This research introduces a recommendation system designed to enhance student success by intelligently personalizing the semester schedules and graduation path based on the student's performance, interests, and background; and inspired by the academic journeys of similar students who have successfully graduated in the past. The proposed recommender system leverages a combination of Markov decision processes, Q-Learning, and collaborative filtering techniques to identify graduation paths with a higher likelihood of success for the student. The proposed model is versatile and generic and can be adapted to various disciplines if sufficient past historical data is available. The proposed model has been prototyped and implemented within the scope of the PERCEPOLIS cyber-infrastructure and its effectiveness has been verified on a testbed of real student records for select academic programs.

This paper proposes an intelligent recommendation approach to facilitate personalized education and help students in planning their path to graduation. The goal is to identify a path that aligns with a student’s interests and career goals and approaches optimality with respect to one or more criteria, such as time-to-graduation or credit hours taken. The approach is illustrated and verified through application to undergraduate curricula at the Missouri University of Science and Technology.

The safety of autonomous vehicles relies on dependable and secure infrastructure for intelligent transportation. The doctoral research described in this paper aims to enable self-healing and survivability of the intelligent transportation systems required for autonomous vehicles (AV-ITS). The proposed approach is comprised of four major elements: qualitative and quantitative modeling of the AV-ITS, stochastic analysis to capture and quantify interdependencies, mitigation of disruptions, and validation of efficacy of the self-healing process. This paper describes the overall methodology and presents preliminary results, including an agent-based model for detection of and recovery from disruptions to the AV-ITS.

Welcome to COMPSAC 2022, the 46th IEEE Computer Society International Conference on Computers, Software & Applications. Our theme this year is “Computers, Software, and Applications in an Uncertain World.” Consequences of the COVID-19 pandemic, ongoing political disputes, and climate change have led to increasing uncertainty in many aspects of our daily life. To thrive in this increasingly uncertain world, innovations in computer hardware, software, and applications have emerged as pressing needs. Safety, security, and resilience of computer hardware and software systems manifest themselves as ongoing concerns, and unique challenges arise in demonstrating and guaranteeing the upholding of these quality attributes despite imperfect knowledge of the environment in which they will operate. The authors whose work is included in these proceedings, as well as all of those who submitted papers to COMPSAC 2022, have seized the opportunity to meet these challenges.

The work presented in this paper demonstrates the use of context-aware recommendation to facilitate personalized education, by assisting students in selecting courses and course content and mapping a trajectory to graduation. The recommendation algorithm considers a student's profile and their program's curricular requirements in generating a schedule of courses, while aiming to reduce attributes such as cost and time-to-degree. The resulting optimization problem is solved using integer linear programming and graph-based heuristics. The course selection algorithm has been developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with assurance that recommended selections are always valid.

A smart grid is a cyber-physical system designed to achieve sustainability by facilitating the use of renewable energy sources, without compromising the reliability of power distribution and transmission. The research presented in this paper seeks to facilitate analysis of the reliability achieved by smart grids. To this end, we present an analytical reliability model that captures the effect of impairments originating from both physical and cyber components, as well as the effect of cyber-physical interdependencies among these components. The model can be instantiated with failure data from field use or simulation. We categorize and quantify dependencies in the smart grid and analyze the impact on reliability of introducing additional interdependencies. Finally, we present a case study that investigates the effect of physical and cyber improvements on overall reliability of the smart grid, and demonstrate that flawed cyber infrastructure can result in reliability lower than that of a conventional power grid with less advanced control.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

The papers in this special section focus on cyber-physical systems and applications for their use. A cyber-physical system (CPS) integrates a vast variety of static and mobile resources, including sensor and actuator networks, swarms of robots, remote-controlled vehicles, critical infrastructures, control and decision software, static data and just-in-time information from sensors, knowledge, data analytics and fusion software, event driven supply chains, and humans, and offers great potential for achieving tasks that are far beyond the capabilities of existing systems [1]. Individual users, organizations, and various communities can transform the vast space of cyberphysical entities into capabilities that no single entity can achieve alone. However, these capabilities do not come easily. Intelligence is needed for just-in-time composition of resources into services. Associated challenges include how to manage the vast number and diverse varieties of static and mobile physical entities, how to describe the capabilities of the cyber-physical entities, how to decompose high level goals into low-level control commands for the individual entities, how to achieve intelligent coordination and manage information flow among the entities.

Presents a listing of the Symposium organizing committee.

