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The Appalachian Cohort for Engineering (ACE) at Ohio University is an NSF S-STEM funded scholarship program for students in Engineering and Computer Science that combines intensive early intervention approaches (i.e., learning communities, peer-led team learning, midterm progress reports, and purposeful selection of academic advisors) with peer advising and cohort building activities. The intent of this program is to help academically capable but financially insecure students from the economically disadvantaged Appalachian counties of Ohio and surrounding states to complete important early milestones in their academic careers. The ultimate goal of this project is to build evidence-based approaches for encouraging retention and early academic milestone completion for a broad range of undergraduate students in the STEM disciplines. The research component of this project seeks to determine which services are most effective at encouraging and supporting these students to reach early academic milestones to promote long-term retention and degree completion. This component involves substantial data collection that includes observational field notes and one-on-one contact records, along with the required NSF data elements. Much of the data collection is provided by trained peer advisors; capturing the following dimensions of academic wellness: personal/transitional, social, academic, financial, health and stress-coping, study sessions and tutoring, goal setting, follow up data, and academic reviews by course. In addition, field notes are collected that describe the setting, attendees, acts, and reflections on specific events that happen throughout the year. This project is in its first year of implementation. In this work-in-progress paper, we report on the design of the project, early successes and challenges, the data collection strategy, and the preliminary results of this project. In this first year, early observational results indicated that this body of students needed both social and academic support; with both being equal emphasis. Furthermore, this body of students indicated that one-on-one support and goal setting were important components of their early success.

This paper describes the efforts at Ohio University to incorporate selected topics from the IEEE-TCPP Curriculum Initiative into the Computer Science/Computer Engineering curriculum prior to a transition to semesters at Ohio University that will occur in the Fall of 2012. In particular, this paper describes our efforts to incorporate (and evaluate) selected elements of the IEEE-TCPP Curriculum Initiative into three courses in order to best determine the appropriate placement of topics related to parallel and distributed computing in the new CS/CpE curriculum under the semester calendar. In particular, we plan to add or revise existing modules and assignments for CS2 (CS 240B, CS 240C at Ohio University, CS 2401 under semesters), DS/A (CS 361 Data Structures at Ohio University, CS 3610 under semesters), and Systems (CS 442 Operating Systems and Computer Architecture I, CS 4420 under semesters) to help us determine which curricular recommendations belong in those three courses in the new semesters curriculum and which topics are more appropriately placed in new required courses entitled EE 3613 Computer Organization and CS 4000 Introduction to Parallel, Distributed, and Web-Centric Computing or in other existing advanced courses such as CS 4040 Design and Analysis of Algorithms or CS 4100 Formal Languages and Compilers.

This work-in-progress paper describes a web- based system that is being developed at Ohio University for developing and administering active learning assignments. The paper describes the design of the system, and its intended use in outcomes-based assessment of student learning in Computer Science. The system allows students to access active learning exercises via the web and provides students with immediate feedback on their work. Faculty use the system to design active learning assignments that tie student performance to achievement of specific course outcomes. This work-in-progress paper describes initial experiences with this system in an advanced course where both the software package JFLAP (An Interactive Formal Languages and Automata Package) and C++ programming are used.

This paper describes recent improvements to and experiences with Web-based grading software that has been developed by the author at Ohio University. The software, named WBGP for the web-based grading project, provides facilities to build, test, and annotate student source code with comments concerning programming style and documentation. The software produces a collection of Web-pages for each student project that describes the results of testing, the grading decisions, and the resulting score. The software is able to build student portfolios consisting of all students projects for a given course. In addition, the software is able to generate reports on the type and frequency of grading comments and the correctness of student projects. The paper describes continued experiences in web-based grading of computer science projects at Ohio University. In particular, we describe the various challenges of grading interactive student projects. Finally, this paper describes the results of a student survey concerning the web-based grading software. Students were surveyed concerning the ease of use and understanding of the generated Web-pages, their attitudes towards students portfolios, and their attitudes towards web-based grading. In general, the students responses were positive

Dynamic real-time systems require adaptive resource management to accommodate varying processing needs. This paper addresses the problem of resource management on a single resource for soft real-time systems (no hard deadline requirements) consisting of tasks that have discrete quality of service (QoS) settings that correspond to varying resource requirements and varying utility. Our approach employs a feedback architecture wherein task QoS settings are adjusted on-line in order to maintain a desired amount of resource slack. These adjustments are calculated based on incremental changes in resource slack using computationally efficient algorithms that provide nearly optimal utility with computable performance bounds. The feedback architecture provides robustness in the presence of additional resource load and imperfect task resource requirement specifications.

This paper extends the previous work on the maximal allowable workload (MAW) problem [D. Juedes et al., (2004)] by investigating a resource allocation problem for distributed real-time systems that contain replicable applications. The systems may use multiple resources of a single type and be affected by multiple environmental factors. The approach searches for a feasible allocation that maximizes a user defined metric of stability. Several algorithms were developed and experiments were conducted to demonstrate the relative strength of these algorithms. The results showed that simulated annealing provides results that are the closest to the optimal for maximizing environmental parameter settings. In addition modified greedy first fit is shown to be the best performing algorithm for finding feasible allocations.

