Motivated by a Bayesian Dirichlet prior model , a computationally efficient stochastic approximation version of the marginal likelihood is proposed and large-sample theory is presented . By restricting the support to a finite grid , a simulated annealing method is employed to maximize the marginal likelihood and estimate the support . Real and simulated data examples show that this novel stochastic approximation and simulated annealing procedure compares favorably with existing methods . The search for a solution is made in an iterative manner , by replacing one station , analyzing the result , and deciding whether to keep the solution or not . Due to the enormous number of possible combinations even for small networks , a structured search method , the simulated annealing method , is used to obtain the final network . This study presents a novel co-operative agents approach , Ant Colony Optimization algorithm ( ACO ) scheme , for solving the scheduling mixed-model assembly lines . The results show that the solution which ant algorithm produces is better than the one which Toyota 's goal chasing algorithm , simulated annealing algorithm and genetic algorithm produce . Finally , this example may extend to a bigger scale , and the satisfied solutions , benchmark results and CPU time to generate a satisfied tour are given . The methods were employed for optimising two challenging optimal control problems of fed-batch bioreactors . The main results show that adaptive random search and genetic algorithms are superior at solving these problems than the simulated annealing based method , both in accuracy and in the number of function evaluations . Since not all the links have the same endpoints , some full duplex links leave a duct at points referred to as junction points . In this paper , we propose a Simulated Annealing based algorithm to optimize the topology design of embedded Ethernet networks . This algorithm finds the (near-)optimal positions of a given number of switches and their connections to given nodes . Given an initial rectilinear Steiner tree , the rectilinear Steiner tree can be transformed into a Steiner routing tree by deleting all the corner points in the rectilinear Steiner tree . Based on the definition of the routing flexibility in a Steiner routing tree and the timing-constrained location flexibility of the Steiner-point in any Y-type wire , the simulated-annealing-based approach is proposed to construct a better timing-constrained flexibility-driven Steiner routing tree by reassigning the feasible locations of the Steiner points in all the Y-type wires . The experimental results show that our proposed algorithm , STFSRT , can increase about 0.005 - -0.020 % wire length to improve about 43 - -173 % routing flexibility for the tested benchmark circuits . The proposed evolutionary algorithm showed very satisfactory results in testing on well-known benchmark problems from TSPLIB . Comparisons is performed with the results of numerous implementations of well-known meta-heuristic approaches from the literature , such as ; Honey Bee Mating Optimization , Particle Swarm Optimization , Tabu Search , Greedy Randomized Adaptive Search Procedure , Genetic Algorithm , Ant Colony Optimization , etc . Having found 9 new best solution out of 20 cases , the computational study shows that the GMMS algorithm is a competitive tool for solving the PTSP . Obtaining an optimal solution for this type of complex , large-sized problems in reasonable computational time is extremely difficult . A meta-heuristic method , based on genetic algorithms , is thus proposed and applied to the given problem in order to obtain a good and near-optimal solution , especially for large sizes . Further , the efficiency of the proposed algorithm , based on various test problems , is compared with the Lingo 8.0 software . We created an XML format for the constraints such that any new real world constraint is fit for the evaluation procedure by only expressing it in the XML format . We have tested the approach on real employee rostering data where the evaluation procedure was called from a meta-heuristic procedure . The main advantages of the proposed approach are the fast evaluation of constraints and the simple extendibility of the model with extra constraints . Unlike widely available literatures in hazardous material ( HAZMAT ) transportation that basically aim at finding non dominated paths for a given origin-destination pair , our main focus in this study is on vehicle routing problem with time window ( VRPTW ) aspect of HAZMAT transportation problem that has received very less attention in literatures . We present a new multi-objective optimization model and its meta-heuristic solution technique using Ant Colony System for HAZMAT routing . In contrast to existing local routing models , we consider minimization of risk and transportation cost in both route choice and routing phases of transportation process . This paper addresses this problem with the realistic assumptions of existing time constraints for trucks to depart from the origin and arrive at their destination , and waiting as well as detour possibility . As this problem is NP-hard even in its very simplified forms , a new meta-heuristic solution methodology inspired from ant colony optimisation is proposed to deal with it . Some sample problems of small to large size are generated and solved with our solution approach . In this paper authors will present analysis and implementation of possible solutions of vehicle routing problem that is based on simulated annealing method , which belongs to the category of meta-heuristic problem solving approaches . The described problem is rather complex linear programming problem from the field of operation research . Tabu search is a meta-heuristic problem solving technique that , when applied carefully , provides near optimal solutions in a very short time . In this paper , we have described the use of tabu search for solving problems related to very large scale integrated ( VLSI ) circuit design automation . Many supply chain planning efforts result in sizable and often nonlinear optimization problems that are difficult to solve using standard solution methods . Meta-heuristic and heuristic solution methods have been developed and applied to tackle such modeling complexities . This paper aims to compare and analyze the performance of three meta-heuristic algorithms in solving a nonlinear green supply chain planning problem . Ant Colony Optimization ( ACO ) has been successfully applied to a wide number of complex and real domains . From classical optimization problems to video games , these kind of swarm-based approaches have been adapted , to be later used , to search for new meta-heuristic based solutions . This paper presents a simple ACO algorithm that uses a specifically designed heuristic , called common-sense , which has been applied in the classical video game Lemmings . An interference-aware MDMP routing is formulated for single-radio networks by employing a time-division link scheduling method to eliminate interference , based on which an optimal path selection associated with the minimum reconstruction distortion of received video may be obtained with high complexity . A heuristic solution of low complexity is developed for the interference-aware multipath routing , by introducing a path metric taking into account intra-flow interference , inter-flow interference , link bandwidth and link ' ' up ' ' probability . The second problem touches on unbalanced-path based joint MD video coding and packet allocation for multiple paths of varying bandwidth and loss probabilities . The results on a Dutch case study indicate that adopting a demand-oriented approach for the management of disruptions not only is possible , but may lead to significant improvement in passenger satisfaction , associated with a low operational cost of the disposition timetable . For a more efficient investigation of the multiple dimensions of the problem , a heuristic solution algorithm based on adaptive large neighborhood search is also presented . The timetable is optimized using operators inspired directly from recovery strategies used in practice ( such as canceling , delaying or rerouting trains , or scheduling additional trains and buses ) , and from optimization methods ( e.g. , feasibility restoration operators ) . In contrast , in Consistent Neighborhood Search ( CNS ) , after each variable assignment , the conflicting variables are deleted to keep the partial solution feasible , and the search can stop when all the variables have a value . In this paper , we formally propose a new heuristic solution method , CNS , which has a search behavior between exhaustive tree search and local search working with complete solutions . We then discuss , with a unified view , the great success of some existing heuristics , which can however be considered within the CNS framework , in various fields : graph coloring , frequency assignment in telecommunication networks , vehicle fleet management with maintenance constraints , and satellite range scheduling . The location routing problem ( LRP ) , known to be the combination of the facility location and vehicle routing problems , is solved in the literature by