varunr14/newsummary huggingface dataset:
Detecting covert information in images by means of steganalysis techniques has become increasingly necessary due to the amount of data being transmitted mainly through the Internet . However these techniques are computationally expensive and not much attention has been paid to reduce their cost by means of available parallel computational platforms . This article presents two computational models for the Subtractive Pixel Adjacency Model which has shown the best detection rates among several assessed steganalysis techniques . A hardware architecture tailored for reconfigurable fabrics is presented achieving high performance and fulfilling hard real time constraints . On the other hand a parallel computational model for the CUDA architecture is also proposed . This model presents high performance during the first stage but it faces a bottleneck during the second stage of the SPAM process . Both computational models are analyzed in detail in terms of their algorithmic structure and performance results . To the best of Authors knowledge these are the first design proposals to accelerate the SPAM model calculation . 


Summary: Two computational parallel models are developed using FPGA and GPU platforms. To the best of our knowledge these are the first acceleration schemas for SPAM. An architectural and performance analysis of both computational models is presented. The FPGA architecture accelerates SPAM making an optimal use of hardware resources. The GPU model accelerates SPAM s 1st stage facing a bottleneck on its 2nd stage.
Improving image resolution by refining hardware is usually expensive and or time consuming . A critical challenge is to optimally balance the trade off among image resolution Signal to Noise Ratio and acquisition time . Super resolution an off line approach for improving image resolution is free from these trade offs . Numerous methodologies such as interpolation frequency domain regularization and learning based approaches have been developed for SR of natural images . In this paper we provide a survey of the existing SR techniques . Various approaches for obtaining a high resolution image from a single and or multiple low resolution images are discussed . We also compare the performance of various SR methods in terms of Peak SNR and Structural Similarity index between the super resolved image and the ground truth image . For each method the computational time is also reported . 


Summary: Comprehensive survey of super resolution algorithms. Performance comparison among different super resolution algorithms. Super resolution techniques applied to natural images.
To solve the problems of long time delay and low reliability in existing systems a Two level Load balance Monitoring Strategy TLLBMS is proposed in this paper. In monitoring terminal level based on customizable System on Chip A2F500 monitoring terminals implement parallel Principal Component Analysis and Independent Component Analysis PCA ICA algorithm to fulfil quickly preprocessing and feature extraction of electrocardiogram ECG signals and give a pre classification and alarming mechanism. Depending on the results of pre classification monitoring terminals transmit only eigenvectors to monitoring center at a Dynamically Variable Time Interval DVTI . This will reduce the quantity of data transmission dramatically which means it will lead to high robustness of communication and short time delay. In monitoring center level monitoring server processes eigenvectors directly and realizes quick classification diagnosis and rescuing. To ensure the correctness of communication and protect data from being falsified Message Digest Algorithm MD5 is realized to verify the integrity of data. 


Summary: A Two level Load balance Monitoring Strategy TLLBMS is proposed. Parallel PCA ICA Algorithm is realized to fulfill quick preprocessing and feature extraction. Only eigenvectors are transmitted at a Dynamically Variable Time Interval DVTI . MD5 hash algorithm is realized to ensure the correctness of communication.
In this paper we investigate the achievable bit error rate performance of a transmitter preprocessing aided cooperative downlink multi carrier code division multiple access system by employing three cooperation strategies . In a multi user cooperative DL communication multi user interference at the relays which prevents the achievement of relay diversity is suppressed with the aid of the TP operated at the base station . In addition inter relay interference at the destination mobile station is mitigated by the TP employed at the relays . Furthermore the channel impulse responses required for formulating the TP at the BS is based on the quantized CIRs feedback in contrast to the perfect CIRs assumption at the relays . Our study shows that the resultant BER performance of the quantized CIRs feedback based TP at the BS coupled with the PCIR based TP at the relays remains close to that attained with the PCIRs knowledge at the BS and relays . 


Summary: The achievable BER when a DMS is supported by a cluster of relays PCIRs and VQ CIRs . The attainable BER when a DMS is assisted by one relay PCIRs and VQ CIRs . The achievable BER when one relay assists all the K DMSs PCIRs and VQ CIRs .
Anycast is an important way of communication for Mobile Ad hoc Networks in terms of resources robustness and efficiency for replicated service applications . Most of the anycast routing protocols select unstable and congested intermediate nodes thereby causing frequent path failures and packet losses . We propose a mobility and quality of service aware anycast routing scheme in MANETs that employs three models node movement stability channel congestion and link route expiry time . These models coupled with Dynamic Source Routing protocol are used in the route discovery process to select nearest k servers . A server among k servers is selected based on less congestion route expiry time number of hops and better stability . The simulation results indicate that proposed MQAR demonstrates reduction in control overheads path delays and improved packet delivery ratio compared to existing methods such as flooding DSR and load balanced service discovery . 


