Automated grading systems, or auto-graders, have become ubiquitous in programming education, and the way they generate feedback has become increasingly automated as well. However, there is insufficient evidence regarding auto-grader feedback's effectiveness in improving student learning outcomes, in a way that differentiates students who utilized the feedback and students who did not. In this study, we fill this critical gap. Specifically, we analyze students' interactions with auto-graders in an introductory Python programming course, offered at five community colleges in the United States. Our results show that students checking the feedback more frequently tend to get higher scores from their programming assignments overall. Our results also show that a submission that follows a student checking the feedback tends to receive a higher score than a submission that follows a student ignoring the feedback. Our results provide evidence on auto-grader feedback's effectiveness, encourage their increased utilization, and call for future work to continue their evaluation in this age of automation

The accelerating pace of developments in Artificial Intelligence (AI) and the increasing role that technology plays in society necessitates substantial changes in the structure of the workforce. Besides scientists and engineers, there is a need for a very large workforce of competent AI technicians (i.e., maintainers, integrators) and users (i.e., operators). As traditional 4-year and 2-year degree-based education cannot fill this quickly opening gap, alternative training methods have to be developed. We present the results of the first four years of the AI Technicians program which is a unique collaboration between the U.S. Army's Artificial Intelligence Integration Center (AI2C) and Carnegie Mellon University to design, implement and evaluate novel rapid occupational training methods to create a competitive AI workforce at the technicians level. Through this multi-year effort we have already trained 59 AI Technicians. A key observation is that ongoing frequent updates to the training are necessary as the adoption of AI in the U.S. Army and within the society at large is evolving rapidly. A tight collaboration among the stakeholders from the army and the university is essential for successful development and maintenance of the training for the evolving role. Our findings can be leveraged by large organizations that face the challenge of developing a competent AI workforce as well as educators and researchers engaged in solving the challenge.

Self-efficacy has been claimed to be a predictor of students' motivation and learning [1]. It has been found to be sensitive to students' success, and to affect their academic achievement. In the CS/IT education context, where the drop rates are high, it is important that students not only gain knowledge and skills, but also self-efficacy, so that they persist in the program. In this study, we investigate 602 students taking an introductory Python course via different delivery methods: (i) traditional in-person; (ii) cohort in-person; (iii) synchronous online; and (iv) asynchronous online. Although modality predicted retention and success, we found no apparent links among learning, student retention, and self-efficacy. However we found evidence that cohort learning may in particular help struggling students catch up with their peers.

In this poster we propose an approach to designing auto-graded programming course projects that are modular and easily manageable by an instructor. Based on our experiences with the Sail() platform which supports auto-grading and feedback generation in multiple contexts, we design the approach to overcome the challenges we observed. The approach is especially focused on designing projects that can be utilized by multiple instructors who may have various scopes or students with varying backgrounds. The approach enables differentiated learning-thereby improving learning experiences and outcomes. We also discuss challenges of using such a modular approach to auto-graded projects.

We conduct a preliminary study of the effect of GPT's temperature parameter on the diversity of GPT4-generated questions. We find that using higher temperature values leads to significantly higher diversity, with different temperatures exposing different types of similarity between generated sets of questions. We also demonstrate that diverse question generation is especially difficult for questions targeting lower levels of Bloom's Taxonomy.

The rapid adoption of emergent technologies is creating significant shortfall in the CS/IT workforce. With not enough students in the educational pipeline to meet the forthcoming demand over the next decade, community colleges are making the effort to train confident, knowledgeable, and self-driven workers in this field. Project-based learning (PBL) has been shown to be effective for these ends, but it poses distinct challenges in resource-limited community college contexts since it may require more time, preparation, and motivation than other teaching modalities, from both the student and the instructor. We studied fifteen sections of an introductory project-based Python course taught at six community colleges, investigating several features of PBL theorized to be particular barriers to student persistence, particularly among women and other identities traditionally underrepresented in technical fields. We describe successes and challenges faced by students in these areas and suggest implications for project-based learning curriculum and platform design.

