Teaching assistants (TAs) play a critical role in computing and HCI education, yet little is known about how they perceive and use AI tools or imagine their future pedagogical uses. We report on a series of design workshops with 131 computing (CS) TAs across two U.S. universities. These workshops invited TAs to reflect on current AI use and envision future AI-enhanced tools and practices. Drawing on surveys and design artifacts, we (1) develop a cross-institutional typology of situated TA uses of AI, revealing opportunities and tensions; (2) show how TAs’ visions of AI are shaped by disciplinary norms, institutional structures, and their intermediary position as student-instructors; and (3) reveal ethical dilemmas. Our findings contribute to HCI by positioning TAs as AI-supported knowledge workers in the education domain; illustrating how design and speculation are shaped by people’s situated understandings of AI and their institutional contexts; and identifying a core tension in which TAs simultaneously preserve and erode the human dimensions of their work, with implications for future instructional tools and human–AI collaboration.

In modern science, the growing complexity of large-scale scientific projects has led to an increasing reliance on cross-facility scientific workflows, where resources and expertise from multiple institutions and geographic locations are leveraged to accelerate scientific discovery. These workflows often require transmitting huge amounts of scientific data through wide-area networks. Although high-speed networks like ESnet and transfer services such as Globus have improved data mobility, several challenges remain. The sheer volume of data can overwhelm network bandwidth, widely used transport protocols such as TCP suffer from inefficiencies due to retransmissions triggered by packet loss, and existing fault-tolerance mechanisms like erasure coding introduce substantial overhead.

As computing skills become increasingly vital across disciplines, programs designed as ''bridges'' between prior degrees and a master's in computing have emerged as important pathways for individuals with non-computing backgrounds to prepare for graduate study in computing-related fields. The bridge typically consists of a set of courses providing a foundational understanding of topics often covered in an undergraduate computing program.

The rapid expansion of K–12 computer science (CS) education has outpaced the supply of teachers with formal CS training, leaving many educators to teach outside their expertise. This creates challenges in assessing student understanding and interpreting conceptual growth through code. To address this, we introduce ARCHIE (Assessment and Recognition of Coding Habits for Instructional Enhancement), a system that extracts conceptual patterns from student programs and visualizes them at both individual and class levels. We solicited feedback from 20 K–12 CS teachers regarding their confidence in analyzing code and their perceptions of ARCHIE. Teachers reported that ARCHIE helped make student thinking more visible, supported formative assessment, and showed strong potential to enhance instructional decision-making. These findings suggest ARCHIE could provide valuable support for K–12 CS teachers, particularly those without a computing background.

Access to Computer Science (CS) education in the United States remains uneven, with disparities linked to geography, income, and demographics. While state dashboards report enrollment and disparities, they rarely show how local community factors shape access and participation. This project presents an interactive dashboard combining Georgia Department of Education enrollment data with National Center for Education Statistics attributes and U.S. Census Bureau data. By linking school-level participation with demographic, socioeconomic, and geographic context, the dashboard reveals differences in representation and resources across communities and highlights how these factors influence opportunities for CS participation. This integration of school- and community-level data provides a more nuanced view of disparities than standard state reports alone.

Computer Science (CS) students, particularly those who are first-generation college students or lack industry exposure, often struggle to see the connections between the courses they take and the wide range of career opportunities available to them. Many have narrow views of career options and limit their vision to a future as a software developer. They may not understand how individual courses, or a combination of them, can prepare them for diverse roles in the computing workforce.

Teaching assistants (TAs) play an increasingly vital role in computer science (CS) education, particularly amid rising enrollments, expanding instructional modalities, and the emergence of generative AI tools. In this evolving landscape, CS TAs are taking on greater responsibilities and often serve as the primary point of personal interaction for students, particularly through recitations, lab sessions, and office hours. However, many CS TAs receive limited preparation in inclusive and responsive teaching practices, limiting their ability to effectively support students from diverse cultural and educational backgrounds. To address this gap, we developed and delivered a series of responsive pedagogy workshops at two diverse institutions. These workshops aimed to deepen CS TAs' understanding of inclusive and responsive teaching strategies, support their implementation in practice, and create space for co-design by positioning TAs not only as learners, but as partners in imagining how responsive pedagogy principles could be more effectively integrated into the courses and contexts in which they teach. In this experience report, we describe the design and implementation of these workshops with 117 TA participants, share workshop materials for broader adoption, and reflect on key findings related to integrating responsive pedagogy into CS education through TA training.