either assuming planar or spherical surfaces . In this work , the manifold location routing problem ( MLRP ) , that is an LRP on Riemannian manifold surfaces , is explained for the 2-facility ( 2-MLRP ) case with the corresponding heuristic algorithm solution . The 2-MLRP problem is a mixed integer non-linear programming problem that is determined to be NP-hard . But due to improper arrangement of bins and unavailability of proper optimization techniques the space gets wasted and damage of goods occur in container . To utilize the maximum occupancy of the container in this project we are proposing the heuristic algorithm solution with weight which consider the dimension of the items and container like length , height , width , weight . This project aims to generate a system for optimizing a container space using user friendly Excel VBA . However we find that the relative performance of the heuristic algorithm deteriorates with increasing degree of spatial dispersion of unelectrified settlements , as well as increasing spatial remoteness of the settlements from the grid network , suggesting that the effectiveness of the heuristic algorithm is context specific . Further , we find that allocation of off grid sources in the heuristic algorithm solution is often significantly greater than in the MINLP model suggesting that heuristic methods can overstate the role of off-grid solutions in certain situations . A generalized hub location and VRM is developed which encompasses the determination of the number , locations of hubs and depots and their assigned service areas as well as the routes between the demand points and consolidation points ( depots , hubs ) . Because this task leads to a very complex transport design problem , a heuristic solution concept has to be developed . The applicability to a case study is demonstrated ; the case of completely new system configuration for Austria is considered . In addition to the typical costs associated with facility operation and transportation , we explicitly consider the pivotal online customer service rate between the distribution centers ( DCs ) and their assigned customers . This study proposes a heuristic solution scheme to resolve this multi-objective programming problem , by integrating genetic algorithms , a clustering analysis , a Non-dominated Sorting Genetic Algorithm II ( NSGA-II ) , and a Technique for Order Preference by Similarity to Ideal Solution ( TOPSIS ) . Several experiments are simulated to demonstrate the possibility and efficacy of the proposed approach . A computational study shows that this approach out-performs the previous methods found in the literature for a set of randomly generated instances . A heuristic version of the solution approach is also proposed and tested on larger instances . Considering this challenging , but realistic practical scenario , various sub-optimal M-MIMO detection structures are evaluated in terms of complexity and bit error rate ( BER ) performance trade-off . Specifically , the successive interference cancellation , lattice reduction ( LR ) and likelihood ascent search ( LAS ) schemes , as well as a hybrid version combining such structures with conventional linear MIMO detection techniques are comparatively investigated , aiming to improve performance . Hence , to provide a comprehensive analysis , we consider the number of antennas varying in a wide range ( from conventional to massive MIMO condition ) , as well as low and high modulation orders , aiming to verify the potential of each MIMO detection technique according to its performance – complexity trade-off . During recent years stochastic algorithms have deserved much attention from the computational biology research communities . In this paper we derive a hybrid version of the formerly known Metabolic Algorithm that is enriched with stochastic features , whose impact on the dynamics of the system is especially prominent when the amount of metabolite becomes smaller . This hybrid procedure represents a first attempt to let the Metabolic Algorithm deal with low concentrations of substances according to a non-deterministic policy . The proposed CSLM algorithm is trained on XOR and OR datasets . The experimental results show that the proposed CSLM algorithm has better performance than other similar hybrid variants used in this study . The latter can be seen as a generalization of SLS-resolution . It provides a basis for hybrid implementations combining Prolog with constraint solvers ( such as ontology reasoners ) . Soundness of the operational semantics is proven . The national regulatory bodies need to address the potential issues of wireless systems mutual interference ( either accidental or deliberate ) and guarantee their stakeholders efficiently policed national spectrum usage . One of the cornerstones in this aspect is the accurate and real-time geolocation of wireless transmitters using methods such as TDoA , AoA and/or hybrid ones . However , the practical implementation of a fully operable spectrum monitoring and wireless transmitters geolocation capable system is a challenging task . Abstract Knowledge of trip travel times serves an important role in transportation management and control . Existing travel time estimation approaches generally cover empirical ones , statistical ones and hybrid ones . Despite strong tractability , the empirical approaches can not sufficiently capture diverse travel time distributions ( TTDs ) and often encounter some issues ( e.g. , assumption of a predefined distribution , failure of significance tests ) . The communication system designer must not only integrate the antenna and its associated circuits but also the phase shifter , amplifiers and control circuits . the use of integrated monolithic antennas fabricated in a single , high resolution , integrated circuit process offers the potential of closer dimensional accuracy , ruggedness , simplicity and better reliability and reproducibility in comparison to their hybrid counterparts , being excellent candidates for integrated quasi-optical power combining systems . In previous chapters , we have emphasized on the planar antenna technology and described various techniques to improve the directivity of the antenna . The use of hybrid algorithms for solving real-world optimization problems has become popular since their solution quality can be made better than the algorithms that form them by combining their desirable features . The newly proposed hybrid method which is called Hybrid Differential , Particle , and Harmony ( HDPH ) algorithm is different from the other hybrid forms since it uses all features of merged algorithms in order to perform efficiently for a wide variety of problems . In the proposed algorithm the control parameters are randomized which makes its implementation easy and provides a fast response . To provide the best network performance , the MAC protocol shall be adaptive enough in order to satisfy the traffic intensity and variation of parking sensors . In this paper , we study the heavy-tailed parking and vacant time models from SmartSantander , and then we apply the traffic model in the simulation with four different kinds of MAC protocols , that is , contention-based , schedule-based and two hybrid versions of them . The result shows that the packet interarrival time is no longer heavy-tailed while collecting a group of parking sensors , and then choosing an appropriate MAC protocol highly depends on the network configuration . Our implementation analyzes multi-million-line programs quickly and in just a fraction of the memory required by prior approaches . When memory alone is insufficient , our approach falls back on disk using several hybrid configurations tuned to put all available resources to good use . This problem is important in the maritime domain where Autonomous Underwater Vehicles ( AUVs ) can greatly benefit from an Autonomous Surface Vehicle ( ASV ) acting as a Communication Relay ( CR ) and/or a Navigational Aid ( NA ) , typically using acoustic communication . The proposed approach is an extension of Hybrid-A*(HA * ) , a hybrid version of A * which enables the derivation of paths that are obstacle free and feasible by the vehicle . The proposed algorithm finds a solution , if it exists , for scenarios where the leader and the follower operate under the same kinematic constraints as well as when they differ . While the globally - trained combination of neural nets and HMMs for speech and handwriting goes back to the 1990s , only due to recent developments HMM / DNN hybrid modeling became dominant in ASR . In the context of these hybrid models , the use of CNNs is relatively recent . CNNs were shown to achieve state of the art performance on the benchmark datasets Broadcast News and Switchboard 300 . For that reason , we argue that SMT provides a more appropriate architecture to find an initial alignment between the languages , but NMT is ultimately a better architecture to model the translation process . Following this observation , we propose a hybrid approach that uses unsupervised SMT to warm up a dual NMT model trained through iterative back - translation . More concretely , we first train two SMT systems in opposite directions as described in Section [ reference ] , and use