Cubic Hermite collocation solution of Kuramoto–Sivashinsky equation

The solution and analysis of Kuramoto–Sivashinsky equation by cubic Hermite collocation method is performed and a bound for maximum norm of the semi-discrete solution is derived by using Lyapunov functional. Error estimates are also obtained for semi-discrete solutions and verified by numerical experiments.     

Replicating data objects in large distributed database systems: an axiomatic game theoretic mechanism design approach

Data object replication onto distributed servers can potentially alleviate bottlenecks, reduce network traffic, increase scalability, add robustness, and decrease user perceived access time. The decision of selecting data object and server pairs requires solving a constraint optimization problem that in general is NP-complete. In this paper, we abstract the distributed database system as an agent-based model, wherein agents continuously compete for allocation and reallocation of data objects. Each agent aims to replicate objects onto its server such that the communication cost is minimized. However, these agents do not have a global view of the system. Thereby, the optimization process becomes highly localized. Such localized optimization may severely affect the overall system performance. To cope with such localized optimization, we propose a “semi-distributed” axiomatic game theoretical mechanism. The mechanism’s control is unique in its decision making process, wherein all the heavy processing is done on the servers of the distributed system and the central body is only required to take a binary decision: (0) not to replicate or (1) to replicate. The cost model used by the agents in the mechanism for the purpose of identifying beneficial data objects is tailored made so that even though the agents take decisions based on their local knowledge domain, the effect is translated into a system-wide performance enhancement. The proposed mechanism is extensively compared against seven well-known conventional and three game theoretical replica allocation methods, namely, branch and bound, greedy, genetic, data-aware replication, tree inspired bottom-up procedure, tree inspired min-max procedure, Benders’ decomposition based procedure, game theoretical English auction, game theoretical Dutch auction, and game theoretical selfish replication procedure. The experimental setup incorporates GT-ITM, Inet network topology generators, Soccer World Cup 1998 access logs, and NASA Kennedy Space Center access logs to closely mimic the Web in its infrastructure and user access patterns. The experimental results reveal that the proposed technique despite its non-cooperative nature improves the solution quality and reduces the execution time compared to other techniques.     

Evolutionary Algorithms for Allocating Data in Distributed Database Systems

A major cost in executing queries in a distributed database system is the data transfer cost incurred in transferring relations (fragments) accessed by a query from different sites to the site where the query is initiated. The objective of a data allocation algorithm is to determine an assignment of fragments at different sites so as to minimize the total data transfer cost incurred in executing a set of queries. This is equivalent to minimizing the average query execution time, which is of primary importance in a wide class of distributed conventional as well as multimedia database systems. The data allocation problem, however, is NP-complete, and thus requires fast heuristics to generate efficient solutions. Furthermore, the optimal allocation of database objects highly depends on the query execution strategy employed by a distributed database system, and the given query execution strategy usually assumes an allocation of the fragments. We develop a site-independent fragment dependency graph representation to model the dependencies among the fragments accessed by a query, and use it to formulate and tackle data allocation problems for distributed database systems based on query-site and move-small query execution strategies. We have designed and evaluated evolutionary algorithms for data allocation for distributed database systems.     

Multiprocessing ocean circulation: Modeling, implementation, and performance on the Intel Paragon

In this paper we present the modeling and implementation of a grand challenge problem in the field of scientific computation: the primitive-equation numerical ocean circulation model. We present the mathematical formulation of the model and propose a scheme for its parallel implementation. Optimizations are made through collective communications and various partitioning schemes. In our experiments, which use up to 100 processors on the Intel Paragon parallel computer, the proposed strategy yields an encouraging speedup and exhibits a sustained scalability with increasing problem and machine sizes. We consider barotropic continental shelf waves in a periodic channel as a test problem. The model has numerous applications in environmental studies and ocean sciences.     

