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A semantic context model for managing privacy on smartphones
No full textIn this work we describe an approach to protecting the privacy and security of user data on mobile devices using a richer semantic model of a user's context. Mobile OS frameworks like Android always lacked mechanisms for dynamic privacy control, while recent advances in context modeling, tracking and collaborative localization has led to the emergence of a new class of smartphone applications that can access and share embedded sensor data. Existing literature on context based privacy and security has predominantly focused on device user's context for probing privacy vulnerability and enforcing security at runtime. We bring into picture the most important component of privacy vulnerability on smartphones, the resident applications themselves and we introduce the novel idea of application provenance. Our context model is realized as a dynamic knowledge base of RDF [67] triples grounded in an ontology in the semantic web language OWL. Policies in the form of rules over this knowledge base monitor and control application access to sensitive information and sensor data. The policies filter data flowing from sensor resources to applications to reduce disclosure by generalizing or obfuscating data. Our ontology includes the ability to represent application provenance and other metadata that can be used by the policies. The resulting system provides fine-grained, context-dependent control to sensitive user data
FATIGUE AND FRACTURE PROPERTIES OF HUMAN CORONAL DENTIN: AGING, ETHNICITY AND MICROSTRUCTURE.
No full textFailures of restorations resulting from tooth fracture are one of the primary problems to lasting oral health. Fatigue cracks are often present in the dentin of restored teeth and their incidence increases with age of the patient. The primary objective of this investigation was to characterize the influence of microstructure, age and ethnicity on the crack growth resistance of human coronal dentin. Compact tension (CT) specimens were prepared from three regions (i.e. inner, middle and outer dentin) of the crown of 3rd molars obtained from young and old patients of the US and Colombia. Stable crack extension was achieved under Mode I quasi�static and cyclic loading. To understand the fundamental mechanisms of crack growth extension, the fracture surfaces were evaluated using scanning electron microscopy (SEM) and image processing techniques. Metrics related to the microstructure were correlated with the fatigue and fracture responses. Lastly, a hybrid approach was also used to quantify the contribution of toughening mechanisms to the overall toughness. Results from the fatigue crack growth experiments showed that deep dentin exhibited the lowest resistance to the initiation of cyclic extension, as indicated by the stress intensity threshold (DKth=0.8 MPa*m0.5), and the highest fatigue crack growth rate. Cracks in the inner dentin underwent incremental extension under cyclic stresses 40% lower than that required in peripheral dentin and grew 1,000 times faster than in peripheral dentin. In addition, the average fatigue crack growth rates increased significantly with tubule density, and with increasing patient age. There was no significant difference between the fatigue crack growth resistance of middle dentin obtained from patients of the US and Colombia. Results from the monotonic crack growth experiments showed that coronal dentin undergoes an increase in the growth resistance with crack extension. Stable crack growth initiated at a stress intensity of 1.35 MPa*m0.5 or greater, and the growth toughness ranged from 0.5 to 2.2 MPa*m0.5. The initiation toughness for outer dentin was approximately 60% higher than that for inner dentin. Furthermore, the fracture toughness of inner dentin (2.2�0.5 MPa*m0.5) was significantly lower than that of middle (2.7�0.2 MPa*m0.5) and outer dentin (3.4�0.3 MPa*m0.5). Overall, cracks oriented perpendicular to the dentin tubules exhibited the lowest crack growth resistance for both quasi-static and cyclic loading. Extrinsic toughening, composed mostly of crack bridging, was estimated to cause an average increase in the fracture energy of 26% in all three regions. The microstructural analysis revealed that a combination of toughening mechanisms were present during crack extension including, micro-cracking of the peritubular cuffs, crack bridging, branching and curving. The potency of these mechanisms of toughening varies with lumen occlusion, density and orientation. Based on these findings, dental restorations extended into deep dentin are much more likely to cause tooth fracture due to the decrease in the crack growth resistance with depth and greater potential for introduction of flaws. That knowledge will help to recognize the critical aspects of the present restorative treatments on the incidence of tooth fracture, and the possible need for new approaches in treatment of senior patients
