N-opcode analysis for android malware classification and categorization

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach.

Enzyme-induced Aggregation of Plasmonic Nanoparticles for the Development of Label-free and Ultrasensitive Detection of Bacterial DNA

Sensitive detection of pathogens is critical to ensure the safety of food supplies and to prevent bacterial disease infection and outbreak at the first onset. While conventional techniques such as cell culture, ELISA, PCR, etc. have been used as the predominant detection workhorses, they are however limited by either time-consuming procedure, complicated sample pre-treatment, expensive analysis and operation, or inability to be implemented at point-of-care testing. Here, we present our recently developed assay exploiting enzyme-induced aggregation of plasmonic gold nanoparticles (AuNPs) for label-free and ultrasensitive detection of bacterial DNA. In the experiments, AuNPs are first functionalized with specific, single-stranded RNA probes so that they exhibit high stability in solution even under high electrolytic condition thus exhibiting red color. When bacterial DNA is present in a sample, a DNA-RNA heteroduplex will be formed and subsequently prone to the RNase H cleavage on the RNA probe, allowing the DNA to liberate and hybridize with another RNA strand. This continuously happens until all of the RNA strands are cleaved, leaving the nanoparticles ‘unprotected’. The addition of NaCl will cause the ‘unprotected’ nanoparticles to aggregate, initiating a colour change from red to blue. The reaction is performed in a multi-well plate format, and the distinct colour signal can be discriminated by naked eye or simple optical spectroscopy. As a result, bacterial DNA as low as pM could be unambiguously detected, suggesting that the enzyme-induced aggregation of AuNPs assay is very easy to perform and sensitive, it will significantly benefit to development of fast and ultrasensitive methods that can be used for disease detection and diagnosis.

Clinical Outcomes in 3343 Children and Adults with Rheumatic Heart Disease from 14 Low and Middle Income Countries: 2-Year Follow-up of the Global Rheumatic Heart Disease Registry (the REMEDY study)

BACKGROUND: -There are few contemporary data on the mortality and morbidity associated with rheumatic heart disease (RHD) or information on their predictors. We report the two year follow-up of individuals with RHD from 14 low and middle income countries in Africa and Asia.

METHODS: -Between January 2010 and November 2012, we enrolled 3343 patients from 25 centers in 14 countries and followed them for two years to assess mortality, congestive heart failure (CHF), stroke or transient ischemic attack (TIA), recurrent acute rheumatic fever (ARF), and infective endocarditis (IE).

RESULTS: -Vital status at 24 months was known for 2960 (88.5%) patients. Two thirds were female. Although patients were young (median age 28 years, interquartile range 18 to 40), the two year case fatality rate was high (500 deaths, 16.9%). Mortality rate was 116.3/1000 patient-years in the first year and 65.4/1000 patient-years in the second year. Median age at death was 28.7 years. Independent predictors of death were severe valve disease (hazard ratio (HR) 2.36, 95% confidence interval (CI) 1.80-3.11), CHF (HR 2.16, 95% CI 1.70-2.72), New York Heart Association functional class III/IV (HR 1.67, 95% CI 1.32-2.10), atrial fibrillation (AF) (HR 1.40, 95% CI 1.10-1.78) and older age (HR 1.02, 95% CI 1.01-1.02 per year increase) at enrolment. Post-primary education (HR 0.67, 95% CI 0.54-0.85) and female sex (HR 0.65, 95%CI 0.52-0.80) were associated with lower risk of death. 204 (6.9%) had new CHF (incidence, 38.42/1000 patient-years), 46 (1.6%) had a stroke or TIA (8.45/1000 patient-years), 19 (0.6%) had ARF (3.49/1000 patient-years), and 20 (0.7%) had IE (3.65/1000 patient-years). Previous stroke and older age were independent predictors of stroke/TIA or systemic embolism. Patients from low and lower-middle income countries had significantly higher age- and sex-adjusted mortality compared to patients from upper-middle income countries. Valve surgery was significantly more common in upper-middle income than in lower-middle- or low-income countries.

