Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving dynamic analysis of android apps using hybrid test input generation

The Android OS has become the most popular mobile operating system leading to a significant increase in the spread of Android malware. Consequently, several static and dynamic analysis systems have been developed to detect Android malware. With dynamic analysis, efficient test input generation is needed in order to trigger the potential run-time malicious behaviours. Most existing dynamic analysis systems employ random-based input generation methods usually built using the Android Monkey tool. Random-based input generation has several shortcomings including limited code coverage, which motivates us to explore combining it with a state-based method in order to improve efficiency. Hence, in this paper, we present a novel hybrid test input generation approach designed to improve dynamic analysis on real devices. We implemented the hybrid system by integrating a random based tool (Monkey) with a state based tool (DroidBot) in order to improve code coverage and potentially uncover more malicious behaviours. The system is evaluated using 2,444 Android apps containing 1222 benign and 1222 malware samples from the Android malware genome project. Three scenarios, random only, state-based only, and our proposed hybrid approach were investigated to comparatively evaluate their performances. Our study shows that the hybrid approach significantly improved the amount of dynamic features extracted from both benign and malware samples over the state-based and commonly used random test input generation method.
Replication study of 34 common SNPs associated with prostate cancer in the Romanian population

Prostate cancer is the third‐most common form of cancer in men in Romania. The Romanian unscreened population represents a good sample to study common genetic risk variants. However, a comprehensive analysis has not been conducted yet. Here, we report our replication efforts in a Romanian population of 979 cases and 1027 controls, for potential association of 34 literature‐reported single nucleotide polymorphisms (SNPs) with prostate cancer. We also examined whether any SNP was differentially associated with tumour grade or stage at diagnosis, with disease aggressiveness, and with the levels of PSA (prostate specific antigen). In the allelic analysis, we replicated the previously reported risk for 19 loci on 4q24, 6q25.3, 7p15.2, 8q24.21, 10q11.23, 10q26.13, 11p15.5, 11q13.2, 11q13.3. Statistically significant associations were replicated for other six SNPs only with a particular disease phenotype: low‐grade tumour and low PSA levels (rs1512268), high PSA levels (rs401681 and rs11649743), less aggressive cancers (rs1465618, rs721048, rs17021918). The strongest association of our tested SNP's with PSA in controls was for rs2735839, with 29% increase for each copy of the major allele G, consistent with previous results. Our results suggest that rs4962416, previously associated only with prostate cancer, is also associated with PSA levels, with 12% increase for each copy of the minor allele C. The study enabled the replication of the effect for the majority of previously reported genetic variants in a set of clinically relevant prostate cancers. This is the first replication study on these loci, known to associate with prostate cancer, in a Romanian population.
Robust shape extraction for automatically segmenting raw LiDAR data of outdoor scenes

ABSTRACT Light detection and ranging (LiDAR) scanning has become a prevalent technique for digitalizing outdoor scenes with three-dimensional (3D) point clouds. An automatic segmentation of the LiDAR data is important for understanding and reconstructing the outdoor scenes. However, it is still a challenging task due to complex and various objects in the outdoor scenes and some scanning drawbacks in the LiDAR data. Observing that most of the objects, such as the ground, road, roof, and facade, can be locally described with a group of geometric shapes, e.g. plane, sphere, and cylinder, we propose an automatic method to segment raw LiDAR data by robustly extracting the shapes in this paper. Firstly, our method divides the raw LiDAR data into a number of supervoxels considering the geometric and spectral consistencies of LiDAR data. Secondly, we robustly extract shapes from each supervoxels using a random sample consensus (RANSAC)-based method and evaluates and optimises the extracted shapes with a density loss estimation technique. Finally, our method outputs the segmentation result by merging the extracted shapes into a group of complete shapes, each of them represents a meaningful object in the outdoor scene. Experiments show that the method is efficient and robust for extracting most of the shapes in the outdoor scenes from a given raw LiDAR point cloud, and no preprocessing is required.
Phylogenetic Evidence against Evolutionary Stasis and Natural Abiotic Reservoirs of Influenza A Virus