them to assist the training of another two NMT systems in opposite directions . In this paper , we investigate the problem of designing the minimum number of required mobile elements tours such that each sensor node is either on the tour or one hop away from the tour , and the length of the tour to be bounded by pre-determined value L. To address this problem , we propose heuristic-based solution . This solution works by directing the mobile element tour towards the highly dense area in the network . The caching point nodes are selected with the aim of reducing the energy expenditures due to multi-hop forwarding . We provide a heuristic-based solution for this problem . We evaluate the performance of our algorithm by comparing it to the best well-known algorithms from the literature . The gathering points/nodes are selected with the aim of reducing the energy expenditures due to multi-hop forwarding . We also present a heuristic-based solution and validate its performance through an extensive set of experiments . While there exist clear methods for validating and ensuring the quality of solutions generated by forest planning heuristic techniques , the use of these methods in the literature varies from one situation to the next . Based on our experience developing and using heuristic forest planning techniques , we describe six levels of heuristic validation that are currently in use , ranging from no validation ( Level 1 ) on one end of the spectrum , to the comparison of heuristic technique solutions with an exact solution obtained using mathematical programming methods ( Level 6 ) on the other end . The reasons why authors may choose or reviewers may require levels of validation are proposed . The knowledge acquired can be expressed in different knowledge representations such as computer programs , first-order logical relations , or fuzzy Petri nets ( FPNs ) . In this paper , we present a flexible knowledge discovery system called generic genetic programming ( GGP ) that applies genetic programming ( GP ) and logic grammars to learn knowledge in various knowledge representation formalisms . An experiment is performed to demonstrate that GGP can discover knowledge represented in FPNs that support fuzzy and approximate reasoning . However , they are restricted in the computer languages in which programs can be induced . Generic Genetic Programming ( GGP ) is a novel , powerful , and general approach that combines ILP and GP . Using GGP , programs in various programming languages such as Lisp , Prolog , and Fuzzy Prolog can be evolved . In this study we integrate a Self-guided Genetic Algorithm with dominance properties ( DPs ) which is named DP-Self-guided GA . Self-guided GA [ 8 ] is belonged to the category of evolutionary algorithms based on probabilistic models ( EAPM ) and it is effective and efficient in solving the scheduling problems . This paper proposed a new algorithm , termed as Self-Guided genetic algorithm , which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models ( EAPM ) . Previous EAPM research explicitly used the probabilistic model from the parental distribution , then generated solutions by sampling from the probabilistic model without using genetic operators . The IEEE 802.11 standards do not provide any mechanism for load distribution and as a result user quality of service ( QoS ) degrades significantly in congested networks where large numbers of users tend to congregate in the same area . The objective of this paper is to provide load balancing techniques that optimise network throughput in areas of user congestion , thereby improving user QoS. Specifically , we develop micro-genetic and standard genetic algorithm approaches for the WLAN load balancing problem , and we analyse their strengths and weaknesses . We also compare the performance of these algorithms with schemes currently in use in IEEE 802.11 WLANs . A kind of improved symmetry and a kind of improved asymmetry genetic algorithm were introduced to solve the general multi-objective optimization problem according to the different important degree of objective functions and constraint functions . It can search the optimum solution from many initial points at the same time . Psychologists and statisticians have developed a number of useful methods for detecting and/or validating taxa underlying psychological disorders . These methods include the taxometric techniques developed by Meehl and his colleagues ( 7 ) , admixture models ( 8) , molecular genetic studies ( 9 ) , and multivariate behavior genetic studies ( 10 ) . Although none of these methods by itself can provide definitive confirmation of taxa , consistent findings of taxonicity across multiple methods offer converging evidence that a psychiatric condition is categorical at a latent level ( 11 ) . It plays an important role in the state of the art trend where traditional medical treatments are to be replaced by personalized genetic medicine , i.e. individualized therapy based on the patient 's genetic heritage . However , fast , robust , and precise image processing tools are required for the prospective practical use of microarray-based genetic testing for predicting disease susceptibilities and drug effects in clinical practice , which require a turn-around timeline compatible with clinical decision-making . In this paper we have developed a fully-automated image analysis platform for the rapid investigation of hundreds of genetic variations across multiple genes . We first define the absolute deviation of random fuzzy variable and then employ it as risk measure to formulate mean-absolute deviation portfolio optimization models . To find the optimal portfolio , we design random fuzzy simulation and simulation-based genetic algorithm to solve the proposed models . Finally , a numerical example for synthetic data is presented to illustrate the validity of the method . The main goal of the Ward group is the development and optimization of artificial metalloenzymes for their use in asymmetric catalysis . These hybrid catalysts , resulting from an incorporation of a metal – containing cofactor within a protein or DNA scaffold , can be optimized by either chemical or genetic means . The present work aims at the genetic optimization of the protein scaffold and exploration of the use of the resulting hybrid catalysts in nano – applications . This scheme was implemented to synthesize bandpass filters for 802.11 a and HIPERLAN wireless LAN applications in the range of 5–6 GHz . This research also develops a novel neuro-genetic design centering methodology for yield enhancement and design for manufacturability of microwave devices and circuits . A neural network model is used to calculate yield using Monte Carlo methods . The method proposed in this paper is a data analysis method that intelligently analyzes power big data and realizes the load forecasting of power grid . The method calls for the corresponding data from each database of big data platform by accepting the load forecast request from the client , and performs the load forecasting in the big data by improving the gray model of chaos genetic algorithm ( CGA ) . After the completion of load forecasting , the final output to the client load forecasting results . In order to investigate the skill learning of heuristics problem-solving , we took simplified 4 × 4 Sudoku as a new task . Brain activity was recorded when participants solved Sudoku problems with fMRI , and compared before and after plenty of practice . Subjects performed a set of heuristic problems in functional Magnetic Resonance Imaging ( fMRI ) environment . Then a computational cognitive model is set up to simulate processes of information processing of the heuristics problem solving . During the modeling , we found that two main factors , visual attention and goal control , are responsible for speeding up heuristic search in problem solving , where visual attention captures a target selectively with the goal state-directed control . Additional cuts are achieved using aspiration windows and principal variation search . Other pruning strategies include null move pruning ( which assumes a pass move should be worse than any variation , in positions that are unlikely to be in zugzwang , as determined by simple heuristics ) , futility pruning ( which assumes knowledge of the maximum possible change in evaluation ) , and other domain - dependent pruning rules ( which assume knowledge of the value of captured pieces ) . The search is focused on promising variations both by extending the search depth of promising variations , and by reducing the search depth of unpromising variations based on heuristics like history , static - exchange evaluation ( SEE ) , and moving piece type . None of these methods apply to gradient - based learning of high - capacity models such as modern deep networks . [ reference ] and [ reference ] propose heuristics based on uncertainty and gradient magnitudes , respectively , and apply their methods to convolutional neural networks . Another recent work uses multi - agent reinforcement learning [ reference ] . Nevertheless , natural defects call for protection through design for fault detection and reliability . In this paper , we present implications of fault detection cryptographic