Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Detection of Cache-Based Exploits

Over the last decades the digitalization has become an integral part of daily life. Computer systems complexity has also grown at a rapid pace. New business models have emerged to optimize utilization and maintenance cost of these complex systems, but neglecting the introduction of new security threats. Cloud computing, for instance, has been established as an important part of the modern IT infrastructure ignoring the potential security risks entailed in its pervasive usage. A popular threat in the Cloud and other complex systems that are reliant on the usage of shared resources stems from the exploitation of side channels. In the context of co-location of mutually untrusted users on the same hardware, the confidentiality of user data has to be guaranteed. However, the class of side-channel and covert-channel attacks has been demonstrated to circumvent the secure isolation between co-located users both in the Cloud and in a native environment by exploiting hardware side effects, e.g., through timing analyses of accesses to CPU caches. The threat related to these exploits has been known for decades, but its relevance has grown with the increasing popularity of Cloud services. In this context, the cache is leveraged as an illegal channel to convey information either from one adversary to another in a covert-channel attack or to leak information from a victim to an attacker in a side-channel attack. As cache usage does not require any privileges, addressing the threat resulting from such an exploit turns out to be a challenging task. On this background, this thesis aims at enhancing systems security by considering the cache-based covert-channel and side-channel attacks. We develop a classification of existing attacks by exploring their feasibility depending on the execution environment context, and construct an information leakage model which includes the CPU scheduling effect on the core-private cache exploitability. To delve into the specifics of detecting cache exploits, we define a set of indicators of compromise and investigate their correlation with the success of a core-private cache exploit. To account for the effect of the hypervisor scheduling configuration on the exploitability of the core-private cache, we empirically assess the success of a covert-channel attack while varying hypervisor scheduling parameters. We employ software events and performance counters to develop a reliable detection mechanism tailored to find contemporary side-channel attacks. The results presented in the thesis demonstrate that by utilizing deliberately selected indicators of compromise along with a comprehensive analysis, systems security can be significantly enhanced with respect to the cache exploitability.
Modeling Polarized Reflectance of Natural Land Surfaces Using Generalized Regression Neural Networks

Retrieval of complete aerosol properties over land through remote sensing requires accurate information about the polarization characteristics of natural land surfaces. In this paper, a new bidirectional polarization distribution function (BPDF) is proposed, using the generalized regression neural network (GRNN). This GRNN-based BPDF model builds a quite accurate nonlinear relationship between polarized reflectance and four input parameters, i.e., Fresnel factor, scattering angle, red, and near-infrared reflectances. It learns fast because only a smoothing parameter needs to be adjusted. The GRNN-based model is compared to six widely used BPDF models (i.e., Nadal–Bréon, Maignan, Waquet, Litivinov, Diner, and Xie–Cheng models), using the Polarization and Directionality of the Earth’s Reflectance (POLDER) measurements. Experiments suggest that the GRNN-based BPDF model is more accurate than these models. Compared with the best current models, the averaged root-mean-square error (RMSE) from the GRNN-based BPDF model can be reduced by 13.4% by using data collected during the whole year and is lower for 97.4% cases with data collected during every month. Moreover, compared to the widely used BPDF models, the GRNN-based BPDF model provides better performance when the scattering angle is small, and it is the first model that is able to reproduce negative polarized reflectance. The GRNN-based BPDF model is thus useful for the remote sensing of complete aerosol properties over land.
Comparing Performances of Five Distinct Automatic Classifiers for Fin Whale Vocalizations in Beamformed Spectrograms of Coherent Hydrophone Array

