Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Disassembly using Dynamic Programming

Most commercial anti-virus software uses signature based techniques to detect whether a file is infected by a virus or not. However, signature based detection systems are unable to detect metamorphic viruses, since such viruses change their internal structure from generation to generation. Previous work has shown that hidden Markov models (HMMs) can be used to detect metamorphic viruses. In this technique, the code is disassembled and the resulting opcode sequences are used for training and detection. Due to the disassembly step, this process is not efficient enough to use when a decision has to be made in real time. In this project, we explore whether dynamic programming can be used to speed up the process of disassembling, with minimal loss of accuracy. Dynamic programming is generally used to solve problems having two key attributes: optimal substructure and overlapping sub problems. During each iteration our algorithm reads part of the input stream from the executable file and determines assembly instructions, thus dividing problems into sub problems. We have created a score matrix representing digraphs of the most common opcode instructions and we have implanted a dynamic program based on this scoring matrix. For various file sizes, we determine the time taken by our dynamic program and we show that our approach is significantly faster than a standard disassembler (OllyDbg). Finally, we analyze the accuracy of our results. ii Acknowledgements I would like to thank Dr. Mark Stamp for guiding and encouraging me throughout the project. I would also like to thank my committee members, Dr. Sami Khuri and Dr. Robert Chun for helping me during the project.
Calcitonin receptor-mediated CFTR activation in human intestinal epithelial cells

High levels of calcitonin (CT) observed in medullary thyroid carcinoma and other CT‐secreting tumours cause severe diarrhoea. Previous studies have suggested that CT induces active chloride secretion. However, the involvement of CT receptor (CTR) and the molecular mechanisms underlying the modulation of intestinal electrolyte secreting intestinal epithelial cells have not been investigated. Therefore, current studies were undertaken to investigate the direct effects of CT on ion transport in intestinal epithelial cells. Real time quantitative RT‐PCR and Western blot analysis demonstrated the expression of CTR in intestinal epithelial T84 cells. Exposure of T84 cells to CT from the basolateral but not from apical side significantly increased short circuit current (ISC) in a dose‐dependent manner that was blocked by 1 μM of CTR antagonist, CT8–32. CT‐induced ISC was blocked by replacing chloride in the bath solutions with equimolar gluconate and was significantly inhibited by the specific cystic fibrosis transmembrane conductance regulator (CFTR) inhibitor, CFTR127inh. Further, biotinylation studies showed that CT increased CFTR levels on the apical membrane. The presence of either the Ca2+ chelator, bis(2‐aminophenoxy)ethane tetraacetic acid‐acetoxymethyl (BAPTA‐AM) ester or the protein kinase A (PKA) inhibitor, H89, significantly inhibited ISC induced by CT (∼32–58% reduction). Response to CT was retained after permeabilization of the basolateral or the apical membranes of T84 cells with nystatin. In conclusion, the activation of CTR by CT induced chloride secretion across T84 monolayers via CFTR channel and the involvement of PKA‐ and Ca2+‐dependent signalling pathways. These data elucidate the molecular mechanisms underlying CT‐induced diarrhoea.
Multi-Objective Optimization of Integrated Power System Expansion Planning with Renewable Energy-Based Distributed Generation

Multi-objective-based power system expansion planning considering distributed generation is presented in this study. An integrated model of electrical power system expansion planning with renewable energy-based distributed generation is proposed. Two objective functions, cost and the emission objective function, are implemented in the proposed model. The cost function comprises investment cost and operation cost. The investment cost covers the cost of the new generation unit, the new distributed generation unit, and the investment for the new transmission circuit. The operation cost covers the operation cost of the installed and new generation unit and the operation cost of the installed and new distributed generation unit. Two emission gasses, CO2 and NOx, are considered in the proposed model. These two gasses are expressed in the same unit as Global Warming Potential. The developed model is implemented into the IEEE 14 bus system. Lexicographic optimization combined with the epsilon constraint technique is used to solve the multi-objective problem. A Pareto optimal solution is generated by this method. Then, the fuzzy decision-making process is implemented to select the best solution from the Pareto set. The simulation results show that the distributed generation significantly reduces the overall cost of power system expansion planning. With a 30% DG penetration level, overall planning costs can be reduced by 12.62% compared to without DG penetration with an equal weighting factor for each objective function. Details of the capacity expansion of the generation unit, distributed generation unit, and transmission line are presented in this study.
The structure of multivariable systems under the action of constant output feedback control laws

The structure of linear, time-invariant, completely controllable and observable multivariable systems under the action of constant output feedback control laws is studied. A structure theorem is stated and proved. Following Dickinson (1976), the notion of covariant output feedback control laws is introduced. It is shown that these results can be used to investigate the problems concerning (i) our ability to alter some of the Popov (1972) invariants by employing constant output feedback control, and (ii) the asymptotic behaviour of the closed loop poles under the variation of the output feedback gain matrix.
Accurately measuring collective operations at massive scale

