Living analytics methods for the social web

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Parallel particle swarm optimization based on spark for academic paper co-authorship prediction

The particle swarm optimization (PSO) algorithm has been widely used in various optimization problems. Although PSO has been successful in many fields, solving optimization problems in big data applications often requires processing of massive amounts of data, which cannot be handled by traditional PSO on a single machine. There have been several parallel PSO based on Spark, however they are almost proposed for solving numerical optimization problems, and few for big data optimization problems. In this paper, we propose a new Spark-based parallel PSO algorithm to predict the co-authorship of academic papers, which we formulate as an optimization problem from massive academic data. Experimental results show that the proposed parallel PSO can achieve good prediction accuracy

Clustering in Recommendation Systems Using Swarm Intelligence

Ένα σύστημα συστάσεων είναι μία εφαρμογή που εκμεταλλεύεται πληροφορίες για να βοηθήσει τους χρήστες στη λήψη αποφάσεων προτείνοντας αντικείμενα που μπορεί να τους αρέσουν. Ένα σύστημα συστάσεων που βασίζεται στην τεχνική του συνεργατικού φιλτραρίσματος (collaborative filtering) δημιουργεί συστάσεις στους χρήστες με βάση τις προτιμήσεις παρόμοιων χρηστών. Ωστόσο, αυτός ο τύπος συστήματος συστάσεων δεν είναι τόσο αποτελεσματικός όταν τα δεδομένα αυξάνονται σε μεγάλο βαθμό (scalability) ή όταν δεν υπάρχει αρκετή πληροφορία (sparsity), καθώς δεν ομαδοποιούνται σωστά οι παρόμοιοι χρήστες. Αυτή η διπλωματική εργασία προτείνει τρείς υβριδικούς αλγορίθμους που ο καθένας συνδυάζει τον αλγόριθμο k-means με έναν αλγόριθμο ευφυΐας σμήνους για να βελτιώσει την ομαδοποίηση των χρηστών, και κατ’ επέκταση την ποιότητα των συστάσεων. Οι αλγόριθμοι ευφυΐας σμήνους που χρησιμοποιούνται είναι o αλγόριθμος τεχνητής κοινωνίας μελισσών (artificial bee colony), ο αλγόριθμος βελτιστοποίησης αναζήτησης κούκων (cuckoo search optimization) και ο αλγόριθμος βελτιστοποίησης γκρίζων λύκων (grey-wolf optimization). Οι προτεινόμενες μέθοδοι αξιολογήθηκαν χρησιμοποιώντας ένα σύνολο δεδομένων του MovieLens. Η αξιολόγηση δείχνει πως τα προτεινόμενα συστήματα συστάσεων αποδίδουν καλύτερα σε σύγκριση με τις ήδη υπάρχουσες τεχνικές όσον αφορά τις μετρικές του μέσου απόλυτου σφάλματος (mean absolute error - MAE), της ακρίβειας (precision), του αθροίσματος των τετραγωνικών σφαλμάτων (sum of squared errors - SSE) και της ανάκλησης (recall). Επιπλέον, τα αποτελέσματα της αξιολόγησης δείχνουν πως ο υβριδικός αλγόριθμος που χρησιμοποιεί την μέθοδο της τεχνητής κοινωνίας μελισσών αποδίδει ελαφρώς καλύτερα από τους άλλους δύο προτεινόμενους αλγορίθμους.A recommender system (RS) is an application that exploits information to help users in decision making by suggesting items they might like. A collaborative recommender system generates recommendations to users based on their similar neighbor’s preferences. However, this type of recommender system faces the data sparsity and scalability problems making the neighborhood selection a challenging task. This thesis proposes three hybrid collaborative recommender systems that each one combines the k-means algorithm with a different bio-inspired technique to enhance the clustering task, and therefore to improve the recommendation quality. The used bio-inspired techniques are artificial bee colony (ABC), cuckoo search optimization (CSO), and grey-wolf optimizer (GWO). The proposed approaches were evaluated over a MovieLens dataset. The evaluation shows that the proposed recommender systems perform better compared to already existing techniques in terms of mean absolute error (MAE), precision, sum of squared errors (SSE), and recall. Moreover, the experimental results indicate that the hybrid recommender system that uses the ABC method performs slightly better than the other two proposed hybrid algorithms