architectures ( Pomaranch in the hardware profile of European Network of Excellence for Cryptology ) for smart infrastructures . In addition , we present low-power architectures for its nine-to-seven uneven substitution box [ tower field architectures in GF(33 ) ] . Our protocols are specifically designed to be highly efficient in terms of communication and bandwidth , requiring only one or two rounds of communication and thus enabling scalable parallel queries . We rigorously prove our protocols to be secure based on cryptographic games and instantiate our technique with three of the most important types of biomedical data – namely DNA , microRNA expression , and DNA methylation . Our experimental results show that our protocols can compute a similarity query over a typical number of positions against a database of 1,000 patients in a few seconds . Detection and Analysis of a potential malware specifically , used for ransom is a challenging task . Recently , intruders are utilizing advance cryptographic techniques to get hold of digital assets and then demand ransom . It is believed that generally , the files comprise of some attributes , states , and patterns that can be recognized by a machine learning technique . Here , we are using 3-LSB bit substitution technique for image steganography and Bit Plane slicing algorithm is used to slice the mosaic image which is used as cover image . Advance cryptographic algorithm like Blowfish is used for encryption and decryption of data and images on both sender and receiver side . Keywords : Steganography ; Image Steganography ; Cryptography ; LSB ; Encryption ; Decryption ; PSNR ratio ; Mosaic Image ; Cover Media ; Slicing . The most precious resource that frequently comes under attack is the distributed ( network ) file system , because this usually holds the entire contents of a company 's electronically stored information . This study starts by claiming that microprocessors have just become fast enough to support full cryptographic controls for all read and write operations in performance-critical systems . This claim is proven through the design , implementation and analysis of a decentralized security system for network attached disks . Integrity and authentication using keyed-hash and RSA as well as confidentiality using RC5 are tested . This analysis demonstrates that full cryptographic controls are feasible in a distributed network file system , by showing the performance overhead for including signature , hash and encryption algorithms on various embedded and workstation computers . The results from these experiments are used to predict the performance impact using three proposed network disk security schemes . This may completely jeopardise the security of embedded watermarks once this information is revealed . To overcome this problem recent work on secure watermark detection proposes cryptographic proofs that perform the detection on concealed detection information . The proposed solutions focus on correlation-based detection and can be applied to any watermarking scheme whose detection criteria can be expressed as a polynomial relation between the quantities required for the detection . Proofs in the standard model ( as opposed to the random oracle model ) are the gold standard of cryptographic proofs , and most cryptographic protocols strive to achieve them . The burgeoning complexity of cryptographic proofs implies that such proofs are prone to errors , and are hard to write , much less verify . In this paper , we propose techniques to generate automated proofs for attribute-based encryption schemes in the standard model , building upon a prototype tool , AutoG&P due to Barthe et al. In doing so , we significantly expand the scope of AutoG&P to support a rich set of data types such as multi-dimensional arrays , and constructs commonly used in cryptographic protocols such as monotone-access structures , and linear secret-sharing schemes . We are going to implement the " The Simon and The Speck “ the cryptography algorithm on a FPGA . Keywords-Cryptographic algorithm , block cipher , FPGA . Introduction : In today ’s world while communicating through an untrusted media or network such as internet it is necessary to keep the system secure , The cryptography is the study of techniques which can used to protect secrets and secure personal information . Implementation of cryptographic algorithms on hardware runs faster than on software and at the same time offering more intrinsic security . The paper involves the design of high performance architecture of a well known cryptographic algorithmRC4 stream cipher and its performance analysis . The cipher architecture is designed using VerilogHDL and finally programmed into Spartan3 FPGA . The optimal cloner is realized by quantum channels with memory , and greatly outperforms the optimal measure-and-reprepare cloning strategy . Applications are outlined , including two-way quantum cryptographic protocols . In order to strengthen the confidentiality of data , this paper proposes a kind of Kernel-level Cryptographic File System(KCFS ) combining the optimized data read/write flow . The kernel-level cryptographic file system can overcome the inconvenience of encryption application and the low efficiency of user-level encryption file system , so as to provide the encryption/decryption function in the kernel-level file system , protecting the data stored in the storage system . The eVoting systems we evaluate in this paper reflect the wide variety of technologies currently available . They are ; electronic tallying tools , scan based systems , DREs with VVPATs , and two cryptographic eVoting systems , Pretˆ a Voter and Scantegrity II . As a result ` we found out that none of these systems fully meet the criteria and , for the purpose of possible implementation for the local election in Germany , need to be improved .
Motivated by a Bayesian Dirichlet prior model , a computationally efficient stochastic approximation version of the marginal likelihood is proposed and large-sample theory is presented . By restricting the support to a finite grid , a simulated annealing method is employed to maximize the marginal likelihood and estimate the support . Real and simulated data examples show that this novel stochastic approximation and simulated annealing procedure compares favorably with existing methods .
The search for a solution is made in an iterative manner , by replacing one station , analyzing the result , and deciding whether to keep the solution or not . Due to the enormous number of possible combinations even for small networks , a structured search method , the simulated annealing method , is used to obtain the final network .
This study presents a novel co-operative agents approach , Ant Colony Optimization algorithm ( ACO ) scheme , for solving the scheduling mixed-model assembly lines . The results show that the solution which ant algorithm produces is better than the one which Toyota 's goal chasing algorithm , simulated annealing algorithm and genetic algorithm produce . Finally , this example may extend to a bigger scale , and the satisfied solutions , benchmark results and CPU time to generate a satisfied tour are given .
The methods were employed for optimising two challenging optimal control problems of fed-batch bioreactors . The main results show that adaptive random search and genetic algorithms are superior at solving these problems than the simulated annealing based method , both in accuracy and in the number of function evaluations .
Since not all the links have the same endpoints , some full duplex links leave a duct at points referred to as junction points . In this paper , we propose a Simulated Annealing based algorithm to optimize the topology design of embedded Ethernet networks . This algorithm finds the (near-)optimal positions of a given number of switches and their connections to given nodes .
Given an initial rectilinear Steiner tree , the rectilinear Steiner tree can be transformed into a Steiner routing tree by deleting all the corner points in the rectilinear Steiner tree . Based on the definition of the routing flexibility in a Steiner routing tree and the timing-constrained location flexibility of the Steiner-point in any Y-type wire , the simulated-annealing-based approach is proposed to construct a better timing-constrained flexibility-driven Steiner routing tree by reassigning the feasible locations of the Steiner points in all the Y-type wires . The experimental results show that our proposed algorithm , STFSRT , can increase about 0.005 - -0.020 % wire length to improve about 43 - -173 % routing flexibility for the tested benchmark circuits .
The proposed evolutionary algorithm showed very satisfactory results in testing on well-known benchmark problems from TSPLIB . Comparisons is performed with the results of numerous implementations of well-known meta-heuristic approaches from the literature , such as ; Honey Bee Mating Optimization , Particle Swarm Optimization , Tabu Search , Greedy Randomized Adaptive Search Procedure , Genetic Algorithm , Ant Colony Optimization , etc . Having found 9 new best solution out of 20 cases , the computational study shows that the GMMS algorithm is a competitive tool for solving the PTSP .
