Preparing a Data Scientist: A Pedagogic Experience in Designing a Big Data Analytics Course

In this article, we present an experiential perspective on how a big data analytics course was designed and delivered to students at a major Midwestern university. In reference to the MSIS 2006 Model Curriculum, we designed this course as a level 2 course, with prerequisites in databases, computer programming, statistics, and data mining. Students in the class were mostly seniors or at the graduate level, and had a strong technical and quantitative background. We include details of concepts covered in the course, as well as summaries of four major sample course assignments used. Some of the concepts covered include large‐scale data collection and management using the Hadoop ecosystem, stream mining, visual analytics, and social network analytics. Besides Hadoop, the course also introduced various IBM and Teradata big data tools. We show how the course modules align with the intended learning goals and course objectives. A post‐course survey indicated that the structure and organization of the course helped students clearly and concisely assimilate the course content.     

Off line simulation system of machining processes

The primary objective for modeling of machining processes is to develop a predictive capability of machining performance in order to facilitate effective planning of machining operations. This capability leads to faster implementation, higher performance, quality at a lower cost. This comes about due to improved selection of machining parameters, optimal fixture design and the avoidance of tool failure. The simulation system presented simultaneously considers the effect of cutter geometry, the cutter's initial position errors, workpiece geometry, machine tool dynamics, and workpiece/fixture system dynamics on the machining process.The integration of all of the above in one model provides an off-line tool to simulate and optimize the machining parameters and the fixture configuration cutting both lead and production time. The modular nature of the simulation system presented allows for the study of many different machining processes. The cutting forces in this system are modeled using a mechanistic approach. NURBS curves and surfaces are utilized for the geometric modeling and simulation of the machining process. While a finite element method is used to model and analyze the workpiece/fixture dynamics. Two case studies are presented to demonstrate the practical application of the presented simulation. The first case presents the optimization of the fixture configuration of a generic automotive component. While the second case presents the results of simulations performed on a novel mill/grind machining process. This process is a combination of face milling and grinding in one operation. Some simulated results are presented along with experimental validation.     

H∞ dynamic observer-based fuzzy integral sliding mode control with input magnitude and rate constraints

This paper focuses on a systematic constrained fuzzy integral sliding mode controller design for a class of uncertain discrete-time nonlinear systems which can be represented as Takagi-Sugeno (T-S) fuzzy models. The contributions are to consider constraints on the control input amplitude and control input amplitude rate and to extend the existing pole-placement design technique for designing gain matrices of the fuzzy sliding surface. Moreover, a dynamic-gain observer along with ${\mathit{H}}_{\infty }$ performance is proposed for attenuating disturbance, which generalizes the existing results on the Proportional Observer (PO), the Proportional Integral Observer (PIO) and the dynamic observer (DO). Finally, the dynamic-observer-based constrained fuzzy integral sliding mode controller is designed. All the proposed design conditions are represented in terms of LMIs-based ones. The methods are studied for not only single-input single-output (SISO) but also multi-input multi-output (MIMO) systems. In the end, the proposed approaches are evaluated on practical and numerical systems to illustrate the superiority of the proposed control scheme.     

Optimization of fuzzy model using genetic algorithm for process control application

A technique for the modeling of nonlinear control processes using fuzzy modeling approach based on the Takagi-Sugeno fuzzy model with a combination of genetic algorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzy model. For the antecedent fuzzy parameters, genetic algorithm is used to tune them while at the consequent part, recursive least squares approach is used to identify the system parameters. This approach is applied to a process control rig with three subsystems: a heating element, a heat exchanger and a compartment tank. Experimental results show that the proposed approach provides better modeling when compared with Takagi Sugeno fuzzy modeling technique and the linear modeling approach.     

