Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Aspect Extraction and Program Analysis for Effective Software Development

Aspect-Oriented Programming is proposed to improve modularity for crosscutting concerns. In aspect-oriented programming, an aspect can capture crosscutting structure based on Join Point Model which abstracts an execution of program as a sequence of join points. A join point represents a well-defined event during program execution such as method call, method execution, field access and exception handling. A concern is called as a crosscutting concern when the related code fragments of the concern spread across (crosscuts) the modules. A crosscutting concern is hard to maintain since a developer needs to maintain all code fragments consistently and code fragments of multiple crosscutting concerns are often mixed at different places. Although such crosscutting concerns are found in many programs, rewriting all of them in aspect-oriented language is infeasible. Therefore, finding concerns for which aspect-oriented programming is suitable and effective is important. In this paper, we propose and discuss two aspects related to objectoriented software development. 1. Dynamic analysis recording program execution 2. Modularization of assertions crosscutting objects Dynamic analysis is a process collecting execution history of a program. The collected information is used by debugger and several analysis tools since the information reflects the actual behavior of the program. Dynamic analysis needs to watch the behavior of objects in the program. A developer is required to implement a program transformation tool in order to achieve dynamic analysis. We have proposed to implement a dynamic analysis tool based on AOP to reduce efforts for the tool development. Comparing an approach using a customized compiler which generates code for dynamic analysis for program slicing, aspect-oriented analyzer improves modularity and maintainability of the analysis system. Another development aspect is modularization of assertions crosscutting objects. Assertion checking is a powerful tool to detect software faults during debugging, testing and maintenance. Although assertion documents the behavior of
Technological, Social, and Economic Trends That Are Increasing U.S. Vulnerability to Insider Espionage

Abstract : This study explores ten technological, social, and economic trends in the United States and globally that are serving to increase opportunity and motivation for espionage. Findings suggest that American "insiders" have an unprecedented level of access to classified and proprietary information due to technological advances in information storage and retrieval. American employees have greater opportunity to establish contact with foreign entities and to transfer information to them through traveling internationally more often and by participating in international research and business ventures more frequently. Internet use is expanding globally and computer-users are becoming more culturally and linguistically diverse. The Internet can now be used to transmit massive amounts of digitized information to multiple foreign parties simultaneously. Finally, the market for U.S. information is expanding. American insiders can sell more types of information to a broader range of foreign buyers than ever before. In addition to these new opportunities for espionage, American employees are more often encountering situations that can provide motivation for this crime. More insiders are experiencing financial problems and gambling addiction, both of which can provide impetus for workplace theft. Loyalty to organizations is diminishing and a greater proportion of American workers are at risk for becoming disgruntled. A growing number of insiders have emotional and financial ties to other countries. Under some circumstances, insiders with loyalties to other peoples may be less inclined to view espionage as morally wrong. It is possible that some insiders with a global orientation to world affairs will view espionage as morally justifiable if they feel that sharing information will benefit the "world community" or prevent armed conflict.
Learning Statistical Features of Scene Images

Scene perception is a fundamental aspect of vision. Humans are capable of analyzing behaviorally-relevant scene properties such as spatial layouts or scene categories very quickly, even from low resolution versions of scenes. Although humans perform these tasks effortlessly, they are very challenging for machines. Developing methods that well capture the properties of the representation used by the visual system will be useful for building computational models that are more consistent with perception. While it is common to use hand-engineered features that extract information from predefined dimensions, they require careful tuning of parameters and do not generalize well to other tasks or larger datasets. This thesis is driven by the hypothesis that the perceptual representations are adapted to the statistical properties of natural visual scenes. For developing statistical features for global-scale structures (low spatial frequency information that encompasses entire scenes), I propose to train hierarchical probabilistic models on whole scene images. I first investigate statistical clusters of scene images by training a mixture model under the assumption that each image can be decoded by sparse and independent coefficients. Each cluster discovered by the unsupervised classifier is consistent with the high-level semantic categories (such as indoor, outdoor-natural and outdoor-manmade) as well as perceptual layout properties (mean depth, openness and perspective). To address the limitation of mixture models in their assumptions of a discrete number of underlying clusters, I further investigate a continuous representation for the distributions of whole scenes. The model parameters optimized for natural visual scenes reveal a compact representation that encodes their global-scale structures. I develop a probabilistic similarity measure based on the model and demonstrate its consistency with the perceptual similarities. Lastly, to learn the representations that better encode the manifold structures in general high-dimensional image space, I develop the image normalization process to find a set of canonical images that anchors the probabilistic distributions around the real data manifolds. The canonical images are employed as the centers of the conditional multivariate Gaussian distributions. This approach allows to learn more detailed structures of the local manifolds resulting in improved representation of the high level properties of scene images.
Robust Adaptive Beamforming in Cognitive Radio Networks