CONCLUSIONS: -Patients with clinical RHD have high mortality and morbidity despite being young; those from low and lower-middle income countries had a poorer prognosis associated with advanced disease and low education. Programs focused on early detection and treatment of clinical RHD are required to improve outcomes.

Data Mining for Network Intrusion Detection : A comparison of data mining algorithms and an analysis of relevant features for detecting cyber-attacks

Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.

Sensing parasites: proteomic and advanced bio-detection alternatives

[EN] Parasitic diseases have a great impact in human and animal health. The gold standard for the diagnosis of the majority of parasitic infections is still conventional microscopy, which presents important limitations in terms of sensitivity and specificity and commonly requires highly trained technicians. More accurate molecular-based diagnostic tools are needed for the implementation of early detection, effective treatments and massive screenings with high-throughput capacities. In this respect, sensitive and affordable devices could greatly impact on sustainable control programmes which exist against parasitic diseases, especially in low income settings. Proteomics and nanotechnology approaches are valuable tools for sensing pathogens and host alteration signatures within micro fluidic detection platforms. These new devices might provide novel solutions to fight parasitic diseases. Newly described specific parasite derived products with immune-modulatory properties have been postulated as the best candidates for the early and accurate detection of parasitic infections as well as for the blockage of parasite development. This review provides the most recent methodological and technological advances with great potential for biosensing parasites in their hosts, showing the newest opportunities offered by modern “-omics” and platforms for parasite detection and control.

Modeling, Simulation, and Characterization of Space Debris in low-Earth Orbit

Every space launch increases the overall amount of space debris. Satellites have limited awareness of nearby objects that might pose a collision hazard. Astrometric, radiometric, and thermal models for the study of space debris in low-Earth orbit have been developed. This modeled approach proposes analysis methods that provide increased Local Area Awareness for satellites in low-Earth and geostationary orbit. Local Area Awareness is defined as the ability to detect, characterize, and extract useful information regarding resident space objects as they move through the space environment surrounding a spacecraft. The study of space debris is of critical importance to all space-faring nations. Characterization efforts are proposed using long-wave infrared sensors for space-based observations of debris objects in low-Earth orbit. Long-wave infrared sensors are commercially available and do not require solar illumination to be observed, as their received signal is temperature dependent. The characterization of debris objects through means of passive imaging techniques allows for further studies into the origination, specifications, and future trajectory of debris objects. Conclusions are made regarding the aforementioned thermal analysis as a function of debris orbit, geometry, orientation with respect to time, and material properties. Development of a thermal model permits the characterization of debris objects based upon their received long-wave infrared signals. Information regarding the material type, size, and tumble-rate of the observed debris objects are extracted. This investigation proposes the utilization of long-wave infrared radiometric models of typical debris to develop techniques for the detection and characterization of debris objects via signal analysis of unresolved imagery. Knowledge regarding the orbital type and semi-major axis of the observed debris object are extracted via astrometric analysis. This knowledge may aid in the constraint of the admissible region for the initial orbit determination process. The resultant orbital information is then fused with the radiometric characterization analysis enabling further characterization efforts of the observed debris object. This fused analysis, yielding orbital, material, and thermal properties, significantly increases a satellite’s Local Area Awareness via an intimate understanding of the debris environment surrounding the spacecraft.

Optical redox ratio and endogenous porphyrins in the detection of urinary bladder cancer:a patient biopsy analysis

Bladder cancer is among the most common cancers in the UK and conventional detection techniques suffer from low sensitivity, low specificity, or both. Recent attempts to address the disparity have led to progress in the field of autofluorescence as a means to diagnose the disease with high efficiency, however there is still a lot not known about autofluorescence profiles in the disease. The multi-functional diagnostic system "LAKK-M" was used to assess autofluorescence profiles of healthy and cancerous bladder tissue to identify novel biomarkers of the disease. Statistically significant differences were observed in the optical redox ratio (a measure of tissue metabolic activity), the amplitude of endogenous porphyrins and the NADH/porphyrin ratio between tissue types. These findings could advance understanding of bladder cancer and aid in the development of new techniques for detection and surveillance.