A large variety of sound sources in the ocean, including biological, geophysical, and man-made, can be simultaneously monitored over instantaneous continental-shelf scale regions via the passive ocean acoustic waveguide remote sensing (POAWRS) technique by employing a large-aperture densely-populated coherent hydrophone array system. Millions of acoustic signals received on the POAWRS system per day can make it challenging to identify individual sound sources. An automated classification system is necessary to enable sound sources to be recognized. Here, the objectives are to (i) gather a large training and test data set of fin whale vocalization and other acoustic signal detections; (ii) build multiple fin whale vocalization classifiers, including a logistic regression, support vector machine (SVM), decision tree, convolutional neural network (CNN), and long short-term memory (LSTM) network; (iii) evaluate and compare performance of these classifiers using multiple metrics including accuracy, precision, recall and F1-score; and (iv) integrate one of the classifiers into the existing POAWRS array and signal processing software. The findings presented here will (1) provide an automatic classifier for near real-time fin whale vocalization detection and recognition, useful in marine mammal monitoring applications; and (2) lay the foundation for building an automatic classifier applied for near real-time detection and recognition of a wide variety of biological, geophysical, and man-made sound sources typically detected by the POAWRS system in the ocean.
Power-Management Strategies for Medical Information Transmission in Wireless Body Sensor Networks

To minimize and manage the power drain, and extend battery lifetime of wireless body sensor networks (WBSN) is one of the major challenges. There are three key purposes of this survey article, first, to examine the downsides of the classical power-management methods in WBSNs; second, considering the life-critical applications and emergency contexts that are encompassed by WBSN; and, third, studying the impact of power-management techniques on resource-confined networks for economical healthcare. A specific power-management solution is also discussed.
Pollution Trends in China from 2000 to 2017: A Multi-Sensor View from Space

Satellite sensors can provide unique views of global pollution information from space. In particular, a series of aerosol and trace gas monitoring instruments have been operating for more than a decade, providing the opportunity to analyze temporal trends of major pollutants on a large scale. In this study, we integrate aerosol products from MODIS (MODIS Resolution Imaging Spectroradiometer, all abbreviations and their definitions are listed alphabetically in Abbreviations) and MISR (Multi-angle Imaging Spectroradiometer), the AAI (Absorbing Aerosol Index) product from OMI (Ozone Monitoring Instrument), column SO2 and NO2 concentrations from OMI, and tropospheric column ozone concentration from OMI/MLS (Microwave Limb Sounder) to study temporal changes in major pollutants over China. MODIS and MISR consistently revealed that column AOD (Aerosol Optical Depth) increased from 2000, peaked around 2007, and started to decline afterward, except for northwest and northeast China, where a continuous upward trend was found. Extensive negative trends in both SO2 and NO2 have also been found over major pollution source regions since ~2005. On the other hand, the OMI AAI exhibited significant increases over north China, especially the northeast and northwest regions. These places also have a decreased Angstrom exponent as revealed by MISR, indicating an increased fraction of large particles. In general, summer had the largest AOD, SO2, and NO2 trends, whereas AAI trends were strongest for autumn and winter. A multi-regression analysis showed that much of the AOD variance over major pollution source regions could be explained by SO2, NO2, and AAI combined, and that the SO2 and NO2 reduction was likely to be responsible for the negative AOD trends, while the AOD increase over NE and NW China may be associated with an increase of coarse particles revealed by increased AAI and decreased AE. In contrast to aerosols, tropospheric ozone exhibited a steady increase from 2005 throughout China. This indicates that although the recent emission control effectively reduced aerosol pollutants, ozone remains a challenging issue and may dominate future air pollution.
Blockchain Applied to Vehicular Odometers

Fraud in automobile odometers is a billion dollar problem worldwide. The technical requirements behind any solution for this problem go through the perennial, secure, and immutable registration of vehicular data. Such requirements immediately raise the question of how blockchain technology could be used for this purpose. In this article, the requirements, design choices, and construction details of a blockchain architecture for vehicular odometers are presented and discussed. The architecture advances the state of the art by adopting a more efficient consensus algorithm than the traditional ones, such as proof of work. Results from a prototype proof ofconcept of the proposed architecture validate the solution, which is able to validate a new block in the blockchain every two seconds. Applicability in real scenarios is one step ahead and very promising.
Maintaining Semantic Information across Generic 3D Model Editing Operations

