1.
16 February 2007

2. UMBC and Ebiquity
UMBC is a research extensive University with a major focus on Information Technology
Ebiquity is a large and active research group with the goal of
“Building intelligent systems in open, heterogeneous, dynamic, distributed environments”
Current research includes mobile and pervasive computing, security/trust/privacy, semantic web, multiagent systems, advanced databases, and high performance computing
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3. CSEE @ UMBC Computer Science & Electrical Engineering
UMBC’s largest department with 45 faculty, ~1000 undergrad, ~200 grad students
Degree programs (graduate and undergraduate)
Computer Science, Computer Engineering, Electrical Engineering
Among research universities, UMBC is #1 in BS CS production and #18 in PhD production
Many institutes, centers and labs
Institute for Language and Information Technology, Center for Information Security and Assurance, Center for Photonics, Lab For Advanced Information Technology, VLSI Lab, CADIP, …
Breadth and focus in research areas
~ $5M/year in sponsored research from Government and Industry
Pervasive computing, AI, security, information retrieval, graphics, databases, VLSI, …
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4.
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5. People and funding Faculty: Finin, Yesha, Joshi, Peng, Halem
Colleagues: Pinkston, Segall, …
Students: ~10 PhD, ~10 MS, ~5 undergrad
Funding:
Current: DARPA (Trauma Pod, STTRs), NSF (two ITRs, Cybertrust, NSG, …), Intelligence community, NASA, NIST, Industry (IBM, Fujitsu, …)
Recent: DARPA (CoABS, GENOA II, DAML), NSF (CAREER)
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6. Ebiquity Research SpaceKR
information
extraction
user modeling
semantic web
machine
learning IR AI
data
mining
Intelligent Information Systems
web services/SOC DB
knowledge management
wearable computing
mobility
policies
HPCC
assurance
Networking & Systems
wireless
Security
trust
context awareness
DRM
pervasive computing
intrusion detection
privacy
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7. Ebiquity Research Space
language technology
robotics
HCI
planning KR
Building intelligent systems in
open, heterogeneous, dynamic, distributed environments
user modeling
semantic web
data
mining
machine
learning AI DB
Intelligent Information Systems
knowledge management
web services IR
service oriented computing
wearable computing
policies
Networking & Systems
wireless
Security
mobility
assurance
context awareness
pervasive computing
intrusion detection
privacy
trust
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8. Some Current Projects Managing spam in social media
Influence and opinions in social media
Recognizing communities using opinionated links
The Semantic Web
Location based advertising
Trustworthy data management in ad hoc networks
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9. Spam in the Blogosphere
Pranam Kolari
Ph.D. Candidate

10. Spam in the Blogosphere
Types: comment spam, ping spam, spam blogs
Akismet: “87% of all comments are spam”
75% of update pings are spam (ebiquity 2005)
20% of indexed blogs by popular blog search engines is spam (Umbria 2006, ebiquity 2006)
“Spam blogs, sometimes referred to by the neologism splogs, are weblog sites which the author uses only for promoting affiliated websites”
“Spings, or ping spam, are update pings sent from spam blogs”
1Wikipedia
Filip Perich
5/1/2019

11. Plagiarized/Scraped Content
Profitable Contexts
Plagiarized/Scraped Content
Filip Perich
5/1/2019

12. Spam Pings –
Inlets to Search Index
Filip Perich
5/1/2019

13. Source of Splogs
“Honestly, Do you think people who make $10k/month from adsense make blogs manually? Come on, they need to make them as fast as possible. Save Time = More Money! It's Common SENSE! How much money do you think you will save if you can increase your work pace by a hundred times? Think about it…”
“Discover The Amazing Stealth Traffic Secrets Insiders Use To Drive Thousands Of Targeted Visitors To Any Site They Desire!”
“Holy Grail Of Advertising... “
$ 197
“Easily Dominate Any Market, Any Search Engine, Any Keyword.”
Filip Perich
5/1/2019

14. Splog Detection – Home Page Based
top features
blogs splogs
SVM based probabilistic splog detection (Kolari et al., 2006)
Hand verified training set of blogs and splogs
SVM Based Model
Bag-of-words
Binary Features
Linear Kernel
Precision/Recall of 87%
Bag-of-words based feature using text on blog home-page
Published at AAAI 2006 we
what
was my
org
flickr
paper
words
weblog
motion me
thank go
january
trackback
archives
political
find
info
news
your
another
website
best
articles on
perfect
products
uncategorized
hot
resources
inc
three
copyright
Filip Perich
5/1/2019

15. 56% of all blogs are splogs! Silicon Valley and Splog Valley?1.
Mountain View, CA 2.
Washington DC 3.
San Francisco, CA 4.
Orlando, FL 5.
Lansing, MI
High PPC contexts are primary spam drivers
auto buy california cancer card casino cheap consolidation credit debt diet discount equipment estate finance florida forex free gift golf health hotel insurance jewelry lawyer loan loans medical money mortgage new online phone poker rental sale software texas trading travel used vacation video wedding
Filip Perich
5/1/2019