Detection of African Swine Fever Virus Antibodies in Serum and Oral Fluid Specimens Using a Recombinant Protein 30 (p30) Dual Matrix Indirect ELISA

In the absence of effective vaccine(s), control of African swine fever caused by African swine fever virus (ASFV) must be based on early, efficient, cost-effective detection and strict control and elimination strategies. For this purpose, we developed an indirect ELISA capable of detecting ASFV antibodies in either serum or oral fluid specimens. The recombinant protein used in the ELISA was selected by comparing the early serum antibody response of ASFV-infected pigs (NHV-p68 isolate) to three major recombinant polypeptides (p30, p54, p72) using a multiplex fluorescent microbead-based immunoassay (FMIA). Non-hazardous (non-infectious) antibody-positive serum for use as plate positive controls and for the calculation of sample-to-positive (S:P) ratios was produced by inoculating pigs with a replicon particle (RP) vaccine expressing the ASFV p30 gene. The optimized ELISA detected anti-p30 antibodies in serum and/or oral fluid samples from pigs inoculated with ASFV under experimental conditions beginning 8 to 12 days post inoculation. Tests on serum (n = 200) and oral fluid (n = 200) field samples from an ASFV-free population demonstrated that the assay was highly diagnostically specific. The convenience and diagnostic utility of oral fluid sampling combined with the flexibility to test either serum or oral fluid on the same platform suggests that this assay will be highly useful under the conditions for which OIE recommends ASFV antibody surveillance, i.e., in ASFV-endemic areas and for the detection of infections with ASFV isolates of low virulence.

DETECTION OF SPORES AND HYPHAE OF ARTWORKS’ BIODETERIOGENIC FILAMENTOUS FUNGI BY RNA-FISH

Filamentous fungi are a threat to the conservation of Cultural Heritage. Thus, detection and identification of viable filamentous fungi are crucial for applying adequate Safeguard measures. RNA-FISH protocols have been previously applied with this aim in Cultural Heritage samples. However, only hyphae detection was reported in the literature, even if spores and conidia are not only a potential risk to Cultural Heritage but can also be harmful for the health of visitors, curators and restorers. Thus, the aim of this work was to evaluate various permeabilizing strategies for their application in the detection of spores/conidia and hyphae of artworks’ biodeteriogenic filamentous fungi by RNA-FISH. Besides of this, the influence of cell aging on the success of the technique and on the development of fungal autofluorescence (that could hamper the RNA-FISH signal detection) were also investigated. Five common biodeteriogenic filamentous fungi species isolated from biodegradated artworks were used as biological model: Aspergillus niger, Cladosporium sp, Fusarium sp, Penicillium sp. and Exophialia sp. Fungal autofluorescence was only detected in cells harvested from Fusarium sp, and Exophialia sp. old cultures, being aging-dependent. However, it was weak enough to allow autofluorescence/RNA-FISH signals distinction. Thus, autofluorescence was not a limitation for the application of RNA-FISH for detection of the taxa investigated. All the permeabilization strategies tested allowed to detect fungal cells from young cultures by RNA-FISH. However, only the combination of paraformaldehyde with Triton X-100 allowed the detection of conidia/spores and hyphae of old filamentous fungi. All the permeabilization strategies failed in the Aspergillus niger conidia/spores staining, which are known to be particularly difficult to permeabilize. But, even in spite of this, the application of this permeabilization method increased the analytical potential of RNA FISH in Cultural Heritage biodeterioration. Whereas much work is required to validate this RNA-FISH approach for its application in real samples from Cultural Heritage it could represent an important advance for the detection, not only of hyphae but also of spores and conidia of various filamentous fungi taxa by RNA-FISH.