The growing demand for computing professionals calls for innovative pathways that welcome individuals from non-computing backgrounds. Post-baccalaureate bridge programs help broaden access by supporting learners from diverse academic disciplines, career trajectories, and socioeconomic backgrounds. The MS Pathways to Computing (MSPC) Consortium, a network of 36 U.S. institutions, advances these efforts to create equitable pathways to master's degrees in computer science.

Recently, there has been a growing interest in the role of teaching assistants (TAs) in computer science (CS). This interest is due to the vital role CS TAs play in supporting student learning and their expanding responsibilities driven by growing enrollments in CS programs worldwide. While much of this research focuses on the technical and pedagogical aspects of CS TAs' duties, researchers recognize the need to further explore the unique value human CS TAs provide, particularly with the rise of AI tools and assistants. In this paper, we use qualitative methods to analyze 109 survey responses collected across two different institutions in the United States as part of a larger design-based research project to make two contributions. First, we illustrate how CS TAs adopt humanistic stances and demonstrate care in their roles, thereby expanding prevailing understandings of CS TAs. Second, we detail similarities and differences across CS TAs' experiences at each institution that underscore the importance of understanding CS TAs as they are situated in different institutional contexts. We conclude by discussing implications of this work for computing instruction and TA training, emphasizing the importance of foregrounding the roles and values brought by TAs.

Despite significant efforts in recent years towards broadening participation in computing, the representation of Black women remains disproportionately low. Many Black women who start a computer science (CS) program often face barriers prompting them to reconsider and ultimately change majors. Recent work centered on the intersectional experiences of Black Women identify the nuances of what is needed to aid in the persistence of Black women. For example, career awareness and faculty mentorship are found to be especially important to Black women's computing persistence. Other studies have highlighted the importance of peer mentoring and a sense of belonging. In this study, we report on a research-based, targeted intervention for Black women in computing that aims to increase their persistence in the field. This intervention is aimed at first-year, traditional freshmen who are Black Women majoring in CS at a research university. The RISE Stars program consists of four main components designed to build and sustain a supportive community among the students: peer-based mentoring, a group-based social impact project, monthly cohort meetings, and social media platforms. This experience report describes the first year of organizing this program, shares initial promising outcomes, and describes lessons learned that can serve as a model to others to replicate.

Computer Science (CS) classes teach technical skills, topics and knowledge areas - often without context where they will be used in future classes. Often times, students routinely struggle and ask questions like ''Why am I learning this?'' and ''What value does it hold?''. In addition to not seeing the correlation on topics, students fail to see what applications these concepts could lead to in the future. When asked what they see themselves doing in the future, we have observed that a significant majority of CS majors respond, ''software developer''.

In 2016, College Board launched Advanced Placement (AP) Computer Science Principles (CSP) as a college-level introductory course to the breadth of Computer Science (CS). This was a joint effort with National Science Foundation to produce a course with a lower barrier of entry to create new pathways of introducing CS to traditionally underrepresented groups in computing; a high school course designed for every student. The sole recommended prerequisite for the course is Algebra I; however, we found that often additional prerequisites and barriers have been imposed. These additional requirements can inadvertently undermine the inclusive vision that underpinned the course's creation. We focus on public high schools in Georgia and discuss different policy barriers that can impact enrollment in AP CSP.