Courses with programming assignments have long faced the issue of academic integrity violations (AIV) where cheating could harm the outcome of student learning. Checking code similarity in students' final submissions is a common way to mitigate this issue. But this single analysis is insufficient as 1) students can refactor their code to evade the check, 2) mere code similarity may not be strong enough evidence to support an AIV case, particularly for simpler assignments that may have similar solutions, and 3) code similarity cannot reveal much about the actual circumstances and behaviors of plagiarism. Due to the lack of supporting data or tools, many educators either abandon solving these challenges or rely on manual approaches that are not feasible at scale. In this paper, we propose a workflow to solve the above challenges for large programming classes by providing supporting evidence of cheating with additional learner data: detailed submission timelines with scores and source code. Running this workflow in a large advanced programming course over several years has helped us identify many cheating cases effectively and efficiently.

AI's pervasive role in various fields highlights the imperative for the workforce to adeptly leverage its potential. While numerous courses cater to developers, there exists a discernible void for the wider community of AI users. To address this, our study introduces 'AI User'-a suite of interactive modules hosted on the Sail() platform, designed specifically for non-technical individuals utilizing Goal-Based Scenario (GBS) learning. We conducted a controlled experiment to ascertain whether GBS offers superior learning gains in AI literacy compared to traditional deliberate practice using multiple choice questions.

This paper studies recent developments in large language models’ (LLM) abilities to pass assessments in introductory and intermediate Python programming courses at the postsecondary level. The emergence of ChatGPT resulted in heated debates of its potential uses (e.g., exercise generation, code explanation) as well as misuses in programming classes (e.g., cheating). Recent studies show that while the technology performs surprisingly well on diverse sets of assessment instruments employed in typical programming classes the performance is usually not sufficient to pass the courses. The release of GPT-4 largely emphasized notable improvements in the capabilities related to handling assessments originally designed for human test-takers. This study is the necessary analysis in the context of this ongoing transition towards mature generative AI systems. Specifically, we report the performance of GPT-4, comparing it to the previous generations of GPT models, on three Python courses with assessments ranging from simple multiple-choice questions (no code involved) to complex programming projects with code bases distributed into multiple files (599 exercises overall). Additionally, we analyze the assessments that were not handled well by GPT-4 to understand the current limitations of the model, as well as its capabilities to leverage feedback provided by an auto-grader. We found that the GPT models evolved from completely failing the typical programming class’ assessments (the original GPT-3) to confidently passing the courses with no human involvement (GPT-4). While we identified certain limitations in GPT-4’s handling of MCQs and coding exercises, the rate of improvement across the recent generations of GPT models strongly suggests their potential to handle almost any type of assessment widely used in higher education programming courses. These findings could be leveraged by educators and institutions to adapt the design of programming assessments as well as to fuel the necessary discussions into how programming classes should be updated to reflect the recent technological developments. This study provides evidence that programming instructors need to prepare for a world in which there is an easy-to-use widely accessible technology that can be utilized by learners to collect passing scores, with no effort whatsoever, on what today counts as viable programming knowledge and skills assessments.

We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate Python programming courses at the postsecondary level. Discussions of potential uses (e.g., exercise generation, code explanation) and misuses (e.g., cheating) of this emerging technology in programming education have intensified, but to date there has not been a rigorous analysis of the models' capabilities in the realistic context of a full-fledged programming course with diverse set of assessment instruments. We evaluated GPT on three Python courses that employ assessments ranging from simple multiple-choice questions (no code involved) to complex programming projects with code bases distributed into multiple files (599 exercises overall). Further, we studied if and how successfully GPT models leverage feedback provided by an auto-grader. We found that the current models are not capable of passing the full spectrum of assessments typically involved in a Python programming course (<70% on even entry-level modules). Yet, it is clear that a straightforward application of these easily accessible models could enable a learner to obtain a non-trivial portion of the overall available score (>55%) in introductory and intermediate courses alike. While the models exhibit remarkable capabilities, including correcting solutions based on auto-grader's feedback, some limitations exist (e.g., poor handling of exercises requiring complex chains of reasoning steps). These findings can be leveraged by instructors wishing to adapt their assessments so that GPT becomes a valuable assistant for a learner as opposed to an end-to-end solution.

The potential for academic integrity violations increases in online courses and instructors must place extra attention on academic integrity, since cheating techniques and costs are different than in the physical classroom. Although students are less supervised and able to study in a self-paced mode in online learning, unauthorized collaboration is still considered to be a serious integrity violation. However, online learning platforms have the advantage that they may capture detailed timelines of student activity. Analysis of these can enable instructors to detect many patterns of collaboration, e.g., working on assessments together, or copying solutions from unauthorized web pages. In this paper, we describe detection methods for several common patterns of alignment between work timelines of pairs of students, and these patterns' relationship with corroborative evidence such as similar answers and unusually fast completion times. We describe data collection necessary to apply the timeline analysis technique to weekly quiz assessments and project submissions, and discuss the strength of evidence the technique can provide in different situations. We have been applying these techniques in an online project-based course over several years, and it has helped instructors to successfully identify potential cheating cases.

