Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy-Preserving and Lightweight Verification of Deep Packet Inspection in Clouds

In the trend of network middleboxes as a service, enterprise customers adopt in-the-cloud deep packet inspection (DPI) services to protect networks. As network misconfigurations and hardware failures notoriously exist, recent efforts envision to ensure the execution integrity of DPI services in untrusted clouds. However, they either require enterprise customers to know proprietary DPI rulesets of cloud providers or introduce forbidden overhead in the network context. In the paper, we propose a privacy-preserving and lightweight verification scheme that efficiently checks whether in-the-cloud DPI services run correctly without leaking private DPI rulesets. Particularly, our design introduces one trusted third party to perform privacy-preserving and trustworthy ruleset evaluation and DPI execution verification. Meanwhile, it devises a novel DPI ruleset authentication method that enables tamper-proof DPI operations and facilitates fast proof generation. The proofs can be verified without requiring the verifier to always maintain all rulesets. To further reduce the verification costs while resisting cloud cheating behaviors like bias treatments of packets, it employs a commitment-based delayed sampling mechanism which requires the DPI services to first demonstrate that all packets have been processed before receiving sampling decisions. Moreover, extensive experiments are conducted based on Click modules. The results show that the proposed scheme is practical and only incurs the real-time overhead of 10–20 microseconds.
Filmmusik – analytische und musikpädagogische Aspekte einer Gattung in der Dekonstruktion

Ich erinnere mich, Ihnen gesagt zu haben, dass ich trotz aller Missstände und Ärgernisse gern Filmmusik schreibe. Diese Aussage ziehe ich zurück. Ich denke, dass ich für einige Zeit von diesem Verlangen kuriert sein werde. Mag die Arbeit für die Leinwand auch noch so verlockend sein, man muss begreifen, dass der Zugang in musikalischen Belangen von einem großen Maß Ignoranz, Dummheit und schlechtem Geschmack derart blockiert wird, dass es wirklich hoffnungslos scheint. (Toch 1944)
Feasibility Study for a Near Term Demonstration of Laser-Sail Propulsion from the Ground to Low Earth Orbit

This paper adds to the body of research related to the concept of propellant-less in-space propulsion utilizing an external high energy laser (HEL) to provide momentum to an ultra-lightweight (gossamer) spacecraft. It has been suggested that the capabilities of Space Situational Awareness assets and the advanced analytical tools available for fine resolution orbit determination make it possible to investigate the practicalities of a ground to Low Earth Orbit (LEO) demonstration at delivered power levels that only illuminate a spacecraft without causing damage to it. The degree to which this can be expected to produce a measurable change in the orbit of a low ballistic coefficient spacecraft is investigated. Key system characteristics and estimated performance are derived for a near term mission opportunity involving the LightSail 2 spacecraft and laser power levels modest in comparison to those proposed previously by Forward, Landis, or Marx. [1,2,3] A more detailed investigation of accessing LightSail 2 from Santa Rosa Island on Eglin Air Force Base on the United States coast of the Gulf of Mexico is provided to show expected results in a specific case.
Modelling of spatial causality among distinctive properties of an image using conditional random field for image classification

ABSTRACT In this paper, we proposed an ordered patch-based method using conditional random field (CRF) in order to encode local properties and their spatial relationship in the images to address texture classification, face recognition and scene classification problems. Typical image classification approaches classify images without considering spatial causality among distinctive properties of an image to represent it in the feature space. In this method first, each image is encoded as a sequence of ordered patches including local properties. Second, the sequence of these ordered patches is modelled as a probabilistic feature vector using CRF to model spatial relationship of these local properties; and finally, image classification is performed on such probabilistic image representation. Experimental results on several standard image datasets indicate that the proposed method outperforms some of existing image classification methods.
Improvement and invariance analysis of Zernike moments

An improved Zernike moment using a region-based shape descriptor is presented. The improved Zernike moment not only has rotation invariance, but also has scale invariance that the unimproved Zernike moment does not have. The experimental results show that the improved Zernike moment has better invariant properties than the unimproved Zernike moment using a region-based shape descriptor.
Probing the dark universe with gravitational lensing

