Trace Oblivious Program Execution

The big data era has dramatically transformed our lives; however, security incidents such as data breaches can put sensitive data (e.g. photos, identities, genomes) at risk. To protect users' data privacy, there is a growing interest in building secure cloud computing systems, which keep sensitive data inputs hidden, even from computation providers. Conceptually, secure cloud computing systems leverage cryptographic techniques (e.g., secure multiparty computation) and trusted hardware (e.g. secure processors) to instantiate a “secure” abstract machine consisting of a CPU and encrypted memory, so that an adversary cannot learn information through either the computation within the CPU or the data in the memory. Unfortunately, evidence has shown that side channels (e.g. memory accesses, timing, and termination) in such a “secure” abstract machine may potentially leak highly sensitive information, including cryptographic keys that form the root of trust for the secure systems. This thesis broadly expands the investigation of a research direction called trace oblivious computation, where programming language techniques are employed to prevent side channel information leakage. We demonstrate the feasibility of trace oblivious computation, by formalizing and building several systems, including GhostRider, which is a hardware-software co-design to provide a hardware-based trace oblivious computing solution, SCVM, which is an automatic RAM-model secure computation system, and ObliVM, which is a programming framework to facilitate programmers to develop applications. All of these systems enjoy formal security guarantees while demonstrating a better performance than prior systems, by one to several orders of magnitude.

MINING AND VERIFICATION OF TEMPORAL EVENTS WITH APPLICATIONS IN COMPUTER MICRO-ARCHITECTURE RESEARCH

Computer simulation programs are essential tools for scientists and engineers to understand a particular system of interest. As expected, the complexity of the software increases with the depth of the model used. In addition to the exigent demands of software engineering, verification of simulation programs is especially challenging because the models represented are complex and ridden with unknowns that will be discovered by developers in an iterative process. To manage such complexity, advanced verification techniques for continually matching the intended model to the implemented model are necessary. Therefore, the main goal of this research work is to design a useful verification and validation framework that is able to identify model representation errors and is applicable to generic simulators. The framework that was developed and implemented consists of two parts. The first part is First-Order Logic Constraint Specification Language (FOLCSL) that enables users to specify the invariants of a model under consideration. From the first-order logic specification, the FOLCSL translator automatically synthesizes a verification program that reads the event trace generated by a simulator and signals whether all invariants are respected. The second part consists of mining the temporal flow of events using a newly developed representation called State Flow Temporal Analysis Graph (SFTAG). While the first part seeks an assurance of implementation correctness by checking that the model invariants hold, the second part derives an extended model of the implementation and hence enables a deeper understanding of what was implemented. The main application studied in this work is the validation of the timing behavior of micro-architecture simulators. The study includes SFTAGs generated for a wide set of benchmark programs and their analysis using several artificial intelligence algorithms. This work improves the computer architecture research and verification processes as shown by the case studies and experiments that have been conducted.

Network Construction and Graph Theoretical Analysis of Functional Language Networks in Pediatric Epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient’s extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.

Digital literacy matters. Increasing workforce productivity through blended English language programmes

Current workplace demands newer forms of literacies that go beyond the ability to decode print. These involve not only competence to operate digital tools, but also the ability to create, represent, and share meaning in different modes and formats; ability to interact, collaborate and communicate effectively using digital tools, and engage critically with technology for developing one’s knowledge, skills, and full participation in civic, economic, and personal matters. This essay examines the application of the ecology of resources (EoR) model for delivering language learning outcomes (in this case, English) through blended classroom environments that use contextually available resources. The author proposes the implementation of the EoR model in blended learning environments to create authentic and sustainable learning environments for skilling courses. Applying the EoR model to Indian skilling instruction contexts, the article discusses how English language and technology literacy can be delivered using contextually available resources through a blended classroom environment. This would facilitate not only acquisition of language and digital literacy outcomes, but also consequent content literacy gain to a certain extent. This would ensure satisfactory achievement of not only communication/language literacy and technological literacy, but also active social participation, lifelong learning, and learner autonomy.