Detection of ochratoxin A in tropical wine and grape juice from Brazil.

Ochratoxin A (OTA) is the main mycotoxin found in grapes, wines and grape juices and is considered one of the most harmful contaminants to human health. In this study, samples of tropical wines and grape juices from different grape varieties grown in Brazil were analysed for their OTA content by high-performance liquid chromatography. The detection and quantification limits for OTA were 0.01 and 0.03 ?g L?1 respectively. OTA was detected in 13 (38.24%) of the samples analysed, with concentrations ranging from <0.03 to 0.62 micron g L-1. OTA was not detected in any of the grape juice samples. Most of the red wine samples proved to be contaminated with OTA (75%), while only one white wine sample was contaminated. However, the OTA levels detected in all samples were well below the maximum tolerable limit (2 micron g L-1) in wine and grape juice established by the European Community and Brazilian legislature. The results of this study indicate a low risk of exposure to OTA by consumption of tropical wines and grape juices from Brazil.

Safe and Collaborative Navigation & Interaction With Mobile Manipulators in Domestic Appliance Test Labs

Safe collaboration between a robot and human operator forms a critical requirement for deploying a robotic system into a manufacturing and testing environment. In this dissertation, the safety requirement for is developed and implemented for the navigation system of the mobile manipulators. A methodology for human-robot co-existence through a 3d scene analysis is also investigated. The proposed approach exploits the advance in computing capability by relying on graphic processing units (GPU’s) for volumetric predictive human-robot contact checking. Apart from guaranteeing safety of operators, human-robot collaboration is also fundamental when cooperative activities are required, as in appliance test automation floor. To achieve this, a generalized hierarchical task controller scheme for collision avoidance is developed. This allows the robotic arm to safely approach and inspect the interior of the appliance without collision during the testing procedure. The unpredictable presence of the operators also forms dynamic obstacle that changes very fast, thereby requiring a quick reaction from the robot side. In this aspect, a GPU-accelarated distance field is computed to speed up reaction time to avoid collision between human operator and the robot. An automated appliance testing also involves robotized laundry loading and unloading during life cycle testing. This task involves Laundry detection, grasp pose estimation and manipulation in a container, inside the drum and during recovery grasping. A wrinkle and blob detection algorithms for grasp pose estimation are developed and grasp poses are calculated along the wrinkle and blobs to efficiently perform grasping task. By ranking the estimated laundry grasp poses according to a predefined cost function, the robotic arm attempt to grasp poses that are more comfortable from the robot kinematic side as well as collision free on the appliance side. This is achieved through appliance detection and full-model registration and collision free trajectory execution using online collision avoidance.

Monitoring and Prediction of Thermal Emergencies in High Performance Computing Systems

Modern scientific discoveries are driven by an unsatisfiable demand for computational resources. High-Performance Computing (HPC) systems are an aggregation of computing power to deliver considerably higher performance than one typical desktop computer can provide, to solve large problems in science, engineering, or business. An HPC room in the datacenter is a complex controlled environment that hosts thousands of computing nodes that consume electrical power in the range of megawatts, which gets completely transformed into heat. Although a datacenter contains sophisticated cooling systems, our studies indicate quantitative evidence of thermal bottlenecks in real-life production workload, showing the presence of significant spatial and temporal thermal and power heterogeneity. Therefore minor thermal issues/anomalies can potentially start a chain of events that leads to an unbalance between the amount of heat generated by the computing nodes and the heat removed by the cooling system originating thermal hazards. Although thermal anomalies are rare events, anomaly detection/prediction in time is vital to avoid IT and facility equipment damage and outage of the datacenter, with severe societal and business losses. For this reason, automated approaches to detect thermal anomalies in datacenters have considerable potential. This thesis analyzed and characterized the power and thermal characteristics of a Tier0 datacenter (CINECA) during production and under abnormal thermal conditions. Then, a Deep Learning (DL)-powered thermal hazard prediction framework is proposed. The proposed models are validated against real thermal hazard events reported for the studied HPC cluster while in production. This thesis is the first empirical study of thermal anomaly detection and prediction techniques of a real large-scale HPC system to the best of my knowledge. For this thesis, I used a large-scale dataset, monitoring data of tens of thousands of sensors for around 24 months with a data collection rate of around 20 seconds.

