Unlike any time before, society is at risk of cyber-attacks and lives under the oppressive threat of information being stolen with a “click.” The emerging threats against the electric grid and other elements of the country’s Critical Infrastructure and Key Resources (CIKR) have created an ever-increasing need for men and women to defend it.
The Cybersecurity Team at the University of Maine has taken it upon themselves to make themselves as competitive as possible this year. For new members, the team has provided hands on training and tutorials to catch them up to speed with current topics. For advanced members they selected special focus topics and have guided them through complex topics. Along with a revamped meeting structure, there has been an increased engagement with the student body and a swell in membership. The team actively provides the necessary resources for personal cyber research and teaches security related topics to new members.
This semester, the Team will have plenty of events and workshops for people of all skillsets. Next Wednesday, the team is hosting a physical security lab revolving around the art of “lock picking,” presented by TOOOL. Later this month, they will be attending the first inaugural Bangor Information Security Professionals meeting.
Alumni and students in the Cybersecurity Team have progressed to work at the elite companies of the field. They include: MITRE Corp., Dell Secureworks, BAE Systems, Sony, Nvidia, Cisco, nVisium, Networkmaine. Students have also gone on to graduate school, such as the Masters of Information Assurance Program at Northeastern University and the Masters of Information Systems at the University of Maine.
Meeting times are 5:30 pm Wednesday and 12 pm Saturday in 224 East Annex. For further information on how to get involved, email the team at email@example.com or connected with them on Facebook.
On Wednesday, February 3rd, students are invited to attend the 2016 UMaine Career Fair, which will be in the New Balance Recreation Center from 10 am to 3 pm. Open to all UMaine undergraduate and graduate students, this event will feature 140 businesses, universities, and graduate programs from Maine and around the country. Many of these businesses are specifically looking to recruit computer science undergraduate students for internships and full time positions in the fields of computer programming, database management, software development, and others. The National Geospatial Intelligence Agency, who named UMaine as a Center of Academic Excellence will also be at the fair recruiting for various careers and internships. Be sure to dress in business attire and bring several copies of an updated resume, and see the Career Center if you need any help with writing a resume or learning an elevator pitch!
On December 10th, undergraduate and graduate computer science students were able to demonstrate their creativity and programming ability in a fun an exciting way, through gaming. Students in COS 125 (Introduction to Problem Solving Using Computer Programming), COS 312 (An Introduction to Video Game Programming with the Unity Game Engine), and COS 598 (Advanced Game Design) were able to compete against each other to see who among them could create the best video game. Students from each level were able to showcase their work, and visitors had the opportunity to play the students’ games and vote for their favorite. The teams with the most votes will receive cash prizes, and will be announced on this website.
Local television coverage of the event can be found at http://www.wcsh6.com/story/news/2015/12/10/student-video-game-exhibition/77092746/.
Recently, Professor Roy Turner presented three papers at CONTEXT’15 (International and Interdisciplinary Conference on Modeling and Using Context) conference in Larnaca, Cyprus, which was held on November 2-6. In addition to the papers that were presented, Professor Turner also chaired a panel at the conference.
The first paper was entitled ” Modeling erroneous human behavior: A context-driven approach,” and was done in collaboration with PhD candidate Chris Wilson. In this paper, they presented a context-driven approach to modeling plausible human behavior and a framework for modeling erroneous behavior which focuses on impairing an agent’s ability to recognize and deal effectively with anticipated contextual changes.
The second paper was entitled ” Representing and communicating context in multiagent systems” and was done in collaboration with MS candidate Sonia Rode. In this paper they propose a new, related representation of contextual knowledge using description logic and a shared ontology, and present a technique for communicating contextual knowledge while respecting bandwidth limitations.
The final paper that was presented was entitled “Using contextual knowledge for trust strategy selection,” and was done in collaboration with Larry Whitsel of the University of Maine at Augusta. In this paper, they present an implemented approach that explicitly represents the agent’s context, informed by known contexts, and that uses that contextual knowledge to select the best strategy, even in the presence of untrustworthy agents.
