An Efficient Approach to Escalate the Speed of Training Convolution Neural Networks

Deep neural networks excel at image identification and computer vision applications such as visual product search, facial recognition, medical image analysis, object detection, semantic segmentation,instance segmentation, and many others. In image and video recognition applications, convolutional neural networks(CNNs) are widely employed. These networks provide better performance but at a higher cost of computation. With the advent of big data, the growing scale of datasets has made processing and model training a time-consuming operation, resulting in longer training times. Moreover, these large scale datasets contain redundant data points that have minimum impact on the final outcome of the model. To address these issues, an accelerated CNN system is proposed for speeding up training by eliminating the noncritical data points during training alongwith a model compression method. Furthermore, the identification of the critical input data is performed by aggregating the data points at two levels of granularity which are used for evaluating the impact on the model output.Extensive experiments are conducted using the proposed method on CIFAR-10 dataset on ResNet models giving a 40% reduction in number of FLOPs with a degradation of just 0.11% accuracy.

Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

Online Signature Verification (OSV), as a personal identification technology, is widely used in various industries.However, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational costs.Additionally, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component Analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

Intelligent Task Offloading and Collaborative Computation over D2D Communication

In this paper,the problem of computation offloading in the edge server is studied in a mobile edge computation(MEC)-enabled cell networks that consists of a base station(BS)integrating edge servers,several terminal devices and collaborators.In the considered networks,we develop an intelligent task offloading and collaborative computation scheme to achieve the optimal computation offloading.First,a distance-based collaborator screening method is proposed to get collaborators within the distance threshold and with high power.Second,based on the Lyapunov stochastic optimization theory,the system stability problem is transformed into a queue stability issue,and the optimal computation offloading is obtained by solving these three sub-problems:task allocation control,task execution control and queue update,respectively.Moreover,rigorous experimental simulation shows that our proposed computation offloading algorithm can achieve the joint optimization among the system efficiency,energy consumption and time delay compared to the mobility-aware and migration-enabled approach,Full BS and Full local.

SDN assisted Stackelberg Game model for LTE-WiFi offloading in 5G networks

The data traffic that is accumulated at the Macro Base Station(MBS)keeps on increasing as almost all the people start using mobile phones.The MBS cannot accommodate all user’s demands,and attempts to offload some users to the nearby small cells so that the user could get the expected service.For the MBS to offload data traffic to an Access Point(AP),it should offer an optimal economic incentive in a way its utility is maximized.Similarly,the APs should choose an optimal traffic to admit load for the price that it gets from MBS.To balance this tradeoff between the economic incentive and the admittance load to achieve optimal offloading,Software Defined Networking(SDN)assisted Stackelberg Game(SaSG)model is proposed.In this model,the MBS selects the users carefully to aggregate the service with AP,so that the user experiencing least service gets aggregated first.The MBS uses the Received Signal Strength Indicator(RSSI)value of the users as the main parameter for aggregating a particular user for a contract period with LTE and WiFi.Each player involved in the game tries to maximize their payoff utilities,and thus,while incorporating those utilities in real-time scenario,we obtain maximum throughput per user which experiences best data service without any lack in Quality of Experience(QoE).Thus,the proposed SaSG model proves better when compared with other game theory models,and hence an optimal data offloading is achieved.

First Principles Study on Mechanical Properties of Superhard α-Ga Boron

The mechanical properties and intrinsic hardness of the α-Ga boron phase （α-Ga-B） are studied by using the combination of first-principles calculations and a semiempirieal macroscopic hardness model. It is found that α- Ga-B is mechanically stable and possesses higher bulk/shear modulus as compared with γ-B28, a newly discovered high-pressure boron phase. The theoretical hardness of α-Ga-B is estimated to be 45 GPa, which is much higher than 38 GPa for γ-B28. The results strongly indicate that α-Ga-B is a potential superhard boron phase. To further obtain insight into the superhard nature of α-Ga-B, we simulate stress-strain curves under tensile and shear deformation. Meanwhile, the microscopic mechanism driving the tensile and shear deformation modes in α-Ga-B is discussed in detail.

Adaptive control of anti-aircraft missile launcher mounted on a mobile base

This paper presents the problem of control of anti-aircraft missile launcher mounted on a moving carrier-vehicle. Direct excitations on the vehicle from the road cause adverse vibrations of the launcher. In order to increase the precision of the guiding system in the conditions of self-propelled movement of the setup on a bumpy road, the adaptive control algorithm was proposed. Some research results of computer simulation are presented in a graphical form.

