The scaling problem in the pattern recognition approach to machine translation

Statistical machine translation (SMT) has proven to be an interesting pattern recognition framework for automatically building machine translations systems from available parallel corpora. In the last few years, research in SMT has been characterized by two significant advances. First, the popularization of the so called phrase-based statistical translation models, which allows to incorporate local contextual information to the translation models. Second, the availability of larger and larger parallel corpora, which are composed of millions of sentence pairs, and tens of millions of running words. Since phrase-based models basically consists in statistical dictionaries of phrase pairs, their estimation from very large corpora is a very costly task that yields a huge number of parameters which are to be stored in memory. The handling of millions of model parameters and a similar number of training samples have become a bottleneck in the field of SMT, as well as in other well-known pattern recognition tasks such as speech recognition or handwritten recognition, just to name a few. In this paper, we propose a general framework that deals with the scaling problem in SMT without introducing significant time overhead by means of the combination of different scaling techniques. This new framework is based on the use of counts instead of probabilities, and on the concept of cache memory.     

Assessing creative problem-solving with automated text grading

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational–statistical machine learning methods to grade students’ natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit students’ constructed responses are beneficial. But the high cost required in manually grading constructed responses could become an obstacle in applying open-ended questions. In this study, automated grading schemes have been developed and evaluated in the context of secondary Earth science education. Empirical evaluations revealed that the automated grading schemes may reliably identify domain concepts embedded in students’ natural language responses with satisfactory inter-coder agreement against human coding in two sub-tasks of the test (Cohen’s Kappa = .65–.72). And when a single holistic score was computed for each student, machine-generated scores achieved high inter-rater reliability against human grading (Pearson’s r = .92). The reliable performance in automatic concept identification and numeric grading demonstrates the potential of using automated grading to support the use of open-ended questions in science assessments and enable new technologies for science learning.     

A simulation-based heuristic algorithm for disposition of direction finders

A direction finder is a military weapon that is used to find locations of targets that emit radio frequencies. Multiple direction finders are used in a direction finder system for finding locations of targets in an area of interest. We present a two-stage heuristic algorithm for disposing direction finders in a direction finder system for the objective of maximizing the accuracy of estimation of the location of a target that is assumed to be located in the area of interest. In the suggested heuristic algorithm, a simulation-based method is used for estimating the probability of coverage, the probability that a target is in a given region (of a given size) surrounding the estimated location of the target, and another simulation-based method and a local search method are used to determine locations of direction finders that result in the maximum probability of coverage. Performance of the suggested algorithm is evaluated through computational experiments and results show that the algorithm gives a good disposition plan in a reasonable amount of computation time.     

Computer-aided measurement plan for an impeller on a coordinate measurement machine with a rotating and tilting probe

This paper presents a computer-aided measurement plan (CAMP) for an impeller on a coordinate measurement machine (CMM) with a rotating/tilting probe. The blade surfaces of a machined impeller were measured exactly to secure the machining tolerance and surface finish of the impeller. CMM with a rotating and tilting probe is commonly used for this measurement. However, it is not easy to evaluate all the points on the impeller surfaces due to the time-consuming nature of the CMM measurement. In addition, many points cannot be easily accessed by the CMM probe due to the interference between the probe and the impeller blades. Furthermore, the inherent complexity of the existing teaching operation of a probe using a joystick suggests the necessity of developing a new method for the measurement planning of an impeller. Thus, this paper proposes a CAMP for effectively gauging the inspection points based on the ruled line information of the impeller blade surfaces. The proposed plan partitions the surfaces into several measurement regions based on the ruled lines of a blade surface and the approach vectors of the probe. Finally, a case example for an impeller is presented to demonstrate the effectiveness of the proposed strategy.     