For more information, see http://orca.umcs.maine.edu/Sites/mainesail/.
Over the years, the University of Maine School of Computing and Information Science (SCIS) has become a major player in the fields of computer science and spatial informatics. Through dozens of published papers, conference presentations, books, research projects, and major grants, UMaine has established itself as a pivotal player in academia around the world. The Geosensor Networks Lab, coordinated by Professor Silvia Nittel, seeks to take some of these advancements in academia and create real-world applications that can help the people of Maine.
The Geosensor Lab specializes in just that; geosensor networks, which are networks of sensors placed in and on the earth to measure natural phenomena. These sensors can be used to record seismic activity, soil moisture, pH, temperature, and just about any other property of soil and air that you can think of. After several months of research and development, Dr. Nittel and graduate students in the Geosensor Networks Lab created a sensor to measure soil moisture tension and temperature, both of which are important to know in an agricultural setting. The sensor works by measuring the soil moisture tension and temperature at regular intervals, which it then encoded by an Arduino processor, very similar to the processor that you have in your cell phone. The sensors are powered by solar panels and use radios to wirelessly send the data to a “Raspberry Pi” minicomputer in another location in the field for further encoding, data storage and connection to the Internet. While the newly created sensor network configuration works in theory, it was still untested in practice. The researchers looked to the most logical place; the Maine blueberry industry.
There is no mistaking that Maine is a powerhouse for low-bush blueberry production; not only is does it account for 10% of the world’s blueberry production, but the state is actually the world’s largest producer of the fruit. This comes from a deep-rooted heritage in Downeast and Coastal Maine, where blueberry cultivation was first carried out thousands of years ago by Native Americans and continues today in large-scale agricultural operations. With over 44,000 acres of blueberries providing thousands of jobs to Mainers, the researchers at the Geosensor Networks Lab saw not only an opportunity to test cutting-edge techniques, but to help the Maine economy. Through a grant from the University of Maine Cooperative Extension, the lab was able to make this work possible.
To do this, the lab partnered with Cherryfield Foods to monitor one of their fields in Washington County. The lab had two goals, which included testing the ability of their newly constructed sensor network, and to see if the data gathered by the network contributed to growing healthier and heavier blueberries consistently across an entire field. What they found were several valuable facts; not only was the data highly useful in optimizing water distribution, but the sensors worked reliably throughout the 2 month deployment and were able to communicate with the Raspberry pi computer from over a mile away.
Now that the summer is over, the lab is working hard to implement the lessons learned over the summer in the blueberry field. Currently, the lab is developing a “field model,” which will allow them to not only record moisture from the sensors in the ground, but to be able to use that data to calculate moisture in other parts of the field in order to understand the field as a whole. The applications of this work can take many forms, including better sensor arrays for seismic activities in California, optimizing data gathering techniques to measure climate change, or even measuring precipitation in Maine potato fields in order to anticipate where water should be delivered to help crop production. With all of these potential applications it is hard not to be excited about the work that is coming out of the Geosensor Networks Lab. What will they come up with next?
Image Description: Researchers hard at work in the lab
Image Description: The team deploys the sensor network in a blueberry field.
Image Description: One of the computers that was made in the lab to record the data.
From October 12-16, the School of Information and Computing Science at the University of Maine sent several researchers to COSIT, one of the most significant conferences in the world for spatial information theory. Established in 1993 and only held every two years, COSIT seeks to bring together scientists from different disciplines for an intensive and deep exchange of knowledge and research. The conference is a single-track meeting that focuses on recent, innovative and significant contributions in the field. This year, University of Maine researchers including professors, graduate students and undergraduate students, presented an impressive three of the total 24 papers at the conference.
