As embedded systems with manycores and Network-on-Chips (NoCs) become ubiquitous, emerging hardware and software vulnerabilities have made it challenging to ensure system integrity especially when third-party intellectual property (IP) is used for rapid prototyping. Prior works have evaluated hardware monitors for ensuring correctness of the system by threat assessment and effective mitigation. However, none have evaluated models that combine both computation (processor pipeline) and communication (NoC) vulnerabilities simultaneously. In this paper, we propose a high-level policy language called d-GUARD that is used to define runtime security policies that can be compiled into hardware monitors. The advantage of this new language is the ability to dynamically change policies based on program’s runtime behavior. To translate high-level policies into low-level hardware monitors, we describe a compiler for d-GUARD that synthesizes policies into Verilog modules. Instead of simply evaluating the design of secure policies for processor pipelines, we extend to secure NoC microarchitectures, including policies for links and routers, as well as policies to prevent Denial-of-Service (DoS) attacks. To mitigate attacks against secure microarchitectures, we also propose fault-tolerant routing approaches to avoid rogue routers when the number of policy violations exceeds a certain threshold. Our secure policies for processor pipelines and NoC microarchitectures consume marginal area and power overhead when compared to baseline making it well suited for low-cost embedded systems. • A high-level security policy language called D-GUARD, which is expressive, modular and compatible with power-efficient hardware is proposed. • The proposed dynamic policies implemented at various levels of the embedded enable low-cost monitors that can be modified based on application demands. • The runtime costs of the processor pipeline using OptimSoC, an open-source implementation of OpenRISC1000 architecture where processor policies are evaluated, which showed less than 1% overhead in power and area when implemented in 14 nm and 45 nm technologies and no performance overhead when implemented on BEEBS benchmark suite.

As low-level embedded systems are vulnerable to attacks that exploit flaws in either hardware or software, it is essential to enforce secure policies to protect the system from malicious instructions that significantly alter program behavior. To improve efficiency of implementation, high-level secure policy languages have been defined such that the policies can be directly synthesized into hardware monitors. However, the language semantics define policies that are static throughout the program execution which limits the flexibility. Moreover, secure policies target processor pipelines and not the network-on-chip (NoC) connecting several processor where denial-of-service attacks could originate. In this paper, we enable dynamically reconfigurable security policies through a high-level language called D-GUARD that target both processor pipeline and NoC architecture in mutlicore embedded systems. Alongside static policies, D-GUARD’s semantics support policies that dynamically change behavior in response to program conditions at runtime. In addition, we also propose policies to thwart denial-of-service attacks by rate limiting the packet flow into the network using the same dynamic policies expressed by D-GUARD. We describe a Verilog compiler to support realizing policies as hardware monitors for both processor pipelines and network interfaces. D-GUARD is developed using the Coq proof assistant, enabling the formal verification of policy correctness and other properties. This approach takes advantage of the abstractions and expressiveness of a higher-level language while minimizing the overhead that comes with other general-purpose approaches implemented purely in hardware, as well as offering the groundwork for a formally verified tool chain.

In this paper, we recount efforts in developing cybersecurity workshops for K-12 teachers, intended to learn skills to better educate the cybersecurity workers of tomorrow. In 2021, we provided two one-day virtual workshops and in 2022 we provided one two-day in-person workshop to high school teachers to increase cybersecurity awareness in three areas: general cybersecurity issues, software security, and hardware security. Both the online and in-person workshops employed Google classroom and Jupyter Notebooks, and high school teachers were provided with Raspberry Pi Zeros to use as part of the workshops. This paper describes the design and implementation of the workshops and also provides evidence demonstrating the effectiveness of the workshops, as well as commentary to provide guidance for future efforts.