Perceptions of School Climate, Psychological Sense of Community, and Gay-Straight Alliances: A Mixed Method Examination
No full textThe high prevalence of anti-LGBTQ (lesbian, gay, bisexual, transgender, and queer/questioning) bullying in U.S. schools is extreme and commonplace. LGBTQ students - and heterosexual students who are perceived to be gay - routinely experience discrimination and sexual prejudice. This mixed method study examined student perceptions of school climate, psychological sense of community (PSOC), and Gay-Straight Alliances (GSAs) in U.S. high schools. Data was analyzed to explore whether students from high schools with GSAs report a more positive general school climate, less homophobic school climate, and higher PSOC than students from high schools without GSAs, regardless of their sexual orientation. Quantitative findings of mean differences between participants from high schools with and without GSAs were nonsignificant, however, meaningful qualitative themes emerged that helped to contextualize these results. Qualitative analysis yielded themes related to geographical factors, values of those in power, GSA effectiveness, multiple PSOC, covert and overt homophobia, and bystander intervention. The mixed method findings provided a nuanced understanding of students' perceptions and experiences
A specialist approach for the classification of column data
No full textUnderstanding the tables on the web and creating knowledge repositories from them is a topic widely researched. By classifying the columns of the table, we can relate context to it helping understand what the table data represents. Our work deals with classification of columns into an appropriate class. We describe a `specialist' approach for classification in which different specialists work together to come up with a ranked list for the given input column. We use three types of specialists, namely- regular expressions based, dictionary based and classifier based. We discuss a serial and parallel framework for the specialists. We evaluate our system in two ways- by testing individual specialist for accuracy and by testing the performance of the overall system in terms of generation of ranked list. We also discuss the scalability of the system in terms of addition of new specialists and performance impact for systems with hundreds of specialists
ESSAYS ON THE ECONOMICS OF PRESCRIPTION DRUGS: PRICE ELASTICITY OF DEMAND AND IMPACT OF ADHERENCE
No full textABSTRACT By 2014, national health expenditures are expected to exceed 290 billion annually (NEHI, 2009). This dissertation is comprised of three essays that examine the economics of prescription drugs. Specifically, the value of medication adherence is estimated in terms of its offsetting effects on the utilization and costs of other health services. The impact of adherence on worker absenteeism and short-term disability is also investigated. Finally, since patient cost-sharing is often cited as a key compliance barrier, the price elasticity of demand for prescription drugs is rigorously measured to gain insight into whether or not Value Based Insurance Design represents a cost-effective tool for improving medication adherence
Making English Grammar Meaningful and Useful Mini Lesson #17
No full textIf Clauses, Present, Past, English GrammarThis lesson was developed by John Nelson and Tymofey Wowk, 2012
Making English Grammar Meaningful and Useful
Mini Lesson #17
If Clauses: Native Speakers Don’t Know Them Very Well
The purpose of this lesson is to describe two kinds of If Clauses used with reference to both
present time and past time.
There are 2 kinds of If Clauses: CONTRARY-TO-FACT Clauses and CONDITIONAL
Clauses.
CONTRARY-TO-FACT If Clauses are used to express ideas that are the opposite of reality.
In the following example, the man is not rich.
If he were rich, he would take a long vacation.
CONDITIONAL If Clauses are used to describe situations which are not known for sure. In
the following sentence, the man may be on vacation or he may not be.
If he is on a vacation, I hope he is enjoying it.
The time expressed by the If Clauses in these two sentences is the present. In the first sentence,
he is not rich now. In the second sentence, he might be on vacation now and he might not be.
The difference in meaning is conveyed by the use of ‘were’ in the first sentence and the use of
‘is’ in the second sentence. “If he were” means “He is not”, and “If he is” means “Maybe he
is, maybe he is not”.
Similar If Clauses can be used in sentences about the past.
This CONTRARY-TO-FACT sentence is used to describe a situation that did not happen. She
was not at the meeting and she did not chair the meeting.