The first paper presented by University of Maine researchers is titled “From Metric to Topology: Determining Relation in Discrete Spaces.” This paper details a set of metrics that can uniquely identify simple topological relations between pixelized objects where the boundary is considered to be the outer set of pixels. This research is particularly pertinent in applications where images need to be analyzed for natural language interfaces, such as with drones, robots, satellites, and other camera-assisted technologies. This work can also have significant application with forest fire management through imagery and also in the understanding of temporal patterns in meteorology and other areas. The method proposed identifies the relation between the objects, which comes from common vocabulary within whichever language the user would prefer the information in. This publication is the culmination of a project with Upward Bound Math-Science student Jordan Barrett, now attending Syracuse University for Mathematics and Physics. Max Egenhofer also collaborated on this paper.
The second paper presented is titled “Swiss Canton Regions: A Model for Complex Objects in Geographic Partitions.” This paper constructs a method for maintaining topological equivalence up to the level of homeomorphism. In other words, this method helps to determine whether two areal objects separate a space in exactly the same way conceptually. This work is the culmination of a group research course in Spatial Reasoning, one of the core benefits of attending the Spatial Informatics program at the University of Maine. The benefit of this research is in the ability to construct queries that go beyond the simple topological queries in commercial GIS and enter into a more complex representation where separations, holes, and self-adjacency matter. Areas such as land cover analysis can gain substantially from this sort of approach. Furthermore, these types of queries are essential in cover analysis for sensor networks, helping to identify coverage issues in the deployment. Professor Max Egenhofer and current Ph.D. student Joshua Lewis, M.S. student Shirly Stephen, and former student Mark Plummer are also authors on the paper.
The third paper presented at COSIT is titled “Shape Similarity Based on the Qualitative Spatial Reasoning Calculus eOPRA.” The paper investigated the use of qualitative spatial representations (QSR) about relative direction and distance for shape representation. The new approach has the advantage that it can generate prototypical shapes from the authors’ abstract representation in first-order predicate calculus. Using the conceptual neighborhood which is an established concept in QSR the authors can directly establish a conceptual neighborhood between shapes that translates into a similarity metric for shapes. This similarity measure was applied to a challenging computer vision problem and achieved promising first results. This paper was authored by Ph.D. student Christopher Dorr, Professor Reinhard Moratz, and Longin Jan Latecki of the Temple University.
The fourth paper is entitled “What is in a Contour Map? A Region-based Logical Formalization of Contour Semantics.” This paper analyses and formalizes contour semantics in a first-order logic ontology that forms the basis for enabling computational common sense reasoning about contour information. The elicited contour semantics comprises four key concepts – contour regions, contour lines, contour values, and contour sets – and their subclasses and associated relations, which are grounded in an existing qualitative spatial ontology. All concepts and relations are illustrated and motivated by physical-geographic features identifiable on topographic contour maps. The encoding of the semantics of contour concepts in first-order logic and a derived conceptual model as basis for an OWL ontology lay the foundation for fully automated, semantically-aware qualitative and quantitative reasoning about contours. This paper was authored by Professor Torsten Hahmman and Lynn Usery of the U.S. Geological Survey.
Professor Max Egenhofer of the School of Computing and Information Science remarked at the impressive nature of not only the quality of the research produced by UMaine researchers, but also at how influential the institution has become in the field of spatial information theory. “The Conference on Spatial Information Theory is the intellectual highlight every two years in the field of Spatial Information Science. The papers presented there are of journal-like quality as they go through a rigors reviewing and selection process. The fact that this year four of the twenty-two papers that were chosen for publication and presentations were authored by UMaine researchers indicates most strongly what a powerhouse UMaine is this field. I was particularly delighted that three of these fours papers were authored or co-authored by a total of five of our graduate students, plus another co-author who is currently an undergraduate student. Many of the conference attendees approached me, commenting on the excellent presentations that our students gave. This involvement of students into top-level research highlights the kind of education that students get in our environment.”
Information on the conference and the conference program may be found at http://www.unm.edu/~sfreunds/COSIT_main/Home.html.
Matt Dube (Ph.D. Candidate in Spatial Information Science and Engineering in the School of Computing and Information Science) has had a very busy and successful semester. He was recently Faculty of the Month for the month of September from the sisters of the Maine Alpha Chapter of Pi Beta Phi sorority. Matt was recognized for his hard work and dedication to the enrichment of his students, care and concern for them, and also for his acumen as a teacher.