Language planning in Malaysia： The first hundred years

The past,present and prospects of a linguistic range that includes Malay,Mandarin,Tamil,and English [A version,adapted for ET,of the opening address to the conference‘Congress of the world's major languages,’held under the auspices of the Dewan Bahasa dan Pustaka(‘Lan- guage and Literacy Agency’),in Kuala Lumpur,Malaysia,5- 8 October 2004]     

Factors affecting attitudes and intentions towards knowledge sharing in the Dubai Police Force

This study contributes to the limited research base on knowledge sharing in public sector organisations, specifically police forces, and organisations in the Middle East through a case study investigation into the factors that affect knowledge sharing in the Dubai Police Force. A questionnaire-based survey was conducted with staff in key departments in the Dubai Police Force. Informed by the literature and by interviews conducted in a previous phase, the core of the questionnaire was a bank of Likert-style questions covering the dependent variables intention to knowledge share, and attitude towards knowledge sharing, and the independent variables, trust, organisational structure, leadership, reward, time, and information technology. Data was analysed using structured equation modelling, in order to test the measurement model using confirmatory factor analysis, and to test the structural model. The structural model suggests a strong relationship between attitude to knowledge sharing, and intention to share knowledge. Hypotheses regarding the influence of leadership, trust, organisational structure, time, and information technology on attitude to knowledge sharing were upheld. Rewards did not to influence attitude to knowledge sharing. Recommendations are offered for practice and further research.     

Growth and yield of rice as affected by transplanting dates and seedlings per hill under high temperature of Dera Ismail Khan, Pakistan

Studies were initiated for two consecutive years to find out the effect of time of transplanting and seedlings hill^-l on the productivity of rice in Dera Ismail Khan district of North West Frontier Province （NWFP）, Pakistan. The experiment was laid out in a randomized complete block design with split plot arrangements. Main plots consisted of four transplanting dates viz. 20th and 27th of June and 4th and 1 lth of July while sub-plots contained 1, 2, 3 or 4 seedlings hill^-1. Among transplanting dates, June 20th planted crop gave highest paddy yield and net return with I seedling hill^-1. It explains that the use of more seedlings hill^-1 not only adds to cost but is also a mere wastage of natural resources. Based on research findings, we conclude that the use of I seedling hill^-1 is most appropriate for timely sowing otherwise 4 seedlings hill^-1 should be used to compensate for the yield gap in late transplanted rice.     

Evaluating research performance of research institutes within Malaysian universities: an alternative assessment framework

Tertiary Education and Management - Evaluation of research performance has significantly increased in importance, particularly in many emerging higher education systems. In Malaysia, the Malaysian...     

Temperamental Shyness,Frontal EEG Theta/Beta Ratio,and Social Anxiety in Children

The authors examined how children’s frontal electroencephalogram (EEG) theta/beta ratio—an index of neurocognitive control—changed from baseline to a social stressor, and whether these EEG changes moderated the relation between temperament and anxiety. Children (N = 152; M_age = 7.82 years, 52% male, 81% White) had their EEG recorded during a baseline and speech anticipation condition. Children’s frontal theta/beta ratio decreased from baseline to speech anticipation, and this baseline-to-task change moderated the relation between temperamental shyness and social anxiety. Temperamental shyness was related to higher state and trait social anxiety only among children with large baseline-to-task decreases in theta/beta ratio. Findings are consistent with theoretical models hypothesizing that temperamentally shy children with heightened neurocognitive control may be at greater risk for anxiety.     

Optimal sizing of piezo-actuators for minimum-deflection design of frames under uncertain loads

Optimal piezo-actuator sizes and locations for a frame structure under bending load uncertainty are determined with the objective of minimizing the deflection under worst case of loading. The bending moments generated by the piezo-actuators are used for deflection control, i.e., to minimize the maximum deflection. The frame is subjected to a tip load which lies in an uncertainty domain with regard to its magnitude and direction. The specific uncertain loading studied in the present paper involves a load of given magnitude but of unknown direction, which should be determined to produce the highest deflection. The worst case of loading depends on the size and location of the actuators leading to a nested problem of optimization (design) and anti-optimization (uncertainty problem). Results are given for deterministic and uncertain loading conditions.