Obtaining an optimal solution for this type of complex , large-sized problems in reasonable computational time is extremely difficult . A meta-heuristic method , based on genetic algorithms , is thus proposed and applied to the given problem in order to obtain a good and near-optimal solution , especially for large sizes . Further , the efficiency of the proposed algorithm , based on various test problems , is compared with the Lingo 8.0 software .
We created an XML format for the constraints such that any new real world constraint is fit for the evaluation procedure by only expressing it in the XML format . We have tested the approach on real employee rostering data where the evaluation procedure was called from a meta-heuristic procedure . The main advantages of the proposed approach are the fast evaluation of constraints and the simple extendibility of the model with extra constraints .
Unlike widely available literatures in hazardous material ( HAZMAT ) transportation that basically aim at finding non dominated paths for a given origin-destination pair , our main focus in this study is on vehicle routing problem with time window ( VRPTW ) aspect of HAZMAT transportation problem that has received very less attention in literatures . We present a new multi-objective optimization model and its meta-heuristic solution technique using Ant Colony System for HAZMAT routing . In contrast to existing local routing models , we consider minimization of risk and transportation cost in both route choice and routing phases of transportation process .
This paper addresses this problem with the realistic assumptions of existing time constraints for trucks to depart from the origin and arrive at their destination , and waiting as well as detour possibility . As this problem is NP-hard even in its very simplified forms , a new meta-heuristic solution methodology inspired from ant colony optimisation is proposed to deal with it . Some sample problems of small to large size are generated and solved with our solution approach .
In this paper authors will present analysis and implementation of possible solutions of vehicle routing problem that is based on simulated annealing method , which belongs to the category of meta-heuristic problem solving approaches . The described problem is rather complex linear programming problem from the field of operation research .
Tabu search is a meta-heuristic problem solving technique that , when applied carefully , provides near optimal solutions in a very short time . In this paper , we have described the use of tabu search for solving problems related to very large scale integrated ( VLSI ) circuit design automation .
Many supply chain planning efforts result in sizable and often nonlinear optimization problems that are difficult to solve using standard solution methods . Meta-heuristic and heuristic solution methods have been developed and applied to tackle such modeling complexities . This paper aims to compare and analyze the performance of three meta-heuristic algorithms in solving a nonlinear green supply chain planning problem .
Ant Colony Optimization ( ACO ) has been successfully applied to a wide number of complex and real domains . From classical optimization problems to video games , these kind of swarm-based approaches have been adapted , to be later used , to search for new meta-heuristic based solutions . This paper presents a simple ACO algorithm that uses a specifically designed heuristic , called common-sense , which has been applied in the classical video game Lemmings .
An interference-aware MDMP routing is formulated for single-radio networks by employing a time-division link scheduling method to eliminate interference , based on which an optimal path selection associated with the minimum reconstruction distortion of received video may be obtained with high complexity . A heuristic solution of low complexity is developed for the interference-aware multipath routing , by introducing a path metric taking into account intra-flow interference , inter-flow interference , link bandwidth and link ' ' up ' ' probability . The second problem touches on unbalanced-path based joint MD video coding and packet allocation for multiple paths of varying bandwidth and loss probabilities .
The results on a Dutch case study indicate that adopting a demand-oriented approach for the management of disruptions not only is possible , but may lead to significant improvement in passenger satisfaction , associated with a low operational cost of the disposition timetable . For a more efficient investigation of the multiple dimensions of the problem , a heuristic solution algorithm based on adaptive large neighborhood search is also presented . The timetable is optimized using operators inspired directly from recovery strategies used in practice ( such as canceling , delaying or rerouting trains , or scheduling additional trains and buses ) , and from optimization methods ( e.g. , feasibility restoration operators ) .
In contrast , in Consistent Neighborhood Search ( CNS ) , after each variable assignment , the conflicting variables are deleted to keep the partial solution feasible , and the search can stop when all the variables have a value . In this paper , we formally propose a new heuristic solution method , CNS , which has a search behavior between exhaustive tree search and local search working with complete solutions . We then discuss , with a unified view , the great success of some existing heuristics , which can however be considered within the CNS framework , in various fields : graph coloring , frequency assignment in telecommunication networks , vehicle fleet management with maintenance constraints , and satellite range scheduling .
The location routing problem ( LRP ) , known to be the combination of the facility location and vehicle routing problems , is solved in the literature by either assuming planar or spherical surfaces . In this work , the manifold location routing problem ( MLRP ) , that is an LRP on Riemannian manifold surfaces , is explained for the 2-facility ( 2-MLRP ) case with the corresponding heuristic algorithm solution . The 2-MLRP problem is a mixed integer non-linear programming problem that is determined to be NP-hard .
But due to improper arrangement of bins and unavailability of proper optimization techniques the space gets wasted and damage of goods occur in container . To utilize the maximum occupancy of the container in this project we are proposing the heuristic algorithm solution with weight which consider the dimension of the items and container like length , height , width , weight . This project aims to generate a system for optimizing a container space using user friendly Excel VBA .
However we find that the relative performance of the heuristic algorithm deteriorates with increasing degree of spatial dispersion of unelectrified settlements , as well as increasing spatial remoteness of the settlements from the grid network , suggesting that the effectiveness of the heuristic algorithm is context specific . Further , we find that allocation of off grid sources in the heuristic algorithm solution is often significantly greater than in the MINLP model suggesting that heuristic methods can overstate the role of off-grid solutions in certain situations .
A generalized hub location and VRM is developed which encompasses the determination of the number , locations of hubs and depots and their assigned service areas as well as the routes between the demand points and consolidation points ( depots , hubs ) . Because this task leads to a very complex transport design problem , a heuristic solution concept has to be developed . The applicability to a case study is demonstrated ; the case of completely new system configuration for Austria is considered .
In addition to the typical costs associated with facility operation and transportation , we explicitly consider the pivotal online customer service rate between the distribution centers ( DCs ) and their assigned customers . This study proposes a heuristic solution scheme to resolve this multi-objective programming problem , by integrating genetic algorithms , a clustering analysis , a Non-dominated Sorting Genetic Algorithm II ( NSGA-II ) , and a Technique for Order Preference by Similarity to Ideal Solution ( TOPSIS ) . Several experiments are simulated to demonstrate the possibility and efficacy of the proposed approach .
A computational study shows that this approach out-performs the previous methods found in the literature for a set of randomly generated instances . A heuristic version of the solution approach is also proposed and tested on larger instances .
Considering this challenging , but realistic practical scenario , various sub-optimal M-MIMO detection structures are evaluated in terms of complexity and bit error rate ( BER ) performance trade-off . Specifically , the successive interference cancellation , lattice reduction ( LR ) and likelihood ascent search ( LAS ) schemes , as well as a hybrid version combining such structures with conventional linear MIMO detection techniques are comparatively investigated , aiming to improve performance . Hence , to provide a comprehensive analysis , we consider the number of antennas varying in a wide range ( from conventional to massive MIMO condition ) , as well as low and high modulation orders , aiming to verify the potential of each MIMO detection technique according to its performance – complexity trade-off .
