Wireless sensor networks (WSNs) are gaining more interest in variety of applications. Of their different characteristics and challenges, network lifetime and efficiency are the most considered issues in WSN-based systems. The scarcest WSN's resource is energy, and one of the most energy-expensive operations is route discovery and data transmission. This paper presents a novel design of a cooperative nodes learning scheme for cooperative energy-efficient routing (CEERA) in wireless sensor networks. In CEERA, nodes perform a cooperative learning in delivering data to the base station. The retransmission of packets is controlled through an address-based timer. CEERA achieves overhead reduction and energy conservation by controlling various parameters that affect the overall network efficiency. Performance results are evaluated using NS2 simulator and our own implemented event-driven simulation. The simulation results show that our algorithm minimizes the overall energy consumption of the WSN, extends network operational lifetime, and improves network efficiency and throughput.
{"title":"A Cooperative Learning Scheme for Energy Efficient Routing in Wireless Sensor Networks","authors":"S. Al-Wakeel, N. Al-Nabhan","doi":"10.1109/ICMLA.2012.143","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.143","url":null,"abstract":"Wireless sensor networks (WSNs) are gaining more interest in variety of applications. Of their different characteristics and challenges, network lifetime and efficiency are the most considered issues in WSN-based systems. The scarcest WSN's resource is energy, and one of the most energy-expensive operations is route discovery and data transmission. This paper presents a novel design of a cooperative nodes learning scheme for cooperative energy-efficient routing (CEERA) in wireless sensor networks. In CEERA, nodes perform a cooperative learning in delivering data to the base station. The retransmission of packets is controlled through an address-based timer. CEERA achieves overhead reduction and energy conservation by controlling various parameters that affect the overall network efficiency. Performance results are evaluated using NS2 simulator and our own implemented event-driven simulation. The simulation results show that our algorithm minimizes the overall energy consumption of the WSN, extends network operational lifetime, and improves network efficiency and throughput.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117236463","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 introduces a Protein Query Language (PQL) for querying protein structures in an expressive yet concise manner. One of the objectives of the paper is to demonstrate how such a language would be beneficial to protein researchers to obtain in-depth protein data from a relational database without extensive SQL knowledge. The language features options such as limiting query results by key protein characteristics such as methyl donated hydrogen bond interactions, minimum and maximum phi and psi angles, repulsive forces, CH/Pi calculations, and other pertinent factors. A backend data model was designed to support storage and retrieval of protein primary and secondary sequences, atomic-level data, as well as calculations on said data. A relational DBMS is used as the persistent storage backend, with every effort made to ensure transparent portability to most relational database systems. In addition, front end applications can be developed to support retrieving, transforming, and preprocessing of information from the Research Collaboratory for Structural Bioinformatics (RCSB) into the backend data repository. The new language and associated architecture allow users to load additional protein files from RCSB into the database, issue standard queries to download pertinent data in user-friendly formats including CSV files, issue non-standard queries against secondary structures via the protein query language, and run error-detection routines against data in the database. Query results may include normalized or denormalized data, model and chain data, residue data, atom detail data, and primary as well as secondary structure data.
{"title":"PQL: Protein Query Language","authors":"S. Elfayoumy, Paul Bathen","doi":"10.1109/ICMLA.2012.217","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.217","url":null,"abstract":"This paper introduces a Protein Query Language (PQL) for querying protein structures in an expressive yet concise manner. One of the objectives of the paper is to demonstrate how such a language would be beneficial to protein researchers to obtain in-depth protein data from a relational database without extensive SQL knowledge. The language features options such as limiting query results by key protein characteristics such as methyl donated hydrogen bond interactions, minimum and maximum phi and psi angles, repulsive forces, CH/Pi calculations, and other pertinent factors. A backend data model was designed to support storage and retrieval of protein primary and secondary sequences, atomic-level data, as well as calculations on said data. A relational DBMS is used as the persistent storage backend, with every effort made to ensure transparent portability to most relational database systems. In addition, front end applications can be developed to support retrieving, transforming, and preprocessing of information from the Research Collaboratory for Structural Bioinformatics (RCSB) into the backend data repository. The new language and associated architecture allow users to load additional protein files from RCSB into the database, issue standard queries to download pertinent data in user-friendly formats including CSV files, issue non-standard queries against secondary structures via the protein query language, and run error-detection routines against data in the database. Query results may include normalized or denormalized data, model and chain data, residue data, atom detail data, and primary as well as secondary structure data.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"321 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116121785","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, an automated method for generation of tests against inefficient solutions for programming challenge tasks on graph theory is proposed. The method is based on the use of (1+1) evolution strategy and is able to defeat several kinds of inefficient solutions. The proposed method was applied to a task from the Internet problem archive, the Timus Online Judge.