Global Optimization: Software and Applications

Mathematical models are a gateway into both theoretical and experimental understand- ing. However, sometimes these models need certain parameters to be established in order to obtain the optimal behaviour or value. This is done by using an optimization method that obtains certain parameters for optimal behaviour, as described by an objective function that may be a minimum (or maximum) result. Global optimization is a branch of optimization that takes a model and determines the global minimum for a given domain. Global opti- mization can become extremely challenging when the domain yields multiple local minima. Moreover, the complexity of the mathematical model and the consequent lengths of calcu- lations tend to increase the amount of time required for the solver to find the solution. To address these challenges, two software packages were developed to aid a solver in optimizing a black box objective function. The first software package is called Computefarm, a distributed local-resource computing software package that parallelizes the iteration step of a solver by distributing objective function evaluations to idle computers. The second software package is an Optimization Database that is used to monitor the global optimization process by storing information on the objective function evaluation and any extra information on the objective function. The Optimization Database is also used to prevent data from being lost during a failure in the optimization process. In this thesis, both Computefarm and the Optimization Database are used in the context of two particular applications. The first application is quantum error correction gate design. Quantum computers cannot rely on software to correct errors because of the quantum me- chanical properties that allow non-deterministic behaviour in the quantum bit. This means the quantum bits can change states between (0, 1) at any point in time. There are various ways to stabilize the quantum bits; however, errors in the system of quantum bits and the sys- tem to measure the states can occur. Therefore, error correction gates are designed to correct for these different types of errors to ensure a high fidelity in the overall circuit. A simulation of a quantum error correction gate is used to determine the properties of components needed to correct for errors in the circuit of the qubit system. The gate designs for the three-qubit and four-qubit systems are obtained by solving a feasibility problem for the intrinsic fidelity ii(error-correction percentage) to be above the prescribed 99.99% threshold. The Optimization Database is used with the MATLAB ’s Global Search algorithm to obtain the results for the three-qubit and four-qubit systems. The approach used in this thesis yields a faster high- fidelity (≤ 99.99%) three-qubit gate time than obtained previously, and obtained a solution for a fast high-fidelity four-qubit gate time. The second application is Rational Design of Materials, in which global optimization is used to find stable crystal structures of chemical compositions. To predict crystal structures, the enthalpy that determines the stability of the structure is minimized. The Optimization Database is used to store information on the obtained structure that is later used for identification of the crystal structure and Compute- farm is used to speed up the global optimization process. Ten crystal structures for carbon and five crystal structures for silicon-dioxide are obtained by using Global Convergence Par- ticle Swarm Optimization. The stable structures, graphite (carbon) and cristobalite (silicon dioxide), are obtained by using Global Convergence Particle Swarm Optimization. Achieving these results allows for further research on the stable and meta-stable crystal structures to understand various properties like hardness and thermal conductivity

A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

Since its emergence, quantum computing has enabled a wide spectrum of new possibilities and advantages, including its efficiency in accelerating computational processes exponentially. This has directed much research towards completely novel ways of solving a wide variety of engineering problems, especially through describing quantum versions of many mathematical tools such as Fourier and Laplace transforms, differential equations, systems of linear equations, and optimization techniques, among others. Exploration and development in this direction will revolutionize the world of engineering. In this manuscript, we review the state of the art of these emerging techniques from the perspective of quantum computer development and performance optimization, with a focus on the most common mathematical tools that support engineering applications. This review focuses on the application of these mathematical tools to quantum computer development and performance improvement/optimization. It also identifies the challenges and limitations related to the exploitation of quantum computing and outlines the main opportunities for future contributions. This review aims at offering a valuable reference for researchers in fields of engineering that are likely to turn to quantum computing for solutions. Doi: 10.28991/ESJ-2023-07-01-020 Full Text: PD

Personalized Recommendation Systems (PRES): A Comprehensive Study and Research Issues.