Also, he recently traveled to Santa Fe, New Mexico for the 12th Biannual Conference on Spatial Information Theory (COSIT). In doing so, he became the first graduate student in the history of the conference to be the first author on two papers at the same COSIT. At the previous COSIT, he became one of 22 researchers in the spatial domain to be published in ACM SIGSPATIAL, GIScience, and COSIT, and at this COSIT has become one of even fewer researchers to be first author on papers at these three venues.
On top of his work in the field of spatial informatics, Dube has conducted cutting-edge research in Political Science, around the realm of redistricting. “Determining an Expected House Majority Using Pattern Analysis,” written in conjunction Professor Richard Powell (Political Science) and undergraduate Political Science student Jesse Clark, will be presented at the Northeastern Political Science Association Conference in Philadelphia, Pennsylvania. First of its kind, the paper seeks to find evidence suggesting or refuting impropriety in Congressional redistricting.
Dube has gone further to diversify his academic work by authoring research in the field of veterinary science with Professor Mick Peterson of the Mechanical Engineering department, Professor Robert Causey of the Animal and Veterinary Science department, and other foreign and domestic researchers. The paper under review, entitled “Effects of Turn Geometry and Surface Type on Injury Laterality in Thoroughbred Racehorse Fatalities” explores how the type of surface of the track and its turn size impacts the distribution of injuries amongst the four limbs of racehorses.
He has also been active in Upward Bound Math-Science, a program for first-generation and low income aspiring college students, preparing them for the rigors and trials of academic life. One of his papers, “From Metric to Topology: Determining Relations in Discrete Space,” is the culmination of his mentorship and work with Jordan Barrett, formerly of Oxford Hills Comprehensive High School, and a current undergraduate student at Syracuse University in Mathematics and Physics. Beyond his extensive research mentorship with this program, he also provides mathematics courses ranging from geometry to research statistics.
Beyond his work in the academic realm, Dube has been extensively involved in Alternative Breaks, and serving as the Chapter Counselor for the UMaine chapter of the Sigma Phi Epsilon fraternity. He is also an avid movie enthusiast, a local official for youth and high school sports, and prides himself in fantasy baseball and trivia knowledge.
Matt is currently applying for faculty positions around the United States in Applied Mathematics, Geography, Computer Science, Information Science, and Data Science. More information on his work can be found at http://www.spatial.maine.edu/~matthew.dube.
Image Description: Maine Business School students Jacqueline McLaughlin (left) and Nevada Horne (right) presented the award.
In the Spring 2015 semester, several Ph.D. candidates successfully presented and defended their dissertations, including Liping Yang, Qinghan Liang, Francis Neville, and Lisa Walton. The Abstracts of these dissertations are below, and full texts are available for review in written form through the School of Computing and Information Science, and will soon be available for download at http://www.library.umaine.edu/theses/.
“Theories and Models of Indoor Space”
Liping Yang (Advisor: Dr. Kate Beard)
Although people’s daily lives are situated in both outdoor and indoor space, they actually spend most of their time in indoor environments. However, traditional geospatial science focuses mainly on outdoor space. Also, when we consider informatic assistance for the task of navigation, GPS has been indispensable for assisting navigation in outdoors. Up to now, navigation assistance for indoor space is much less well developed. But applications need to consider both outdoor and indoor spaces. Therefore, research on providing theories and models of indoor space is necessary.
This dissertation provides the development of theories and models representing the structure of indoor space and supporting navigation within it. A fundamental technique used is ontology development, which is the computerized specification of the meaning of terms used in specific domains. After investigating the similarities and differences between outdoor and indoor spaces in the context of navigation, the ontology of indoor space that can be integrated with outdoor space is developed. Four levels of ontologies are constructed based on the idea of modularization: upper ontology (the most general concepts), domain ontologies (concerned with the specific structure of the spaces), navigation task ontology, and application ontologies (specific user types and applications). We also work on making extensions to existing formal spatial models and developing related computational algorithms. A pure topological structure combinatorial map is extended to consider geometric information. A new formal concept dual map is proposed in order to make a correct dual connection between the structure of an indoor space and its navigation construct. Using the theories and algorithms developed in this research, we develop an approach to automatic construction of navigation graphs (underlying data structures that systems use to support human wayfinding) from building plans. The approach integrates topology, geometry, and semantics.
