In the frame of our previous experiments for proof based synthesis of sorting algorithms for lists and for binary trees, we employed certain special techniques which are able to generate multiple variants of sorting and merging, by investigating all combinations of auxiliary functions available for composing objects (lists, respectively trees). The purpose of this paper is to describe this technique and the results obtained. We present the main principles and the application of this technique to merging of sorted binary trees into a sorted one. Remarkably, merging requires a nested recursion, for which an appropriate induction principle is difficult to guess. Our method is able to find it automatically by using a general Noetherian induction and the combinatorial technique.
{"title":"Combinatorial Techniques for Proof-Based Synthesis of Sorting Algorithms","authors":"I. Dramnesc, T. Jebelean, Sorin Stratulat","doi":"10.1109/SYNASC.2015.30","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.30","url":null,"abstract":"In the frame of our previous experiments for proof based synthesis of sorting algorithms for lists and for binary trees, we employed certain special techniques which are able to generate multiple variants of sorting and merging, by investigating all combinations of auxiliary functions available for composing objects (lists, respectively trees). The purpose of this paper is to describe this technique and the results obtained. We present the main principles and the application of this technique to merging of sorted binary trees into a sorted one. Remarkably, merging requires a nested recursion, for which an appropriate induction principle is difficult to guess. Our method is able to find it automatically by using a general Noetherian induction and the combinatorial technique.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"13 1","pages":"137-144"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85192355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper addresses the development of general purpose game agents able to learn a vast number of games using the same architecture. The article analyzes the main existing approaches to general game playing, reviews their performance and proposes future research directions. Methods such as deep learning, reinforcement learning and evolutionary algorithms are considered for this problem. The testing platform is the popular video game console Atari 2600. Research into developing general purpose agents for games is closely related to achieving artificial general intelligence (AGI).
{"title":"Comparative Analysis of Existing Architectures for General Game Agents","authors":"Ionel-Alexandru Hosu, A. Urzica","doi":"10.1109/SYNASC.2015.48","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.48","url":null,"abstract":"This paper addresses the development of general purpose game agents able to learn a vast number of games using the same architecture. The article analyzes the main existing approaches to general game playing, reviews their performance and proposes future research directions. Methods such as deep learning, reinforcement learning and evolutionary algorithms are considered for this problem. The testing platform is the popular video game console Atari 2600. Research into developing general purpose agents for games is closely related to achieving artificial general intelligence (AGI).","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"709 1","pages":"257-260"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90960677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The research area of sentiment analysis, opinion mining, sentiment mining and sentiment extraction has gained popularity in the last years. Online reviews are becoming very important criteria in measuring the quality of a business. This paper presents a sentiment analysis approach to business reviews classification using a large reviews dataset provided by Yelp: Yelp Challenge dataset. In this work, we propose several approaches for automatic sentiment classification, using two feature extraction methods and four machine learning models. It is illustrated a comparative study on the effectiveness of the ensemble methods for reviews sentiment classification.
{"title":"Business Reviews Classification Using Sentiment Analysis","authors":"Andreea Salinca","doi":"10.1109/SYNASC.2015.46","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.46","url":null,"abstract":"The research area of sentiment analysis, opinion mining, sentiment mining and sentiment extraction has gained popularity in the last years. Online reviews are becoming very important criteria in measuring the quality of a business. This paper presents a sentiment analysis approach to business reviews classification using a large reviews dataset provided by Yelp: Yelp Challenge dataset. In this work, we propose several approaches for automatic sentiment classification, using two feature extraction methods and four machine learning models. It is illustrated a comparative study on the effectiveness of the ensemble methods for reviews sentiment classification.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"1 1","pages":"247-250"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89048279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Error Threshold is a concept from molecular biology that has been introduced [G. Ochoa (2006) Error Thresholds in Genetic Algorithms. Evolutionary Computation Journal, 14:2, pp 157-182, MIT Press] in Genetic Algorithms and has been linked to the concept of Optimal Mutation Rate. In this paper, the author expands previous works with a study of Error Thresholds near 1 (i.e. mutation probabilities of approx. 0.95), in the context of binary encoded chromosomes. Comparative empirical tests are performed, and the author draws conclusions in the context of population consensus sequences, population size, mutation rates and error thresholds.