ABSTRACT Zhang et al. (G. Zhang, D. Shoham, D. Gilichinsky, S. Davydov, J. D. Castello, and S. O. Rogers, J. Virol. 80:12229-12235, 2006) have claimed to have recovered influenza A virus RNA from Siberian lake ice, postulating that ice might represent an important abiotic reservoir for the persistence and reemergence of this medically important pathogen. A rigorous phylogenetic analysis of these influenza A virus hemagglutinin gene sequences, however, indicates that they originated from a laboratory reference strain derived from the earliest human influenza A virus isolate, WS/33. Contrary to Zhang et al.'s assertions that the Siberian “ice viruses” are most closely related either to avian influenza virus or to human influenza virus strains from Asia from the 1960s (Zhang et al., J. Virol. 81:2538 [erratum], 2007), they are clearly contaminants from the WS/33 positive control used in their laboratory. There is thus no credible evidence that environmental ice acts as a biologically relevant reservoir for influenza viruses. Several additional cases with findings that seem at odds with the biology of influenza virus, including modern-looking avian influenza virus RNA sequences from an archival goose specimen collected in 1917 (T. G. Fanning, R. D. Slemons, A. H. Reid, T. A. Janczewski, J. Dean, and J. K. Taubenberger, J. Virol. 76:7860-7862, 2002), can also be explained by laboratory contamination or other experimental errors. Many putative examples of evolutionary stasis in influenza A virus appear to be due to laboratory artifacts.
Robots as assistive technology - does appearance matter?

This work studies the effect of a robot's design (appearance) in facilitating and encouraging interaction of children with autism with a small humanoid robot. The paper compares the children's level of interaction with the response to the robot in two different scenarios: one where the robot was dressed like a human (with a 'pretty-girl' appearance) with an uncovered face, and the other, when it appeared with plain clothing and with a featureless, masked face. The results of these, trials clearly indicate the children's preference in their initial response for interaction with a plain, featureless robot over interaction with a human like robot.
Paleoclimatic and archaeological evidence from Lake Suches for highland Andean refugia during the arid middle-Holocene

Severe aridity during the mid-Holocene, ca. 8.0–4.0 kyr BP, led to extreme ecological stress in the tropical Andes. Here, we report paleolimnological and archeological data from Lake Suches in southern Peru (70° 24’ 12” W, 16° 55’ 35” S) spanning 13.6–4.4 kyr BP. Integrated paleoclimate and archeological data reveal that moisture was locally available and the basin served as an ecological refugium throughout the mid-Holocene. Mid-Holocene aridity was established no later than 7.2 kyr BP, with maximum aridity ca. 5.5–4.8 kyr BP. However, water levels in Lake Suches were sustained throughout peak middle-Holocene aridity, even as other systems desiccated. Isotopic enrichment of water in Lake Suches (δ18Olake) and extensive wetlands (δ18Obofedal) surrounding the lake indicate prolonged residence time. These reservoirs, combined with elevation-linked hydrographic factors, mitigated mid-Holocene net decreases in atmospheric moisture. Archeological data from Suches indicate successive population increases beginning ca. 11.0–9.8 kyr BP as drier but more stable early Holocene conditions were established regionally. Population maxima in Suches during the mid-Holocene/mid-Archaic period ca. 9.0–7.0 kyr BP coincide with peak aridity in the Titicaca and Atacama systems, as well as documented archeological hiatuses in these regions. Population decreases coincide with peak aridity recorded in Lake Suches ca. 6.0–5.0 kyr BP, but the basin was never fully abandoned. Evidence for refugial microenvironments is key to understanding the persistence of human populations and other endemic Andean flora and fauna during the highly adverse climates of the middle-Holocene. We outline several mechanisms which likely explain the formation of refugia linked to bofedales and hydrographic characteristics of Suches. Understanding refugial dynamics will be key to understanding the effects of past climatic change, as well as addressing current warming and decreased precipitation trends in the tropical Andes.
Better ϵ-Dependencies for Offline Approximate Nearest Neighbor Search, Euclidean Minimum Spanning Trees, and ϵ-Kernels