Since its early success as an experimental test of the theory of general relativity in 1919, gravitational lensing has come a long way and is firmly established as an indispensable element for many astrophysical applications. In this thesis, we explore novel applications of gravitational lensing that further our understanding of the dark sectors of the cosmos and other astrophysical objects, namely dark matter nanostructure, black holes and the Galactic disk. We pay particular attention to developing concrete and optimal statistical methodologies and numerical implementations for these novel probes. We start by developing a statistical framework to measure the dark matter power spectrum in the deep nonlinear regime, using transient weak lensing, and simultaneously measure the time delays for strongly lensed quasars. We then outline how observations of microlensing in optical and radio can unravel the structure, dynamics, and content of the Galactic disk, and in particular, be used to detect stellar mass black holes. Lastly, using the shadow images of the super-massive black holes caused by extreme lensing effect, we can learn about the structure of space-time, accretion flows and astrophysical jets. We present a Bayesian framework for analyzing the data from the Event Horizon Telescope Collaboration.
Statistical Methods for Dating Collections of Historical Documents

Statistical Methods for Dating Collections of Historical Documents Gelila Tilahun Doctor of Philosophy Graduate Department of Statistics University of Toronto 2011 The problem in this thesis was originally motivated by problems presented with documents of Early England Data Set (DEEDS). The central problem with these medieval documents is the lack of methods to assign accurate dates to those documents which bear no date. With the problems of the DEEDS documents in mind, we present two methods to impute missing features of texts. In the first method, we suggest a new class of metrics for measuring distances between texts. We then show how to combine the distances between the texts using statistical smoothing. This method can be adapted to settings where the features of the texts are ordered or unordered categoricals (as in the case of, for example, authorship assignment problems). In the second method, we estimate the probability of occurrences of words in texts using nonparametric regression techniques of local polynomial fitting with kernel weight to generalized linear models. We combine the estimated probability of occurrences of words of a text to estimate the probability of occurrence of a text as a function of its feature – the feature in this case being the date in which the text is written. The application and results of our methods to the DEEDS documents are presented.
De-noising remotely sensed digital imagery

This paper applies two recent methods to denoise remotely sensed images - wavelet based Markov Random Field (MRF) methods, Independent Component Analysis (ICA) and compares them with the standard Wiener filter. In order to facilitate the continued use of these methods in remote sensing the theory behind each method is discussed in detail. Subsequently they are applied to de-noising remotely sensed images. The efficiency of each algorithm is obtained by computing the signal to noise ratio (SNR) before and after de-noising. Results indicate that the MRF based methods, though slightly more complicated to program and only marginally slower than ICA de-noising, generally perform better than both ICA and Wiener filtering. The article ends by discussing future areas of research in de-noising remotely sensed images.
The role of recombination landscape in species hybridisation and speciation

It is now well recognised that closely related species can hybridize and exchange genetic material, which may promote or oppose adaptation and speciation. In some cases, interspecific hybridisation is very common, making it surprising that species identity is preserved despite active gene exchange. The genomes of most eukaryotic species are highly heterogeneous with regard to gene density, abundance of repetitive DNA, chromatin compactisation etc, which can make certain genomic regions more prone or more resistant to introgression of genetic material from other species. Heterogeneity in local recombination rate underpins many of the observed patterns across the genome (e.g. actively recombining regions are typically gene rich and depleted for repetitive DNA) and it can strongly affect the permeability of genomic regions to interspecific introgression. The larger the region lacking recombination, the higher the chance for the presence of species incompatibility gene(s) in that region, making the entire non- or rarely recombining block impermeable to interspecific introgression. Large plant genomes tend to have highly heterogeneous recombination landscape, with recombination frequently occurring at the ends of the chromosomes and central regions lacking recombination. In this paper we review the relationship between recombination and introgression in plants and argue that large rarely recombining regions likely play a major role in preserving species identity in actively hybridising plant species.
How Well Can the Met Office Unified Model Forecast Tropical Cyclones in the Western North Pacific