Integration of middleware scheduling and resource management services enables open distributed real-time embedded (DRE) applications to meet end-to-end quality of service (QoS) requirements in highly variable operating environments. This paper describes our research on integrating CORBA scheduling and resource management services, and presents experiments we conducted to validate and quantify the benefits of this integration. Our experimental results show that integrating distributed scheduling and resource management in middleware for open DRE systems can offer significant improvements in predictability. Specifically, integrating our stand-alone resource management service with a previously unmanaged experimental baseline application reduced the ratio of missed deadlines from 26% to 10%, and the same application performed even better under the control of integrated scheduling and resource management services, with a missed deadline ratio of only 1%.

This paper describes a project to build a Web-based grading system and reports on experiences in using a Web-based grading system in a sophomore-level course on data structures. The Web-based grading system consists of electronic submission and retrieval programs, automated testing and Web-based evaluation software, Web-based code annotation software, and Web-based report generation software. The software was designed with electronic grading and delivery as the primary objective and with course-based assessment as a secondary objective. In addition, the software provides the means to capture electronic examples of student work for assessment and accreditation purposes and the means to create electronic portfolios for individual students. The results of our experience with the Web-based grading software in a sophomore-level course on data structures are also described. The software system provided a means to examine detailed information on the overall performance of students on programming projects. In particular, the software recorded information concerning the performance of student projects on test cases and recorded the frequency and type of design and documentation errors. This information was used to evaluate the overall performance of students and to identify common errors that merited additional course time and review.

Summary form only given. We describe the quality-based adaptive resource management architecture, QARMA, a framework for resource management within CORBA. QARMA consists of three major components: the system repository service, the resource management service, and the enactor service. QARMA serves as a basis for integration of existing CORBA services and management mechanisms into a single, coherent framework for resource management. QARMA supports the management of a wide variety of applications developed using various development paradigms, easily integrates with other management and infrastructure components that already exist as CORBA services, and is easily extended to allow the use of new resource management mechanisms as they become available.

Summary form only given. We examine several heuristic algorithms for the maximum allowable workload (MAW) problem for real-time systems with tasks having variable workloads. Briefly, the problem concerns the allocation of tasks to m processors, where each task t is characterized by a function t.r(w) that gives the running time of the task in terms of its workload (or input size) w. The objective of the maximum allowable workload problem is to find an allocation of tasks to processors so that the allocation is feasible (no task misses its deadline) when each task is given a workload of w or smaller and w is maximized. This optimization problem uses a robustness measure that is closely related to the MAIL (maximum allowable increase in load) metric recently proposed by Gertphol et al. The main contribution of this paper is the comparison of several heuristic algorithms for the MAW-RMS problem. Hillclimbing, random search, simulated annealing, and first-fit heuristics are presented and evaluated via simulation. As we show here, the first-fit greedy heuristic produces solutions of a reasonable quality compared to the other algorithms. In addition, we demonstrate the applicability of our model in air defense systems.

Summary form only given. We propose architecture and a general optimization framework for dynamic, distributed real-time systems. Interesting features of this model include the consideration of adaptive applications and utility functions. We extend by formalizing the corresponding multicriterial optimization problem. As the most difficult part of this problem, we identified the evaluation and comparison of the quality of single allocations and sets of allocations, respectively. To this end, we propose and examine metrics for measuring the goodness of solutions within our general resource management framework. These metrics lay the basis for further work on developing both online and offline algorithms to tackle the general optimization problem and provide an efficient adaptive resource manager for dynamic, distributed real-time systems.

Measure-theoretic aspects of the /spl les//sub m//sup P/-reducibility structure of exponential time complexity classes E=DTIME(2/sup linear/) and E/sub 2/=DTIME(2/sup polynomial/) are investigated. Particular attention is given to the complexity (measured by the size of complexity cores) and distribution (abundance in the sense of measure) of languages that are /spl les//sub m//sup P/-hard for E and other complexity classes. Tight upper and lower bounds on the size of complexity cores of hard languages are derived. The upper bounds say that the /spl les//sub m//sup P/-hard languages for E are unusually simple in, the sense that they have smaller complexity cores than most languages in E. It follows that the /spl les//sub m//sup P/-complete languages for E form a measure 0 subset of E (and similarly in E/sub 2/). This latter fact is seen to be a special case of a more general theorem, namely, that every /spl les//sub m//sup P/-degree (e.g. the degree of all /spl les//sub m//sup P/-complete languages for NP) has measure 0 in E and in E/sub 2/. >

Given a collection of biologically related protein or DNA sequences, the basic multiple sequence alignment problem is to determine the most biologically plausible alignment of these sequences. Under the assumption that the collection of sequences arose from some common ancestor, an alignment can be used to infer the evolutionary history among the sequences, i.e., the most likely pattern of insertions, deletions and mutations that transformed one sequence into another. The general multiple sequence alignment problem is known to be NP-hard, and hence the problem of finding the best possible multiple sequence alignment is intractable. However, this does not preclude the possibility of developing algorithms that produce near optimal multiple sequence alignments in polynomial time. We examine techniques to combine efficient algorithms for near optimal global and local multiple sequence alignment with evolutionary computation techniques to search for better near optimal sequence alignments. We describe our evolutionary computation approach to multiple sequence alignment and present preliminary simulation results on a set of 17 clusters of orthologous groups of proteins (COGs). We compare the fitness of the alignments given by the proposed techniques with the fitness of CLUSTAL W alignments given in the COG database.