Recently, Arya, da Fonseca, and Mount [STOC 2011, SODA 2012] made notable progress in improving the ϵ-dependencies in the space/query-time tradeoffs for (1 + ϵ)-factor approximate nearest neighbor search in fixed-dimensional Euclidean spaces. However, ϵ-dependencies in the preprocessing time were not considered, and so their data structures cannot be used to derive faster algorithms for offline proximity problems. Known algorithms for many such problems, including approximate bichromatic closest pair (BCP) and approximate Euclidean minimum spanning trees (EMST), typically have factors near (1/ϵ)d/2±O(1) in the running time when the dimension d is a constant. We describe a technique that breaks the (1/ϵ)d/2 barrier and yields new results for many well-known proximity problems, including: • an O((1/ϵ)d/3+O(1) n)-time randomized algorithm for approximate BCP, • an O((1/ϵ)d/3+O(1) n log n)-time algorithm for approximate EMST, and • an O(n log n + (1/ϵ)d/3+O(1) n)-time algorithm to answer n approximate nearest neighbor queries on n points. Using additional bit-packing tricks, we can shave off the log n factor for EMST, and even move most of the ϵ-factors to a sublinear term. The improvement arises from a new time bound for exact "discrete Voronoi diagrams", which were previously used in the construction of ϵ-kernels (or extent-based coresets), a well-known tool for another class of fundamental problems. This connection leads to more results, including: • a streaming algorithm to maintain an approximate diameter in O((1/ϵ)d/3+O(1)) time per point using O((1/ϵ)d/2+O(1)) space, and • a streaming algorithm to maintain an ϵ-kernel in O((1/ϵ)d/4+O(1)) time per point using O((1/ϵ)d/2+O(1)) space.
Growth of MRSA and Pseudomonas aeruginosa in a fine-celled foam model containing sessile commensal skin bacteria

Abstract Sessile cultures of the skin bacteria Staphylococcus saprophyticus and Corynebacterium xerosis were grown using novel fine-celled foam substrata to test the outcome of challenge by methicillin-resistant Staphylococcus aureus or Pseudomonas aeruginosa under three growth medium regimens (simulated sweat, simulated serum or simulated sweat substituted with simulated serum during the microbial challenge). S. saprophyticus and C. xerosis significantly limited MRSA and P. aeruginosa immigration respectively, under the simulated sweat and serum medium regimes. Under the substitution medium regime however, MRSA and P. aeruginosa integrated into pre-established biofilms to a significantly greater extent, attaining cell densities similar to the axenic controls. The outcome of challenge was influenced by the medium composition and test organism but could not be predicted based on planktonic competition assays or growth dynamics. Interactions between skin and wound isolates could be modelled using the fine-celled foam-based system. This model could be used to further investigate interactions and also in preclinical studies of antimicrobial wound care regimens.
Angiotensin converting enzyme insertion/deletion polymorphism: association with ethnic origin

Objective To determine the distribution of the insertion/deletion (I/D) polymorphism of the angiotensin converting enzyme (ACE) gene in several ethnic groups: Caucasian Europeans, Black Nigerians, Samoan Polynesians and Yanomami Indians. Results The ratio of the frequencies of the II, ID and DD genotypes were 1:2:1 in the Europeans, but there was a tendency towards a higher frequency of the D allele in the Nigerians. In contrast, the Samoans and the Yanomami Indians displayed a much higher frequency of the I allele than of the D allele. Conclusion The relationship between ACE genotype and disease in these latter groups is still not known, but the present results clearly suggest that ethnic origin should be carefully considered in the increasing number of studies on the association between I/D ACE genotype and disease aetiology.
Event-Based Optimization of Markov Systems

Recent research indicates that Markov decision processes (MDPs) and perturbation analysis (PA) based optimization can be derived easily from two fundamental performance sensitivity formulas. With this sensitivity point of view, an event-based optimization approach, including event-based sensitivity analysis and event-based policy iteration, was proposed via an example by X. R. Cao (Discrete Event Dyn. Syst.: Theory Appl., vol. 15, pp. 169-197, 2005). This approach utilizes the special feature of a system and illustrates how the potentials can be aggregated using the special feature. The approach applies to many practical problems that do not fit well the standard MDP formulation. This note provides a mathematical formulation and proves the main results for this approach.
Semi-Supervised Learning for Web Text Clustering