AbstractConvection-permitting numerical weather prediction models are a key tool for forecasting tropical cyclone (TC) intensities, intensity changes, and precipitation. The Met Office has been routinely running a regional (4.4-km grid spacing), explicit convection version of its Unified Model (UM) over the Philippines since August 2014, driven by its operational global model. The principal aim of this study is to assess the performance of this model relative to the driving global model. By evaluating over a year’s worth of operational TC forecasts, it is shown that the Philippines regional model offers clear benefits for TC forecasting compared with the Met Office global model. In particular, it provides much improved predictions for the intensities of strong storms (category 3 and above) and can successfully capture some rapid intensification (RI) events, whereas the global model cannot predict RI at all. The spatial location of rainfall within intense TCs is also more skillfully predicted by the region.
Seagrass macroinvertebrate bycatches support mosquito net trawl fishery in Madagascar

The use of mosquito nets, primarily for targeting small and/or juvenile fish, has rapidly increased in Western Indian Ocean shallow seagrass beds and coral reefs over the last 20 years. However, to date, invertebrate by-catch by locally-made fishing gear has not been reported. We studied the mosquito net trawl fishery in seagrass areas in the Bay of Toliara, Southwest Madagascar through the GPS tracking of fishers from August 2018 to February 2019. Catches were monitored through monthly landing surveys to characterize spatial temporal patterns in the abundance and diversity of macroinvertebrates in the seagrass beds. Fishing was carried out at low tide, mostly at night, by fishers pulling a hand-made trawl net of varying dimensions. Overall, 43 macroinvertebrate taxa were identified out of 217,080 individuals collected from 109 catch samples. Catches were generally composed of crustaceans (mainly Portunidae, Processidae, Penaeidae, and Alpheidae). The crab Thalamita mitsiensis largely dominated the abundance and biomass of the macroinvertebrate assemblage (from 6% to 100% and from 5 to 100% of the overall density and biomass, respectively). Macroinvertebrates composed 1.5% to 100% of the total catch per trip (i.e., 4–55 kg trip−1). They were sold for human consumption or animal feed, which provided additional income to fishers (USD 1–24 trip−1 and 1–72% of catch income per trip). This study revealed that macroinvertebrate resources provide valuable by-catch to small-scale fishers in Southwest Madagascar. This bycatch generates income that further encourages the use of mosquito net trawls and exacerbates their negative effects on coastal seagrass ecosystems and fisheries.
Assessment of Production Performance and Egg Quality of Commercial Laying Hens Fed Black Pepper and Red Pepper Additives

With the increasing demand for poultry products (meat and eggs) worldwide, poultry farmers want to improve the productivity of their flocks. This challenge has necessitated poultry nutritionists to offer specific nutritional strategies for improved productivity. Feed additives are recommended as one of such strategic options, and plant materials, otherwise known as phytogenics, are being extensively investigated. Therefore, the study's objective was to assess the production performance and egg quality of commercial laying hens fed black pepper and red pepper additives. A total of 210 commercial laying hens at 24 weeks of age were allotted into seven dietary treatments in a completely randomized design. Each treatment had thirty (30) birds each, replicated three times to give ten (10) birds per replicate in a study that lasted 12 weeks. The formulated diets included: a control diet with no additives; Treatments 2 and 3 had 1% and 1.5% black pepper powder; Treatments 4 and 5 had 1% and 1.5% red pepper powder; Treatment 6 had a mixture of 0.5% each of black pepper and red pepper, while treatment 7 had a mixture of 0.75% each of black pepper and red pepper. All data collected were subjected to a one-way analysis of variance using the general linear model procedure of SAS (2012). From the results obtained, the hen day production (%) was significantly highest in hens fed the diet with 1% red pepper (83.40%) and the least from hens in the control (65.56%). The control treatment also recorded the least performances in egg mass (48.76g/bird/day) and feed conversion ratios (2.58 and 2.30). Shell thickness was least (0.43mm) in hens fed the diet with 1.5% red pepper, while the thickest shell measurement of 0.50mm was recorded in Treatment 6. The highest Haugh unit of 103.77 was obtained from Treatment 4, while the least value of 96.66 was obtained from the control. Yolk colour was significantly improved in the treated groups as against what was obtained from the control group. From the results, it can be concluded that black pepper and red pepper, having improved production performance and some egg quality characteristics in the treated groups compared to the control hold great potential as dietary additives.
Assessment of transfer learning models for grading of diabetic retinopathy