Accurate, reproducible and comparable measurement of collective operations is a complicated task. Although different measurement schemes are implemented in well- known benchmarks, many of these schemes introduce different systematic errors in their measurements. We characterize these errors and select a window-based approach as the most accurate method. However, this approach complicates measurements significantly and introduces a clock synchronization as a new source of systematic errors. We analyze approaches to avoid or correct those errors and develop a scalable synchronization scheme to conduct benchmarks on massively parallel systems. Our results are compared to the window-based scheme implemented in the SKaMPI benchmarks and show a reduction of the synchronization overhead by a factor of 16 on 128 processes.
A SiGe BiCMOS phase shifter based on quarter‐wave coupled resonators

This work presents the first example of monolithically integrated phase shifter based on a pass‐band filter architecture. The proposed configuration was realized mapping a classical quarter‐wave coupled filter circuit into its lumped element equivalent. Phase control is achieved by controlling the pass‐band through tunable tanks employing varactor diodes. A demonstrator was prototyped in the 24 GHz ISM band using a 0. 25μm SiGe BiCMOS technology. Experimental results show 180° of phase range and maximum transmission losses of 8 dB. The main feature of this configuration is that it allows controlling the transmission losses by design and that its size is extremely compact.
Managing heterogeneous resources in data mining applications on grids using XML-based metadata

The Grid supports the sharing and coordinated use of resources in dynamic heterogeneous distributed environments. The effective use of a Grid requires the definition of an approach to manage the heterogeneity of the involved resources that can include computers, data, network facilities and software tools provided by different organizations. This issue get more importance when complex applications, such as data-intensive simulations and data mining applications, executed on a Grid. This paper is concerned with heterogeneous resource management in Grid-based data mining applications. It discusses how resources are represented and managed in the KNOWLEDGE GRID and how XML-based metadata are used to describe data mining tools, data sources, mining models and execution plans, and how those metadata are used for the design and execution of distributed data mining applications on Grids.
Biomechanical characterization with inverse FE model parameter estimation: Macro and micro applications

An inverse finite element (FE) model parameter estimation algorithm can be used to characterize a realistic biomechanical properties, which consider (or include) the material nonlinearity, contact mechanics, complex boundary conditions, and geometrical constraints in the modeling. In this study, biomechanical experiments on macro and micro samples are conducted. Macro indentation experiments on porcine livers were performed to measure the force behavior using one-dimensional indentation device. The force response of the human liver cancer cells was also measured by the atomic force microscope (AFM). The mechanical behavior of porcine livers (macro) and human liver cancer cells (micro) were characterized with inverse FE model parameter estimation algorithm via hyperelastic and linear viscoelastic models. The developed models are suitable for computing accurate reaction force on tools and deformation of biomechanical tissues.
Ontology matching techniques for enterprise architecture models

Current Enterprise Architecture (EA) approaches tend to be generic, based on broad meta-models that cross-cut distinct architectural domains. Integrating these models is necessary to an effective EA process, in order to support, for example, benchmarking of business processes or assessing compliance to structured requirements. However, the integration of EA models faces challenges stemming from structural and semantic heterogeneities that could be addressed by ontology matching techniques. For that, we used AgreementMakerLight, an ontology matching system, to evaluate a set of state of the art matching approaches that could adequately address some of the heterogeneity issues. We assessed the matching of EA models based on the ArchiMate and BPMN languages, which made possible to conclude about not only the potential but also of the limitations of these techniques to properly explore the more complex semantics present in these models. Enterprise Architecture (EA) is a practice to support the analysis, design and implementation of a business strategy in an organization, considering its relevant multiple domains. In recent years, a variety of Enterprise Architecture [5] languages have been established to manage the scale and complexity of this domain. Integration of EA models is necessary to support EA processes, however structural and semantic heterogeneities hinder integration. Ontology matching has been proposed as a useful technique to help address this challenge [4]. Ontologies and associated techniques are increasingly being recognized as valuable tools in the EA domain (e.g., [1]). To evaluate the applicability of ontology matching techniques to address the heterogeneity between EA models, we have selected four case studies that demonstrate heterogeneity challenges at the model level. Cases 1 and 2 showcase Abstraction Level Incompatibilities between models encoded in different languages (ArchiMate and BPMN), that represent similar situations. Cases 3 and 4 illustrate both Abstraction Level and Element Description heterogeneties between models using the same language, where both pairs of models represent the same situation encoded by different modelers. To support the matching tasks we have used AgreementMakerLight (AML) [2],
Noise enhancement and SNR equivalence in Bernoulli nonuniform sampling