Summary: Anycast routing is important in terms of resources robustness and efficiency. The proposed model is based on node stability congestion and link route expiry time. Performs better in terms of control overheads PDR and end to end delay.
Graphical abstract The system bifurcation diagrams for x 1 are shown for harmonic excitation force 1.14 10 3 0.1 1.0 and 2.0 respectively . There are two intervals of nonlinear motion at 0 0.006 and 1.248 2.0 respectively when harmonic excitation force increases to 2.0 . Image graphical abstract 


Summary: Highlight Analyze the nonlinear cubic stiffness coefficient ratio effect for ultrasonic cutting system. Ultrasonic cutting system is designed for stable and synchronized vibration when the excitation force is controlled at 2.0 with the interval of nonlinear cubic stiffness coefficient ratio 0.006 1.248. For small values of the non periodic motion is more obvious meaning that the larger hardening cubic stiffness coefficient causes more severely unsteady behavior. For the variation of dynamic behavior ultrasonic cutting system includes periodic quasi periodic and chaotic motions under specific operating parameters.
This paper addresses the problem of identifying meaningful patterns and trends in data via clustering . The clustering framework that we propose is based on the generalized Dirichlet distribution which is widely accepted as a flexible modeling approach and a hierarchical Dirichlet process mixture prior . A main advantage of the adopted hierarchical Dirichlet process is that it provides a principled elegant nonparametric Bayesian approach to model selection by supposing that the number of mixture components can go to infinity . In addition to capturing the structure of the data the combination of hierarchical Dirichlet process and generalized Dirichlet distribution is shown to offer a natural efficient solution to the feature selection problem when dealing with high dimensional data . We develop two variational learning approaches for learning the parameters of the proposed model . The batch algorithm examines the entire data set at once while the incremental one learns the model one step at a time . The utility of the proposed approach is demonstrated on real applications namely face detection facial expression recognition human gesture recognition and off line writer identification . The obtained results show clearly the merits of our statistical framework . 


Summary: A statistical framework based on hierarchical Dirichlet processes and generalized Dirichlet distribution is developed. The framework simultaneously performs model parameters estimations as well as model complexity determination. The learning of the model is done via variational Bayes inference. The efficiency of the proposed algorithm is validated via challenging applications.
Thresholding is a popular image segmentation method that converts a grayscale image into a binary image . In this paper we propose a cloud model based framework for range constrained thresholding with uncertainty and improve four traditional methods . The method involves four major steps including representing the image using cloud model estimating the automatic threshold for gray level ranges of object and background implementing image transformation to focus on mid region of the image and determining the binary threshold within the constrained gray level range . Cloud model can effectively represent various visual properties of the image such as intensity based class uncertainty intra class homogeneity and between class contrast . The approach is validated both quantitatively and qualitatively . Compared with the traditional state of art algorithms on a variety of synthetic and real images with or without noisy as well as laser cladding images the experimental results suggest that the presented method is efficient and effective . 


Summary: A cloud model based framework for range constrained thresholding with uncertainty. Improving four traditional methods under the new framework. Representing the image using cloud model. Implementing image transformation to focus on mid region of the image. Cloud model based framework is efficient and effective.
Current Critical Infrastructures need intelligent automatic active reaction mechanisms to protect their critical processes against cyber attacks or system anomalies and avoid the disruptive consequences of cascading failures between interdependent and interconnected systems . In this paper we study the Intrusion Detection Prevention and Response Systems that can offer this type of protection mechanisms their constituting elements and their applicability to critical contexts . We design a methodological framework determining the essential elements present in the IDPRS while evaluating each of their sub components in terms of adequacy for critical contexts . We review the different types of active and passive countermeasures available categorizing them and assessing whether or not they are suitable for Critical Infrastructure Protection . Through our study we look at different reaction systems and learn from them how to better create IDPRS solutions for CIP . 