Using syntactic and semantic features for classifying mo- dal values in the portuguese language

This paper presents a study made in a field poorly explored in the Portuguese language – modality and its automatic tagging. Our main goal was to find a set of attributes for the creation of automatic tag- gers with improved performance over the bag-of-words (bow) approach. The performance was measured using precision, recall and F1. Because it is a relatively unexplored field, the study covers the creation of the corpus (composed by eleven verbs), the use of a parser to extract syntac- tic and semantic information from the sentences and a machine learning approach to identify modality values. Based on three different sets of attributes – from trigger itself and the trigger’s path (from the parse tree) and context – the system creates a tagger for each verb achiev- ing (in almost every verb) an improvement in F1 when compared to the traditional bow approach.

Towards Unstructured Knowledge Integration in Natural Language Processing

In the last decades, Artificial Intelligence has witnessed multiple breakthroughs in deep learning. In particular, purely data-driven approaches have opened to a wide variety of successful applications due to the large availability of data. Nonetheless, the integration of prior knowledge is still required to compensate for specific issues like lack of generalization from limited data, fairness, robustness, and biases. In this thesis, we analyze the methodology of integrating knowledge into deep learning models in the field of Natural Language Processing (NLP). We start by remarking on the importance of knowledge integration. We highlight the possible shortcomings of these approaches and investigate the implications of integrating unstructured textual knowledge. We introduce Unstructured Knowledge Integration (UKI) as the process of integrating unstructured knowledge into machine learning models. We discuss UKI in the field of NLP, where knowledge is represented in a natural language format. We identify UKI as a complex process comprised of multiple sub-processes, different knowledge types, and knowledge integration properties to guarantee. We remark on the challenges of integrating unstructured textual knowledge and bridge connections with well-known research areas in NLP. We provide a unified vision of structured knowledge extraction (KE) and UKI by identifying KE as a sub-process of UKI. We investigate some challenging scenarios where structured knowledge is not a feasible prior assumption and formulate each task from the point of view of UKI. We adopt simple yet effective neural architectures and discuss the challenges of such an approach. Finally, we identify KE as a form of symbolic representation. From this perspective, we remark on the need of defining sophisticated UKI processes to verify the validity of knowledge integration. To this end, we foresee frameworks capable of combining symbolic and sub-symbolic representations for learning as a solution.

Influence of English language in the number of citations of articles published in Brazilian journals of Ophthalmology

PURPOSE: To determine the association between language and number of citations of ophthalmology articles published in Brazilian journals. METHODS: This study was a systematic review. Original articles were identified by review of documents published at the two Brazilian ophthalmology journals indexed at Science Citation Index Expanded - SCIE [Arquivos Brasileiros de Oftalmologia (ABO) and Revista Brasileira de Oftalmologia (RBO)]. All document types (articles and reviews) listed at SCIE in English (English Group) or in Portuguese (Portuguese Group) from January 1, 2008 to December 31, 2009 were included, except: editorial materials; corrections; letters; and biographical items. The primary outcome was the number of citations through the end of second year after publication date. Subgroup analysis included likelihood of citation (cited at least once versus no citation), journal, and year of publication. RESULTS: The search at the web of science revealed 382 articles [107 (28%) in the English Group and 275 (72%) in the Portuguese Group]. Of those, 297 (77.7%) were published at the ABO and 85 (23.3%) at the RBO. The citation counts were statistically significantly higher (P<0.001) in the English Group (1.51 - SD 1.98 - range 0 to 11) compared with the Portuguese Group (0.57 - SD 1.06 - range 0 to 7). The likelihood citation was statistically significant higher (P<0.001) in the English Group (70/107 - 65.4%) compared with the Portuguese Group (89/275 - 32.7%). There were more articles published in English at the ABO (98/297 - 32.9%) than at the RBO (9/85 - 10.6%) [P<0.001]. There were no significant difference (P=0.967) at the proportion of articles published in English at the years 2008 (48/172 - 27.9%) and 2009 (59/210 - 28.1%). CONCLUSION: The number of citations of articles published in Portuguese at Brazilian ophthalmology journals is lower than the published in English. The results of this study suggest that the editorial boards should strongly encourage the authors to adopt English as the main language in their future articles.