In this paper, we describe efforts to promote a robust cyber security workforce through a series of online workshops for K-12 teachers and grades 7-12 students. In 2021, we provided virtual workshops to high school teachers and students to increase cyber security awareness in three areas, (i) general cybersecurity issues, (ii) software security, and (iii) hardware security. The workshops employed Google classroom and Jupyter Notebooks, and high school teachers were provided with hardware (Raspberry Pi Zeros) to use as part of the workshops. We describe the design and implementation of the workshops and share evidence to demonstrate the effectiveness of the workshops and provide insights for future professional development for teachers. Our central question was: What impact do workshops have on teachers' preparation to effectively teach cybersecurity topics to their students and what do teachers report learning from their experiences in the workshop? We provide insights into workshops' effectiveness for computer science teachers and for STEM teachers who are not computer science teachers.

MOTIVATION: De novo motif discovery algorithms find statistically over-represented sequence motifs that may function as transcription factor binding sites. Current methods often report large numbers of motifs, making it difficult to perform further analyses and experimental validation. The motif selection problem seeks to identify a minimal set of putative regulatory motifs that characterize sequences of interest (e.g. ChIP-Seq binding regions). RESULTS: In this study, the motif selection problem is mapped to variants of the set cover problem that are solved via tabu search and by relaxed integer linear programing (RILP). The algorithms are employed to analyze 349 ChIP-Seq experiments from the ENCODE project, yielding a small number of high-quality motifs that represent putative binding sites of primary factors and cofactors. Specifically, when compared with the motifs reported by Kheradpour and Kellis, the set cover-based algorithms produced motif sets covering 35% more peaks for 11 TFs and identified 4 more putative cofactors for 6 TFs. Moreover, a systematic evaluation using nested cross-validation revealed that the RILP algorithm selected fewer motifs and was able to cover 6% more peaks and 3% fewer background regions, which reduced the error rate by 7%. AVAILABILITY AND IMPLEMENTATION: The source code of the algorithms and all the datasets are available at https://github.com/YichaoOU/Set_cover_tools. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Data mining algorithms and sequencing methods (such as RNA-seq and ChIP-seq) are being combined to discover genomic regulatory motifs that relate to a variety of phenotypes. However, motif discovery algorithms often produce very long lists of putative transcription factor binding sites, hindering the discovery of phenotype-related regulatory elements by making it difficult to select a manageable set of candidate motifs for experimental validation. To address this issue, the authors introduce the motif selection problem and provide coverage-based search heuristics for its solution. Analysis of 203 ChIP-seq experiments from the ENCyclopedia of DNA Elements project shows that our algorithms produce motifs that have high sensitivity and specificity and reveals new insights about the regulatory code of the human genome. The greedy algorithm performs the best, selecting a median of two motifs per ChIP-seq transcription factor group while achieving a median sensitivity of 77 percent.

This paper describes the design and initial implementation of a new CS0 course for CS majors at Ohio University and an associated computer science placement exam. It was our expectation that this course would help improve overall retention and academic performance of CS majors. The new CS0 course was intended for new students with little or no programming experience. Hence, we designed CS0 by selecting some of the best components of other CS0 efforts from the literature, namely, introductory programming in Python, computational thinking, and motivational material aimed at instilling ownership of the major. In order to direct students into CS0 or CS1 appropriately, a placement exam was given to students during orientation. The placement process resulted in a mixed cohort consisting of students with no programming experience as well as other students with programming experience who did not pass the placement exam. Longitudinal analysis of student grades in CS0 and CS1 showed that these were two distinct populations with very different needs. A statistically significant difference was found in the CS1 grades for CS0 students from these populations. Surprisingly, CS0 students without prior programming experience got significantly higher grades in CS1 than CS0 students who had programmed before. We share lessons learned from our experience, including a revised placement process to address these separate populations.

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.

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%.