Summary: We study IDPRS solutions for CIP considering their components and constraints. We design a methodological framework for IDPRS solutions within critical scenarios. ICS need automated intelligent solutions for early detection and protection. Current IDPRS solutions for CIP lack automatic active reaction mechanisms. We give recommendations for adaptation or development of IDPRS solutions for CIP.
Backbone nodes are effective for routing in wireless networks because they reduce the energy consumption in sensor nodes . Packet delivery only occurs through the backbone nodes which depletes the energy in the backbone drastically . Several backbone construction algorithms including energy aware virtual backbone tree virtual backbone tree algorithm for minimal energy consumption and multihop cluster based stable backbone tree fail to form a complete backbone when converting important nodes such as a cut vertex tree node to a non backbone node . Thus we propose a fault tolerant virtual backbone tree algorithm that addresses all of these conflicts and we give theoretical derivations of the bounds for the probability that a sensor node can connect with the backbone . Furthermore randomized FTVBT improves FTVBT by redistributing non tree nodes randomly among all the eligible tree nodes based on their fitness values thereby decreasing the rapid depletion of energy in a particular node and increasing the network lifetime . We performed simulations in NS2 and analyzed the experimental results . 


Summary: Need of energy efficient backbone routing for WSNs. FTVBT virtual backbone tree construction algorithm proposed to minimize energy consumption. Identifies hotspots distributes dependents across tree nodes and the distribution is also bounded. Randomized FTVBT elects tree nodes based on a weight function and increases network lifetime.
This work proposes a stochastic shape optimization method for continuous structures using the level set method . Such a method aims to minimize the expected compliance and its variance as measures of the structural robustness . The behavior of continuous structures is modeled by linear elasticity equations with uncertain loading and material . This uncertainty can be modeled using random variables with different probability distributions as well as random fields . The proper problem formulation is ensured by the proof of the existence colorrev of solution under certain geometrical constraints on the set of admissible shapes . The proposed method addresses the stochastic linear elasticity problem in its weak form obtaining the explicit expressions for the continuous shape derivatives . Some numerical examples are presented to show the effectiveness of the proposed approach . 


Summary: Robust shape optimization considering uncertainty in loads and material is proposed. Stochastic shape derivatives of continuous structures are obtained. The existence of a solution for the robust shape optimization problem is proved. Different probability distributions and random fields are considered in experiments.
The biometrics the password and the storage device are the elements of the three factor authentication . In 2013 Yeh et al . proposed a three factor user authentication scheme based on elliptic curve cryptography . However we find that it has weaknesses including useless user identity ambiguous process no session key and no mutual authentication . Also it can not resist the user forgery attack and the server spoofing attack . Moreover Khan et al . propose a fingerprint based remote authentication scheme with mobile devices . Unfortunately it can not withstand the user impersonation attack and the De synchronization attack . Furthermore the user s identity can not be anonymous either . To overcome the disadvantages we propose a new three factor remote authentication scheme and give a formal proof with strong forward security . It could provide the user s privacy and is secure . Compared to some recent three factor authentication schemes our scheme is secure and practical . 


Summary: We point out that Yeh et al. s scheme is not secure because it has several disadvantages in security. We point out that Khan et al. s scheme is not secure with some weaknesses. We present a new three factor scheme based on ECC. We prove our scheme secure with a formal proof and analysis. By comparing with some latest schemes our scheme is more practical for application due to the security and efficiency.
A honeypot system can be deployed to decoy and record malicious intrusions over the Internet . However events logged by a honeypot can rapidly accumulate an enormous amount of data which an administrator will be unable to handle . The proposed system combines episode mining and pruning and allows an administrator to identify suspected intrusions and thus focus his energy on addressing them instead of reading enormous amounts of raw data . An attack episode is composed of a series of events and represents an Internet intrusion as a series of relevant events occurring to a victim host in a specific sequence . Due to the variety of internet attacks this paper focuses on discovering attack episodes for the Server Message Block protocol which provides Microsoft Windows Network services . Experiments show that the proposed approach can locate suspicious episodes that are very likely novel attacks from an immense amount of logged data . 


Summary: All on line attempts to access a honeypot can be regarded as attacks. A honeypot s logs can be very large which an administrator will be unable to handle. We apply serial episode mining and two round pruning to identify suspected attack. The experiments conducted in this paper focus on port 445 for the SMB protocol.
This paper presents a novel topology optimization method for designing structures with multiphase embedded components under minimum distance constraints in the level set framework . By using the level set representation for both the component layout and the host structure topology the shapes of the components can be easily preserved and optimal structural topologies with smooth boundary material interface can be obtained . With the purpose of preventing the components moving too close to each other a minimum distance constraint based on virtual boundary offset is proposed . Different from existing distance detection methods relying on explicit topology representation the proposed constraint is imposed as a unified integral form for which the design sensitivity can be readily obtained . Moreover this constraint is effective for detecting the distance between any complex shaped components . Several numerical examples are presented to demonstrate the validity and effectiveness of the proposed method . 