Diabetic retinopathy is a potentially mortal diabetic complication. The severity level of DR must be identified earlier to reduce the medical complications. Effective automated ways for identifying DR and classifying its severity stage are necessary to reduce the burden on ophthalmologists. Transfer learning methods are utilized to automatically grade the severity of diabetic retinopathy in this study. The stages of DR are diagnosed using pretrained VGG16, Inception v3, and ResNet50 models on pre-processed retinal images of DDR dataset. Out of three implemented models, Inception v3 achieved higher validation accuracy of 76.47% and testing accuracy of 67% compared to VGG16 and ResNet50 models. This research contributes to the analysis of deep learning architectures for the creation of automated diabetic retinopathy stage diagnosis and grading
Defining Continental Lithosphere as a Layer With Abundant Frozen‐In Structures That Scatter Seismic Waves

We investigate the structure of the continental lithosphere by combining two approaches: a systematic survey of abrupt changes in seismic properties detected by P‐to‐S converted body waves and an integrated geophysical‐petrological inversion for temperature and density in the upper mantle. We refine the global thermo‐chemical model WINTERC‐G in eastern North America by including detailed regional information on the crust into petrological inversions and combine it with the upper mantle layering beneath eastern North America yielded by anisotropy‐aware receiver‐function analysis. Eastern North America's Archean, Proterozoic and Paleozoic lithospheres show an excellent agreement between the depth to the 1,300°C isotherm that bounds the lithosphere and the depth range where converted waves detect abrupt changes in seismic properties. Boundaries with these abrupt changes reside within the rigid mechanical lithosphere and are uncommon in the convecting mantle beneath it. The boundaries include both impedance increases and decreases with depth, as well as anisotropy changes, and must have developed over the course of the assembly and evolution of the lithosphere. In the asthenosphere below, such heterogeneities appear to have been largely mixed out by convection. The existence of abundant interfaces with diverse origin can account for the commonly observed scattered signals from within the continental lithosphere and presents an alternative to the end‐member concept of the mid‐lithospheric discontinuity as a ubiquitous feature with a uniform origin. Generally, we can define continental lithosphere as a region of conductive heat transport and steep geotherm that is characterized by pervasive internal layering of density, elastic moduli and texture.
Avian biodiversity assessment studies in a Neotropical wetland – the combination of sampling methods makes the difference

In studies of avian diversity, many different methods have been applied. Since methodological approaches may affect research results, the choice of a given methodology must be consistent with the scientific objectives. The aim of this study was to investigate how different methodologies with their intrinsic limitations help detect and monitor birds to evaluate how they complement each other in the survey of species. Three different assessment methods, mist nets, point counts, and autonomous acoustic recordings were used to serve this purpose in a study of different Pantanal habitats, such as savannas and forests. The point counts detected more species (126 species) than the two other methods autonomous acoustic recordings (113 species) and mist nets (79 species). We observed significant differences in the number of species detected by mist nets and the other two methods. Each survey method identified exclusive species. When comparing habitats, all three methods showed significant differences in bird species composition. Savannas were richer in bird species than forests, and replacement was the main driver responsible for the differences in beta diversity between the habitats. The three methodologies, when applied together, proved to be complementary in avian species detection.
Improving Smart Infrastructure Monitoring System as a Response to Prevalent Pandemic