{"title":"High Probability Mutation and Error Thresholds in Genetic Algorithms","authors":"Nicolae-Eugen Croitoru","doi":"10.1109/SYNASC.2015.51","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.51","url":null,"abstract":"Error Threshold is a concept from molecular biology that has been introduced [G. Ochoa (2006) Error Thresholds in Genetic Algorithms. Evolutionary Computation Journal, 14:2, pp 157-182, MIT Press] in Genetic Algorithms and has been linked to the concept of Optimal Mutation Rate. In this paper, the author expands previous works with a study of Error Thresholds near 1 (i.e. mutation probabilities of approx. 0.95), in the context of binary encoded chromosomes. Comparative empirical tests are performed, and the author draws conclusions in the context of population consensus sequences, population size, mutation rates and error thresholds.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"8 1","pages":"271-276"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79707998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We use several geometric techniques to characterize identifiability and to estimate noise robustness in the framework of l1-analysis regularization. This extends several recent theoretical results and algorithms that deal with the same issues in the less complex case of l1-synthesis regularizations.
{"title":"Identifiability and Noise Robustness for l1-Analysis Regularizations in Compressive Sensing","authors":"Nicodeme Marc, Turcu Flavius, D. Charles","doi":"10.1109/SYNASC.2015.21","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.21","url":null,"abstract":"We use several geometric techniques to characterize identifiability and to estimate noise robustness in the framework of l1-analysis regularization. This extends several recent theoretical results and algorithms that deal with the same issues in the less complex case of l1-synthesis regularizations.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"70 1","pages":"79-84"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85968480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Pop, V. Chifu, I. Salomie, Cristian Prigoana, Tiberiu Boros, Dorin Moldovan
This paper models the problem of generating healthy menu recommendations for older adults as an optimization problem and proposes a hybrid Honey Bees Mating Optimization method for solving this problem. The method hybridizes the state of the art Honey Bees Mating Optimization meta-heuristic by injecting strategies inspired from Genetic Algorithms, Hill Climbing, Simulated Annealing, and Tabu Search into the steps that generate new solutions of the optimization problem. The method has been integrated in a food ordering system enabling older adults to order food daily. Experiments have been conducted on several hybridization configurations to identify the most appropriate hybridization that leads to the healthy menu recommendation that best satisfies the older adult's diet recommended by the nutritionist, its culinary preferences and time and price constraints.
{"title":"Generating Healthy Menus for Older Adults Using a Hybrid Honey Bees Mating Optimization Approach","authors":"C. Pop, V. Chifu, I. Salomie, Cristian Prigoana, Tiberiu Boros, Dorin Moldovan","doi":"10.1109/SYNASC.2015.73","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.73","url":null,"abstract":"This paper models the problem of generating healthy menu recommendations for older adults as an optimization problem and proposes a hybrid Honey Bees Mating Optimization method for solving this problem. The method hybridizes the state of the art Honey Bees Mating Optimization meta-heuristic by injecting strategies inspired from Genetic Algorithms, Hill Climbing, Simulated Annealing, and Tabu Search into the steps that generate new solutions of the optimization problem. The method has been integrated in a food ordering system enabling older adults to order food daily. Experiments have been conducted on several hybridization configurations to identify the most appropriate hybridization that leads to the healthy menu recommendation that best satisfies the older adult's diet recommended by the nutritionist, its culinary preferences and time and price constraints.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"98 1","pages":"452-459"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81197075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we would like to introduce a GPU accelerated solver for systems of linear equations with an infinite precision. The infinite precision means that the system can provide a precise solution without any rounding error. These errors usually come from limited precision of floating point values within their natural computer representation. In a simplified description, the system is using modular arithmetic for transforming an original SLE into dozens of integer SLEs that are solved in parallel via GPU. In the final step, partial results are used for a calculation of the final solution. The usage of GPU plays a key role in terms of performance because the whole process is computationally very intensive. The GPU solver can provide about one magnitude higher performance than a multithreaded one.