This study explores how Microsoft AirSim and OpenAI's Natural Language Processing capabilities can enable drone navigation within a campus simulation. Utilizing Unreal Engine, we create a 3D simulation of Georgia State University's campus to investigate language-based drone control. Our implementation integrates three key technologies: (1) Microsoft's AirSim platform for simulating drone physics, (2) OpenAI's ChatGPT API for natural language interpretation and command processing, and (3) a detailed campus environment within Unreal Engine. This integration replaces traditional drone control interfaces, allowing users to operate simulated drones through natural language instructions. By translating user commands into navigation directions, this technology showcases the practical applications of language models. Our findings indicate that this approach enhances campus navigation simulations and provides a secure environment for testing drone operations in urban settings. This study highlights the potential of combining language processing with drone control systems, particularly in educational simulations.

Most required computer science curricula focus on the fundamental concepts of computer science (CS) and do not cover topics regarding professional development. So how do students know and learn why they should get internships, create an online professional presence in places like GitHub, or even craft a compelling resume? In this paper, we present preliminary findings from a pilot project to pair students in CS1 and CS2 classes with industry mentors to increase this awareness, internship placement and ultimately improve student success and graduation rates. We found a positive response from the mentees that participated. Feedback provided by the mentors indicate that a significant majority saw an increase in understanding of these issues after multiple sessions with their paired mentees.

OBJECTIVE: The proliferation of m-health interventions has led to a growing research area of app analysis. We derived RACE (Review, Assess, Classify, and Evaluate) framework through the integration of existing methodologies for the purpose of analyzing m-health apps, and applied it to study opioid apps. MATERIALS AND METHODS: The 3-step RACE framework integrates established methods and evidence-based criteria used in a successive manner to identify and analyze m-health apps: the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, inter-rater reliability analysis, and Nickerson-Varshney-Muntermann taxonomy. RESULTS: Using RACE, 153 opioid apps were identified, assessed, and classified leading to dimensions of Target Audience, Key Function, Operation, Security & Privacy, and Impact, with Cohen's kappa < 1.0 suggesting subjectivity in app narrative assessments. The most common functions were education (24%), prescription (16%), reminder-monitoring-support (13%), and treatment & recovery (37%). A majority are passive apps (56%). The target audience are patients (49%), healthcare professionals (39%), and others (12%). Security & Privacy is evident in 84% apps. DISCUSSION: Applying the 3-step RACE framework revealed patterns and gaps in opioid apps leading to systematization of knowledge. Lessons learned can be applied to the study of m-health apps for other health conditions. CONCLUSION: With over 350 000 existing and emerging m-health apps, RACE shows promise as a robust and replicable framework for analyzing m-health apps for specific health conditions. Future research can utilize the RACE framework toward understanding the dimensions and characteristics of existing m-health apps to inform best practices for collaborative, connected and continued care.

Recent work proposed to improve the security of CPSs by authenticating the CPS devices through the device operation times in the response packets from the devices, due to the strong correlation between the timing fingerprints and the physics of the devices. Although such a technique may be effective in defending against naive attackers, an advanced attacker may monitor the operation of the CPS before launching a device physics aware mimicry attack. In this paper, we show how the spoofed response packets can be crafted by an attacker to deceive the CPS device authentication method based on the device operation times. Specifically, we use the timing and physical measurements embedded in the packets to reconstruct the devices in the physical system, which can be used to spoof response packets corresponding to the actual model and configuration of the devices in the CPS. We demonstrate the performance of our technique in realistic testbeds with real devices. Finally, we propose an upgraded defense mechanism that may be used against such mimicry attacks.

More and more applications are promoting cus-tomized or personalized services. In order for these applications to provide meaningful output, it collects users’ personal information over time. Some personal information (e.g. education level or income level) can only be captured by users actively updating their profile to reflect these changes. We refer to these as long-term time series data, as they do not change frequently. If applications can keep up to date on a diverse and large set of personal features, they can provide higher quality service. However, this quality of service comes at the cost of the user sacrificing their privacy. There has been numerous research on protecting privacy of time series data for context aware services, but the privacy leakage of personal information updates during the whole life-cycle of the series has received only scant attention.Motivated users concerned about their privacy, we discuss in detail the privacy leakage risk, focusing on long-term time-series data from the perspective of game theory. Then, we propose a reward-privacy model, targeting the privacy-aware data-updates for the entire life-cycle in context-aware services by leveraging a three-party Stackelberg game. We theoretically prove that a Nash Equilibrium exists in the proposed model, and then use simulations to validate that a Nash Equilibrium exists for different parameters of the productivity function. By using our proposed framework, users have guidance to decide not only the timing of submitting personal updates, but also the granularity or obscurity level for their data.