In this paper, we propose an adaptive non-intrusive cognitive radio network based on smart antenna technologies. The cognitive transmitter (CT), which is equipped with antenna array, first estimates and tracks the composite steering vectors of each primary (licensed) user. In what follows, CT forms transmit beamforming to place nulls to primary receivers (PR) based on the estimated spatial signatures. Moreover, we extensively analyze performance degradation caused by spatial signatures mismatch (estimation error) and verify that the proposed robust nulls-steering beamformer can comprehensively alleviate the mismatch effect. Simulation results demonstrate convergence of the proposed gradient-based recursive least squares (RLS) algorithm. Furthermore, we have shown that outage probability can be kept extremely low under appropriate array size, which ensures the practicability of the proposed scheme.
Regression Model Based on Sparse Bayesian Learning

Sparse Bayesian learning (SBL) and relevance vector machines(RVM) have received much attention in the machine learning, which as a means of achieving regression. The methodology relies on a parameterized prior that encourages models with few non-zero weights. In this paper, we present a new and efficient algorithm which exploits properties of the marginal likelihood function to enable maximisation via a principled and efficient sequential addition and deletion of candidate basis functions. Meanwhile, regression model has been built based on this algorithm.
What does improved fuel economy cost consumers and what does it cost taxpayers? Some illustrations

“Green growth” is an emerging paradigm that integrates several policy aspirations, including the durability of economic activity, reduced environmental impacts, and sustained growth in high-quality employment in such a way as to foster coherent, cross-sectoral policy design. Focusing on “green growth” highlights the need for governments to assess policies on their long-term economic, environmental and social impacts, recognizing that there can be synergies but also tradeoffs among the broad policy aims. As we hope to show in this paper, an examination of “green growth” policies in the transport sector provides an interesting case in point. Reducing emissions comes at a cost to consumers and taxpayers and if fuel tax revenues decline strongly it may be necessary to review the way the transport sector is taxed and contributes to aggregate tax revenue.
Altered expression of mir-222 and mir-25 influences diverse gene expression changes in transformed normal and anaplastic thyroid cells, and impacts on MEK and TRAIL protein expression

Thyroid cancer is the most common endocrine malignancy and accounts for the majority of endocrine cancer-related deaths each year. Our group and others have previously demonstrated dysfunctional microRNA (miRNA or miR) expression in the context of thyroid cancer. The objective of the present study was to investigate the impact of synthetic manipulation of expression of miR-25 and miR-222 in benign and malignant thyroid cells. miR-25 and miR-222 expression was upregulated in 8505C (an anaplastic thyroid cell line) and Nthy-ori (a SV40-immortalised thyroid cell line) cells, respectively. A transcriptomics-based approach was utilised to identify targets of the two miRNAs and real-time PCR and western blotting were used to validate a subset of the targets. Almost 100 mRNAs of diverse functions were found to be either directly or indirectly targeted by both miR-222 and miR-25 [fold change ≥2, false discovery rate (FDR) ≤0.05]. Gene ontology analysis showed the miR-25 gene target list to be significantly enriched for genes involved in cell adhesion. Fluidigm real-time PCR technologies were used to validate the downregulation of 23 and 22 genes in response to miR-25 and miR-222 overexpression, respectively. The reduction of the expression of two miR-25 protein targets, TNF-related apoptosis-inducing ligand (TRAIL) and mitogen-activated protein kinase kinase 4 (MEK4), was also validated. Manipulating the expression of both miR-222 and miR-25 influenced diverse gene expression changes in thyroid cells. Increased expression of miR-25 reduced MEK4 and TRAIL protein expression, and cell adhesion and apoptosis are important aspects of miR-25 functioning in thyroid cells.
Working Memory after Traumatic Brain Injury: The Neural Basis of Improved Performance with Methylphenidate

Traumatic brain injury (TBI) often results in cognitive impairments for patients. The aim of this proof of concept study was to establish the nature of abnormalities, in terms of activity and connectivity, in the working memory network of TBI patients and how these relate to compromised behavioral outcomes. Further, this study examined the neural correlates of working memory improvement following the administration of methylphenidate. We report behavioral, functional and structural MRI data from a group of 15 Healthy Controls (HC) and a group of 15 TBI patients, acquired during the execution of the N-back task. The patients were studied on two occasions after the administration of either placebo or 30 mg of methylphenidate. Between group tests revealed a significant difference in performance when HCs were compared to TBI patients on placebo [F(1, 28) = 4.426, p < 0.05, ηp2 = 0.136]. This difference disappeared when the patients took methylphenidate [F(1, 28) = 3.665, p = 0.66]. Patients in the middle range of baseline performance demonstrated the most benefit from methylphenidate. Changes in the TBI patient activation levels in the Left Cerebellum significantly and positively correlated with changes in performance (r = 0.509, df = 13, p = 0.05). Whole-brain connectivity analysis using the Left Cerebellum as a seed revealed widespread negative interactions between the Left Cerebellum and parietal and frontal cortices as well as subcortical areas. Neither the TBI group on methylphenidate nor the HC group demonstrated any significant negative interactions. Our findings indicate that (a) TBI significantly reduces the levels of activation and connectivity strength between key areas of the working memory network and (b) Methylphenidate improves the cognitive outcomes on a working memory task. Therefore, we conclude that methylphenidate may render the working memory network in a TBI group more consistent with that of an intact working memory network.
Long-term predictability of mean daily temperature data