Summary: We present topology optimization with distance control of embedded components. Both structural topology and multi component layout are represented by level set. Specified geometries of component can be well preserved thanks to level set model. A unified level set based distance constraint for arbitrary components is proposed. Explicit integral form of the distance constraint facilitates sensitivity analysis.
With the rapid advancement in very large scale integration technology it is the utmost necessity to achieve a reliable design with low power consumption . The Quantum dot Cellular Automata can be such an architecture at nano scale and thus emerges as a viable alternative for the current CMOS VLSI . This work targets design of logic module in QCA . It reports a modular design methodology to build the fault tolerant 1 multiplexer with optimized wire crossings delay and power consumption . A 2 1 QCA multiplexer is proposed as the basic logic module that in turn is utilized to synthesize 4 1 and 8 1 multiplexers . It shows significant achievement in terms of clock speed wire crossing fault tolerance and power consumption over the existing designs . The effectiveness of proposed multiplexer is further established through synthesis of configurable logic block for field programmable gate arrays . 


Summary: A modular design methodology around multiplexer is designed. This work make a trade off between modular design and its reliability associated with fault tolerance and energy consumptions. Cascading lower order multiplexer to synthesize higher order multiplexer mitigating wire crossing and delay. Application of proposed design in CLB is also done.
A major optimization problem in the synthesis of sequential circuits is State Assignment or State Encoding in Finite State Machines . The state assignment of an FSM determines the complexity of its combinational circuit and thus area delay testability and power dissipation . Since optimal state assignment is an NP hard problem and existing deterministic algorithms produce solutions far from best known solutions we resort to the use of non deterministic iterative optimization heuristics . This paper proposes the use of cuckoo search optimization algorithm for solving the state assignment problem of FSMs with the aim of minimizing area of the resulting sequential circuit . Results obtained from the CSO algorithm are compared with those obtained from binary particle swarm optimization algorithm genetic algorithm and the well known deterministic methods of NOVA and JEDI . The results indicate that CSO outperforms deterministic methods as well as other non deterministic heuristic optimization methods . 


Summary: A major optimization problem in synthesis of sequential circuits is State Assignment SA . Cuckoo search optimization CSO algorithm is employed for solving the SA problem. CSO targets area minimization of synthesized sequential circuits. CSO results outperform deterministic and non deterministic heuristic optimization methods.
Genetic programming is an evolutionary method that allows computers to solve problems automatically . However the computational power required for the evaluation of billions of programs imposes a serious limitation on the problem size . This work focuses on accelerating GP to support the synthesis of large problems . This is done by completely exploiting the highly parallel environment of graphics processing units . Here we propose a new quantum inspired linear GP approach that implements all the GP steps in the GPU and provides the following significant performance improvements in the GP steps elimination of the overhead of copying the fitness results from the GPU to the CPU and incorporation of a new selection mechanism to recognize the programs with the best evaluations . The proposed approach outperforms the previous approach for large scale synthetic and real world problems . Further it provides a remarkable speedup over the CPU execution . 


Summary: A new quantum inspired linear genetic programming system that runs on the GPU. Allows the synthesis of solutions for large scale real world problems. Eliminates the overhead of copying the fitness results from the GPU to the CPU. Proposes a new selection mechanism to recognize the programs with best evaluations. Improves performance of the GP execution through exploiting the GPU environment.
Supervisory control and data acquisition systems currently use the polling technique for monitoring electric utility networks . Unfortunately conventional SCADA systems do not suit the needs of smart grids in terms of the required data rate . Polling based wireless networks can extend the capabilities of SCADA systems as they provide low cost transceivers and bounded packet delay . However the harsh environment of power stations negatively impacts the performance of wireless links . This paper introduces a field measurement based study that focuses on the effect of power system noise and transients on packet delivery reliability of Zigbee and WiFi polling based wireless networks . Extensive experiments show that the electromagnetic interference emitted from high voltage substations during normal operation conditions do not significantly affect wireless communication in the gigahertz range . Moreover we analytically and experimentally demonstrate that abnormal operation conditions of power systems may negatively impact the reliability of packet delivery in polling based wireless networks . Furthermore we show that this negative impact can be mitigated by following some proposed technical considerations regarding the wireless standard the operating frequency the location and the number of wireless transceivers used . 


Summary: We measure and model the path loss for wireless links in low voltage substations. No effect of EMI on Zigbee links is normally observed in high voltage substations. The probability of unsuccessful polling transactions P ut is analytically studied. Sending multiple copies of data by WiFi 5GHz transmitters achieves near zero P ut