During recent years stochastic algorithms have deserved much attention from the computational biology research communities . In this paper we derive a hybrid version of the formerly known Metabolic Algorithm that is enriched with stochastic features , whose impact on the dynamics of the system is especially prominent when the amount of metabolite becomes smaller . This hybrid procedure represents a first attempt to let the Metabolic Algorithm deal with low concentrations of substances according to a non-deterministic policy .
The proposed CSLM algorithm is trained on XOR and OR datasets . The experimental results show that the proposed CSLM algorithm has better performance than other similar hybrid variants used in this study .
The latter can be seen as a generalization of SLS-resolution . It provides a basis for hybrid implementations combining Prolog with constraint solvers ( such as ontology reasoners ) . Soundness of the operational semantics is proven .
The national regulatory bodies need to address the potential issues of wireless systems mutual interference ( either accidental or deliberate ) and guarantee their stakeholders efficiently policed national spectrum usage . One of the cornerstones in this aspect is the accurate and real-time geolocation of wireless transmitters using methods such as TDoA , AoA and/or hybrid ones . However , the practical implementation of a fully operable spectrum monitoring and wireless transmitters geolocation capable system is a challenging task .
Abstract Knowledge of trip travel times serves an important role in transportation management and control . Existing travel time estimation approaches generally cover empirical ones , statistical ones and hybrid ones . Despite strong tractability , the empirical approaches can not sufficiently capture diverse travel time distributions ( TTDs ) and often encounter some issues ( e.g. , assumption of a predefined distribution , failure of significance tests ) .
The communication system designer must not only integrate the antenna and its associated circuits but also the phase shifter , amplifiers and control circuits . the use of integrated monolithic antennas fabricated in a single , high resolution , integrated circuit process offers the potential of closer dimensional accuracy , ruggedness , simplicity and better reliability and reproducibility in comparison to their hybrid counterparts , being excellent candidates for integrated quasi-optical power combining systems . In previous chapters , we have emphasized on the planar antenna technology and described various techniques to improve the directivity of the antenna .
The use of hybrid algorithms for solving real-world optimization problems has become popular since their solution quality can be made better than the algorithms that form them by combining their desirable features . The newly proposed hybrid method which is called Hybrid Differential , Particle , and Harmony ( HDPH ) algorithm is different from the other hybrid forms since it uses all features of merged algorithms in order to perform efficiently for a wide variety of problems . In the proposed algorithm the control parameters are randomized which makes its implementation easy and provides a fast response .
To provide the best network performance , the MAC protocol shall be adaptive enough in order to satisfy the traffic intensity and variation of parking sensors . In this paper , we study the heavy-tailed parking and vacant time models from SmartSantander , and then we apply the traffic model in the simulation with four different kinds of MAC protocols , that is , contention-based , schedule-based and two hybrid versions of them . The result shows that the packet interarrival time is no longer heavy-tailed while collecting a group of parking sensors , and then choosing an appropriate MAC protocol highly depends on the network configuration .
Our implementation analyzes multi-million-line programs quickly and in just a fraction of the memory required by prior approaches . When memory alone is insufficient , our approach falls back on disk using several hybrid configurations tuned to put all available resources to good use .
This problem is important in the maritime domain where Autonomous Underwater Vehicles ( AUVs ) can greatly benefit from an Autonomous Surface Vehicle ( ASV ) acting as a Communication Relay ( CR ) and/or a Navigational Aid ( NA ) , typically using acoustic communication . The proposed approach is an extension of Hybrid-A*(HA * ) , a hybrid version of A * which enables the derivation of paths that are obstacle free and feasible by the vehicle . The proposed algorithm finds a solution , if it exists , for scenarios where the leader and the follower operate under the same kinematic constraints as well as when they differ .
While the globally - trained combination of neural nets and HMMs for speech and handwriting goes back to the 1990s , only due to recent developments HMM / DNN hybrid modeling became dominant in ASR . In the context of these hybrid models , the use of CNNs is relatively recent . CNNs were shown to achieve state of the art performance on the benchmark datasets Broadcast News and Switchboard 300 .
For that reason , we argue that SMT provides a more appropriate architecture to find an initial alignment between the languages , but NMT is ultimately a better architecture to model the translation process . Following this observation , we propose a hybrid approach that uses unsupervised SMT to warm up a dual NMT model trained through iterative back - translation . More concretely , we first train two SMT systems in opposite directions as described in Section [ reference ] , and use them to assist the training of another two NMT systems in opposite directions .
In this paper , we investigate the problem of designing the minimum number of required mobile elements tours such that each sensor node is either on the tour or one hop away from the tour , and the length of the tour to be bounded by pre-determined value L. To address this problem , we propose heuristic-based solution . This solution works by directing the mobile element tour towards the highly dense area in the network .
The caching point nodes are selected with the aim of reducing the energy expenditures due to multi-hop forwarding . We provide a heuristic-based solution for this problem . We evaluate the performance of our algorithm by comparing it to the best well-known algorithms from the literature .
The gathering points/nodes are selected with the aim of reducing the energy expenditures due to multi-hop forwarding . We also present a heuristic-based solution and validate its performance through an extensive set of experiments .
While there exist clear methods for validating and ensuring the quality of solutions generated by forest planning heuristic techniques , the use of these methods in the literature varies from one situation to the next . Based on our experience developing and using heuristic forest planning techniques , we describe six levels of heuristic validation that are currently in use , ranging from no validation ( Level 1 ) on one end of the spectrum , to the comparison of heuristic technique solutions with an exact solution obtained using mathematical programming methods ( Level 6 ) on the other end . The reasons why authors may choose or reviewers may require levels of validation are proposed .
The knowledge acquired can be expressed in different knowledge representations such as computer programs , first-order logical relations , or fuzzy Petri nets ( FPNs ) . In this paper , we present a flexible knowledge discovery system called generic genetic programming ( GGP ) that applies genetic programming ( GP ) and logic grammars to learn knowledge in various knowledge representation formalisms . An experiment is performed to demonstrate that GGP can discover knowledge represented in FPNs that support fuzzy and approximate reasoning .
However , they are restricted in the computer languages in which programs can be induced . Generic Genetic Programming ( GGP ) is a novel , powerful , and general approach that combines ILP and GP . Using GGP , programs in various programming languages such as Lisp , Prolog , and Fuzzy Prolog can be evolved .
In this study we integrate a Self-guided Genetic Algorithm with dominance properties ( DPs ) which is named DP-Self-guided GA . Self-guided GA [ 8 ] is belonged to the category of evolutionary algorithms based on probabilistic models ( EAPM ) and it is effective and efficient in solving the scheduling problems .