If she had been at the meeting, she would have chaired it.
This CONDITIONAL sentence is used to suggest what might have happened, but it is unknown.
If she was at the meeting, I am sure she expressed her opinion.
The time expressed in the If Clauses of these two sentences is the past. In the first sentence, she
was not at the meeting. In the second sentence, maybe she was at the meeting, maybe she
This lesson was developed by John Nelson and Tymofey Wowk, 2012
was not at the meeting. The difference in meaning in these sentences is conveyed by the use of
‘had been’ in the first sentence and of ‘was’ in the second sentence.
The two kinds of IF Clause sentences are presented in the following Chart. Notice that each
sentence has an IF Clause followed by a Main Clause.
KINDS OF IF CLAUSE SENTENCES
Time Contrary-To-Fact Conditional
Present
Time
If she wrote it,
it would be well written.
(She doesn’t write it.)
If she writes it,
it is well written.
(Maybe she writes it, maybe not.)
Past
Time
If she had written it,
it would have been well written
(She didn’t write it.)
If she wrote it,
it was well written
(Maybe she wrote it, maybe not.)
With CONTRARY-TO-FACT If Clauses, the Main Clauses usually use Modal Helping
Verbs. In CONDITIONAL If Clauses, the Main Clauses do not necessarily use Modal
Helping Verbs.
IF Clause sentences can mix times between the If Clause and the Main Clause. This usually
occurs when the IF Clause refers to a past time, and the Main Clause refers to a present time.
Both CONTRARY-TO-FACT and CONDITIONAL If Clause sentences can have mixed
times as shown in these examples.
If he had gone yesterday, he would be telling me about it now.
(He didn’t go yesterday, and he is not telling me about it now.)
If he went there yesterday, he is probably on his way back now.
(Maybe he went there yesterday, and if so, he is coming back now.)
If Clauses are very difficult to use correctly. Native speakers of American English frequently do
not use them correctly
Proteomic Identification and Biological Validation of Novel Breast Cancer MHC II Peptide Vaccine Candidates and Immunosuppressive Mechanisms
No full textBreast cancer is readily treatable at early stages; however few if any treatments exist for patients diagnosed with late stage metastatic disease. Because CD4+ T cells are crucial for long term immunologic memory and may prevent or eliminate dissemination of latent metastases, this thesis is focused on identifying novel immunogenic major histocompatibility class (MHC) II-restricted peptides that activate tumor-specific CD4+ T cells. MHC II cell-based vaccines (human breast cancer cells transduced with MHC II and costimulatory molecules) efficiently activate healthy donors' and breast cancer patients' CD4+ T cells, provided vaccines do not express chaperone protein Invariant chain (Ii). Therefore, it was hypothesized that in the absence of Ii, novel immunogenic MHC II-restricted peptides would be presented by tumor cells. To prove this hypothesis, MHC II-restricted peptides from Ii- vaccine cells and Ii+ tumor cells were sequenced using mass spectrometry-based peptidomics. Four hundred and thirty peptides were identified, 92 of which were uniquely presented by Ii- MHC II cell-based vaccines. Seven of these peptides efficiently activated tumor-specific T cells from healthy donors and breast cancer patients. Therefore, these studies established that Ii regulates the peptide repertoire presented by MHC II+ tumor cells and identified peptides that are potential candidates for breast cancer vaccines. Most breast cancer patients are immune suppressed, and therefore may be restricted in their ability to respond to vaccine immunotherapy. Among the most potent mediators of tumor-induced immune suppression are myeloid-derived suppressor cells (MDSC). Heightened inflammation exacerbates MDSC accumulation and immune suppressive potency. To understand the mechanisms by which inflammation regulates MDSC, the proteins and cellular pathways of MDSC induced in less inflammatory conditions (conventional MDSC) and in highly inflammatory conditions (inflammatory MDSC) were compared using mass spectrometry. Pathway analysis and biological experiments revealed that inflammation enhanced MDSC accumulation by making MDSC less susceptible to apoptosis. Collectively, these studies indicate that immunization with the immunogenic MHC II-restricted peptides while targeting inflammatory pathways that cause MDSC resistance to apoptosis may be a promising approach for breast cancer immunotherapy
APPROACH TO UNWRAP A 3D FINGERPRINT TO A 2D EQUIVALENT FINGERPRINT