An overview of rate control techniques in HEVC and SHVC video encoding

Video standards are crucial for exchanging video content, enabling a myriad of services and supporting a wide variety of devices ranging from personal devices to clouds and IoT. One of the core requirements in video standards is the rate control that regulates the bit allocation and picture quality. This paper presents an overview of rate control techniques in the HEVC video coding standard. While providing an insight into the rate control mechanism specific to HEVC, it describes the basic operating principle of rate control algorithms, including their essential parameter, outputs, and performance measures. We review rate control in past coding standards and bring out the basic features of HEVC that drive the need for new rate control algorithms. Alongside, we delineate the Rate-Distortion model-based taxonomy of various algorithms, including their classification criteria. The paper gives out another classification of the rate control algorithms based on their basic principle and mechanisms. The article also explains the scalable extension of HEVC, namely SHVC, while highlighting some of the possible SHVC rate control design challenges. Finally, we present some of the unresolved research issues in HEVC rate control and outline possible future research directions.

     

Improved Ant Lion Optimizer with Deep Learning Driven Arabic Hate Speech Detection

Arabic is the world’s first language, categorized by its rich and complicated grammatical formats. Furthermore, the Arabic morphology can be perplexing because nearly 10,000 roots and 900 patterns were the basis for verbs and nouns. The Arabic language consists of distinct variations utilized in a community and particular situations. Social media sites are a medium for expressing opinions and social phenomena like racism, hatred, offensive language, and all kinds of verbal violence. Such conduct does not impact particular nations, communities, or groups only, extending beyond such areas into people’s everyday lives. This study introduces an Improved Ant Lion Optimizer with Deep Learning Dirven Offensive and Hate Speech Detection (IALODL-OHSD) on Arabic Cross-Corpora. The presented IALODL-OHSD model mainly aims to detect and classify offensive/hate speech expressed on social media. In the IALODL-OHSD model, a three-stage process is performed, namely pre-processing, word embedding, and classification. Primarily, data pre-processing is performed to transform the Arabic social media text into a useful format. In addition, the word2vec word embedding process is utilized to produce word embeddings. The attention-based cascaded long short-term memory (ACLSTM) model is utilized for the classification process. Finally, the IALO algorithm is exploited as a hyperparameter optimizer to boost classifier results. To illustrate a brief result analysis of the IALODL-OHSD model, a detailed set of simulations were performed. The extensive comparison study portrayed the enhanced performance of the IALODL-OHSD model over other approaches.     

A Pure Nash Equilibrium-Based Game Theoretical Method for Data Replication across Multiple Servers

This paper proposes a non-cooperative game based technique to replicate data objects across a distributed system of multiple servers in order to reduce user perceived Web access delays. In the proposed technique computational agents represent servers and compete with each other to optimize the performance of their servers. The optimality of a non-cooperative game is typically described by Nash equilibrium, which is based on spontaneous and non-deterministic strategies. However, Nash equilibrium may or may not guarantee system-wide performance. Furthermore, there can be multiple Nash equilibria, making it difficult to decide which one is the best. In contrast, the proposed technique uses the notion of pure Nash equilibrium, which if achieved, guarantees stable optimal performance. In the proposed technique, agents use deterministic strategies that work in conjunction with their self-interested nature but ensure system-wide performance enhancement. In general, the existence of a pure Nash equilibrium is hard to achieve, but we prove the existence of such equilibrium in the proposed technique. The proposed technique is also experimentally compared against some well-known conventional replica allocation methods, such as branch and bound, greedy, and genetic algorithms.     

Adaptive output-feedback control for a class of nonlinear systems with actuator failures and input quantisation

In this paper, an adaptive output feedback control technique is proposed for a class of nonlinear systems with unknown parameters, unknown nonlinear functions, quantised input and possible actuator failures up to infinity. A modified backstepping approach is proposed by the use of high-gain K-filters, hyperbolic tangent function property and bound-estimation approach to compensate for the effect of possible number of actuator failures up to infinity, input quantisation and unknown nonlinear functions. It is proved both mathematically and by simulation that with the proposed controller, all the signals of the closed-loop system are globally bounded despite of input quantisation, unknown nonlinear functions and possible number of actuator failures up to infinity.     

Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.