This paper proposed a new algorithm , termed as Self-Guided genetic algorithm , which is one of the algorithms in the category of evolutionary algorithm based on probabilistic models ( EAPM ) . Previous EAPM research explicitly used the probabilistic model from the parental distribution , then generated solutions by sampling from the probabilistic model without using genetic operators .
The IEEE 802.11 standards do not provide any mechanism for load distribution and as a result user quality of service ( QoS ) degrades significantly in congested networks where large numbers of users tend to congregate in the same area . The objective of this paper is to provide load balancing techniques that optimise network throughput in areas of user congestion , thereby improving user QoS. Specifically , we develop micro-genetic and standard genetic algorithm approaches for the WLAN load balancing problem , and we analyse their strengths and weaknesses . We also compare the performance of these algorithms with schemes currently in use in IEEE 802.11 WLANs .
A kind of improved symmetry and a kind of improved asymmetry genetic algorithm were introduced to solve the general multi-objective optimization problem according to the different important degree of objective functions and constraint functions . It can search the optimum solution from many initial points at the same time .
Psychologists and statisticians have developed a number of useful methods for detecting and/or validating taxa underlying psychological disorders . These methods include the taxometric techniques developed by Meehl and his colleagues ( 7 ) , admixture models ( 8) , molecular genetic studies ( 9 ) , and multivariate behavior genetic studies ( 10 ) . Although none of these methods by itself can provide definitive confirmation of taxa , consistent findings of taxonicity across multiple methods offer converging evidence that a psychiatric condition is categorical at a latent level ( 11 ) .
It plays an important role in the state of the art trend where traditional medical treatments are to be replaced by personalized genetic medicine , i.e. individualized therapy based on the patient 's genetic heritage . However , fast , robust , and precise image processing tools are required for the prospective practical use of microarray-based genetic testing for predicting disease susceptibilities and drug effects in clinical practice , which require a turn-around timeline compatible with clinical decision-making . In this paper we have developed a fully-automated image analysis platform for the rapid investigation of hundreds of genetic variations across multiple genes .
We first define the absolute deviation of random fuzzy variable and then employ it as risk measure to formulate mean-absolute deviation portfolio optimization models . To find the optimal portfolio , we design random fuzzy simulation and simulation-based genetic algorithm to solve the proposed models . Finally , a numerical example for synthetic data is presented to illustrate the validity of the method .
The main goal of the Ward group is the development and optimization of artificial metalloenzymes for their use in asymmetric catalysis . These hybrid catalysts , resulting from an incorporation of a metal – containing cofactor within a protein or DNA scaffold , can be optimized by either chemical or genetic means . The present work aims at the genetic optimization of the protein scaffold and exploration of the use of the resulting hybrid catalysts in nano – applications .
This scheme was implemented to synthesize bandpass filters for 802.11 a and HIPERLAN wireless LAN applications in the range of 5–6 GHz . This research also develops a novel neuro-genetic design centering methodology for yield enhancement and design for manufacturability of microwave devices and circuits . A neural network model is used to calculate yield using Monte Carlo methods .
The method proposed in this paper is a data analysis method that intelligently analyzes power big data and realizes the load forecasting of power grid . The method calls for the corresponding data from each database of big data platform by accepting the load forecast request from the client , and performs the load forecasting in the big data by improving the gray model of chaos genetic algorithm ( CGA ) . After the completion of load forecasting , the final output to the client load forecasting results .
In order to investigate the skill learning of heuristics problem-solving , we took simplified 4 × 4 Sudoku as a new task . Brain activity was recorded when participants solved Sudoku problems with fMRI , and compared before and after plenty of practice .
Subjects performed a set of heuristic problems in functional Magnetic Resonance Imaging ( fMRI ) environment . Then a computational cognitive model is set up to simulate processes of information processing of the heuristics problem solving . During the modeling , we found that two main factors , visual attention and goal control , are responsible for speeding up heuristic search in problem solving , where visual attention captures a target selectively with the goal state-directed control .
Additional cuts are achieved using aspiration windows and principal variation search . Other pruning strategies include null move pruning ( which assumes a pass move should be worse than any variation , in positions that are unlikely to be in zugzwang , as determined by simple heuristics ) , futility pruning ( which assumes knowledge of the maximum possible change in evaluation ) , and other domain - dependent pruning rules ( which assume knowledge of the value of captured pieces ) . The search is focused on promising variations both by extending the search depth of promising variations , and by reducing the search depth of unpromising variations based on heuristics like history , static - exchange evaluation ( SEE ) , and moving piece type .
None of these methods apply to gradient - based learning of high - capacity models such as modern deep networks . [ reference ] and [ reference ] propose heuristics based on uncertainty and gradient magnitudes , respectively , and apply their methods to convolutional neural networks . Another recent work uses multi - agent reinforcement learning [ reference ] .
Nevertheless , natural defects call for protection through design for fault detection and reliability . In this paper , we present implications of fault detection cryptographic architectures ( Pomaranch in the hardware profile of European Network of Excellence for Cryptology ) for smart infrastructures . In addition , we present low-power architectures for its nine-to-seven uneven substitution box [ tower field architectures in GF(33 ) ] .
Our protocols are specifically designed to be highly efficient in terms of communication and bandwidth , requiring only one or two rounds of communication and thus enabling scalable parallel queries . We rigorously prove our protocols to be secure based on cryptographic games and instantiate our technique with three of the most important types of biomedical data – namely DNA , microRNA expression , and DNA methylation . Our experimental results show that our protocols can compute a similarity query over a typical number of positions against a database of 1,000 patients in a few seconds .
Detection and Analysis of a potential malware specifically , used for ransom is a challenging task . Recently , intruders are utilizing advance cryptographic techniques to get hold of digital assets and then demand ransom . It is believed that generally , the files comprise of some attributes , states , and patterns that can be recognized by a machine learning technique .
Here , we are using 3-LSB bit substitution technique for image steganography and Bit Plane slicing algorithm is used to slice the mosaic image which is used as cover image . Advance cryptographic algorithm like Blowfish is used for encryption and decryption of data and images on both sender and receiver side . Keywords : Steganography ; Image Steganography ; Cryptography ; LSB ; Encryption ; Decryption ; PSNR ratio ; Mosaic Image ; Cover Media ; Slicing .
The most precious resource that frequently comes under attack is the distributed ( network ) file system , because this usually holds the entire contents of a company 's electronically stored information . This study starts by claiming that microprocessors have just become fast enough to support full cryptographic controls for all read and write operations in performance-critical systems . This claim is proven through the design , implementation and analysis of a decentralized security system for network attached disks .
Integrity and authentication using keyed-hash and RSA as well as confidentiality using RC5 are tested . This analysis demonstrates that full cryptographic controls are feasible in a distributed network file system , by showing the performance overhead for including signature , hash and encryption algorithms on various embedded and workstation computers . The results from these experiments are used to predict the performance impact using three proposed network disk security schemes .
This may completely jeopardise the security of embedded watermarks once this information is revealed . To overcome this problem recent work on secure watermark detection proposes cryptographic proofs that perform the detection on concealed detection information . The proposed solutions focus on correlation-based detection and can be applied to any watermarking scheme whose detection criteria can be expressed as a polynomial relation between the quantities required for the detection .