{"title":"Generation of Tests for Programming Challenge Tasks on Graph Theory Using Evolution Strategy","authors":"M. Buzdalov","doi":"10.1109/ICMLA.2012.194","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.194","url":null,"abstract":"In this paper, an automated method for generation of tests against inefficient solutions for programming challenge tasks on graph theory is proposed. The method is based on the use of (1+1) evolution strategy and is able to defeat several kinds of inefficient solutions. The proposed method was applied to a task from the Internet problem archive, the Timus Online Judge.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122905835","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}
Onur Keskin, Ismet Ates, Z. H. Karadeniz, A. Turgut, Z. Kıral
In this paper, we develop a web based interface which performs a wireless communication with ZigBee protocol for monitoring wind energy potential and also gathering custom reports for determination of the interested wind field. A custom printed circuit board layer is designed for interfacing with all the sensors that are in use. Web based interface is a product of responsive design for platform and device independency. This system enables scalable, accessible, reliable, low cost and low power consumption solution for renewable energy systems.
{"title":"Monitoring and Determination of Wind Energy Potential by Web Based Wireless Network","authors":"Onur Keskin, Ismet Ates, Z. H. Karadeniz, A. Turgut, Z. Kıral","doi":"10.1109/ICMLA.2012.205","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.205","url":null,"abstract":"In this paper, we develop a web based interface which performs a wireless communication with ZigBee protocol for monitoring wind energy potential and also gathering custom reports for determination of the interested wind field. A custom printed circuit board layer is designed for interfacing with all the sensors that are in use. Web based interface is a product of responsive design for platform and device independency. This system enables scalable, accessible, reliable, low cost and low power consumption solution for renewable energy systems.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"217 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123027836","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}
Viktor Ten, N. Isembergenov, Y. Akhmetbekov, D. Sarbassov, A. Iglikov, B. Matkarimov
Problem statement for a renewable power system test site located at Nazarbayev University is formulated with consideration of a presence of uncertain disturbances from consumer grid side. Proposed controller is based on the method of additional equilibria. For adjusting of parameters of controller and control plant the genetic algorithm is proposed. Results of MATLAB simulation of designed control system are presented.
{"title":"Approach to Control of the Output Voltage in Renewable Energy Sources on the Basis of AE-method Using Genetic Algorithm","authors":"Viktor Ten, N. Isembergenov, Y. Akhmetbekov, D. Sarbassov, A. Iglikov, B. Matkarimov","doi":"10.1109/ICMLA.2012.168","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.168","url":null,"abstract":"Problem statement for a renewable power system test site located at Nazarbayev University is formulated with consideration of a presence of uncertain disturbances from consumer grid side. Proposed controller is based on the method of additional equilibria. For adjusting of parameters of controller and control plant the genetic algorithm is proposed. Results of MATLAB simulation of designed control system are presented.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130675327","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}
Malak Alshawabkeh, Alma Riska, Adnan Sahin, Motasem Awwad
In this paper, we develop an automated and adaptive framework that aims to move active data to high performance storage tiers and inactive data to low cost/high capacity storage tiers by learning patterns of the storage workloads. The framework proposed is designed using efficient Markov chain correlation based clustering method (MCC), which can quickly predict or detect any changes in the current workload based on what the system has experienced before. The workload data is first normalized and Markov chains are constructed from the dynamics of the IO loads of the data storage units. Based on the correlation of one-step Markov chain transition probabilities k-means method is employed to group the storage units that have similar behavior at each point. Such framework can then easily be incorporated in various resource management policies that aim at enhancing performance, reliability, availability. The predictive nature of the model, particularly makes a storage system both faster and lower-cost at the same time, because it only uses high performance tiers when needed, and uses low cost/high capacity tiers when possible.