To demonstrate and evaluate the proposed navigation graph generation approach, a case study was developed using OpenStreetMap (OSM) based on Boardman Hall and its surroundings, which serves as a test-bed for the developed constructions and algorithms. A simple human subject experiment was also conducted to partially evaluate the case study. Ontologies are also evaluated by the developed case study. The results of the case study and human experiment showed that the generated navigation graph provides a collection of appropriately positioned navigation nodes, as well as appropriate connecting navigation edges.
“Towards The Continuous Spatio-Temporal Field Model for Sensor Data Streams”
Qinghan Liang (Advisor: Dr. Silvia Nittel)
Today, with the availability of inexpensive, wireless enabled sensor nodes, we encounter a massive amount of geo-referenced sensor streams, which are collected continuously, spatially dense, and in real-time. Continuous geographic phenomena such as pollen distribution, extreme weather events, a toxic chemical leak or radioactive fallout now can be observed live and needs to be analyzed in real-time. However, the high volume of continuous sensor data streams pushes the capabilities of traditional sensor data management beyond their limits. Over the last decade, data stream engines (DSE) have been introduced as data management technology, which provide real-time query support for applications with very high throughput rates. However, users are better supported if they would be able to interact with higher-level abstractions of the real-world phenomena, rather than analyzing observations based on individual measurement streams. Dealing with individual streams requires that users need to write code that not only copes with the real-time nature of streams but also that fact that the streams need to be integrated and analyzed, continuously, which is a non-trivial task.
This dissertation introduces the Stream Field Data Model, a DSE data model extension that is based on the concept of a field to represent continuous phenomena over space and time and is formally integrated with the relational and relational-based stream models. Using the high-level abstraction of fields provides an easy-to-use, flexible, mathematically defined and concise data model support for both sensor data streams as well as continuous phenomena. Furthermore, a Stream Query Language for the Field Stream Data Model is proposed with a novel set of stream query operators specifically for spatio-temporal fields. The approach is to lift relational operator to fields, and the semantics of this set of operators are discussed and formalized.
The feasibility of extending DSE for visualizing fields in near real-time based on 100,000 of streams has been investigated. This dissertation proposes and evaluates different strategies to optimize a pipelined stream operator framework to achieve near real-time spatial interpolation throughput, considering the memory footprint, runtime efficiency and interpolation quality.
“Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models”
Francis Neville (Advisor: Dr. Silvia Nittel)
As sensors become increasingly compact and dependable in natural environments, spatially-distributed heterogeneous sensor network systems steadily become more pervasive. However, any environmental monitoring system must account for potential data loss due to a variety of natural and technological causes. Modeling a natural spatial region can be problematic due to spatial nonstationarities in environmental variables, and as particular regions may be subject to specific influences at different spatial scales. Relationships between processes within these regions are often ephemeral, so models designed to represent them cannot remain static. Integrating temporal factors into this model engenders further complexity.
This dissertation evaluates the use of multilayer perceptron neural network models in the context of sensor networks as a possible solution to many of these problems given their data-driven nature, their representational flexibility and straightforward fitting process. The relative importance of parameters is determined via an adaptive backpropagation training process, which converges to a best-fit model for sensing platforms to validate collected data or approximate missing readings. As conditions evolve over time such that the model can no longer adapt to changes, new models are trained to replace the old.
We demonstrate accuracy results for the MLP generally on par with those of spatial kriging, but able to integrate additional physical and temporal parameters, enabling its application to any region with a collection of available data streams. Potential uses of this model might be not only to approximate missing data in the sensor field, but also to flag potentially incorrect, unusual or atypical data returned by the sensor network. Given the potential for spatial heterogeneity in a monitored phenomenon, this dissertation further explores the benefits of partitioning a space and applying individual MLP models to these partitions. A system of neural models using both spatial and temporal parameters can be envisioned such that a spatiotemporal space partitioned by k-means is modeled by k neural models with internal weightings varying individually according to the dominant processes within the assigned region of each. Evaluated on simulated and real data on surface currents of the Gulf of Maine, partitioned models show significant improved results over single global models.