We demonstrate a self-training system for sports involving throwing a ball. We design a do-it-yourself (DIY) machinery that can be assembled using off-the-shelf items and integrates computer vision to visually track the ball throw accuracy. In this work, we demonstrate a system that can identify if the ball went through the hoop and approximately in which of the hoop's inner region. We envision that this preliminary design sets the foundation for a complete DIY sports IoT system that involves a hoola hoop, RaspberryPi, PiCamera and a LED strip, along with advanced ball placement and dynamics tracking.

This Innovative Practice Work-In-Progress paper presents a collaborative virtual computer lab (CVCL) environment to support collaborative learning in cloud-based virtual computer labs. With advances of cloud computing and virtualization technologies, a new paradigm of virtual computer labs has emerged, where students carry out labs on virtualized resources remotely through the Internet. Virtual computer labs bring advantages, such as anywhere, anytime, on-demand access of specialized software and hardware. However, with current implementations, it also makes it difficult for students to collaborate, due to the fact that students are assigned separated virtual working spaces in a remote-accessing environment and there is a lack of support for sharing and collaboration. To address this issue, we develop a CVCL environment that allows students to reserve virtual computers labs with multiple participants and support remote real-time collaboration among the participants during a lab. The CVCL environment will implement several well-defined collaborative lab models, including shared remote collaboration, virtual study room, and virtual tutoring center. This paper describes the overall architecture and main features of the CVCL environment and shows preliminary results.

A recent 2017 study from HigherEd.com, shows that roughly 35% of students transfer colleges during their academic career. While much research has focused on the impact of undergraduate student success factors such as flipped classrooms, hybrid learning, and technology usage, we found no studies comparing transfer students versus non-transfer (native). In our research, we measure impact based on CS course load per semester related to pass/fail rates and contrast student status of transfer versus non-transfer (native). We show that transfer students tend to enroll in more CS courses per semester, beyond the department's recommended two. We also show that their performance is consistently different than that of native students, namely higher CS course fail rates and lower CS GPA scores. The detrimental effects and realization in this study is reason for further investigation. More features will be gathered to be used in our continued analysis and we see the need to examine the difficulty of CS courses taken to indicate why one cohort performs poorly and one does not. We conjecture that most transfer students tend to complete their core classes and are left with only CS courses to complete upon transfer. This results in the transfer students taking a heavier CS load and thus impacting their performance negatively, as compared to native students. Considering that many transfer students start their path way in 2-year institutions, it is imperative that better advising strategies are developed to enable the students to succeed upon their transition.

BACKGROUND: The Epi-Info software suite, built and maintained by the Centers for Disease Control and Prevention (CDC), is widely used by epidemiologists and public health researchers to collect and analyze public health data, especially in the event of outbreaks such as Ebola and Zika. As it exists today, Epi-Info Desktop runs only on the Windows platform, and the larger Epi-Info Suite of products consists of separate codebases for several different devices and use-cases. Software portability has become increasingly important over the past few years as it offers a number of obvious benefits. These include reduced development time, reduced cost, and simplified system architecture. Thus, there is a blatant need for continued research. Specifically, it is critical to fully understand any underlying negative performance issues which arise from platform-agnostic systems. Such understanding should allow for improved design, and thus result in substantial mitigation of reduced performance. In this paper, we present a viable cross-platform architecture for Epi-Info which solves many of these problems. RESULTS: We have successfully generated executables for Linux, Mac, and Windows from a single code-base, and we have shown that performance need not be completely sacrificed when building a cross-platform application. This has been accomplished by using Electron as a wrapper for an AngularJS app, a Python analytics module, and a local, browser-based NoSQL database. CONCLUSIONS: Promising results warrant future research. Specifically, the design allows for cross-platform form-design, data-collection, offline/online modes, scalable storage, automatic local-to-remote data sync, and fast analytics which rival more traditional approaches.