{"title":"Automated Storage Tiering Using Markov Chain Correlation Based Clustering","authors":"Malak Alshawabkeh, Alma Riska, Adnan Sahin, Motasem Awwad","doi":"10.1109/ICMLA.2012.71","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.71","url":null,"abstract":"In this paper, we develop an automated and adaptive framework that aims to move active data to high performance storage tiers and inactive data to low cost/high capacity storage tiers by learning patterns of the storage workloads. The framework proposed is designed using efficient Markov chain correlation based clustering method (MCC), which can quickly predict or detect any changes in the current workload based on what the system has experienced before. The workload data is first normalized and Markov chains are constructed from the dynamics of the IO loads of the data storage units. Based on the correlation of one-step Markov chain transition probabilities k-means method is employed to group the storage units that have similar behavior at each point. Such framework can then easily be incorporated in various resource management policies that aim at enhancing performance, reliability, availability. The predictive nature of the model, particularly makes a storage system both faster and lower-cost at the same time, because it only uses high performance tiers when needed, and uses low cost/high capacity tiers when possible.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123818530","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 presents work done on classification, segmentation and chronological prediction of cinematic sound employing support vector machines (SVM) with sequential minimal optimization (SMO). Speech, music, environmental sound and silence, plus all pair wise combinations excluding silence, are considered as classes. A model considering simple adjacency rules and probabilistic output from logistic regression is used for segmenting fixed-length parts into auditory scenes. Evaluation of the proposed methods on a 44-film dataset against k-nearest neighbor, Naive Bayes and standard SVM classifiers shows superior results of the SMO classifier on all performance metrics. Subsequently, we propose sample size optimizations to the building of similar datasets. Finally, we use meta-features built from classification as descriptors in a chronological model for predicting the period of production of a given soundtrack. A decision table classifier is able to estimate the year of production of an unknown soundtrack with a mean absolute error of approximately five years.
{"title":"Classification, Segmentation and Chronological Prediction of Cinematic Sound","authors":"Pedro Silva","doi":"10.1109/ICMLA.2012.172","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.172","url":null,"abstract":"This paper presents work done on classification, segmentation and chronological prediction of cinematic sound employing support vector machines (SVM) with sequential minimal optimization (SMO). Speech, music, environmental sound and silence, plus all pair wise combinations excluding silence, are considered as classes. A model considering simple adjacency rules and probabilistic output from logistic regression is used for segmenting fixed-length parts into auditory scenes. Evaluation of the proposed methods on a 44-film dataset against k-nearest neighbor, Naive Bayes and standard SVM classifiers shows superior results of the SMO classifier on all performance metrics. Subsequently, we propose sample size optimizations to the building of similar datasets. Finally, we use meta-features built from classification as descriptors in a chronological model for predicting the period of production of a given soundtrack. A decision table classifier is able to estimate the year of production of an unknown soundtrack with a mean absolute error of approximately five years.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"113 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124135803","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 concept paper suggests a new way of thinking about the origin, growth, and spread of a general-purpose "scientific habit of mind" in humans, and discusses how intelligent coaching agents may help. The argument begins with a description of the role of the cognitive cycle in animal thinking. We then examine critical differences between non-human and human cognition, especially in respect to the crucial and yet problematic role of language and linguistic interaction in the "widening spread and deepening hold" of scientific thinking and discourse in human populations. The paper concludes with a vision of a new kind of open, networked learning community inhabited by human learners, human experts, and intelligent agents, and suggests ways of evaluating the development of scientific thinking within these communities using a combination of social network and semantic space analysis.