{"title":"GPU Solver for Systems of Linear Equations with Infinite Precision","authors":"Jiri Khun, I. Šimeček, R. Lórencz","doi":"10.1109/SYNASC.2015.28","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.28","url":null,"abstract":"In this paper, we would like to introduce a GPU accelerated solver for systems of linear equations with an infinite precision. The infinite precision means that the system can provide a precise solution without any rounding error. These errors usually come from limited precision of floating point values within their natural computer representation. In a simplified description, the system is using modular arithmetic for transforming an original SLE into dozens of integer SLEs that are solved in parallel via GPU. In the final step, partial results are used for a calculation of the final solution. The usage of GPU plays a key role in terms of performance because the whole process is computationally very intensive. The GPU solver can provide about one magnitude higher performance than a multithreaded one.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"18 1","pages":"121-124"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85502946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the newest molecular descriptors, the general sum-connectivity index of a graph G is defined as χα(G) = ΣuvϵE(G)(d(u) + d(v))α, where d(u) denotes the degree of vertex u in G and is a real number. The aim of this paper is to determine the trees and the unicyclic graphs with k pendant vertices that maximize the general sum-connectivity index for α ≥ 1,with 2 ≤ k ≤ n for trees and 0 ≤ k ≤ n - 3 for unicyclic graphs.
{"title":"General Sum-Connectivity Index with a = 1 for Trees and Unicyclic Graphs with k Pendants","authors":"Rozica-Maria Tache, I. Tomescu","doi":"10.1109/SYNASC.2015.55","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.55","url":null,"abstract":"One of the newest molecular descriptors, the general sum-connectivity index of a graph G is defined as χ<sub>α</sub>(G) = Σ<sub>uvϵE(G)</sub>(d(u) + d(v))α, where d(u) denotes the degree of vertex u in G and is a real number. The aim of this paper is to determine the trees and the unicyclic graphs with k pendant vertices that maximize the general sum-connectivity index for α ≥ 1,with 2 ≤ k ≤ n for trees and 0 ≤ k ≤ n - 3 for unicyclic graphs.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"82 1","pages":"307-311"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80813575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We introduce the notion of strongly distributed multi-agent systems and present a uniform approach to incremental problem solving on them. The approach is based on the systematic use of two logical reduction techniques: Feferman-Vaught reductions and syntactically defined translation schemes. The multi-agent systems are presented as logical structures A. The problems are represented as boolean or quantitative formulae on them. We propose a uniform template for methods, which allow for a certain cost evaluation of formulae of logic Lover A from values of formulae over its components and values of formulae over the index structure I. We show that our approach works for very many of extensions of First Order Logic.
{"title":"Incremental Reasoning on Strongly Distributed Multi-agent Systems","authors":"E. Ravve, Z. Volkovich, G. Weber","doi":"10.1109/SYNASC.2015.68","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.68","url":null,"abstract":"We introduce the notion of strongly distributed multi-agent systems and present a uniform approach to incremental problem solving on them. The approach is based on the systematic use of two logical reduction techniques: Feferman-Vaught reductions and syntactically defined translation schemes. The multi-agent systems are presented as logical structures A. The problems are represented as boolean or quantitative formulae on them. We propose a uniform template for methods, which allow for a certain cost evaluation of formulae of logic Lover A from values of formulae over its components and values of formulae over the index structure I. We show that our approach works for very many of extensions of First Order Logic.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"14 1","pages":"415-422"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72999664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper makes a short overview of current state of the art monitoring tools for cloud and big data frameworks. In order to effectively create, test and deploy new algorithms or frameworks one needs suitable monitoring solutions. Hence we aim on creating a critical overview for some of the monitoring solutions existing on the market. Also we present relevant metrics used for monitoring cloud and big data applications, focused mainly on cloud deployment scenarios for big data frameworks.
{"title":"An Overview of Monitoring Tools for Big Data and Cloud Applications","authors":"Gabriel Iuhasz, I. Drăgan","doi":"10.1109/SYNASC.2015.62","DOIUrl":"https://doi.org/10.1109/SYNASC.2015.62","url":null,"abstract":"This paper makes a short overview of current state of the art monitoring tools for cloud and big data frameworks. In order to effectively create, test and deploy new algorithms or frameworks one needs suitable monitoring solutions. Hence we aim on creating a critical overview for some of the monitoring solutions existing on the market. Also we present relevant metrics used for monitoring cloud and big data applications, focused mainly on cloud deployment scenarios for big data frameworks.","PeriodicalId":6488,"journal":{"name":"2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)","volume":"305 1","pages":"363-366"},"PeriodicalIF":0.0,"publicationDate":"2015-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75817511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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