Supervised learning algorithms usually require large amounts of training data to learn reasonably accurate classifiers. Yet, for many text classification tasks, providing labeled training documents is expensive, while unlabeled documents are readily available in large quantities. Learning from both, labeled and unlabeled documents, in a semi-supervised framework is a promising approach to reduce the need for labeled training documents. In this paper, a semi-supervised learning method combining rough set and self-organizing maps (SOM) for Web text clustering is proposed. Rough set is used for reducing the irrelevant attributes of text representation on small set of labeled documents. And then using the set of reduced attributes got by rough set method, the SOM is employed for generating Web text clusters. Experimental results show the advantages of our approach to certain extent.
Remote Sensing Identification of Black Cotton Soil based on Deep Belief Network

As a type of expansive soil, black cotton soil swells when absorbing water and shrinks when dehydrated, and the cycle of swellingshrinking movements can readily occur repeatedly. These characteristics result in serious consequences both to land surfaces and to surface buildings such as ground fracturing, building settling, and road buckling and cracking, having extreme adverse effects on the quality and safety of road transportation. With Kitui, Kenya as the research area and a GF-1 remote sensing image as the vector, this study focuses on in-depth exploration of the application of a deep belief network to identify and classify black cotton soil based on the characteristics of the local black cotton soil in the remote sensing image. The results indicate that given the sample database available to this study, when the network depth was 3, the number of nodes in each hidden layer was 60, the learning rate was 0.01, the number of iterations was 20, and the number of samples was 2,000,000. The best classification result could be achieved with a precision of about 90% per the evaluation criteria proposed in this study, indicating a significant advantage of the deep belief network in remote sensing identification of black cotton soil.
Effects of Hyponatremia on the Brain

Hyponatremia is a very common electrolyte disorder, especially in the elderly, and is associated with significant morbidity, mortality and disability. In particular, the consequences of acute hyponatremia on the brain may be severe, including permanent disability and death. Also chronic hyponatremia can affect the health status, causing attention deficit, gait instability, increased risk of falls and fractures, and osteoporosis. Furthermore, an overly rapid correction of hyponatremia can be associated with irreversible brain damage, which may be the result of the osmotic demyelination syndrome. This review analyzes the detrimental consequences of acute and chronic hyponatremia and its inappropriate correction on the brain and the underlying physiopathological mechanisms, with a particular attention to the less known in vivo and in vitro effects of chronic hyponatremia.
In Vivo Magnetic Resonance Imaging of Mesenchymal Stem Cells in Myocardial Infarction

Background—We investigated the potential of magnetic resonance imaging (MRI) to track magnetically labeled mesenchymal stem cells (MR-MSCs) in a swine myocardial infarction (MI) model. Methods and Results—Adult farm pigs (n=5) were subjected to closed-chest experimental MI. MR-MSCs (2.8 to 16×107 cells) were injected intramyocardially under x-ray fluoroscopy. MRIs were obtained on a 1.5T MR scanner to demonstrate the location of the MR-MSCs and were correlated with histology. Contrast-enhanced MRI demonstrated successful injection in the infarct and serial MSC tracking was demonstrated in two animals. Conclusion—MRI tracking of MSCs is feasible and represents a preferred method for studying the engraftment of MSCs in MI.
Periodically Scheduled Burst Flows in Optical Burst Switching Networks

Providing flexible quality of service (QoS) for multiple classes of traffic is one of the important challenges in the design of optical burst switching (OBS) networks. In this paper, we propose a novel bandwidth reservation scheme for periodically scheduled bursts. Multi-protocol label switching (MPLS) is used to construct reliable burst delivery channels across the network. Meanwhile, best effort bursts are transferred using one way offset signaling to improve the wavelength efficiency, Our approach provides flow level QoS control without compromising the performance of best effort bursts. Simulation results show that our approach is better than early dropping schemes.
The Effect of Calcium Channel Blockers on Smoking-Induced Skin Flap Necrosis