Dietary Supplement Usage And Motivation In Brazilian Road Runners.

The consumption of dietary supplements is highest among athletes and it can represent potential a health risk for consumers. The aim of this study was to determine the prevalence of consumption of dietary supplements by road runners. We interviewed 817 volunteers from four road races in the Brazilian running calendar. The sample consisted of 671 male and 146 female runners with a mean age of 37.9 ± 12.4 years. Of the sample, 28.33% reported having used some type of dietary supplement. The main motivation for this consumption is to increase in stamina and improve performance. The probability of consuming dietary supplements increased 4.67 times when the runners were guided by coaches. The consumption of supplements was strongly correlated (r = 0.97) with weekly running distance, and also highly correlated (r = 0.86) with the number of years the sport had been practiced. The longer the runner had practiced the sport, the higher the training volume and the greater the intake of supplements. The five most frequently cited reasons for consumption were: energy enhancement (29.5%), performance improvement (17.1%), increased level of endurance (10.3%), nutrient replacement (11.1%), and avoidance of fatigue (10.3%). About 30% of the consumers declared more than one reason for taking dietary supplements. The most consumed supplements were: carbohydrates (52.17%), vitamins (28.70%), and proteins (13.48%). Supplement consumption by road runners in Brazil appeared to be guided by the energy boosting properties of the supplement, the influence of coaches, and the experience of the user. The amount of supplement intake seemed to be lower among road runners than for athletes of other sports. We recommend that coaches and nutritionists emphasise that a balanced diet can meet the needs of physically active people.

A Rapid And Simple Capillary Electrophoresis Method For Indirect Determination Of The Biocide 2,2-dibromo-3-nitrilo-propionamide (dbnpa) In Cooling Waters.

A capillary zone electrophoresis (CE) method was developed for the determination of the biocide 2,2-dibromo-3-nitrilo-propionamide (DBNPA) in water used in cooling systems. The biocide is indirectly determined by CE measurement of the concentration of bromide ions produced by the reaction between the DBNPA and bisulfite. The relationship between the bromide peak areas and the DBNPA concentrations showed a good linearity and a coefficient of determination (R(2)) of 0.9997 in the evaluated concentration range of 0-75 μmol L(-1). The detection and quantification limits for DBNPA were 0.23 and 0.75 μmol L(-1), respectively. The proposed CE method was successfully applied for the analysis of samples of tap water and cooling water spiked with DBNPA. The intra-day and inter-day (intermediary) precisions were lower than 2.8 and 6.2%, respectively. The DBNPA concentrations measured by the CE method were compared to the values obtained by a spectrophotometric method and were found to agree well.

The academic plagiarism and its punishments - a review

Currently there is an increase in the occurrence of plagiarism in varied types of academic texts. Therefore, in agreement with the Brazilian Coordination of Improvement of Higher Education Personnel (CAPES) policies, Brazilian higher education institutions should establish guidelines for the detection and inhibition of academic plagiarism. However, the notion of plagiarism is extremely complex, since the ability of textual construction acquired during education is also developed using others' words. Thus, it is necessary to better know the concept of plagiarism and its implications, as well as the consequences of plagiarism and the punishments that may result from it. Consequently, rules and policies to be established will be better founded in order to address the problem of plagiarism in academic texts in a comprehensive and consistent way, not only to inhibit plagiarism but also to promote education on how is possible to create texts in an original fashion.