As secondary objectives, we use the case study to assess how SMR schemes compare side-by-side with common and related CAR schemes and whether adjusting for covariates in the design can be as powerful as adjusting for covariates in a parametric model. We find each SMR extension, individually and collectively, to improve covariate balance, estimator efficiency, study power, and quality of matches. We provide a case-study where CAR schemes with randomization-based inference can be as and more powerful than non-CAR schemes with parametric adjustment for covariates.

A DCT probability histogram-based ROI features for content-based natural and medical image retrieval applications

The effectiveness of a Content-based Image Retrieval (CBIR) system depends on selecting representative and salient visual features. Capturing the salient image features for forming a feature vector is an essential part of the CBIR process, as it can significantly affect retrieval efficiency. Most images contain a visually prominent region that perceives maximum image semantic information, known as Regions-of-Interest (ROIs). This paper suggests a new CBIR scheme that employs a Graph-based Visual Saliency (GBVS) map to extract the ROI of the image. Further, block-level Discrete Cosine Transforms (DCT) and subsequent histograms are computed for the DC and significant AC coefficients of the extracted ROI image. In addition, a low-dimensional feature vector is formed from the modified histograms. Subsequently, six statistical features from the background scene have also been computed for the final feature vector construction. As a result, the final feature vector contains ROI and background information in their proper proportion to improve retrieval performance. Moreover, the performance of the proposed CBIR scheme has been evaluated using one medical, three natural, two objects, one texture, and one natural heterogeneous image database. All these databases collectively contain 53732 different images from 200 image classes. The outcomes of the experiments demonstrate substantial improvement in the retrieval process with some existing related schemes. At the same time, it also shows that the performance of the proposed scheme is robust concerning all kinds of image databases.

Argument-Structure Constructions with Organization Names in the English Eurolect

Construction Grammar (Hoffmann & Trousdale 2013) has received very little attention in onomastics, let alone corpus-based approaches, as corpora are just starting to be applied to the empirical study of names (Motschenbacher 2020). This study employs Named Entity Recognition plus verbal pattern extraction in an intermodal corpus (Bernardini 2016) of EU discourse, or Eurolect (Sandrelli 2018). The methodological aim is to mine English argument-structure constructions (Goldberg 1995) with subordinate clauses introduced by that and organization names in the subject slot ([ORG + V + that + SC]). First, the personification recognition method of Dorst, Mulder, and Steen (2011) is applied to quantitatively prove the strong relationship between the extracted argument-structure constructions and personification metaphors in EU discourse. Second, the constructions and their form-meaning pairings are described, both per subcorpus and globally. Results show that, at a macro- and meso-level of schematicity, the [ORG + V + that + SC] construction transversally symbolizes personification as an understanding scheme for institutional relations, constructing organization names with semantically human verbs of belief, speech, and thought. At a microscopic level, however, some constructions occur exclusively in one of the four subcorpora (non-translated, translated, non-interpreted, and interpreted English), meaning that they could be covering specific mediating functions through their name-verb slot choices.

Microstructure design of porous viscoelastic composites with prescribed relaxation moduli by multi-material topology optimization

This paper presents a multi-material topology optimization method for designing the microstructures of porous viscoelastic composites with prescribed relaxation moduli. It is assumed that the composite materials are microscopically composed of periodic unit cells. The effective relaxation moduli of the composites are obtained using the homogenization method. The density-based topology optimization is utilized, and a related material interpolation scheme is introduced. The design objective is to determine an optimal material distribution that exhibits the prescribed modulus in both the glassy and rubbery regions. The design sensitivity is derived analytically using the adjoint variable method. A filtering scheme is adopted to impose a minimum length scale for each material phase. Several numerical examples are presented to demonstrate the effectiveness of the proposed method. Various microstructures with prescribed relaxation moduli are presented and discussed. In addition, the bounds on the relaxation moduli of the optimized microstructures are compared with theoretical bounds.

Equivalent resolvents of Douglas-Rachford splitting and other operator splitting algorithms: a unified degenerate proximal point analysis

We introduce a generalized proximal point algorithm, and perform a detailed convergence analysis with the focus on the case of degenerate metric. The degeneracy leads to a well-defined resolvent form restricted to a reduced dimensional space. This approach unifies the algorithmic structures of Douglas-Rachford splitting and other related operator splitting schemes. Various aspects of these algorithms, in particular, the convergence of Douglas-Rachford splitting in terms of the solution itself and Chambolle-Pock algorithm under the limit setting, are investigated or revisited by the unified degenerate proximal point analysis.