Virtual agents increasingly rely on cloud-based services, which makes them vulnerable to unpredictable network latency and service response times. In this work, we evaluate the use of conversational fillers to mitigate the impact of delay in system response time on users’ perception of a virtual agent. These fillers are uttered by the agent to keep the user engaged until the response is ready. We present the findings of a study run on the Mechanical Turk platform with 360 participants who interacted with a virtual agent. We tested two types of conversational fillers. The first type were generic utterances that simply asked the user to hold. The second adopted contextualized fillers that assume some semantic knowledge of the input and contain some of its elements. To test the generalizability of the different fillers, we ran two task-based experiments. The first task was to get a recipe and the second was to find a restaurant. Contextualized fillers positively affected participants’ rating of the agent’s response time but did not impact the agent’s likeability.

We analyze differences in working habits between well-performing and at-risk students using highly-granular data collected from two semesters of an online project-based, upper-level course on cloud computing at a US institution of higher education. Such differentiating metrics may provide deeper insights than interim grades, which are oftentimes the only quantifiable data that is captured and available to an instructor as a proxy for students' learning. Interim grades provide little insight into students' broader work habits and may mask unsustainable learning strategies that result in shallow learning or quickly-forgotten skills/knowledge. The adoption of technology-enhanced learning tools for course delivery, automatic feedback, and grading enable data-informed insight and reflection into students' working habits. This data could allow the detection of early signs of under-prepared students or students in crisis. We empirically assess what working habits, if any, differ among well-performing and at-risk students. From clickstream and other activity data, we derive 22 metrics such as time spent reading project write-ups, timing of starting and finishing work, or break-taking. We also calculate two measures of consistency of each metric measured by a coefficient of variance and a variance of ranking over the semester as well as outlier behavior of a student. Using Z-test and Kolmogorov-Smirnov test, we confirm differences in multiple behavior patterns. Notably, our data suggest that well-performing students start and finish working on a project earlier than at-risk students but they also tend to have fewer submissions which indicate they are more thoughtful about feedback.

Three previous Working Groups (WG) have met at ITiCSE conferences to explore ways of incorporating cloud computing into courses and curricula by mapping industry job skills to knowledge areas (KAs) and KAs to student learning objectives (LOs) and using these as the framework for a repository of learning materials and course exemplars \citefoster2018, foster2019, adams2020. The ongoing value of the work of these WGs will be enhanced by validating the KAs and LOs and their mapping to current job skills and continuing to build a community of educators who will contribute to and benefit from the repository.

While data science education has gained increased recognition in both academic institutions and industry, there has been a lack of research on automated coding assessment for novice students. Our work presents a first step in this direction, by leveraging the coding metrics from traditional software engineering (Halstead Volume and Cyclomatic Complexity) in combination with those that reflect a data science project’s learning objectives (number of library calls and number of common library calls with the solution code). Through these metrics, we examined the code submissions of 97 students across two semesters of an introductory data science course. Our results indicated that the metrics can identify cases where students had overly complicated codes and would benefit from scaffolding feedback. The number of library calls, in particular, was also a significant predictor of changes in submission score and submission runtime, which highlights the distinctive nature of data science programming. We conclude with suggestions for extending our analyses towards more actionable intervention strategies, for example by tracking the fine-grained submission grading outputs throughout a student’s submission history, to better model and support them in their data science learning process.

Computer science pedagogy has overwhelmingly favored problem-solving practice over methods of engagement like worked-out example study especially in advanced classes. This is due to the belief that while these alternative methods may improve student conceptual learning, they may leave them less able to perform on authentic problem-solving tasks from a lack of hands-on practice. In this paper, we perform a direct comparison of this trade-off in a synchronous collaborative programming project by adjusting the boundary between problem-solving and collaborative reflection based on a worked-out example while keeping the total time on task constant. We find that the more time students spent on worked example study, the more was the observed improvement in the pre- to post-test scores with no significant difference in performance on a subsequent problem-solving task. These results, therefore, challenge the dominant place of problem-solving practice in the advanced curricular context and inform the design of collaborative programming projects at scale.