Inductive transfer with context-sensitive neural networks

Context-sensitive Multiple Task Learning, or csMTL, is presented as a method of inductive transfer which uses a single output neural network and additional contextual inputs for learning multiple tasks. Motivated by problems with the application of MTL networks to machine lifelong learning systems, csMTL encoding of multiple task examples was developed and found to improve predictive performance. As evidence, the csMTL method is tested on seven task domains and shown to produce hypotheses for primary tasks that are often better than standard MTL hypotheses when learning in the presence of related and unrelated tasks. We argue that the reason for this performance improvement is a reduction in the number of effective free parameters in the csMTL network brought about by the shared output node and weight update constraints due to the context inputs. An examination of IDT and SVM models developed from csMTL encoded data provides initial evidence that this improvement is not shared across all machine learning models.     

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models. Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University, Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles at international journals. He is currently working on applications of artificial intelligence and adaptive control theory in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor manufacturing. Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management, inventory control, production planning and scheduling. Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively. From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis, and ubiquitous logistics information systems. An erratum to this article can be found at     

多功能转速测量仪的研制

针对目前在转速测量的过程中,存在着一定的误差,结合转速测量仪的测量方法进行改进,介绍了多功能转速测量仪的研制过程.它与原有的转速测量仪相比,在不降低技术指标的前提下增加了瞬态转速测量、低转速测量、计数、计时、线速度测量、双通道测量等功能.项目成果在今后的转速检测中将显现出良好的社会效益,并为计量行政管理部门提供有力的技术保障.     

基于LSSVM的木材干燥建模研究

针对木材干燥过程的强非线性特点,提出以最小二乘支持向量机LSSVM建立木材干燥基准模型.通过实验用小型木材干燥窑实际干燥过程中采集的数据作为训练样本进行仿真实验,结果表明基于LSSVM的木材干燥模型预测输出能够准确反映干燥过程木材含水率的变化,模型结构简单、预测精度高、泛化能力强,验证了LSSVM对木材干燥过程建模是一种可行而有效的方法.     

AptaCDSS-E: A classifier ensemble-based clinical decision support system for cardiovascular disease level prediction

Conventional clinical decision support systems are generally based on a single classifier or a simple combination of these models, showing moderate performance. In this paper, we propose a classifier ensemble-based method for supporting the diagnosis of cardiovascular disease (CVD) based on aptamer chips. This AptaCDSS-E system overcomes conventional performance limitations by utilizing ensembles of different classifiers. Recent surveys show that CVD is one of the leading causes of death and that significant life savings can be achieved if precise diagnosis can be made. For CVD diagnosis, our system combines a set of four different classifiers with ensembles. Support vector machines and neural networks are adopted as base classifiers. Decision trees and Bayesian networks are also adopted to augment the system. Four aptamer-based biochip data sets including CVD data containing 66 samples were used to train and test the system. Three other supplementary data sets are used to alleviate data insufficiency. We investigated the effectiveness of the ensemble-based system with several different aggregation approaches by comparing the results with single classifier-based models. The prediction performance of the AptaCDSS-E system was assessed with a cross-validation test. The experimental results show that our system achieves high diagnosis accuracy (>94%) and comparably small prediction difference intervals (<6%), proving its usefulness in the clinical decision process of disease diagnosis. Additionally, 10 possible biomarkers are found for further investigation.     

Optimal job sequence determination and operation machine allocation in flexible manufacturing systems: an approach using adaptive hierarchical ant colony algorithm

In this paper, an Adaptive Hierarchical Ant Colony Optimization (AHACO) has been proposed to resolve the traditional machine loading problem in Flexible Manufacturing Systems (FMS). Machine loading is one of the most important issues that is interlinked with the efficiency and utilization of FMS. The machine loading problem is formulated in order to minimize the system unbalance and maximize the throughput, considering the job sequencing, optional machines and technological constraints. The performance of proposed AHACO has been tested over a number of benchmark problems taken from the literature. Computational results indicate that the proposed algorithm is more effective and produces promising results as compared to the existing solution methodologies in the literature. The evaluation and comparison of system efficiency and system utilization justifies the supremacy of the algorithm. Further, results obtained from the proposed algorithm have been compared with well known random search algorithm viz. genetic algorithm, simulated annealing, artificial Immune system, simple ant colony optimization, tabu search etc. In addition, the algorithm has been tested over a randomly generated problem set of varying complexities; the results validate the robustness and scalability of the algorithm utilizing the concepts of ‘heuristic gap’ and ANOVA analysis.