Based on the framework of parameterized complexity theory, we derive tight lower bounds on the computational complexity for a number of well-known NP-hard problems. We start by proving a general result, namely that the parameterized weighted satisfiability problem on depth-t circuits cannot be solved in time n/sup o(k)/poly(m), where n is the circuit input length, m is the circuit size, and k is the parameter, unless the (t - l)-st level W[t $1] of the W-hierarchy collapses to FPT. By refining this technique, we prove that a group of parameterized NP-hard problems, including weighted SAT, dominating set, hitting set, set cover, and feature set, cannot be solved in time n/sup o(k)/poly(m), where n is the size of the universal set from which the k elements are to be selected and m is the instance size, unless the first level W[l] of the W-hierarchy collapses to FPT. We also prove that another group of parameterized problems which includes weighted q-SAT (for any fixed q /spl ges/ 2), clique, and independent set, cannot be solved in time n/sup o(k)/ unless all search problems in the syntactic class SNP, introduced by Papadimitriou and Yannakakis, are solvable in subexponential time. Note that all these parameterized problems have trivial algorithms of running time either n/sup k/ poly(m) or O(n/sup k/).

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.

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.

Most computer science departments provide their students with a mathematical foundation which enables them to master theoretic concepts. However, students often express the concern that they are applying their theoretic background to "toy" problems only. They wonder what "real" problems look like and whether or not their background will be sufficient for them when they are faced with an industrial situation. At the same time companies who are hoping to hire well prepared computer programmers, systems analysts, and other technical staff express their concern that recent graduates entering the workforce are not adequately prepared for dealing with large problems in a real world setting. The approach presented here addresses the problem of bridging the gap between theory and practice by actively seeking out industrial partners who provide academics with real problems that can be addressed by teams of students in the academic setting.

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.

The C++ package ADOL-C described here facilitates the evaluation of first and higher derivatives of vector functions that are defined by computer programs written in C or C++. The resulting derivative evaluation routines may be called from C/C++, Fortran, or any other language that can be linked with C. The numerical values of derivative vectors are obtained free of truncation errors at a small multiple of the run-time and randomly accessed memory of the given function evaluation program. Derivative matrices are obtained by columns or rows. For solution curves defined by ordinary differential equations, special routines are provided that evaluate the Taylor coefficient vectors and their Jacobians with respect to the current state vector. The derivative calculations involve a possibly substantial (but always predictable) amount of data that are accessed strictly sequentially and are therefore automatically paged out to external files.

Measure-theoretic aspects of the $\leq _{\text{m}}^{\text{P}}$-reducibility structure of the exponential time complexity classes ${\text{E}} = {\text{DTIME}}(2^{{\text{linear}}} )$ and ${\text{E}}_2 = {\text{DTIME}}(2^{{\text{polynomial}}} )$ 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 $\leq _{\text{m}}^{\text{P}}$-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 bound says that the $\leq _{\text{m}}^{\text{P}}$-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 $\leq _{\text{m}}^{\text{P}}$-complete languages for E form a measure 0 subset of E (and similarly in ${\text{E}}_2$). This latter fact is seen to be a special case of a more general theorem, namely, that every$\leq _{\text{m}}^{\text{P}}$-degree (e.g., the degree of all $\leq _{\text{m}}^{\text{P}}$-complete languages for NP) has measure 0 in E and in ${\text{E}}_2$.

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/.>

. The numerical methods employed in the solution of many scientific computing problems require the computation of first- or second-order derivatives of a function f : R n !R m . We present an approach that, given a serial C program for the computation of f(x), derives a parallel execution schedule for the computation of f and its derivatives in a completely automatic fashion. This is achieved by overloading the computation of f(x) in C++ to obtain a trace of the computations to be performed and then transforming this trace into a data flow graph for the computation of f(x). In addition to the computation of f(x), this graph also allows us to exactly and inexpensively compute derivates of f by the repeated use of the chain rule. Parallelism is exploited in two ways: rows or columns of derivative matrices can be computed by independent passes through the computational graph, and parallelism within the processing of this computational graph can be exploited by processing independent...