This paper provides a summary of the First International Workshop on Model Based Design for Cyber- Physical Systems (MB4CP 2015) in conjunction with DSN 2015 conference in Rio de Janeiro, Brazil.

Smart grids, where cyber infrastructure is used to make power distribution more dependable and efficient, are prime examples of modern infrastructure systems. The cyber infrastructure provides monitoring and decision support intended to increase the dependability and efficiency of the system. This comes at the cost of vulnerability to accidental failures and malicious attacks, due to the greater extent of virtual and physical interconnection. Any failure can propagate more quickly and extensively, and as such, the net result could be lowered reliability. In this paper, we describe metrics for assessment of two phases of smart grid operation: the duration before a failure occurs, and the recovery phase after an inevitable failure. The former is characterized by reliability, which we determine based on information about cascading failures. The latter is quantified using resilience, which can in turn facilitate comparison of recovery strategies. We illustrate the application of these metrics to a smart grid based on the IEEE 9-bus test system.

This paper illustrates the use of software for monitoring and recording the effects of electrostatic discharge (ESD) on the operation of embedded systems, with the goal of facilitating root-cause analysis of resulting failures. Hardware -- based scanning techniques are typically used for analyzing the effect of ESD on systems by identifying physical coupling paths. This paper proposes software techniques that monitor registers and flags associated with peripherals of embedded systems to detect faults associated with the effects of ESD. A lightweight, cost-effective, and non-intrusive software tool has been developed that monitors and records the status of all registers associated with a designated peripheral under test, identifying the fault propagation caused by ESD in the system, and visually presenting the resulting errors. The tool has been used to detect and visually summarize ESD-induced errors on the SD card peripheral of the S3C2440 development board, using local injection and system-level scanning. Root-cause analysis of these faults can potentially assist in identification of coupling paths of electromagnetic interference, as well as determination of areas of the hardware that are more vulnerable to ESD.

"Smart" technology spans a broad array of application domains, from critical infrastructure systems such as smart power grids to intelligent healthcare and personalized education. The common thread among these applications is their reliance on computing and communication for "intelligence" -- accurately characterizing their operating environment and intended use, adapting their operation based on this characterization, and efficiently delivering the services expected in a dependable fashion.

This paper describes Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support (PERCEPOLIS), where context-aware recommendation algorithms facilitate personalized learning and instruction. Fundamental to PERCEPOLIS are (a) modular course development and offering, which increase the resolution of the curriculum and allow for finer-grained personalization of learning artifacts and associated data collection; (b) blended learning, which allows class time to be used for active learning, interactive problem solving and reflective instructional tasks; and (c) networked curricula, in which the components form a cohesive and strongly interconnected whole where learning in one area reinforces and supports learning in other areas. Intelligent software agents customize the content of a course for each learner, based on his or her academic profile and interests, aided by context-based recommendation algorithms. This paper provides an introduction to the PERCEPOLIS platform, with focus on these algorithms; and describes the educational research that underpins its design.

Sensor network has given rise to a comprehensive view of various environments through their harnessed data. Usage of the data is, however, limited since the current sensor network paradigm promotes isolated networks that are statically tasked. In recent years, users have become mobile entities that require constant access to data for efficient processing. Under the current limitations of sensor networks, users would be restricted to using only a subset of the vast amount of data being collected, depending on the networks they are able to access. Through reliance on isolated networks, proliferation of sensor nodes can easily occur in any area that has high appeals to users. Furthermore, support for dynamic tasking of nodes and efficient processing of data is contrary to the general view of sensor networks as subject to severe resource constraints. Addressing the aforementioned challenges requires the deployment of a system that allows users to take full advantage of data collected in the area of interest to their tasks. In light of these observations, we introduce a hardware-overlay system designed to allow users to efficiently collect and utilize data from various heterogeneous sensor networks. The hardware-overlay takes advantage of FPGA devices and the mobile agent paradigm in order to efficiently collect and process data from cooperating networks. The computational and power efficiency of the system are herein demonstrated.

In cyber-physical systems (CPSs), embedded computing systems and communication capability are used to streamline and fortify the operation of a physical system. Intelligent critical infrastructure systems are among the most important CPSs and also prime examples of pervasive computing systems, as they exploit computing to provide "anytime, anywhere" transparent services. The existing body of knowledge includes techniques for assessment, modeling, and simulation of physical and cyber infrastructures, respectively, but such isolated analysis is incapable of fully capturing the interdependencies that occur when they intertwine to create a CPS. Fundamental differences exist between the attributes of cyber and physical components, significantly complicating representation of their behavior with a single comprehensive model or simulation tool. This paper articulates the challenges present in integrated simulation of a CPS, where the goal is to accurately reflect the operation and interaction of the cyber and physical networks that comprise the system. A solution is presented for the CPS domain of intelligent water distribution networks, using EPANET and Matlab to represent the physical water distribution network and the decision support algorithms used to control the allocation of water, respectively.