The accelerating evolution and adoption of cloud computing services is generating increased demand for job skills in this domain. To address this growth, higher education has identified the importance of cloud computing courses that are practical and compatible with this rapidly changing field. This is especially relevant as cloud services are becoming common computing resources for many new computational approaches and advanced subjects such as machine learning and data science. The ability to incorporate specific components of cloud computing teaching content into a variety of courses has become important. However, the lack of availability of high-quality teaching material that is easy to integrate, when teaching rapidly evolving cloud-related concepts continues to be a challenge for instructors. This working group will try to address this challenge.

Code-review, the systematic examination of source code, is widely used in industry, but seldom used in courses. We designed and implemented a rubric-driven online peer code-review system (PCR) that we have deployed for two semesters, during which 228 students performed over 1003 code reviews. PCR is designed to meet four goals: (1) Provide timely feedback to students on their submissions, (2) Teach students the art of code review, (3) Allow custom feedback on submissions even in massive online classes, and (4) Allow students to learn from each other. We report on using PCR, in particular, the accuracy of student-based reviews, the surprising number of free-form comments made by students, the variability of staff-based reviews, how student engagement impacts the accuracy, the additional workload, and anecdotal perspectives of students. We describe some critical design considerations for PCR including rubric design, the importance of PCR training on each assignment to acclimate students to the rubric, and how we match student reviewers to student submissions.

Cloud Computing is a rapidly evolving field that is triggering a wave of innovations in various domains such as machine learning and artificial intelligence. Cloud skills are becoming essential for any technology-related profession. Furthermore, the accelerated adoption of cloud technologies by industry is increasing the demand for cloud-trained professionals. Thus, higher education institutions are offering training opportunities and programs in cloud computing, however, the lack of well-rounded and high-quality curricular materials continues to be a challenge for educators. A 2018 Working Group (WG) created a report that --among other artifacts-- described fourteen Knowledge Areas (KAs), with numerous Learning Objectives (LOs) for each KA, to teach cloud concepts. Expanding on that work, this WG will focus on providing a collection of resources that would eventually constitute a model cloud curriculum. By relying on two particular surveys: one that looks at the existing curricular offerings, and another one that maps knowledge areas to job titles in cloud computing, we plan to provide a dynamic and configurable curricular exemplars repository for the cloud community-at-large, following the popularity of hands-on, project-based learning methodologies as our primary focus.

Understanding the relationship between a student's broader work habits and their performance, particularly on open-ended programming assignments, is key towards being able to guide students towards success. In spite of this, most evidence of student behavior and its relationship to performance is anecdotal. The advent of large-scale courses which use online tools for delivering course content, monitoring the programming environment, and providing automatic feedback as well as grading now makes it possible to dive into the data and develop data-driven methods for understanding how a student's approach to an assignment---from their first exposure to the description of the problem to their final submission of their completed assignment---influences their final performance.

Understanding the scalability of MapReduce applications is a challenging problem. The difficulty lies in the distributed mapping of the input big data. The distribution of data and compute resources must match with fluctuating network substrates. User-defined Map and Reduce functions over application parameters further complicate the issue. Therefore, it offers great payoff to use small datasets and limited test runs to reveal the behavior of MapReduce applications over big-data. In this paper, we analyze the scaling effects of server cluster-size over varieties of Map- and Reduce-intensive applications. In our study, we discover specific conditions which lead to the power-law conformity in representative MapReduce applications. We report four major discoveries: (1) Within a range of scaling parameters, MapReduce execution time follows the power-law distribution. (2) Power-law scalability for Map-intensive applications work well even with a small cluster size. (3) Shuffle-intensive applications exhibit power-law behavior starting from larger cluster size. (4) The scaling effects may depart from power-law distribution, if the cloud resources are heavily overprovisioned than the workload demands. The above findings enable users to use bounded test runs to allocate and configure virtual and physical resources in large-scale MapReduce applications. These results can be also applied in generating business models for providing cost-effective cloud computing services.