{"title":"Animal Cognition, Epistemic Fluency, Social Networks and the Scientific Habit of Mind","authors":"D. M. Morrison, Xiangen Hu","doi":"10.1109/ICMLA.2012.166","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.166","url":null,"abstract":"This concept paper suggests a new way of thinking about the origin, growth, and spread of a general-purpose \"scientific habit of mind\" in humans, and discusses how intelligent coaching agents may help. The argument begins with a description of the role of the cognitive cycle in animal thinking. We then examine critical differences between non-human and human cognition, especially in respect to the crucial and yet problematic role of language and linguistic interaction in the \"widening spread and deepening hold\" of scientific thinking and discourse in human populations. The paper concludes with a vision of a new kind of open, networked learning community inhabited by human learners, human experts, and intelligent agents, and suggests ways of evaluating the development of scientific thinking within these communities using a combination of social network and semantic space analysis.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121317251","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}
Mobile phones are becoming the latest target of electronic junk mail. Recent reports clearly indicate that the volume of SMS spam messages are dramatically increasing year by year. Probably, one of the major concerns in academic settings was the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. To address this issue, we have recently proposed a new SMS Spam Collection that, to the best of our knowledge, is the largest, public and real SMS dataset available for academic studies. However, as it has been created by augmenting a previously existing database built using roughly the same sources, it is sensible to certify that there are no duplicates coming from them. So, in this paper we offer a comprehensive analysis of the new SMS Spam Collection in order to ensure that this does not happen, since it may ease the task of learning SMS spam classifiers and, hence, it could compromise the evaluation of methods. The analysis of results indicate that the procedure followed does not lead to near-duplicates and, consequently, the proposed dataset is reliable to use for evaluating and comparing the performance achieved by different classifiers.
{"title":"On the Validity of a New SMS Spam Collection","authors":"J. M. G. Hidalgo, Tiago A. Almeida, A. Yamakami","doi":"10.1109/ICMLA.2012.211","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.211","url":null,"abstract":"Mobile phones are becoming the latest target of electronic junk mail. Recent reports clearly indicate that the volume of SMS spam messages are dramatically increasing year by year. Probably, one of the major concerns in academic settings was the scarcity of public SMS spam datasets, that are sorely needed for validation and comparison of different classifiers. To address this issue, we have recently proposed a new SMS Spam Collection that, to the best of our knowledge, is the largest, public and real SMS dataset available for academic studies. However, as it has been created by augmenting a previously existing database built using roughly the same sources, it is sensible to certify that there are no duplicates coming from them. So, in this paper we offer a comprehensive analysis of the new SMS Spam Collection in order to ensure that this does not happen, since it may ease the task of learning SMS spam classifiers and, hence, it could compromise the evaluation of methods. The analysis of results indicate that the procedure followed does not lead to near-duplicates and, consequently, the proposed dataset is reliable to use for evaluating and comparing the performance achieved by different classifiers.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121418837","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}
High dimensionality and class imbalance are the two main problems affecting many software defect prediction. In this paper, we propose a new technique, named SelectRUSBoost, which is a form of ensemble learning that in-corporates data sampling to alleviate class imbalance and feature selection to resolve high dimensionality. To evaluate the effectiveness of the new technique, we apply it to a group of datasets in the context of software defect prediction. We employ two classification learners and six feature selection techniques. We compare the technique to the approach where feature selection and data sampling are used together, as well as the case where feature selection is used alone (no sampling used at all). The experimental results demonstrate that the SelectRUSBoost technique is more effective in improving classification performance compared to the other approaches.
{"title":"A Hybrid Approach to Coping with High Dimensionality and Class Imbalance for Software Defect Prediction","authors":"Kehan Gao, T. Khoshgoftaar, Amri Napolitano","doi":"10.1109/ICMLA.2012.145","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.145","url":null,"abstract":"High dimensionality and class imbalance are the two main problems affecting many software defect prediction. In this paper, we propose a new technique, named SelectRUSBoost, which is a form of ensemble learning that in-corporates data sampling to alleviate class imbalance and feature selection to resolve high dimensionality. To evaluate the effectiveness of the new technique, we apply it to a group of datasets in the context of software defect prediction. We employ two classification learners and six feature selection techniques. We compare the technique to the approach where feature selection and data sampling are used together, as well as the case where feature selection is used alone (no sampling used at all). The experimental results demonstrate that the SelectRUSBoost technique is more effective in improving classification performance compared to the other approaches.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114617622","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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