As the demand for cheaper electronic devices has increased, the location of manufacturing foundries has changed, sometimes to untrusted places in foreign countries. Some of these locations have limited oversight of the manufacturing of complicated and sensitive electronic components including integrated circuits (ICs). The integrated circuits are key component in all current electronic devices and can be modified to be malicious or to monitor the functions of their applications. These malicious modifications on the ICs are called hardware trojans (HWTs). HWTs can be designed to quietly monitor, to actively send out sensitive information, or to destroy their host device completely. The idea of hardware trojans in Wireless Sensor Networks (WSNs) has not been investigated before; thus, our goal is to demonstrate the potential threat that hardware trojans pose for sensor networks. This is important to study, given that in WSNs hundreds of sensors are deployed and in most cases left unattended, which gives the opportunity to an attacker to trigger a HWT on the sensors. For our investigation, we used TelosB sensors that have been used for some WSN applications. An attacker in a network can, for example, take advantage of the SPI bus that is used by the radio to eavesdrop messages and even disrupt communications completely. Currently, security breaches through software is given great importance in the WSN academic and research community. Our research shows that the same level of importance must be given to attacks through hardware to ensure a trusted and secure network.

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. We study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects a snapshot of data to the base station in a distributed manner without the time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Furthermore, we extend ADDC to deal with the continuous data collection issue, and analyze the delay and capacity performances of ADDC for continuous data collection, which are also proven to be order-optimal. Finally, extensive simulation results indicate that ADDC can effectively accomplish a data collection task and significantly reduce data collection delay.

In this paper, we introduce the Bodyguard Allocation Problem (BAP) game, that illustrates the behavior of processes with contradictory individual goals in distributed systems. In particular, the game deals with the conflict of interest between two classes of processes that maximize/minimize their distance to a special process called the root. A solution of the BAP game represents a rooted spanning tree in which there exists a condition of equilibrium with maximum social welfare. We analyze the inefficiency of equilibria of the game based on both a completely cooperative and noncooperative approach. Additionally, we design two algorithms, CBAP and DBAP, that provide approximated solutions for the BAP game. We prove that both algorithms always terminate in a configuration with equilibrium and we analyze their running time based on the approach of cooperation used. We perform experimental simulations to compare the overall quality of equilibria obtained by the proposed algorithms.

As a promising communication paradigm, Cognitive Radio Networks (CRNs) have paved a road for Secondary Users (SUs) to opportunistically exploit unused licensed spectrum without causing unacceptable interference to Primary Users (PUs). In this paper, we study the distributed data collection problem for asynchronous CRNs, which has not been addressed before. First, we study the Proper Carrier-sensing Range (PCR) for SUs. By working with this PCR, an SU can successfully conduct data transmission without disturbing the activities of PUs and other SUs. Subsequently, based on the PCR, we propose an Asynchronous Distributed Data Collection (ADDC) algorithm with fairness consideration for CRNs. ADDC collects data of a snapshot to the base station in a distributed manner without any time synchronization requirement. The algorithm is scalable and more practical compared with centralized and synchronized algorithms. Through comprehensive theoretical analysis, we show that ADDC is order-optimal in terms of delay and capacity, as long as an SU has a positive probability to access the spectrum. Finally, extensive simulation results indicate that ADDC can effectively finish a data collection task and significantly reduce data collection delay.

IEEE 802.15.4 is a standard designed for low data rate wireless personal area networks (WPANs) intended to provide connectivity to mobile devices. Such devices have considerable storage, energy, and communication constraints. However, they can be used in a variety of applications like home/office automation, healthcare, environmental control and more. To extend the lifetime of the WPAN, we propose a backward compatible energy efficient 802.15.4 MAC protocol (DEEP) for beacon-enabled sensor networks. The implementation of DEEP requires modifications to the Superframe Guaranteed Time Slot (GTS) distribution. This modification optimizes the GTS distribution providing reduced energy consumption. We implemented the improvements to the IEEE 802.15.4 protocol using real sensor nodes in a wireless network. Specifically, we conducted an energy study of DEEP's acknowledgmentbased GTS descriptor distribution scheme and compared the results with the standard implementation. Experiments show that DEEP reduces energy consumption up to nearly 50% when 7 devices allocate guaranteed time slots descriptors during normal communication.

