2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)最新文献_第2页

A Multi-objective Hyper-Heuristic for the Flexible Job Shop Scheduling Problem with Additional Constraints 带附加约束的柔性作业车间调度问题的多目标超启发式算法

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.46

J. Grobler

引用次数: 4

Yield Optimization with Binding Latency Constraints 具有绑定延迟约束的成品率优化

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.51

Dmitri I. Arkhipov, John G. Turner, M. Dillencourt, Paul L. Torresz, A. Regan

{"title":"Yield Optimization with Binding Latency Constraints","authors":"Dmitri I. Arkhipov, John G. Turner, M. Dillencourt, Paul L. Torresz, A. Regan","doi":"10.1109/ISCMI.2016.51","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.51","url":null,"abstract":"Programmatic advertising is an actively developing industry and research area. Some of the research in this area concerns the development of optimal or approximately optimal contracts and policies between publishers, advertisers and intermediaries such as ad networks and ad exchanges. Both the development of contracts and the construction of policies governing their implementation are difficult challenges, and different models take different features of the problem into account. In programmatic advertising decisions are made in real time, and time is a scarce resource particularly for publishers who are concerned with content load times. Policies for advertisement placement must execute very quickly once content is requested, this requires policies to either be pre-computed and accessed as needed, or for the policy execution to be very efficient. In this paper we formulate a stochastic optimization problem for per publisher ad sequencing with binding latency constraints. We adopt a well known heuristic optimization technique to this problem and evaluate it's performance on real data instances. Our experimental results indicate that our heuristic algorithm is near optimal for instances where an optimality calculation is feasible, and superior to other reasonable approaches for instances when the calculation is not feasible.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128945496","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}

引用次数: 2

Realtime Haptic Rendering in Hybrid Environment Using Unified SPH Method 基于统一SPH方法的混合环境实时触觉渲染

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.42

Ji Liang, Ge Yu, Kun Wang, Yan Wang, Lili Guo

引用次数: 1

Classification on Grassmann Manifold via Scheiddegger-Watson Distribution using Bayesian Approach 基于Scheiddegger-Watson分布的Grassmann流形贝叶斯分类

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.37

Muhammad Ali, M. Antolovich

引用次数: 0

The Influence of Topologies on the Dynamic Vector Evaluated Particle Swarm Optimisation Algorithm 拓扑结构对动态矢量评估粒子群优化算法的影响

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.43

Mardé Helbig

{"title":"The Influence of Topologies on the Dynamic Vector Evaluated Particle Swarm Optimisation Algorithm","authors":"Mardé Helbig","doi":"10.1109/ISCMI.2016.43","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.43","url":null,"abstract":"Most real world problems have more than one objective, with at least two objectives in conflict with one another and at least one objective that is dynamic in nature. The dynamic vector evaluated particle swarm optimisation (DVEPSO) algorithm is a co-operative algorithm, where each sub-swarm solves only one objective function and therefore, each sub-swarm optimises only a sub-set of decision variables. Knowledge is shared amongst the sub-swarms when the particles' velocity is updated, by using the position of the global guide of the sub-swarm or of another sub-swarm. Each sub-swarm's entities are connected to one another according to a specific topology that determines the communication of particles with one another. This paper investigates the effect of using the star or Von Neumann topology for DVEPSO's sub-swarm's particles. The results indicate that the star topology performed the best with regards to accuracy and the Von Neumann topology performed the best with regards to stability. In addition, the Von Neumann topology performed the best on benchmarks with a non-linear Pareto-optimal set (POS) and in very fast changing environments.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124910226","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}

引用次数: 0

Model of Learning Assessment to Measure Student Learning: Inferring of Concept State of Cognitive Skill Level in Concept Space 衡量学生学习的学习评估模型:概念空间中认知技能水平的概念状态推断

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.26

Rania Aboalela, J. Khan

{"title":"Model of Learning Assessment to Measure Student Learning: Inferring of Concept State of Cognitive Skill Level in Concept Space","authors":"Rania Aboalela, J. Khan","doi":"10.1109/ISCMI.2016.26","DOIUrl":"https://doi.org/10.1109/ISCMI.2016.26","url":null,"abstract":"This research presents a novel learning assessment model to measure student learning in terms of learning cognitive skill levels and the current concept state towards achieving the level. The cognitive skill levels refer to levels such as whether a student has acquired the concept at the level of understanding, or applying, or analyzing, or evaluating, or creating. The concept state refers to whether a student has already learned, or is ready to learn, or is not ready to learn a certain concept. The model is comprised of four constructions: graph paradigm of a semantic/ontological scheme, concept mapped testing and evaluation method, analysis algorithms and methods which produce the sets of concept states, and the assessment analytics, which is the process to estimate the students' concept states. The concept states involved three basic sets of the concepts in the domain: Verified Skills (VS), Derived Skills (DS), and Potential Skills (PS). VS means the concepts are known by evidence. DS means the concepts are known by inference. PS means the concepts are ready to be known by inference. The experiment is conducted to validate the accuracy of the concept states VS, DS, and PS.","PeriodicalId":417057,"journal":{"name":"2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125294493","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}

引用次数: 10

Prediction of Steam Turbine Performance as a Waste Heat Recovery Mechanism on Naturally Aspirated Spark Ignition Engine Using Artificial Neural Network 自然吸气式火花点火发动机余热回收机制汽轮机性能的人工神经网络预测

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.22

S. Herawan, Kamarulhelmy Talib, S. A. Shamsudin, A. Putra, M. T. Musthafah, A. Ismail

引用次数: 2

Mode of Delivery Prognosis through Data Mining 基于数据挖掘的交付预测模型

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.10

H. Alshraideh, A. Khayyat, Mwaffaq Otoom

引用次数: 0

Meta Learning Application in Rank Aggregation Feature Selection 元学习在秩聚合特征选择中的应用

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.55

I. Smetannikov, Alexander Deyneka, A. Filchenkov

引用次数: 3

Visual Programming and Development of Manufacturing Processes Based on Hierarchical Petri Nets 基于层次Petri网的制造过程可视化编程与开发

2016 3rd International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2016-11-01 DOI: 10.1109/ISCMI.2016.12

Daniel Losch, J. Roßmann

引用次数: 5