Cloud Computing has gained significant momentum in the last five years and is regarded as a paradigm shift away from traditional 'silo' based computing. It is no longer seen as a niche area of technology, offering a diverse range of scalable and redundant service deployment models, including Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), and Containers-as-a-Service (CaaS). These models are applied to areas such as IoT, Cyber-Physical Systems, Social Media, Data Science, Media Streaming, Ecommerce, and Health Informatics. The growth in cloud presents challenges for companies to source expertise that securely supports their business when migrating/deploying services to the Cloud - particularly Small-Medium-Enterprises (SME) with limited resources. The UK Government recently published the Digital Skills Crisis report, identifying skill-set challenges facing industry, with a shortage in cloud skills negatively impacting business. While cloud technologies have evolved at significant pace, the development of contemporary Computer Science curriculum in the further and higher education (HE) sector has lagged behind. The challenges faced in the sector includes the training of educators, institutional gaps (software and hardware policies), regulatory constraints, and access to cloud platforms. Collectively these challenges are significant, but not insurmountable. By embedding fundamental cloud skills throughout the educator and student journey, both stakeholders will be better positioned to understand and practically apply the use of appropriate cloud services, and produce graduates that can support the needs of industry. This working group (WG) aims to: i) assess current cloud computing curricula in CS and similar programs, ii) document industry needs for in-demand cloud skills, iii) identify issues and gaps around cloud curriculum uptake, and iv) develop solutions to meet the skill demands on core Cloud Computing topics, technical skills exercises, and modules for integration with contemporary Computer Science curricula.

We present a study involving 160 participants investigating the effect of associating professional status and ethnicity with an agent by manipulating its appearance, language, and level of education. We aim to discern perceptions of status and ethnicity with respect to participants' cultural background by inviting participants from two different cultural groups (Middle Eastern and Western) to take part in our study. Results revealed that participants' cultural background had a strong impact on their ratings of the agent and its message. However, neither the agent's portrayed status nor its ethnicity appeared to have an effect on participants' perceptions of the agent. We further found that participants from both cultural backgrounds holding a negative attitude towards robots in general tend to perceive the presented message by the agent more negatively. Middle Eastern participants had a more positive attitude towards robotic agents than Western participants, which might have been the main influence on their perception of the message presented by the agent. In addition, participants who identified the agent as a member of their own cultural group perceived the presented message more positively than those from the other cultural group. We discuss our results with an intention to inform design implications for agents in a cross-cultural context.

As social robots begin to enter our lives as providers of information, assistance, companionship, and motivation, it becomes increasingly important that these robots are capable of interacting effectively with human users across different cultural settings worldwide. A key capability in establishing acceptance and usability is the way in which robots structure their speech to build credibility and express information in a meaningful and persuasive way. Previous work has established that robots can use speech to improve credibility in two ways: expressing practical knowledge and using rhetorical linguistic cues. In this paper, we present two studies that build on prior work to explore the effects of language and cultural context on the credibility of robot speech. In the first study (n=96), we compared the relative effectiveness of knowledge and rhetoric on the credibility of robot speech between Arabic-speaking robots in Lebanon and English-speaking robots in the USA, finding the rhetorical linguistic cues to be more important in Arabic than in English. In the second study (n=32), we compared the effectiveness of credible robot speech between robots speaking either Modern Standard Arabic or the local Arabic dialect, finding the expression of both practical knowledge and rhetorical ability to be most important when using the local dialect. These results reveal nuanced cultural differences in perceptions of robots as credible agents and have important implications for the design of human-robot interactions across Arabic and Western cultures.

We have designed, developed and administered a course on cloud computing that was taught to over 700 students at our institution over two years. The goal of this project-based course is to provide students with foundational systems concepts as well as experience in developing the required skills to design and deploy viable, robust and elastic web-services within performance and budgetary constraints. We present our objectives, learning outcomes, projects, learning model, outcomes and lessons learned. So far, for this demanding course, our student retention rate is above 80% and enrollment is doubling every year.

In this paper, we delineate the causes of performance variations when scaling provisioned virtual resources for a variety of MapReduce applications. Hadoop MapReduce facilitates the development and execution processes of large-scale batch applications on big data. However, provisioning suitable resources to achieve desired performance at an affordable cost requires expertise into the execution model of MapReduce, the resources available for provisioning and the execution behavior of the application at hand. As an initial step towards automating this process, we characterize the difference in execution response for different MapReduce applications while varying the number of virtualized CPUs and memory resources, number of map slots as well as cluster size on a private cloud. This characterization helps illustrate the performance variation, 5x compared to 36x speedup, of Reduce-intensive and Map-intensive applications at effectively utilizing provisioned resources at different scales (1-64 VMs). By comparing the scalability efficiency, we clearly indicate the under-provisioning or over-provisioning of resources for different MapReduce applications at large scale.