A Molecular Dynamics (MD) system is defined by the position and momentum of particles and their interactions. Solving the dynamics numerically and evaluating the interaction is computationally expensive even for a small number of particles in the system. We are focusing on long-ranged interactions, since the calculation time is O(N2) for an N particle system. There are many existing algorithms aimed at reducing the calculation time of MD simulations. Among the existing algorithms, Multigrid (MG) method [1] reduces O(N2) calculation time to O(N) time while still achieving reasonable accuracy. Another movement to achieve much faster calculation time is running MD simulation on special purpose processors and customized hardware with ASICs or an FPGAs. In this paper, we design and implement FPGA-based MD simulator with an efficient MG method.

Recently, many parallel computing models using dynamically reconfigurable electrical buses have been proposed in the literature. The underlying characteristics are similar among these models, but they do have certain differences that can take form of restrictions on configurations allowed. This paper presents a constant time simulation of an R-mesh on an LR-mesh (a restricted model of the R-mesh), proving that in spite of the differences, the two models possess the same complexity. In other words, the LR-mesh can simulate a step of the R-mesh in constant time with a polynomial increase in size. This simulation is based on Rein-gold's algorithm to solve USTCON in log-space. The simulation is also the first to be executed in constant time.

Microarray data usually contains a huge number of genes (features) and a comparatively small number of samples, which make accurate classification or prediction of diseases challenging. Feature selection techniques can help us identify important and irrelevant (unimportant) features by applying certain selection criteria. However, different feature selection algorithms based on various theoretical arguments often produce different results when applied to the same data set. This makes selecting an optimal or near optimal feature subset for a data set difficult. In this paper, we propose using a genetic algorithm to improve feature subset selection by combining valuable outcomes from multiple feature selection methods. The goal of our genetic algorithm is to achieve a balance between the classification accuracy and the size of the feature subsets selected. The advantages of this approach include the ability to accommodate different feature selection criteria and find small subsets of features that perform well for a particular inductive learning algorithm of interest to build the classifier. The experimental results demonstrate that our approach can find subsets of features with higher classification accuracy and/or smaller size compared with each individual feature selection algorithm.

In this paper, we demonstrate how to implement and improve two ant colony optimization (ACO) algorithms on the optical pipelined reconfigurable mesh (PR-mesh): the generic ACO and the fast ant colony optimization (FACO) algorithm. The run-time complexity of our improved generic ACO algorithm, with x generations each generation having m ants, on an n times n PR-mesh is O((x middot m + n)log n), which outperforms the currently best known electrical model implemented in (Merkle and Middendorf, 2002) with run-time complexity of O(x middot (m + n)log n). Our FACO algorithm on PR-mesh yields O(((z/(n*middot;log2n)) + n/log n) middot log log n) run-time complexity for n2jobs while the existing FACO algorithm on the electrical model yields a run-time complexity of O((z + n)log* n) but can only handle log2n jobs, where z is the total number of ants from all generations. In addition, we propose a theoretical FACO algorithm on a three dimensional PR-mesh solving n2jobs in O(x middot (m + n) middot log n) time

The unidirectional nature of propagation and predictable delays are two characteristics of optically pipelined buses that have made them popular in recent years. Many models have been proposed that use reconfigurable optically pipelined buses. In this paper, we establish a relationship between a one dimensional and a two dimensional model of this type. This simulation shows that the challenge is to map the processors so that those belonging to a two-dimensional bus segment are contiguous and in the same order on the simulating one-dimensional model. We focus on the linear array with a reconfigurable pipelined bus system (LARPBS) and its two dimensional counterpart the pipelined reconfigurable mesh (PR-mesh).