Many of today’s data models for 3D applications, such as City Geography Markup Language (CityGML) or Industry Foundation Classes (IFC) encode rich semantic information in addition to the traditional geometry and materials representation. However, 3D editing techniques fall short of maintaining the semantic information across edit operations if they are not tailored to a specific data model. While semantic information is often lost during edit operations, geometry, UV mappings, and materials are usually maintained. This article presents a data model synchronization method that preserves semantic information across editing operation relying only on geometry, UV mappings, and materials. This enables easy integration of existing and future 3D editing techniques with rich data models. The method links the original data model to the edited geometry using point set registration, recovering the existing information based on spatial and UV search methods, and automatically labels the newly created geometry. An implementation of a Level of Detail 3 (LoD3) building editor for the Virtual Singapore project, based on interactive push-pull and procedural generation of façades, verified the method with 30 common editing tasks. The implementation synchronized changes in the 3D geometry with a CityGML data model and was applied to more than 100 test buildings.
Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion

When extracting land-use information from remote sensing imagery using image segmentation, obtaining fine edges for extracted objects is a key problem that is yet to be solved. In this study, we developed a new weight feature value convolutional neural network (WFCNN) to perform fine remote sensing image segmentation and extract improved land-use information from remote sensing imagery. The WFCNN includes one encoder and one classifier. The encoder obtains a set of spectral features and five levels of semantic features. It uses the linear fusion method to hierarchically fuse the semantic features, employs an adjustment layer to optimize every level of fused features to ensure the stability of the pixel features, and combines the fused semantic and spectral features to form a feature graph. The classifier then uses a Softmax model to perform pixel-by-pixel classification. The WFCNN was trained using a stochastic gradient descent algorithm; the former and two variants were subject to experimental testing based on Gaofen 6 images and aerial images that compared them with the commonly used SegNet, U-NET, and RefineNet models. The accuracy, precision, recall, and F1-Score of the WFCNN were higher than those of the other models, indicating certain advantages in pixel-by-pixel segmentation. The results clearly show that the WFCNN can improve the accuracy and automation level of large-scale land-use mapping and the extraction of other information using remote sensing imagery.
Examples of Practical Use of ISO/IEC 25000 (short paper)

In recent years the ISO/IEC 25000 series of standards seems to have reached their completeness and maturity expanding their definition from software to systems, data and IT service products. The ISO/IEC 25000 application in industry is on a voluntary basis, but it is also supported by public regulatory context. Some actions are also undertaken to apply these standards when new quality measures are defined.
Image-Based Dynamic Quantification of Aboveground Structure of Sugar Beet in Field

Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R2 > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.
An Overview of Respiratory Airflow Estimation Techniques: Acoustic vs Non-Acoustic

The primary objective of this analysis to provide an overview of the respiratory airflow estimation techniques, including both acoustic and non-acoustic approaches. This paper also highlights and addresses the applications where the breathing flow estimation was found to be crucial. The detailed summary of both acoustic and non-acoustic techniques were presented. The non-acoustic methods require airflow measurement devices or sensors whereas the acoustic methods only involve processing of breath sound signals to estimate airflow. A brief discussion of the advantages and disadvantages of the acoustic and non-acoustic approaches of respiratory flow estimation were included in this article. The literature discloses that current advances in computing technology provide a platform to integrate the signal processing methods and machine learning algorithms to estimate airflow non-invasively. The future studies to be extended after estimation the respiratory flow also were addressed in the paper.
Evaluation of Coherent and Incoherent Landslide Detection Methods Based on Synthetic Aperture Radar for Rapid Response: A Case Study for the 2018 Hokkaido Landslides

Damage mapping using Synthetic Aperture Radar (SAR) imagery has been studied in recent decades to support rapid response to natural disasters. Many researches have been developing coherent and incoherent change detection. However, their performances can vary depending on the types of the damages, the characteristics of the scatterers and the corresponding capability of algorithms. In particular, the coherence-based methods have been used as promising detectors over urban areas where high coherences are observed, but their detection accuracies still remain controversial over the area where low coherences are mainly observed such as the 2018 Hokkaido landslides. In order to understand the characteristics of landslide (damage) detectors for low-coherence areas and find an alternative and complementary method, we designed the coherence difference, coherence normalized difference, log-ratio, intensity correlation difference, and normalized differences of the intensity correlation assuming limited availability of dataset, and also developed multi-temporal algorithms using the coherence, intensity, and intensity correlation. They were tested and evaluated using multiple polygons extracted from aerial photos. We were able to observe that the multi-temporal intensity correlation method has the potential to detect the landslides over the low coherence region and all types of land uses.
Chlorophyll Concentration Retrieval by Training Convolutional Neural Network for Stochastic Model of Leaf Optical Properties (SLOP) Inversion