Proofs in the standard model ( as opposed to the random oracle model ) are the gold standard of cryptographic proofs , and most cryptographic protocols strive to achieve them . The burgeoning complexity of cryptographic proofs implies that such proofs are prone to errors , and are hard to write , much less verify . In this paper , we propose techniques to generate automated proofs for attribute-based encryption schemes in the standard model , building upon a prototype tool , AutoG&P due to Barthe et al. In doing so , we significantly expand the scope of AutoG&P to support a rich set of data types such as multi-dimensional arrays , and constructs commonly used in cryptographic protocols such as monotone-access structures , and linear secret-sharing schemes .
We are going to implement the " The Simon and The Speck “ the cryptography algorithm on a FPGA . Keywords-Cryptographic algorithm , block cipher , FPGA . Introduction : In today ’s world while communicating through an untrusted media or network such as internet it is necessary to keep the system secure , The cryptography is the study of techniques which can used to protect secrets and secure personal information .
Implementation of cryptographic algorithms on hardware runs faster than on software and at the same time offering more intrinsic security . The paper involves the design of high performance architecture of a well known cryptographic algorithmRC4 stream cipher and its performance analysis . The cipher architecture is designed using VerilogHDL and finally programmed into Spartan3 FPGA .
The optimal cloner is realized by quantum channels with memory , and greatly outperforms the optimal measure-and-reprepare cloning strategy . Applications are outlined , including two-way quantum cryptographic protocols .
In order to strengthen the confidentiality of data , this paper proposes a kind of Kernel-level Cryptographic File System(KCFS ) combining the optimized data read/write flow . The kernel-level cryptographic file system can overcome the inconvenience of encryption application and the low efficiency of user-level encryption file system , so as to provide the encryption/decryption function in the kernel-level file system , protecting the data stored in the storage system .
The eVoting systems we evaluate in this paper reflect the wide variety of technologies currently available . They are ; electronic tallying tools , scan based systems , DREs with VVPATs , and two cryptographic eVoting systems , Pretˆ a Voter and Scantegrity II . As a result ` we found out that none of these systems fully meet the criteria and , for the purpose of possible implementation for the local election in Germany , need to be improved .
An Approximate Bayesian Marginal Likelihood Approach for Estimating Finite Mixtures Comparison of Variance-Reduction and Space-Filling Approaches for the Design of Environmental Monitoring Networks Ant Colony Optimization Algorithms for Scheduling the Mixed Model Assembly Lines Benchmark testing of simulated annealing, adaptive random search and genetic algorithms for the global optimization of bioprocesses On link harness optimization of embedded Ethernet networks Timing-Constrained Flexibility-Driven Routing Tree Construction A novel evolutionary algorithm for the homogeneous Probabilistic Traveling Salesman Problem A New ILP Model for Identical Parallel-Machine Scheduling with Family Setup Times Minimizing the Total Weighted Flow Time by a Genetic Algorithm Evaluation of Employee Rosters with the Extended Linear Numberings Method Optimization of Vehicle Routing and Scheduling Problem with Time Window Constraints in Hazardous Material Transportation Fuel-efficient truck platooning by a novel meta-heuristic inspired from ant colony optimisation Implementation of Transportation Problem by Using theMethod of Meta-Heuristics Approach On Using Tabu Search for Design Automation of VLSI Systems Planning of complex supply chains: A performance comparison of three meta-heuristic algorithms Micro and Macro Lemmings Simulations Based on Ants Colonies Multi-description multipath video streaming in wireless ad hoc networks Integration of passenger satisfaction in railway timetable rescheduling for major disruptions Consistent Neighborhood Search for Combinatorial Optimization 2-Facility manifold location routing problem Container Space Optimization using Excel VBA How effective are heuristic solutions for electricity planning in developing countries An integrated multi-depot hub-location vehicle routing model for network planning of parcel service Multi-objective evolutionary approach for supply chain network design problem within online customer consideration Simultaneous Vehicle and Crew Scheduling in Urban Mass Transit Systems Low-Complexity Massive MIMO Detectors Under Spatial Correlation and Channel Error Estimates Towards a Hybrid Metabolic Algorithm CSLM: Levenberg Marquardt based Back Propagation Algorithm Optimized with Cuckoo Search Hybrid rules with well-founded semantics Practical evaluation of TDoA, AoA and hybrid methods for geolocation of wireless transmitters A novel generative adversarial network for estimation of trip travel time distribution with trajectory data Development in the Terahertz Communication System Application of Hybrid Optimization Algorithm in the Synthesis of Linear Antenna Array How to choose the relevant MAC protocol for wireless smart parking urban networks? Database-Backed Program Analysis for Scalable Error Propagation Kinodynamic Path Planning for Following and Tracking Vehicles Very deep multilingual convolutional neural networks for LVCSR An Effective Approach to Unsupervised Machine Translation DATA COLLECTION SCHEME FOR WIRELESS SENSOR NETWORK WITH MOBILE COLLECTOR Data Gathering Scheme for Wireless SensorNetworks Using a Single Mobile Element A data caching approach for sensor applications A review of the status and use of validation procedures for heuristics used in forest planning A flexible knowledge discovery system using genetic programming and logic grammars The logic grammars based genetic programming system A Self-guided genetic algorithm with dominance properties for single machine scheduling problems Self-Guided Genetic Algorithm Wireless LAN load balancing with genetic algorithms Improved Genetic Algorithm of Multi-objective Structure Fuzzy Optimization Comorbidity and Chairman Mao. Fully automated analysis of multi-resolution four-channel micro-array genotyping data Random fuzzy mean-absolute deviation models for portfolio optimization problem with hybrid uncertainty Optimization of the performance of an artificial imine reductase by directed evolution and encapsulation Design and Optimization of Microwave Circuits and Systems Using Artificial Intelligence Techniques Load Forecasting Research Based on High Performance Intelligent Data Processing of Power Big Data A Skill Learning during Heuristic Problem Solving: An fMRI Study Interaction between Visual Attention and Goal Control for Speeding Up Human Heuristic Search Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm Multi-Task Learning as Multi-Objective Optimization Reliable And Low-Latency And Low-Complexity For Pomaranch For False-Alarm-Sensitive Cryptographic Applications Privacy-Preserving Similar Patient Queries for Combined Biomedical Data Ransomware Analysis using Feature Engineering and Deep Neural Networks Robust Image Mosaic Scheme Decentralized security for network attached storage An experimental analysis of cryptographic overhead in performance-critical systems Efficient Implementation of Zero-Knowledge Proofs for Watermark Detection in Multimedia Data Exploring Automation in Proofs of Attribute-based Encryption in the Standard Model Implementation of SIMON & SPECK Algorithm HDL Implementation of RC4 Stream cipher for cryptographic applications Optimal cloning of unitary transformation. Kernel-level Cryptographic File System Based on Read/Write Optimization Readiness of various eVoting systems for complex elections
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