16. Influence and Opinions in Social Media
Akshay Java
Ph.D. Candidate

17. Biz Intelligence from Social Media
“Social media describes the online tools and platforms that people use to share opinions, insights, experiences, and perspectives with each other.” – Wikipedia, Feb 07
Social Media is a dynamic and growing area, that includes blogs, wikis, forums, photo and video sharing sites, etc.
Goal: Building scalable, solutions and frameworks for analyzing high volumes of blog data to derive business intelligence
Filip Perich
5/1/2019

18. Knowing & Influencing your Market
Your goal is to market Apple’s iPod phone
How can you track the buzz about it?
What are the relevant communities and blogs?
Which communities are fans, which are suspicious, which are put off by the hype?
Is your advertising having an effect? The desired effect?
Which bloggers are influential in this market? Of these, which are already onboard and which are lost causes?
To whom should you send details or evaluation samples?
Filip Perich
5/1/2019

19. Influence Detection Influence Detection Influence, bias in MSM
Often buyers look for opinions and reviews on blogs
Detecting influential nodes and their role in how people perceive a service is an important tool for marketing
Using topic, social structure, opinions, biases and temporal information we can develop an accurate model for influence
Influence, bias in MSM
Top Democrat MSM Sources
Top Republican MSM Sources
Filip Perich
5/1/2019

20. Opinions in Social Media
TREC 06: Finding opinionated posts, either positive or negative, about a query
2006 TREC Blog corpus:
80K blogs
300K posts
50 test queries
Challenges: open domain sentiment words, slangs, subject
Reader’s Perspective
“Starbucks Sandwiches are bad!”
“I went to school early so I would have time to grab some lunch. Which ended up consisting of a crappy sandwich from starbucks and a chai latte. Lacey came into Starbucks while I was there so we chatted for a little bit and she thought that I might be in her class. After I finished eating I headed to school and checked the board……..”1
Narrative
Expressed Opinions
Opinions can effect buying decisions of customers
[1]
Filip Perich
5/1/2019

21. Finding Feeds That Matter
Analysis of Bloglines Feeds
83K publicly listed subscribers
2.8M feeds, 500K are unique
26K users (35%) use folders to organize subscriptions
Data collected in May 2006
Before Merge
Top Advertising Feeds
1. Adrants » Marketing and Advertising News With Attitude
2. Adverblog: advertising and new media marketing 3.
4. adfreak
5. AdJab
6. MIT Advertising Lab: future of advertising and advertising technology
7. AdPulp: Daily Juice from the Ad Biz
8. Advertising/Design Goodness
Related Tags: advertising  marketing  media  news  design 
After Merge
Filip Perich
5/1/2019

22. Anubhav Kale M.S Student
Link Polarity
Anubhav Kale
M.S Student

23. Modeling Influence Using Link Polarity
Motivation
Growing interest in exploring role of communities in social media
Community detection algorithms rely more on link structure and less on sentiment associated with links
Convert a sparsely connected blog graph into a densely connected graph with sentiment weight attached to every link
Approach
Link Polarity : Analyze text surrounding html links in blog posts to determine bias of bloggers about each other
Trust Propagation: Use trust propagation models to spread the polarity from a small subset of “connected” bloggers to all bloggers.
Experiments
Political blogosphere as a specific domain of study. Goal is to divide the dataset of political blogs into left and right leaning blogs.
Detection of leaning based on positive/neural/negative score from influential bloggers (high in-link blogs) in both communities
Validation with a hand-labeled dataset indicates around 60% correct classification
Filip Perich
5/1/2019

24. Anubhav Kale M.S Student
Semantic Web
Anubhav Kale
M.S Student

25. http://swoogle.umbc.edu/ Running since summer 2004
1.8M RDF docs, 320M triples, 10K ontologies, 15K namespaces, 1.3M classes, 175K properties, 43M instances, 600 registered users
Filip Perich
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26. Applications and Use Cases1
Supporting Semantic Web Developers
Ontology designers, vocabulary discovery, who’s using my ontologies or data?, use analysis, errors, statistics, etc.
Searching Specialized Collections
Spire: aggregating observations and data from biologists
InferenceWeb: searching over and enhancing proofs
Supporting Semantic Web Tools
Triple shop: finding data for SPARQL queries 2 3
Filip Perich
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27. What are body masses of fishes that eat fishes?
Swoogle Triple Shop
What are body masses of fishes that eat fishes?
At UMBC we have designed a TripleShop, a workshop for semantic web data.
We write a database query in a language called SPARQL
Essentially, we are writing patterns that will match the kinds of triples I just showed you.
Roughly, what we’re saying here is:
Find actual foodweb links and
consider the taxa that are predators and the taxa that are prey,
and make sure that these predators and prey are both fish,
and give me the maximum body sizes of these prey and their predators.
IF you are familiar with relational database queries, you might notice that I haven’t specified what data tables the data should come from.
. . . leaving out the FROM clause
Filip Perich
5/1/2019 29