The increasing dependence of critical infrastructures on computer hardware and software has given rise to unanticipated ethical, social, and legal complications [1]. Transportation systems, the power grid, and emergency services, and financial networks all rely on a vast amount of information from diverse sources. Protection of the integrity, confidentiality, and privacy of this information often results in ethical dilemmas, in particular in times of crisis.

Wireless sensor networks are becoming a basis for a rapidly increasing range of applications. Habitat, flood, and wildfire monitoring are interesting examples of such applications. Each application has different requirements in terms of node functionalities, network size, complexity and cost; therefore, it is worthwhile time investment to design and implement a general purpose framework for wireless sensor networks that would be adaptable to any monitoring application of interest with a minimum amount of effort. In this manuscript, we propose a basic structure for such a framework and highlight a number of challenges anticipated during the course of this doctoral research

Wireless devices can be used to monitor and record a broad range of phenomena. Their advantages include ease of installation and maintenance and considerable reduction in wiring costs. The addition of battery power and radio communication to such wireless devices can result in a completely autonomous system. The operating environment of monitoring systems is often hostile, due to temperature fluctuations, humidity, electromagnetic noise, and other interfering phenomena. The system should be able to adapt to changing conditions to maintain dependability in its operations. This paper presents the case study of adapting a flood detection device to the environmental threat of submersion

Component-based software development (CBSD) offers many advantages like reduced product time to market, reduced complexity and cost etc. Despite these advantages its wide scale utilization in developing security critical systems is currently hampered because of lack, of suitable design techniques to efficiently manage the complete system security concerns in the development process. The use of commercial of the shelf (COTS) components can introduce various security and reliability risks in the system. In this paper we propose a methodology for efficient management of all the system security concerns involved in the design of component based systems. Our methodology is based on formally representing the system security specifications and component capabilities. We identify the metrics for correlating both and suggest extensions to a previously proposed software development process, for selection of suitable components and integration mechanisms. The proposed solution ensures due treatment of all the security concerns for the complete system in the acquisition efforts.

Quality and risk concerns currently limit the application of commercial off-the-shelf (COTS) software components to non-critical applications. Software metrics can quantify factors contributing to the overall quality of a component-based system, and models for tradeoffs between cost and various aspects of quality can guide quality and risk management by identifying and eliminating sources of risk. This paper discusses metrics and models that can be used to alleviate quality concerns for COTS-based systems, enabling the use of COTS components in a broader range of applications.

This paper presents a temporal model for the coverage achieved by software testing. The proposed model, which is applicable at any level of the testing hierarchy, can determine the value of test coverage at any given time, as well as predicting future values. The model is comprised of two main components: coverage functions, and the coverage matrix. The coverage functions represent the coverage of a single entity as a function of time and reflect the test environment through their stochastic parameters. The coverage matrix utilizes the coverage functions to depict the coverage attained for each entity by each test within the test suite. A normalized sum of the elements of the coverage matrix is used to represent the overall coverage achieved by the test suite, as a function of time. The application of the model to multi-phase testing is illustrated In the application section, test coverage values from Y2K compliance testing are used to verify model predictions.

The growing reliance on commercial-off-the-shelf (COTS) components for developing large-scale projects introduces a new paradigm in software engineering, which requires the design of new software development and business processes. Large scale component reuse leads to savings in development resources, enabling these resources to be applied to areas such as quality improvement. These savings come at the price of integration difficulties, performance constraints, and incompatibility of components from multiple vendors. Relying on COTS components also increases the system's vulnerability to risks arising from third-party development, which can negatively affect the quality of the system, as well as causing expenses not incurred in traditional software development. We aim to alleviate such concerns by using software metrics to accurately quantify factors contributing to the overall quality of a component-based system, guiding quality and risk management by identifying and eliminating sources of risk.

In this paper, we evaluate two video filtering mechanisms for MPEG-1 video, namely low-pass filtering and selective frame dropping. The evaluation provides tradeoffs between the reduction achieved in bandwidth requirements and the perceptual quality of the video sequences delivered to the client. Extensive experiments revealed that these filtering mechanisms result in a significant reduction in bandwidth requirements while maintaining acceptable perceptual quality.