Study of the Isotope Effects of Novel Superconducting LaH₁₀-LaD₁₀and H₃S-D₃S Systems

This paper is directed to study the isotope effects of some superconducting materials that have a strong coupling coefficient λ > 1.5, and focuses on new superconducting materials whose critical temperature is close to room temperature, specifically LaH10-LaD10 and H3S-D3S systems. The Eliashberg-McMillan (EM) model and the recent Gor’kov-Kresin (GK) model for evaluating the isotope effects coefficient α were examined for these systems. The predicted values of α as a function of pressure, as compared to experimental values led to inference that these two models, despite their importance and simplicity, cannot be considered complete. These models can be used to calculate isotope effect of most superconducting materials with strong coupling coefficients but with critical reliability. The significance of studying the isotope effect lies in the possibility of identifying the interatomic forces that control the properties of superconducting materials such as electrons-mediated phonons and Coulomb interactions.

CF-BFT:A Dual-Mode Byzantine Fault-Tolerant Protocol Based on Node Authentication

The consensus protocol is one of the core technologies in blockchain,which plays a crucial role in ensuring the block generation rate,consistency,and safety of the blockchain system.Blockchain systems mainly adopt the Byzantine Fault Tolerance(BFT)protocol,which often suffers fromslow consensus speed and high communication consumption to prevent Byzantine nodes from disrupting the consensus.In this paper,this paper proposes a new dual-mode consensus protocol based on node identity authentication.It divides the consensus process into two subprotocols:Check_BFT and Fast_BFT.In Check_BFT,the replicas authenticate the primary’s identity by monitoring its behaviors.First,assume that the systemis in a pessimistic environment,Check_BFT protocol detects whether the current environment is safe and whether the primary is an honest node;Enter the fast consensus stage after confirming the environmental safety,and implement Fast_BFT protocol.It is assumed that there are 3f+1 nodes in total.If more than 2f+1 nodes identify that the primary is honest,it will enter the Fast_BFT process.In Fast_BFT,the primary is allowed to handle transactions alone,and the replicas can only receive the messages sent by the primary.The experimental results show that the CF-BFT protocol significantly reduces the communication overhead and improves the throughput and scalability of the consensus protocol.Compared with the SAZyzz protocol,the throughput is increased by 3 times in the best case and 60%in the worst case.

Phish Block:A Blockchain Framework for Phish Detection in Cloud

The data in the cloud is protected by various mechanisms to ensure security aspects and user’s privacy.But,deceptive attacks like phishing might obtain the user’s data and use it for malicious purposes.In Spite of much techno-logical advancement,phishing acts as thefirst step in a series of attacks.With technological advancements,availability and access to the phishing kits has improved drastically,thus making it an ideal tool for the hackers to execute the attacks.The phishing cases indicate use of foreign characters to disguise the ori-ginal Uniform Resource Locator(URL),typosquatting the popular domain names,using reserved characters for re directions and multi-chain phishing.Such phishing URLs can be stored as a part of the document and uploaded in the cloud,providing a nudge to hackers in cloud storage.The cloud servers are becoming the trusted tool for executing these attacks.The prevailing software for blacklisting phishing URLs lacks the security for multi-level phishing and expects security from the client’s end(browser).At the same time,the avalanche effect and immut-ability of block-chain proves to be a strong source of security.Considering these trends in technology,a block-chain basedfiltering implementation for preserving the integrity of user data stored in the cloud is proposed.The proposed Phish Block detects the homographic phishing URLs with accuracy of 91%which assures the security in cloud storage.

An Enhanced Trust-Based Secure Route Protocol for Malicious Node Detection

The protection of ad-hoc networks is becoming a severe concern because of the absence of a central authority.The intensity of the harm largely depends on the attacker’s intentions during hostile assaults.As a result,the loss of Information,power,or capacity may occur.The authors propose an Enhanced Trust-Based Secure Route Protocol(ETBSRP)using features extraction.First,the primary and secondary trust characteristics are retrieved and achieved routing using a calculation.The complete trust characteristic obtains by integrating all logical and physical trust from every node.To assure intermediate node trust-worthiness,we designed an ETBSRP,and it calculates and certifies each mobile node's reputation and sends packets based on that trust.Connection,honesty,power,and capacity are the four trust characteristics used to calculate node repu-tation.We categorize Nodes as trustworthy or untrustworthy according to their reputation values.Fool nodes are detached from the routing pathway and cannot communicate.Then,we use the cryptographic functions to ensure more secure data transmission.Finally,we eliminate the untrustworthy nodes from the routing process,and the datagram from the origin are securely sent to the target,increas-ing throughput by 93.4%and minimizing delay.

Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network(MANET).A MANET’s nodes could engage actively and dynamically with one another.However,MAN-ETs,from the other side,are exposed to severe potential threats that are difficult to counter with present security methods.As a result,several safe communication protocols designed to enhance the secure interaction among MANET nodes.In this research,we offer a reputed optimal routing value among network nodes,secure computations,and misbehavior detection predicated on node’s trust levels with a Hybrid Trust based Reputation Mechanism(HTRM).In addition,the study designs a robust Public Key Infrastructure(PKI)system using the suggested trust evaluation method in terms of“key”generation,which is a crucial component of a PKI cryptosystem.We also concentrate on the solid node authenticating process that relies on pre-authentication.To ensure edge-to-edge security,we assess safe,trustworthy routes to secure computations and authenticate mobile nodes,incorporating uncertainty into the trust management solution.When compared to other protocols,our recommended approach performs better.Finally,we use simulations data and performance evaluation metrics to verify our suggested approach’s validity Our approach outperformed the competing systems in terms of overall end-to-end delay,packet delivery ratio,performance,power consumption,and key-computing time by 3.47%,3.152%,2.169%,and 3.527%,3.762%,significantly.

EIAS:An Efficient Identity-Based Aggregate Signature Scheme for WSNs Against Coalition Attack

Wireless sensor networks(WSNs)are the major contributors to big data acquisition.The authenticity and integrity of the data are two most important basic requirements for various services based on big data.Data aggregation is a promising method to decrease operation cost for resource-constrained WSNs.However,the process of data acquisitions in WSNs are in open environments,data aggregation is vulnerable to more special security attacks with hiding feature and subjective fraudulence,such as coalition attack.Aimed to provide data authenticity and integrity protection for WSNs,an efficient and secure identity-based aggregate signature scheme(EIAS)is proposed in this paper.Rigorous security proof shows that our proposed scheme can be secure against all kinds of attacks.The performance comparisons shows EIAS has clear advantages in term of computation cost and communication cost when compared with similar data aggregation scheme for WSNs.

EECDH to prevent MITM attack in cloud computing

Cloud computing has reached the peak of Gartner hype cycle,and now the focus of the whole telecom industry is the ability to scale data storage with minimal investment.But data privacy and communication issues will occur with the increment of the cloud data storage.The key privacy concern for scalability is caused by the dynamic membership allocation and multi-owner data sharing.This paper addresses the issues faced by multiple owners through a mutual authentication mechanism using the Enhanced Elliptic Curve Diffie-Hellman(EECDH)key exchange protocol along with the Elliptic Curve Digital Signature Algorithm(ECDSA).The proposed EECDH scheme is used to exchange the secured shared key among multiple owners and also to eliminate the Man-In-The-Middle(MITM)attacks with less computational complexity.By leveraging these algorithms,the integrity of data sharing among multiple owners is ensured.The EECDH improves the level of security only slightly increasing the time taken to encrypt and decrypt the data,and it is secured against the MITM attacks,which is experimented using the AVISPA tool.

Genetic Algorithm Routing Protocol for Mobile Ad Hoc Network

Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable infrastructure.Given that nodes are highly mobile,MANET’s topology often changes.These changes increase the difficulty in finding the routes that the packets use when they are routed.This study proposes an algorithm called genetic algorithm-based location-aided routing(GALAR)to enhance the MANET routing protocol efficiency.The GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their destination.The GALAR was constructed based on a genetic optimization scheme that considers all contributing factors in the delivery behavior using criterion function optimization.Simulation results showed that the GALAR algorithm can make the probability of packet delivery ratio more than 99%with less network overhead.Moreover,GALAR was compared to other algorithms in terms of different network evaluation measures.The GALAR algorithm significantly outperformed the other algorithms that were used in the study.

Multi-Constrained QoS Opportunistic Routing by Optimal Power Tuning in Low Duty-Cycle WSNs

Designing a multi-constrained QoS (Quality of service) communication protocol for mission-critical applications that seeks a path connecting source node and destination node that satisfies multiple QoS constrains such as energy cost, delay, and reliability imposes a great challenge in Wireless Sensor Networks (WSNs). In such challenging dynamic environment, traditional routing and layered infrastructure are inefficient and sometimes even infeasible. In recent research works, the opportunistic routing paradigm which delays the forwarding decision until reception of packets in forwarders by utilizing the broadcast nature of the wireless medium has been exploited to overcome the limitations of traditional routing. However, to guarantee the balance between the energy, delay and reliability requires the refinement of opportunistic routing through interaction between underlying layers known as cross-layer opportunistic routing. Indeed, these schemes fail to achieve optimal performance and hence require a new method to facilitate the adoption of the routing protocol to the dynamic challenging environments. In this paper, we propose a universal cross-layered opportunistic based communication protocol for WSNs for guaranteeing the user set constraints on multi-constrained QoS in low-duty-cycle WSN. Extensive simulation results show that the proposed work, Multi-Constrained QoS Opportunistic routing by optimal Power Tuning (MOR-PT) effectively achieves the feasible QoS trade-off constraints set by user by jointly considering the power control and selection diversity over established algorithms like DSF [1] and DTPC [2].