Abstract. We quantify the long-term predictability of global mean daily temperature data by means of the Renyi entropy of second order K 2 . We are interested in the yearly amplitude fluctuations of the temperature. Hence, the data are low-pass filtered. The obtained oscillatory signal has a more or less constant frequency, depending on the geographical coordinates, but its amplitude fluctuates irregularly. Our estimate of K 2 quantifies the complexity of these amplitude fluctuations. We compare the results obtained for the CRU data set (interpolated measured temperature in the years 1901-2003 with 0.5° resolution, Mitchell et al., 2005)with the ones obtained for the temperature data from a coupled ocean-atmosphere global circulation model (AOGCM, calculated at DKRZ). Furthermore, we compare the results obtained by means of K 2 with the linear variance of the temperature data.
Three-algebras, triple systems and 3-graded Lie superalgebras

The three-algebras used by Bagger and Lambert in N = 6 theories of ABJM type are in one-to-one correspondence with a certain type of Lie superalgebras. We show that the description of three-algebras as generalized Jordan triple systems naturally leads to this correspondence. Furthermore, we show that simple three-algebras correspond to simple Lie superalgebras, and vice versa. This gives a classification of simple three-algebras from the well-known classification of simple Lie superalgebras.
From task to dialogue: task-based user interface design

Developing user interfaces is no more a mere technical software development task; successful user interface design has to be interdisciplinary, taking into account other aspects, such as psychological, social, organisational, and cognitive aspects. It is generally accepted that the tasks, the user has to fulfil with a system to be developed should play an important role in its design. Knowing the user's tasks enables the designer to construct user interfaces reflecting the tasks' properties, including efficient usage patterns, easy-to-use interaction sequences, and powerful assistance features.
Sigma Mass in the Large N Limit of QCD

We study the leading contribution to the scalar and the pseudoscalar two-point function in the large N expansion of QCD using the worldline techniques. We find that in this limit there exists a relationship between these two Green's functions which implies that for every massive pseudoscalar meson there exists a scalar meson of identical mass. This is true even when chiral symmetry is spontaneously broken. The relationship between the Green's function further suggests that in the planar limit the sigma mass must be identical to the eta mass. We also discuss the relevance of these results for the quenched lattice QCD simulations and for hadron phenomenology.
Depressive Symptoms and Risk of Type 2 Diabetes in a National Sample of Middle-Aged and Older Adults

OBJECTIVE To examine the association between baseline elevated depressive symptoms and incident type 2 diabetes in a national sample of people aged ≥50 years. RESEARCH DESIGN AND METHODS The sample consisted of 6,111 individuals free from self-reported doctor-diagnosed diabetes at baseline in 2002–2003. The eight-item Center for Epidemiological Studies–Depression (CES-D) scale was the measurement of depressive symptoms. Cox proportional hazards regression models were used to assess whether baseline elevated (≥4) depressive symptoms were associated with a higher risk of type 2 diabetes over 45.8 months of follow-up. RESULTS The hazard ratio (HR) for diabetes was 1.62 (95% CI 1.15–2.29) in a model adjusted for age, sex, marital status, education, total net household wealth, cardiovascular and psychiatric and other noncardiovascular comorbidities, BMI, and health behaviors for participants with elevated CES-D symptoms compared with those without. Complementary analysis performed for a subsample (n = 5,090) showed that additional adjustment of this model for use of antidepressants did not explain the association (HR 1.58, 95% CI 1.09–2.29). CONCLUSIONS Elevated depressive symptoms were associated with a higher risk of developing type 2 diabetes after accounting for sociodemographic, lifestyle, and clinical factors in a national sample of people aged ≥50 years.
Endoscopic ultrasound reliably identifies chronic pancreatitis when other imaging modalities have been non-diagnostic.