F ace masks are no longer an option for protection against airborne diseases brought on by coughing, talking, or sneezing, which can spread germs into the air and infect everyone nearby, especially with the coronavirus pandemic that occurred in 2019. Also, some states and the government have made it mandatory for people to wear face masks. In recent years, Artificial Intelligence has played an important role in the medical field, as Convolution Neural Network techniques have proven to be very useful in image detection applications with different algorithms. In this paper, we propose a model using deep learning algorithms to achieve the most efficient and speedy way to detect the presence of a face mask on people in public places by using RGB cameras. The Alexnet, Googlenet, Resnet 18, and Squeezenet are trained on a dataset that consists of images of people with and without masks and is publicly available as “Mola RGB Covsurv” Mendeley Data, with 80% of the dataset being used for training and 20% for testing to get the most efficient algorithm. The proposal we recommend is Squeeznet's algorithm, which achieved an average precision of 94.1592% with a sensitivity of 91.19533% in 1700 minutes and 10 seconds.
Evaluating the survival and growth rate of Acropora digitifera in wild and mesocosm systems

The growing interest in coral culture for restoration and biotechnological applications has prompted researchers to improve their understanding of coral culture, with a focus on ex-situ production. This study aimed to understand the performance of common hard coral, Acropora digitifera by examining their survival and growth at Pulau Bidong (in-situ) and in a mesocosm system (ex-situ). First, three branches were tagged from each of eight A. digitifera colonies (n = 24). Mortality and linear extension rate were recorded monthly, from July - November 2018. Meanwhile, five branches from each tagged colony were brought back to a mesocosm set up at the hatchery in the Institute of Tropical Aquaculture (AKUATROP), Universiti Malaysia Terengganu (UMT) for the ex-situ experiment. All coral nubbins (n=40) were then divided into two coral size groups: small (<5cm) and large (>5cm). After four months, small nubbins showed 100% survivorship, while large nubbins survived for only two months. In contrast, 67% of wild colonies remained alive. However, nubbins in mesocosm extent with almost 2-folds slower (0.091 + 0.027 mm day-1) than those in the wild (0.166 + 0.033 mm day-1). Coral nubbins in mesocosm form a basal self-attachment “disc” at the bottom. This suggests that fragmented corals invest more energy in self-stabilization, especially in the early stage of transplantation, which affects their linear growth. This study demonstrates the different demographic traits for corals in both the environments.
Prevalence of zoonotic diseases of small ruminants at Savar upazila of Dhaka district

The objective of this study was to assess the prevalence of zoonotic diseases of small ruminants (SR) at Savar upazila of Dhaka district. A questionnaire-based survey was conducted in small and middle-scale goat and sheep farms at Savar Upazila with a total number of 1981 SR (1438 goats and 543 sheep). We found a total of 468 diseased SR (281 goats and 187 sheep), of which 206 cases were zoonotic (41.37%) (124 / 60% goats; 82/40% sheep). Prevalence of bacterial zoonoses (Salmonellosis, Tetanus Dermatophilosis, Brucellosis, Listeriosis and Campylobacteriosis ) was higher (23.78% in goat and 15.53% in sheep) followed by that of parasitic zoonoses (Hydatidosis, Fascioliasis, Amphistomiasis and Cryptosporidiosis) (17.48% in goat and 11.65% in sheep), viral zoonoses (Contagious Ecthyma, Foot and Mouth Disease and Rabies) (12.14% in goat and 7.77% in sheep), rickettsial zoonoses (Q Fever) (2.91% in goat and 1.94% in sheep). The age-wise, sex-wise and breed-wise prevalence of zoonotic diseases in goats and sheep were also determined. The study provided an epidemiological forecast showing the prevalence of zoonotic diseases of SR, which can be helpful for the clinician in the diagnosis of such infections. The present study revealed that the SR at Savar Upazila is susceptible and also infected with various zoonotic diseases. So, prevention and control of these zoonotic diseases are essential. Bangladesh J. Agri. 2023, 48(1): 81-91