How do politeness strategies and cultural aspects affect robot acceptance and anthropomorphization across native speakers of English and Arabic? Previous work in cross-cultural HRI studies has mostly focused on Western and East Asian cultures. In contrast, Middle Eastern attitudes and perceptions of robot assistants are a barely researched topic. We investigated culture-specific determinants of robot acceptance and anthropomorphization by conducting a between-subjects study in Qatar. A total of 92 native speakers of either English or Arabic interacted with a receptionist robot in two different interaction tasks. We further manipulated the robot's verbal behavior in experimental sub-groups to explore different politeness strategies. Our results suggest that Arab participants perceived the robot more positively and anthropomorphized it more than English speaking participants. In addition, the use of positive politeness strategies and the change of interaction task had an effect on participants' HRI experience. Our findings complement the existing body of cross-cultural HRI research with a Middle Eastern perspective that will help to inform the design of robots intended for use in cross-cultural, multi-lingual settings.

MapReduce is now a pervasive analytics engine on the cloud. Hadoop is an open source implementation of MapReduce and is currently enjoying wide popularity. Hadoop offers a high-dimensional space of configuration parameters, which makes it difficult for practitioners to set for efficient and cost-effective execution. In this work we observe that MapReduce application performance is highly influenced by map concurrency. Map concurrency is defined in terms of two configurable parameters, the number of available map slots and the number of map tasks running over the slots. We show that some inherent MapReduce characteristics enable well-informed prediction of map concurrency. We propose Map Concurrency Characterization (MC2), a standalone utility program that can predict the best map concurrency for any given MapReduce application. By leveraging the generated predicted information, MC2 can judiciously guide Map phase configuration and, consequently, improve Hadoop performance. Unlike many of relevant schemes, MC2 does not employ simulation, dynamic instrumentation, and/or static analysis of unmodified job code to predict map concurrency. In contrast, MC2 utilizes a simple, yet effective mathematical model, which exploits the MapReduce characteristics that impact map concurrency. We implemented MC2 and conducted comprehensive experiments on a private cloud and on Amazon EC2 using Hadoop 0.20.2. Our results show that MC2 can correctly predict the best map concurrencies for the tested benchmarks and provide up to 2.2X speedup in runtime.

Understanding performance scalability in MapReduce applications presents a challenging problem. The difficulty lies in the distributed locations of input data and the distributed compute resources that utilize varied network substrates. User-defined Map and Reduce stages, with numerous application parameters, further complicate the problem. Using small datasets and limited test runs to understand how MapReduce applications will behave with "big data" can have a significant payoff. In this paper, we evaluate the impact of cluster-size scaling on execution time for a set of Map- and Reduce-intensive applications. We model the MapReduce framework, specify conditions and implications of power-law conformity, and verify our model with data from benchmark MapReduce applications. Empirical results indicate that: (1) within a range of scaling parameters, MapReduce execution times follow a power-law distribution. (2) Power-law scalability for Map-intensive applications starts from a small cluster size. (3) Shuffle-intensive applications exhibit power-law behavior starting from larger clusters. (4) Cluster-scaling performance gains fail to show power-law behavior when computing resources far exceed those needed. Our findings will facilitate using small-scale test runs to allocate and configure virtual and physical computing resources in large scale clouds.

MapReduce is by far one of the most successful realizations of large-scale data-intensive cloud computing platforms. MapReduce automatically parallelizes computation by running multiple map and/or reduce tasks over distributed data across multiple machines. Hadoop is an open source implementation of MapReduce. When Hadoop schedules reduce tasks, it neither exploits data locality nor addresses partitioning skew present in some MapReduce applications. This might lead to increased cluster network traffic. In this paper we investigate the problems of data locality and partitioning skew in Hadoop. We propose Center-of-Gravity Reduce Scheduler (CoGRS), a locality-aware skew-aware reduce task scheduler for saving MapReduce network traffic. In an attempt to exploit data locality, CoGRS schedules each reduce task at its center-of-gravity node, which is computed after considering partitioning skew as well. We implemented CoGRS in Hadoop-0.20.2 and tested it on a private cloud as well as on Amazon EC2. As compared to native Hadoop, our results show that CoGRS minimizes off-rack network traffic by averages of 9.6% and 38.6% on our private cloud and on an Amazon EC2 cluster, respectively. This reflects on job execution times and provides an improvement of up to 23.8%.