The type of information systems used to recommend items to the users are called Recommendation systems. The concept of recommendations was seen among cavemen, ants and other creatures too. Users often rely on opinion of their peers when looking for selecting something, this usual behavior of the humans, led to the development of recommendation systems. There exist various recommender systems for various areas. The existing recommendation systems use different approaches. The applications of recommendation systems are increasing with increased use of web based search for users’ specific requirements. Recommendation techniques are employed by general purpose websites such as google and yahoo based on browsing history and other information like user’s geographical locations, interests, behavior in the web, history of purchase and the way they entered the website. Document recommendation systems recommend documents depending on the similar search done previously by other users. Clickstream data which provides information like user behavior and the path the users take are captured and given as input to document recommendation system. Movie recommendation systems and music recommendation systems are other areas in use and being researched to improve. Social recommendation is gaining the momentum because of huge volume of data generated and diverse requirements of the users. Current web usage trends are forcing companies to continuously research for best ways to provide the users with the suitable information as per the need depending on the search and preferences. This paper

Active Control of Shocks and Sonic Boom Ground Signal

The manipulation of a flow field to obtain a desired change is a much heightened subject. Active flow control has been the subject of the major research areas in fluid mechanics for the past two decades. It offers new solutions for mitigation of shock strength, sonic boom alleviation, drag minimization, reducing blade-vortex interaction noise in helicopters, stall control and the performance maximization of existing designs to meet the increasing requirements of the aircraft industries. Despite the wide variety of the potential applications of active flow control, the majority of studies have been performed at subsonic speeds. The active flow control cases were investigated in transonic speed in this study. Although the active flow control provides significant improvements, the sensibility of aerodynamic performance to design parameters makes it a nontrivial and expensive problem, so the designer has to optimize a number of different parameters. For the purpose of gaining understanding of the active flow control concepts, an automated optimization cycle process was generated. Also, the optimization cycle reduces cost and turnaround time. The mass flow coefficient, location, width and angle were chosen as design parameters to maximize the aerodynamic performance of an aircraft. As the main contribution of this study, a detailed parametric study and optimization process were presented. The second step is to appraise the practicability of weakening the shock wave and thereby reducing the wave drag in transonic flight regime using flow control devices such as two dimensional contour bump, individual jet actuator, and also the hybrid control which includes both control devices together, thereby gaining the desired improvements in aerodynamic performance of the air-vehicle. After this study, to improve the aerodynamic performance, the flow control and shape parameters are optimized separately, combined, and in a serial combination. The remarkable part of all these studies is both gradient and non-gradient optimization techniques were used to find the global optimum point. The second part of this study includes investigation of the possibility of weakening the shock strength and the reduction of far field signature by using off-body energy addition. The main obstacle for flying supersonically over land is the detrimental effects of sonic boom on general public and structures. The shock waves generated from various parts of an aircraft flying at supersonic speed, coalesce to form a classic sonic boom acoustic signature, \u27N\u27 wave associated with the sonic boom on the ground. High pressure was imposed on certain parts of the computational domain to simulate the pulsed laser effects, and then the propagation and interaction of this pulsed shock with shock waves generated from the diamond shaped model were investigated. Optimization of the location and the power of the pulsed shock were achieved using the non-gradient optimization technique. The main contribution of this study is the optimization of the parameters of pulsed shock

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016)

Proceedings of the First PhD Symposium on Sustainable Ultrascale Computing Systems (NESUS PhD 2016) Timisoara, Romania. February 8-11, 2016.The PhD Symposium was a very good opportunity for the young researchers to share information and knowledge, to present their current research, and to discuss topics with other students in order to look for synergies and common research topics. The idea was very successful and the assessment made by the PhD Student was very good. It also helped to achieve one of the major goals of the NESUS Action: to establish an open European research network targeting sustainable solutions for ultrascale computing aiming at cross fertilization among HPC, large scale distributed systems, and big data management, training, contributing to glue disparate researchers working across different areas and provide a meeting ground for researchers in these separate areas to exchange ideas, to identify synergies, and to pursue common activities in research topics such as sustainable software solutions (applications and system software stack), data management, energy efficiency, and resilience.European Cooperation in Science and Technology. COS

The News Delivery Channel Recommendation Based on Granular Neural Network

With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech recognition, machine translation, image processing, and the creation of recommendation systems. Therefore, many real-world complex problems can be solved by the deep learning techniques. As is known, traditional news recommendation systems mostly employ techniques based on collaborative filtering and deep learning, but the performance of these algorithms is constrained by the sparsity of the data and the scalability of the approaches. In this paper, we propose a recommendation model using granular neural network model to recommend news to appropriate channels by analyzing the properties of news. Specifically, a specified neural network serves as the foundation for the granular neural network that the model is considered to be build. Different information granularities are attributed to various types of news material, and different information granularities are released between networks in various ways. When processing data, granular output is created, which is compared to the interval values pre-set on various platforms and used to quantify the analysis's effectiveness. The analysis results could help the media to match the proper news in depth, maximize the public attention of the news and the utilization of media resources