Miniaturized hyperspectral imaging techniques have developed rapidly in recent years and have become widely available for different applications. Combining calibrated hyperspectral imagery with inverse physically based reflectance models is an interesting approach for estimating chlorophyll concentrations that are good indicators of vegetation health. The objective of this study was to develop a novel approach for retrieving chlorophyll a and b values from remotely sensed data by inverting the stochastic model of leaf optical properties using a one-dimensional convolutional neural network. The inversion results and retrieved values are validated in two ways: A classical machine learning validation dataset and calculating chlorophyll maps from empirical remotely sensed hyperspectral data and comparing them to TCARI OSAVI , an index that has strong negative correlation with chlorophyll concentration. With the validation dataset, coefficients of determination ( R 2 ) of 0.97 were obtained for chlorophyll a and 0.95 for chlorophyll b. The chlorophyll maps correlate with the TCARI OSAVI map. The correlation coefficient (R) is −0.87 for chlorophyll a and −0.68 for chlorophyll b in selected plots. These results indicate that the approach is highly promising approach for estimating vegetation chlorophyll content.
Effectiveness of HIV Anti-retroviral Agents on the HIV/AIDS Epidemic

Aims: The scale up of HAART globally has shifted the mortality time table, decreased viral transmission and improved health related quality of life for patients while emphasizing the importance of adherence to therapy for improved outcomes. Study Design: Review Article. Place and Duration of Study: Department of Medicine SABA University School of Medicine. Methodology: Using Pubmed and Science Direct, research was executed from January 2015 to March 2015. Articles used were limited to a study completion date of less than five years. The type of studies used were case control, cohort, cross sectional, follow up, and qualitative studies on human subjects. All studies originated within the last 20 years but to keep the issue current and relevant, preference was given to studies published within the last five years. Results: The scale up of HAART correlated with a decrease in mortality rates and a lengthening of the death time table. Health Related Quality of Life significantly improved with therapy. Both vertical and horizontal transmission rates showed improvements with the increase of available therapy. Stigma is still a limiting factor to adherence, limiting the gains of HAART. Review Article Faizi et al.; BJMMR, 15(11): 1-13, 2016; Article no.BJMMR. 24788 2 Conclusion: HAART has had an overall positive effect in improving the quality of life of patients suffering from the HIV epidemic while working to combat new infections. Causation can be inferred for some aspects as there is adequate literature while certain topics such as stigma need more research. Ultramini Abstract: HAART has overall improved quality of life for patients while decreasing the mortality time table. It has shifted the perception of HIV from a death sentence to a treatable and preventable diagnosis. Vertical and horizontal transmission have seen great improvements. However, much work has to be done to combat stigma, increase adherence, and decrease attrition to increase the gains of HAART and its effects on the HIV epidemic.
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier

Mapping flood-prone areas is a key activity in flood disaster management. In this paper, we propose a new flood susceptibility mapping technique. We employ new ensemble models based on bagging as a meta-classifier and K-Nearest Neighbor (KNN) coarse, cosine, cubic, and weighted base classifiers to spatially forecast flooding in the Haraz watershed in northern Iran. We identified flood-prone areas using data from Sentinel-1 sensor. We then selected 10 conditioning factors to spatially predict floods and assess their predictive power using the Relief Attribute Evaluation (RFAE) method. Model validation was performed using two statistical error indices and the area under the curve (AUC). Our results show that the Bagging–Cubic–KNN ensemble model outperformed other ensemble models. It decreased the overfitting and variance problems in the training dataset and enhanced the prediction accuracy of the Cubic–KNN model (AUC=0.660). We therefore recommend that the Bagging–Cubic–KNN model be more widely applied for the sustainable management of flood-prone areas.
Towards an Operational, Split Window-Derived Surface Temperature Product for the Thermal Infrared Sensors Onboard Landsat 8 and 9

The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.