28. Location Based Advertising
Olga Ratsimor
Ph.D. Candidate

29. Intelligent Marketing in Mobile Peer-To-Peer Environments eNcentive
MH2
MH1
MH3
Animated Slide
Filip Perich
5/1/2019

30. eNcentive: Targeting Locations
Filip Perich
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31. eNcentive + TrueBahn Trust based mobile shopping
Mobile users are divided into two groups
Kids and Adults
Groups are interlined
Kids have a circle of friends that can grow and shrink
Adults have circle of other Adults that they trust. The circle can grow and shrink.
Kids can shop for items that thy have been authorized to buy
If there is a new item on the wish list then kids need to find an Adult from their trust network to give them authorization.
Filip Perich
5/1/2019

32. Bartering of Digital Goods and Services in Pervasive Environments
Context-based, continuous bartering with peers in pervasive environments
Value in Use - is the value of the particular electronic good or service for the particular user.
Value in Exchange - reflects the potential value of the service against any other service in value-for-value exchanges.
Effects of collaborative strategies on the welfare of the network
Effects of digital rights management restrictions on bartering collaborations
Exploiting relationships between groups of devices
Filip Perich
5/1/2019

33. Jesse English and Sandor Dornbush
With help from: Dr. Tim Oates, Dr. Anupam Joshi, Dr. Zary Segall & Chad Eby.
Filip Perich
5/1/2019

34. Activity Aware Music Feedback loop between the user and their music.
Use the users physiological state to influence the music selection.
The music influences the users mood and mindset.
Filip Perich
5/1/2019

35. Current XPod Platform Nokia 5500 Sport Phone Embedded Accelerometers
MP3 Player
Filip Perich
5/1/2019

36. Current Projects Incorporate song metadata
Human Generated, eg Last.fm
Machine Generated
Incorporate other meta information
Location Information
Recent Phone Calls
Weather
Filip Perich
5/1/2019

37. Advertising Potential
“Cellphone Ads May Take Off Soon,” New York Times Febuary 14th, 2007
Target adds based on activity profiles.
Send running adds to somebody who runs.
Target adds based on musical tastes.
Truck adds for somebody who listens to country.
Filip Perich
5/1/2019

38. Trustworthy Data Management in Ad Hoc Networks
Anand Patwardhan
Ph.D Candidate
Jim Parker
Ph.D Candidate

39. Situation Awareness allows Adaptation
GPS satellite
Localized and distributed
Wireless
Access points
Hazard warnings,
Detours,
Inclement weather,
Road conditions,
Traveler info.
Localized Info-Stream Services
Various forms
of connectivity
Location
& directions
GSM, GPRS,
EDGE, E-VDO
WiMax
GPS
VANET
connectivity
Update propagation
Onboard Computer
with various sensors:
GPS location
Cameras
Engine Condition
Tire pressure etc.
Situation Awareness allows Adaptation
Filip Perich
5/1/2019

40. Securing MANETs Security for resources Trust in other resources
Malicious behavior (Activity monitoring)
Misuse (Resource protection)
Response/recourse (Accountability)
Trust in other resources
Dependence on recommendations (Identities and Reputations)
Reliability of information
Filip Perich
5/1/2019

41. Trust evolution, reputation management,
Cross-layer Analysis
Trust evolution, reputation management,
recourse
Intrusion
Detection
Application
Commendations
Accusations
(to other devices)
Packet dropping,
Mangling, injection
Transport
Routing attacks,
disruptions
Link
Unfair contention,
Jamming
MAC/PHY
Response
Filip Perich
5/1/2019

42. Simulation and Modeling
Identity
Cryptographic Addresses
Mobility, congestion, radio interference
Reduce false positives
Scalability
Large radio-ranges or dense networks
Aggregation of data
Communicate intrusions data to warn others
Filip Perich
5/1/2019

43. Other Current and Recent Projects

44. Other Current and Recent Projects
Pervasive and mobile computing
(1) Trauma Pod
(2) Context aware pervasive computing
(3) Mogatu: Tivo for mobile computing
Semantic Web
(4) Swoogle: searching and indexing Semantic Web data
(5) Semnews: text understanding and extraction
(6) Agents and the Semantic Web
(7) Spire: Semantic Web for data discovery and integration
Security and trust
(8) Semantic policy languages
(9) Semdis: Discovering Semantic Links
(10) Securing ad hoc networks
(11) Privacy for passive RFID tags
Information extraction and retrieval
(12) Recognizing spam weblogs
(13) Extracting opinions from weblogs
(14) Modeling the Spread of Influence on the Blogosphere
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45.
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46.
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