Background: Calcium channel blockers have been shown experimentally to reverse many of the effects of nicotine. The purpose of this study was to assess the effect of calcium channel blockers on smoking-induced skin flap necrosis. Methods: Forty male albino Wistar rats were divided into four groups. Groups A, B, and C were treated in a controlled smoking chamber for 20 minutes daily for 21 days. On day 14, caudally based dorsal skin flaps (3 × 10 cm) were created. On days 14 through 21, group B animals received verapamil (20 mg/kg/day) by gavage. Group C received nifedipine (10 mg/kg/day). On day 21, standardized photographs were taken and flap survival areas determined. Urine cotinine concentrations were measured on days 14 and 21. Results: The mean cotinine level at surgery was 161 ng/ml in group A (smoking), 149 ng/ml in group B (verapamil), and 168 ng/ml in group C (nifedipine). These differences were not statistically significant. Cotinine concentration at surgery for group D (no smoking) was less than 10 ng/ml. The mean flap survival in group D was 79.1 percent, compared with 63.7 percent in group A (p = 0.003). The mean flap survival in group B (verapamil) was 72.8 percent, compared with 73.7 percent in group C (nifedipine). Both values were significantly greater than in group A (p = 0.04 and p = 0.008, respectively). Conclusions: In this study, enteral calcium channel blockers were associated with a statistically significant improvement in flap survival compared with untreated animals with an equivalent smoke exposure. Calcium channel blockers may reduce perioperative risk in active smokers who require skin flap surgery.
Impact of measuring fractional flow reserve on decision-making in the cath lab in a cohort of patients being considered for coronary revascularization.

BACKGROUND Fractional flow reserve (FFR) is an accepted standard to detect the functional significance of coronary stenoses. Recent trials suggest that revascularization of moderate coronary stenoses can be safely deferred if the FFR is > or = 0.75 and FFR can be used to guide therapy in multivessel disease. AIM In a cohort of patients with moderate angiographic coronary disease, we sought to examine the influence of FFR on lesion revascularization and the impact of multivessel FFR assessment on revascularization strategy. METHODS AND RESULTS Patients with FFR measurements taken between April 2005 to October 2007 were included. Out of 300 cases performed in this time, 264 patients were included. Patients were 62 +/- 11 years and 1.3 +/- 0.54 vessels were examined per case. 92.7% of lesions with a FFR < 0.75 underwent revascularizati on an d 93% of lesions with a FFR > or = 0.75 had intervention deferred. FFR was 0.71 +/- 0.07 in the revascularization group (9 coronary artery bypass graft surgery, 64 percutaneous coronary interventions) and 0.86 +/- 0.06 in the deferred group (p < 0.001). Overall, 75% of patients avoided revascularization of at least one vessel on the basis of the FFR. CONCLUSIONS Measurement of FFR is clinically useful with a high impact on clinical decision-making in the catheterization laboratory. FFR can be used to reclassify patients with multivessel stenoses, reducing the need for revascularization in the majority of cases.
Modeling the prosody of hidden events for improved word recognition

We investigate a new approach for using speech prosody as a knowledge source for speech recognition. The idea is to penalize word hypotheses that are inconsistent with prosodic features such as duration and pitch. To model the interaction between words and prosody we modify the language model to represent hidden events such as sentence boundaries and various forms of disfluency, and combine with it decision trees that predict such events from prosodic features. N-best rescoring experiments on the Switchboard corpus show a small but consistent reduction of word error as a result of this modeling. We conclude with a preliminary analysis of the types of errors that are corrected by the prosodically informed model.
Diaphragmatic palsy after cardiac surgical procedures in patients with congenital heart

Paralysis of diaphragm on one or, exceptionally, both sides is a common cause of delayed recovery and excessive morbidity following pediatric cardiac surgery. The consequences of this complication after all forms of congenital heart surgery in newborns and young infants can be potentially serious. The impact of diaphragmatic palsy on the physiology after single ventricle palliations is particularly significant. It is necessary for all professionals taking care of children with heart disease to be familiar with the etiology, diagnosis, and management of this condition. Early recognition and prompt management of diaphragmatic palsy can potentially reduce the duration of mechanical ventilation and intensive care in those who develop this complication. This review summarizes the anatomy of the phrenic nerves, reasons behind the occurrence of diaphragmatic palsy, and suggests practical guidelines for management.