This paper addresses the problem of nonuniform spectral analysis and spectrum sensing by means of Bernoulli sampling. The statistical treatment of sampling shows that nonuniformly sampling below the Nyquist rate produces in average the effects of noise enhancement and power loss on the second-order statistics of the signal (correlation and spectrum). The main focus of the paper is to obtain an equivalence in terms of signal-to-noise ratio (SNR) to model the noise enhancement and power loss effects. The Bernoulli nonuniform sampling is further extended to matrix formulation, which allows the application of spectrum sensing for cognitive radio signal detection. Numerical results assess the noise enhancement effect and SNR equivalence in spectral analysis and spectrum sensing.
Autocrine induction of gliostatin/platelet-derived endothelial cell growth factor (GLS/PD-ECGF) and GLS-induced expression of matrix metalloproteinases in rheumatoid arthritis synoviocytes.

OBJECTIVE The purpose of this study was to examine how gliostatin/platelet-derived endothelial cell growth factor (GLS/PD-ECGF) is involved in the molecular mechanism of cartilage degradation in rheumatoid arthritis (RA) with special reference to the GLS-induced gene expression and protein synthesis of matrix metalloproteinase (MMP)-1 (collagenase-1) and MMP-3 (stromelysin-1). METHODS Fibroblast-like synoviocytes (FLSs) obtained from RA patients were cultured and stimulated by GLS. Changes in the expression levels of GLS, MMP-1 and MMP-3 were assessed by Northern blot analysis and reverse transcription-polymerase chain reaction (RT-PCR) for GLS, and by RT-PCR and enzyme-linked immunosorbent assay for MMPs and tissue inhibitor of metalloproteinase 1. RESULTS GLS demonstrated a self-induction of mRNA in cultured RA FLSs. GLS evoked a dose-dependent induction of MMP-1 and MMP-3 mRNAs, and subsequently their extracellular secretion. CONCLUSION These findings suggest that GLS is a plausible pathogenic factor causing the extensive joint destruction in RA mediated via MMPs.
Survey and evaluation of approaches for the adaptation reasoning framework

Public Abstract / Executive summary: Creation of an adaptation reasoning framework for dynamic, adaptive, and distributed systems is not a trivial task. Such a framework reasons on the variability that is represented by models implementing DiVA's MDE approach and which causes, due to the potential big size of such systems, an explosion of complexity. A dynamic system operates in real-time and is in the potentially constant need to respond fast to component or context changes through global reconfiguration. Complexity explosion would, if not appropriately handled, result in unacceptable calculation costs and too long reaction time spans while deciding which of a large number of possible configuration variants is valid or even optimal. This survey researches and evaluates existing reasoning approaches and shows ways to reduce reasoning complexity in the context of DiVA. Results will be the base of the later Reasoning Framework implementation of WP4 that is coupled to the concepts and implementations of the technical DiVA work packages 1, 2, and 3.
Comprehensive Tracker Based Display Calibration for Holographic Optical See-Through Head-Mounted Display

A holographic display system is able to offer an immersive 3D experience with a controllable focus cue that matches the depth information of virtual content. Proper calibration of an OST-HMD must take into account the specific features of that HMD. Existing calibration methods, however, cannot fully capture the optical characteristics of holographic display systems. With the aid of tracking systems, it is possible to register rendered graphics with tracked objects in the real world, providing an augmented reality experience for the users. We propose a new calibration procedure to properly align the coordinate system of rendering with that of the tracker. In this paper, both internal and external tracking systems are studied; three models of the mapping from tracking space to display space are presented: isometric, affine and perspective transformation. Experimental results demonstrate the validity of our method. Additionally, in a novel approach, the implementation uses self localization and spatial mapping of the HMD with an external tracker to perform this calibration, eliminating the requirement for line of sight from the HMD to the object or tracker. Microsoft HoloLens is used as the holographic OST-HMD for our experiments. The experimental results indicate an accuracy of up to 4 mm in the average reprojection error for the general perspective case and similarly 4 mm in our objective evaluation. To the best of our knowledge, this is the first OST-HMD calibration method that takes the holographic nature of the display technology into account.
Understanding Suicidal Behavior: The Contribution of Recent Resting-State fMRI Techniques