Self-Similar Hermite-Gaussian Spatial Solitons in Two-Dimensional Nonlocal Nonlinear Media

We study analytically and numerically the propagation of spatial solitons in a two-dimensional stronglynonlocal nonlinear medium. Exact analytical solutions in the form of self-similar spatial solitons are obtained involvinghigher-order Hermite-Gaussian functions. Our theoretical predictions provide new insights into the low-energy spatialsoliton transmission with high fidelity.

A novel wavelet method for electric signals analysis in underwater arc welding

Electric signals are acquired and analyzed in order to monitor the underwater arc welding process. Voltage break point and magnitude are extracted by detecting arc voltage singularity through the modulus maximum wavelet （MMW） method. A novel threshold algorithm, which compromises the hard-threshold wavelet （HTW） and soft-threshold wavelet （STW） methods, is investigated to eliminate welding current noise. Finally, advantages over traditional wavelet methods are verified by both simulation and experimental results.

Digital Evidence for Database Tamper Detection

Most secure database is the one you know the most. Tamper detection compares the past and present status of the system and produces digital evidence for forensic analysis. Our focus is on different methods or identification of different locations in an oracle database for collecting the digital evidence for database tamper detection. Starting with the basics of oracle architecture, continuing with the basic steps of forensic analysis the paper elaborates the extraction of suspicious locations in oracle. As a forensic examiner, collecting digital evidence in a database is a key factor. Planned and a modelled way of examination will lead to a valid detection. Based on the literature survey conducted on different aspects of collecting digital evidence for database tamper detection, the paper proposes a block diagram which may guide a database forensic examiner to obtain the evidences.

Energy-Delay Efficient Unified Routing Protocol for Wireless Sensor Networks

One of the most important challenges in the Wireless Sensor Networks is to improve the performance of the network by extending the lifetime of the sensor nodes. So the focus is on obtaining a trade-off between minimizing the delay involved and reducing the energy consumption of the sensor nodes which directly translate to an extended lifetime of the sensor nodes. An effective Sleep-wake scheduling mechanism can prolong the lifetime of the sensors by eliminating idle power listening, which could result in substantial delays. To counter this, an anycast forwarding scheme that could forward the packet opportunistically to the first awaken node may result in retransmissions as if the chosen node falls in resource constraints. The algorithm, namely Prim’s-Dual is proposed to solve the said problem. The algorithm considers five crucial parameters, namely the residual energy of the nodes, transmission power, receiving power, packet loss rate, interference from which the next hop is determined to extend the lifetime of the sensor node. Since the proposed work is framed keeping critical event monitoring in mind, the sleep-wake scheduling is modified as low-power, high-power scheduling where all nodes are in low-power and the nodes needed for data transmission are respectively turned on to high-power mode. The integrated framework provides several opportunities for performance enhancement for conflict-free transmissions. The aim of our algorithm is to show reliable, energy efficient transfer without compromising on lifetime and delay. The further effectiveness of the protocol is verified. The results demonstrate that the proposed protocol can efficiently handle network scalability with acceptable latency and overhead.

Heterogeneous Parallel Algorithm Design and Performance Optimization for WENO on the Sunway TaihuLight Supercomputer

A Weighted Essentially Non-Oscillatory scheme(WENO) is a solution to hyperbolic conservation laws,suitable for solving high-density fluid interface instability with strong intermittency. These problems have a large and complex flow structure. To fully utilize the computing power of High Performance Computing(HPC) systems, it is necessary to develop specific methodologies to optimize the performance of applications based on the particular system’s architecture. The Sunway TaihuLight supercomputer is currently ranked as the fastest supercomputer in the world. This article presents a heterogeneous parallel algorithm design and performance optimization of a high-order WENO on Sunway TaihuLight. We analyzed characteristics of kernel functions, and proposed an appropriate heterogeneous parallel model. We also figured out the best division strategy for computing tasks,and implemented the parallel algorithm on Sunway TaihuLight. By using access optimization, data dependency elimination, and vectorization optimization, our parallel algorithm can achieve up to 172× speedup on one single node, and additional 58× speedup on 64 nodes, with nearly linear scalability.