CONTEXT There are classical radiological features for the diagnosis of chronic pancreatitis when utilising endoscopic retrograde cholangiopancreatography (ERCP), magnetic resonance cholangiopancreatography (MRCP) or computed tomography (CT), however, not all patients exhibit these features despite convincing clinical histories, which may result in diagnostic delay. OBJECTIVE The aim of this study was to assess the use of endoscopic ultrasound (EUS) in the diagnosis of chronic pancreatitis when other imaging modalities had not yielded a diagnosis. METHODS All patients undergoing pancreatic EUS between January 1996 and December 2004 were identified from the radiology computerised database. Sixteen patients with a clinical diagnosis of chronic pancreatitis (10 males, 6 females; mean age 53+/-4 years) underwent EUS after normal conventional imaging. Patients were then followed clinically until December 2007. RESULTS Thirteen patients exhibited features of chronic pancreatitis not identified by other modalities, which included duct dilatation (n=8), calcification (n=7); parenchymal change (n=6), irregular undilated ducts (n=2), pancreatic ductal calculi (n=1), and fine calcification (n=1). Of the remaining 3 patients, a diagnosis of autoimmune pancreatitis was made in one, in another there was a pancreatic duct stricture of uncertain origin that was stented, and in only one case was no diagnosis established. All 13 patients with an EUS diagnosis of chronic pancreatitis subsequently underwent a repeat CT scan for surveillance of their disease and in all cases, the CT scans subsequently demonstrated evidence of chronic pancreatitis indicating radiological progression. No new pancreaticobiliary diagnoses were established during this period. CONCLUSIONS EUS is a useful diagnostic tool confirming the diagnosis of chronic pancreatitis in 13 of 16 cases where histories were suspicious of chronic pancreatitis, and providing an alternative diagnosis in another two cases. EUS should be considered an important tool for diagnosis of chronic pancreatitis and should be used when cross-sectional imaging is non-diagnostic.
Phishing in a university community: Two large scale phishing experiments

Phishing is a type of social engineering where a potential victim is sent a message that impersonates a legitimate source or organization. Phishing attacks typically lure the targets into revealing confidential information such as password, credit card details, bank account numbers, or any other sensitive information. Human behavior and technology are two equally important aspects of phishing attacks, while current anti-phishing research have focused on the technology front, very few real life studies have been performed with a focus on the human aspects of phishing attacks. In this paper, we present the results of two large scale real life phishing attacks conducted on more than 10,000 community members of a university that includes students, alumni, faculty and staff. Our study is the first large scale phishing experiment on human subjects. Previous work suggests that users' demographics are useful indicators in identifying the most vulnerable users to phishing attacks. Our results illustrate that user demographics alone cannot predict user's susceptibility to phishing attacks. We also found that warning users about phishing risks alone is not sufficient to prevent more users from responding to the phishing attack. Even though subjects were warned not to respond to phishing emails, many disregarded the warning. We explain our findings through analysis of the empirical results of the two real life phishing attacks conducted.
Wolbachia Reduces the Transmission Potential of Dengue-Infected Aedes aegypti

Background Dengue viruses (DENV) are the causative agents of dengue, the world’s most prevalent arthropod-borne disease with around 40% of the world’s population at risk of infection annually. Wolbachia pipientis, an obligate intracellular bacterium, is being developed as a biocontrol strategy against dengue because it limits replication of the virus in the mosquito. The Wolbachia strain wMel, which has been introduced into the mosquito vector, Aedes aegypti, has been shown to invade and spread to near fixation in field releases. Standard measures of Wolbachia’s efficacy for blocking virus replication focus on the detection and quantification of virus in mosquito tissues. Examining the saliva provides a more accurate measure of transmission potential and can reveal the extrinsic incubation period (EIP), that is, the time it takes virus to arrive in the saliva following the consumption of DENV viremic blood. EIP is a key determinant of a mosquito’s ability to transmit DENVs, as the earlier the virus appears in the saliva the more opportunities the mosquito will have to infect humans on subsequent bites. Methodology/Principal Findings We used a non-destructive assay to repeatedly quantify DENV in saliva from wMel-infected and Wolbachia-free wild-type control mosquitoes following the consumption of a DENV-infected blood meal. We show that wMel lengthens the EIP, reduces the frequency at which the virus is expectorated and decreases the dengue copy number in mosquito saliva as compared to wild-type mosquitoes. These observations can at least be partially explained by an overall reduction in saliva produced by wMel mosquitoes. More generally, we found that the concentration of DENV in a blood meal is a determinant of the length of EIP, saliva virus titer and mosquito survival. Conclusions/Significance The saliva-based traits reported here offer more disease-relevant measures of Wolbachia’s effects on the vector and the virus. The lengthening of EIP highlights another means, in addition to the reduction of infection frequencies and DENV titers in mosquitoes, by which Wolbachia should operate to reduce DENV transmission in the field.