No full textFingerprints are the most widely used biometric feature for human identification because of their accuracy and uniqueness. Traditional fingerprint acquisition techniques are contact based and result in poor quality images. The new generation of non-contact based scanners capture high resolution and detailed 3D fingerprint scans, which addresses many of the problems of traditional fingerprint acquisition techniques. The majority of existing fingerprint databases available today are 2D, so there is a need for backward compatibility for the 3D scans captured. In order to solve this interoperability issue, I present an algorithm to unwrap the 3D fingerprint to its 2D equivalent image to be used in an Automatic Fingerprint Identification System
Real-Time Progressive Band Processing for Linear Spectral Unmixing and Endmember Extraction
No full textHyperspectral imaging systems generate large volumes of data that contain the subtle yet critical knowledge their users need, if they are processed intelligently. Band selection algorithms attempt to separate image bands with high-quality information from those with irrelevant noise. They are a pre-processing filter applied before several other classes of algorithms to avoid the Hughes phenomenon, of diminishing (and even negative) returns caused by supplying those algorithms with too much data. Effective band selection enables real-time and band-progressive image processing algorithms. Real-time algorithms are ones that process image data at an equal or faster rate than it is acquired, analyzing one pixel before the next one arrives from the imaging device. Band-progressive algorithms process entire image subsets at once, then use the results to process a slightly modified subset with small numbers of bands added or removed. This dissertation develops three major technologies. Progressive Band Selection (PBS) is a method to select the most valuable bands of a hyperspectral image according to a changeable, application-specific criterion, and compose a reduced image subset. Real-time linear unmixing algorithms use bands from this subset to determine the abundance of physical materials in each pixel, where the materials are represented by their spectroscopic profile (spectra). This implies that material spectra data is separate from the image data, and the spectra are available before the image is acquired. However, band-progressive endmember extraction algorithms are designed to detect image pixels that represent pure material spectra, negating the need for a separate library. Each technology is described both from a theoretical standpoint and in terms of their practical implementation, and experiments are performed to demonstrate their value relative to the state of the art
A Trust and Reputation Mechanism Through Behavioral Modeling of Reviewers
No full textTrust and reputation have become important topics in various domains, such as online markets, supply chain management, auctions, social networks, and e-commerce applications. With the significant increase in transactions with people and organizations, especially in online markets, people need to interact with strangers with whom they have few or no previous interactions. Reputation information in the form of word of mouth (i.e., a mechanism for propagating knowledge or opinion (Abdul-Rahman & Hailes 2000)) in auctions and supply chain management, and in the form of provided reviews and ratings on online websites, are two different sources of useful information that can help model trust, in order to mitigate the risk of interacting with an unknown individual. In providing reputation information, people can have different behaviors, such as being biased based on incentives or they can have different preferences and viewpoints. Therefore, in modeling trust and reputation, it is critical to learn the behavior of people who are providing reputation information in order to select trustworthy partners. Such a reputation mechanism is missing from the current trust and reputation literature. In this dissertation, I introduce a novel trust and reputation mechanism that models and learns a reputation provider's behavior using probability theory. This learned behavior is then used to re-interpret the provider's reputation information, thus making use of the entire reputation data effectively, even if the information is biased or based on personal viewpoints and preferences. I show the importance of learning the behavior of reputation providers using different patterns of being biased or having different preferences and satisfaction thresholds in three different settings: an iterated prisoner's dilemma scenario from game-theory, an online rating website, and an online marketplace. My results show that learning the behavior of reputation providers in each of these settings helps individuals to more effectively aggregate and adjust reputation information in order to make decisions, thereby increasing their satisfaction and overall payoffs in their interactions