Health Related Quality of Life Assessment questionnaire for children aged 5 to 11 years with HIV/AIDS: cross-cultural adaptation for the Portuguese language

The objective of this study is to describe preliminary results from the cross-cultural adaptation of the Quality of Life Assessment Questionnaire, used to measure health related quality of life (HRQL) in Brazilian children aged between 5 and 11 with HIV/AIDS. The cross-cultural model evaluated the Concept, Item, Semantic and Measurement Equivalences (internal consistency and intra-observer reliability). Evaluation of the conceptual, item, semantic equivalences showed that the Portuguese version is pertinent for the Brazilian context. Four of seven domains showed internal consistency above 0.70 (α: 0.76-0.90) and five of seven revealed intra-observer reliability (ricc: 0.41-0.70). This first Portuguese version of the HRQL questionnaire can be understood as a valuable tool for assessing children's HRQL, but further studies with large samples and more robust analyses are recommended before use in the Brazilian context.

Defining a role for the subthalamic nucleus within operative theoretical models of subcortical participation in language

Objective:To investigate the effects of bilateral, surgically induced functional inhibition of the subthalamic nucleus (STN) on general language, high level linguistic abilities, and semantic processing skills in a group of patients with Parkinson’s disease. Methods:Comprehensive linguistic profiles were obtained up to one month before and three months after bilateral implantation of electrodes in the STN during active deep brain stimulation (DBS) in five subjects with Parkinson’s disease (mean age, 63.2 years). Equivalent linguistic profiles were generated over a three month period for a non-surgical control cohort of 16 subjects with Parkinson’s disease (NSPD) (mean age, 64.4 years). Education and disease duration were similar in the two groups. Initial assessment and three month follow up performance profiles were compared within subjects by paired t tests. Reliability change indices (RCI), representing clinically significant alterations in performance over time, were calculated for each of the assessment scores achieved by the five STN-DBS cases and the 16 NSPD controls, relative to performance variability within a group of 16 non-neurologically impaired adults (mean age, 61.9 years). Proportions of reliable change were then compared between the STN-DBS and NSPD groups. Results:Paired comparisons within the STN-DBS group showed prolonged postoperative semantic processing reaction times for a range of word types coded for meanings and meaning relatedness. Case by case analyses of reliable change across language assessments and groups revealed differences in proportions of change over time within the STN-DBS and NSPD groups in the domains of high level linguistics and semantic processing. Specifically, when compared with the NSPD group, the STN-DBS group showed a proportionally significant (p

Adult language input in the early childhood educational setting

Age-related changes in the adult language addressed to children aged 2;0-4;0 years in polyadic conditions were investigated in Australian childcare centres. The language that 21 staff members addressed to these children was coded for multiple variables in the broad social categories of prosody, context, speech act and gesture. The linguistic components were coded within the categories of phonology, lexicon, morphology, syntax and referential deixis. Minimal age-related differences were found. Explanations for the similarity of the adult language input across the age groups within the early childhood educational environment, will be discussed

Hemispheric contributions to lexical ambiguity resolution: Evidence from individuals with complex language impairment following left-hemisphere lesions

Nine individuals with complex language deficits following left-hemisphere cortical lesions and a matched control group (n 5 9) performed speeded lexical decisions on the third word of auditory word triplets containing a lexical ambiguity. The critical conditions were concordant (e.g., coin–bank–money), discordant (e.g., river–bank–money), neutral (e.g., day–bank– money), and unrelated (e.g., river–day–money). Triplets were presented with an interstimulus interval (ISI) of 100 and 1250 ms. Overall, the left-hemisphere-damaged subjects appeared able to exhaustively access meanings for lexical ambiguities rapidly, but were unable to reduce the level of activation for contextually inappropriate meanings at both short and long ISIs, unlike control subjects. These findings are consistent with a disruption of the proposed role of the left hemisphere in selecting and suppressing meanings via contextual integration and a sparing of the right-hemisphere mechanisms responsible for maintaining alternative meanings.

Language disorders in dementia of the Alzheimer type

The language profile of a group of 18 Alzheimer patients is documented and their performance on a standard aphasia test battery compared to a group of institutionalized, nonneurologically impaired control subjects matched for age, sex, and educational level. The Alzheimer patients scored significantly lower than the controls in the areas of verbal expression, auditory comprehension, repetition, reading, and writing. Articulation abilities were the same in each group. A language deficit was evident in all Alzheimer patients. The language disorder exhibited resembled a transcortical sensory aphasia. Syntax and phonology remained relatively intact but semantic abilities were impaired. The results support the inclusion of a language deficit as a diagnostic criterion of Alzheimer's disease.