IEEE 802.15.4 is a new standard intended to serve a set of simple but important applications with very low power consumption and relaxed data rate requirements. Among the applications are residential networking, medical sensor networking, and industrial monitoring. We implement the 802.15.4 simulation module in the Georgia Tech Network Simulator (GTNetS). Simulation results are provided and analyzed to verify the implementation

Future Web applications will be more collaborative and will use the standard and ubiquitous Internet protocols. We have previously developed system on mobile devices (SyD) middleware to rapidly develop and deploy collaborative applications over heterogeneous and possibly mobile devices hosting web objects. In this paper, we present the software engineering methodology for developing SyD-enabled Web applications and illustrate it through a case study on a system of calendar application, with implementation on iPAQs and its performance metrics study. SyD-enabled Web objects allow us to create a collaborative application rapidly with limited coding. In this case study, the modular software architecture allowed us to hide the inherent heterogeneity among devices, data stores, and networks by presenting a uniform and persistent object view of mobile calendar objects interacting through XML/SOAP requests and responses. The performance results we obtained show that the application scales well as we increase the group size and adapts well within the constraints of mobile devices.

In this paper we propose a constant-time algorithm for parallel implementation of the message-passing decoder of low density parity check (LDPC) codes on the linear array with a reconfigurable pipelined bus system (LARPBS), achieving the minimum number of processors required for a fully parallel implementation. Dynamic reconfiguration provides flexibility to code changes and efficient message routing. To decode a different code, we may simply set up the required connections between the bit-nodes and check-nodes by modifying the initialization phase of the LARPBS algorithm. No extra wiring or hardware changes are required, as compared to other existing approaches. Moreover, the same hardware can implement the decoder in both probability and logarithm domains. The LARPBS also allows reducing the number of the bus cycles required for processor communications to a small constant, regardless of the code length. We illustrate that the LARPBS is an efficient and fast model for implementing the decoder.

This paper describes our proposed power management schemes, directional local recovery and dynamic power management, to reduce and balance power consumption. We introduce the power variance as a metric to evaluate the performance of power balancing. Using ns-2 and the ad hoc on-demand distance vector routing (AODV) protocol, we compare the performance of AODV with our extension against the original AODV. Simulation results show that our schemes provide substantial energy savings and thus prolong the battery life of mobile nodes while introducing limited increase in end-to-end delay.

Summary form only given. This paper considers fault-tolerance on the R-Mesh and LR-Mesh models. We propose a technique to identify a healthy submesh from a faulty model using the removal fault model. Then, we use scalable algorithms to simulate the faulty model on the resulting healthy submesh. We also extend this work to cover more restrictive variations of the reconfigurable mesh, specifically, the NXR-Mesh and NXLR-Mesh. The overhead for the R-Mesh and NXR-Mesh is O(log n), and we obtain a constant overhead for the LR-Mesh and NXLR-Mesh.

Summary form only given. We present a simulation of an acyclic n/spl times/n DR-Mesh on an n/spl times/n LR-Mesh. The simulation is efficient in regards to size since both models use the same number of processors. The worst execution time for this simulation is O(n/sup 2/) time, but we demonstrate that its average execution time is O(log n). The existing fastest simulation takes O(log n) time, but it uses an extremely large number of processors. On the other hand, the most efficient simulation in terms of size takes O(log/sup 2/ n) time with O(n/sup 4//log/sup 2/ n) processors. Both of the existing simulations are for the unrestricted DR-Mesh. This paper provides an important step to efficiently simulate the unrestricted DR-Mesh on weaker models such as the R-Mesh and the LR-Mesh.

Recently, many models using reconfigurable optically pipelined buses have been proposed in the literature. We present simulations for a number of these models and establish that they possess the same complexity, so that any of these models can simulate a step of one of the other models in constant time with a polynomial increase in size. Specifically, we determine the complexity of three optical models (the PR-Mesh, APPBS, and AROB) to be the same as the well known LR-Mesh and the cycle-free LR-Mesh.