MapReduce offers a promising programming model for big data processing. Inspired by functional languages, MapReduce allows programmers to write functional-style code which gets automatically divided into multiple map and/or reduce tasks and scheduled over distributed data across multiple machines. Hadoop, an open source implementation of MapReduce, schedules map tasks in the vicinity of their inputs in order to diminish network traffic and improve performance. However, Hadoop schedules reduce tasks at requesting nodes without considering data locality leading to performance degradation. This paper describes Locality-Aware Reduce Task Scheduler (LARTS), a practical strategy for improving MapReduce performance. LARTS attempts to collocate reduce tasks with the maximum required data computed after recognizing input data network locations and sizes. LARTS adopts a cooperative paradigm seeking a good data locality while circumventing scheduling delay, scheduling skew, poor system utilization, and low degree of parallelism. We implemented LARTS in Hadoop-0.20.2. Evaluation results show that LARTS outperforms the native Hadoop reduce task scheduler by an average of 7%, and up to 11.6%.

HRI studies in a Middle Eastern environment are subject to nuances and subtleties. This study explores the nature of interactions, in an uncontrolled environment, between a permanently deployed bi-lingual robot-receptionist and interlocutors of varied native tongues. We correlate an interlocutor's native language with their propensity for accepting an invite and the duration of the ensuing conversation. Subsequently, we present results that demonstrate significant disparity in interactional patterns between English and Arabic speakers. We also assess the importance of a transliterated Arabic input mode for encouraging user interaction.

Cloud computing offers a paradigm shift in management of computing resources for large-scale applications. Using the Infrastructure-as-a-service (IaaS) cloud computing model, users today can request dynamically provisioned, virtualized resources such as CPU, memory, disk, and network access in the form of virtualized resources. The client typically requests resources based on computational needs and pays for resource instances based on their capacity and time utilized. Mapping these virtual resource requests to physical hardware could vary for identical requests. This can potentially cause variations in the performance of applications deployed on such resources. The performance of the application can vary according to the physical layout of the provisioned hardware (the number of virtual machines (VMs), the size/configuration of the VMs and the inter-VM locality). In this paper, we study the effects of this “provisioning variation” and its impact on application performance using suitable benchmarks as well as demonstrate their effect on a few MapReduce workloads. Our initial findings indicate that provisioning variation can impact performance by a factor of 5 primarily due to I/O contention.

Hala is a bilingual (Arabic and English) culturally-sensitive robot receptionist located at Carnegie Mellon University in Qatar. We report results from Hala's deployment by comparing her English dialogue corpus to that of a similar monolingual robot (named "Tank") located at CMU's Pittsburgh campus. Specifically, we compare the average number of turns per interaction, duration of interactions, frequency of interactions with personal questions, rate of non-understandings, and rate of thanks after the robot's answer. We provide possible explanations for observed similarities and differences and highlight potential cultural implications on the interactions.

There is growing interest in studying the possibility of reconfigurable architectures as replacements for general purpose computing for certain application domains. Reconfigurable systems can take advantage of deep computational pipelines, perform concurrent execution and are inherently data flow in nature. Furthermore, these systems have the capability of 'on the fly' reconfiguration of all or portions of the hardware to represent all the functionality required to complete the execution of an application. However, these architectures suffer from slow run time reconfiguration (RTR) due to the fact that the configuration memory resides off-chip and hence requires high access latency. This disadvantage limits the system performance and the application domain in which reconfigurable systems could prove effective. To overcome slow RTR, recent approaches include on-chip configuration memory to cache the next possible configurations. This approach trades off die area for fast RTR which diminishes the processing power of the reconfigurable processor. The high cost of adding configuration cache, up to 50% of the die area, would considerably increase the number of hardware reconfigurations required compared to architectures without on-chip cache. This paper presents an alternative reconfigurable architecture which overcomes these limitations by exploiting high bandwidth optical channels. We develop a performance model to analyze and compare the performance of cache based RTR architectures, optical based RTR architectures and hybrid optical-cache based RTR architectures.

Modern computer buses are typically organized by the three functions of data transfer, addressing, and arbitration/control. In this paper we present a fiber-based bus design which provides optical solutions for each of these functions. The design includes an all-optical addressing system, based on coincident pulse addressing, which eliminates the latency contribution and bandwidth limitation associated with electronic address decoding. The control system uses time-of-flight relationships between a priority chain and a feedback waveguide to implement fully distributed asynchronous and self-timed bus arbitration.>