“Bigraphs for Goal-directed Indoor Navigation”
Lisa Walton (Advisor: Michael Worboys)
Formal models of indoor space for reasoning about pedestrian navigation tasks should capture key static and dynamic properties and relationships between agents and indoor spaces, and provide an effective framework for reasoning about change. Of particular interest are changes in properties or relationships that affect an agent’s ability to carry out a goal directed navigation task. We focus on changes that occur in response to key indoor events, especially those that modify locality or connectivity relationships between agents and physical or functional spaces. This thesis presents a framework for formally representing indoor environments, the events that occur in them, and their effects on the topological properties and relationships between indoor spaces and agents. The main goal is to provide a computational foundation for qualitative spatiotemporal reasoning about indoor pedestrian navigation by modeling the effects of key indoor events on agent behaviors and relationships in indoor environments. To this end, we capture important aspects of agent wayfinding behaviors in the context of spatial image schema, which are abstractions of spatiotemporal perceptual patterns (e.g., an agent can perceive a room as a CONTAINER that she can move IN and OUT of), and spatial affordances, which are objectively measurable actions an agent can take given their current capabilities and environment (e.g., a stairway affords an ambulatory adult the ability to move between floors). The framework has three major components: (i) an indoor space ontology, (ii) an indoor bigraph model, and (iii) an indoor event calculus.
The indoor space ontology formally captures the typology of agents, objects, places and events that are utilized in the indoor bigraph model and event calculus. The indoor bigraph model provides formal algebraic specifications of indoor environments that independently represent agent and place locality (e.g., building hierarchies) and connectivity (e.g., path based navigation graphs). A typed indoor event calculus provides a logic-based formalism for representing the effects of indoor space events on key indoor relationships. By defining indoor fluents IN and LINKED with associated events INTO, LINK, and UNLINK and appropriate effect axioms we construct narratives about indoor navigation tasks as potential sequences of events and their consequences in indoor environments. For example, given an agent’s starting situation and a particular goal-directed navigation task we determine potential sequences of events that would lead to satisfying her goal (e.g., if a fire occurs in the building, how can she straightforwardly reach an exit?). Next, we show that the indoor bigraph model together with the indoor event calculus can be used to describe scenes and narratives with incomplete information, and that bigraph composition and joining operations can be used to adjust the granularity of scenes and to compose partial scenes to provide additional context. Finally we evaluate the framework by implementing the indoor event calculus in an abduction planner and running case studies based on human subject experiments designed for a companion study (NSF grant IIS-0916219) to determine if effective goal-directed navigation plans can be derived for an indoor environment. The goal is to evaluate the thesis framework by comparing the planning narratives produced by reasoning in the calculus to actual navigation strategies produced by humans or software agents in the experimental settings.
Dr. Roy Turner will be presenting three papers co-authored with others within the School of Computing and Information Science at the Ninth International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT’15) in Cyprus in early November. These papers include:
As part of the Springer Series on AI, all three accepted submissions will be published in the Proceedings of the Ninth International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT’15), Lecture Notes in Artficial Intelligence, H. Christiansen et al. (eds.), November 2-6, 2015, Lanarca, Cyprus.
Dr. Torsten Hahmann (Link ) co-organized the Joint Ontology Workshops held at the premier Artificial Intelligence conference, IJCAI, recently held in Buenos Aires. The four workshops included:
Workshop on Modular Ontologies (WoMO)
Formal Ontologies for Artificial Intelligence (FOfAI)
Ontologies and logic programming for query answering
Workshop on Belief Change and Non Monotonic Reasoning in Ontologies and Databases
Details on the workshops and the conference may be found at http://www.iaoa.org/jowo//.