Suicidal behavior is a relevant and multifaceted public health issue and is commonly associated with a significant disability and psychosocial impairment. To date, no available biomarkers are able to predict which subjects will develop suicide over time, and this is hardly surprising given the number of factors that have been hypothesized to modulate suicide risk based on the current literature (1). In the effort to solve this shortcoming, a possible approach is represented by the search of those patterns of brain activation that are associated with suicidal behavior and may be identified using functional neuroimaging techniques. To date, the most commonly used functional neuroimaging technique is represented by functional magnetic resonance imaging (fMRI). fMRI may detect the local changes in the relative concentrations of oxy- and deoxy-hemoglobin, induced by local metabolic demand [i.e., it measures the so-called blood–oxygen level-dependent (BOLD) signals] (2). fMRI data can be also acquired while the imaged subject is performing a given task (i.e., task-dependent fMRI) or at rest (resting-state fMRI – rsfMRI). There are studies showing aberrant neural activity patterns in suicide attempters that were carried out using task-based BOLD fMRI (3). Indeed, task-based fMRI has been used to probe the neural substrates of specific cognitive and emotional intermediate phenotype of suicide, such as error monitoring (4) and decision-making (5), but task-based fMRI is inherently limited by the need of active collaboration by the scanned subject as well as by the nature of the task during fMRI data acquisition. fMRI data can be also acquired while the subject is not performing any task – i.e., at rest (rsfMRI) – to evaluate which brain regions present same patterns of activation over time that are supposed to represent a valid surrogate marker of functional connectivity between different gray matter areas and over the whole brain (6). Compared with task-based fMRI, rsfMRI is not dependent on subject collaboration (except for the requirement to lay in the scanner as much as possible), thus increasing its inter-subject and intra-subject reproducibility. Moreover, rsfMRI allows to explore the resting-state brain networks, in particular, the default mode network (DMN), that have been reported to be altered in several psychopathological conditions and may be not easily investigated using the commonly available task-based fMRI (7, 8). Finally, as rsfMRI data can be analyzed over the whole brain, they do not require to have an a priori hypothesis regarding the involvement of specific brain regions.
Logic-based Plan Recognition for Intelligent Help Systems

Intelligent help systems aim at providing optimal help to the users of complex software application systems. In this context plan recognition is essential for a cooperative system behavior in that it allows to predict the user's future actions, to determine suboptimal action sequences or even serves as a basis for user-adapted tutoring or learning components. In this paper a new approach to incremental plan recognition based on a modal temporal logic is described. This logic allows for an abstract representation of plans including control structures such as loops and conditionals which makes it particularly well-suited for the above-mentioned tasks in command-language environments. There are two distinct phases: With a generalized abductive reasoning mechanism the set of valid plan hypotheses is determined in each recognition step. A probabilistic selection, based on Dempster-Shafer Theory, then serves to determine the "best" hypotheses in order to be able to provide help whenever required.
A Five-Gene Hedgehog Signature Developed as a Patient Preselection Tool for Hedgehog Inhibitor Therapy in Medulloblastoma

Purpose: Distinct molecular subgroups of medulloblastoma, including hedgehog (Hh) pathway–activated disease, have been reported. We identified and clinically validated a five-gene Hh signature assay that can be used to preselect patients with Hh pathway–activated medulloblastoma. Experimental Design: Gene characteristics of the Hh medulloblastoma subgroup were identified through published bioinformatic analyses. Thirty-two genes shown to be differentially expressed in fresh-frozen and formalin-fixed paraffin-embedded tumor samples and reproducibly analyzed by RT-PCR were measured in matched samples. These data formed the basis for building a multi-gene logistic regression model derived through elastic net methods from which the five-gene Hh signature emerged after multiple iterations. On the basis of signature gene expression levels, the model computed a propensity score to determine Hh activation using a threshold set a priori. The association between Hh activation status and tumor response to the Hh pathway inhibitor sonidegib (LDE225) was analyzed. Results: Five differentially expressed genes in medulloblastoma (GLI1, SPHK1, SHROOM2, PDLIM3, and OTX2) were found to associate with Hh pathway activation status. In an independent validation study, Hh activation status of 25 medulloblastoma samples showed 100% concordance between the five-gene signature and Affymetrix profiling. Further, in medulloblastoma samples from 50 patients treated with sonidegib, all 6 patients who responded were found to have Hh-activated tumors. Three patients with Hh-activated tumors had stable or progressive disease. No patients with Hh-nonactivated tumors responded. Conclusions: This five-gene Hh signature can robustly identify Hh-activated medulloblastoma and may be used to preselect patients who might benefit from sonidegib treatment. Clin Cancer Res; 21(3); 585–93. ©2014 AACR.
The Link between Identity and Role Performance

Despite interest in the influence of self-concept on behavior, research has been limited by (I) the need to better understand the mechanism by which self influences role behaviors, (2) a reliance on self-esteem as the only measure of self-concept, and (3) the absence of quantitative measures of self-concept. The research reported here is designed to test one formulation ofthe link between identity and behavior. It is based on recent theoretical conceptions of identity, advances in its measurement, and the assumption that identities motivate behaviors that have meanings consistent (isomorphic) with the identity. Data obtained from 640 college students are used to discover and measure four dimensions of meaning pertaining to the college student identity, and to assess the impact of student identities on the two "performance" variables of educational plans and participation in social activities. The findings strongly support the hypothesized link of identity and performance through common meanings.