Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated analysis of spinal MRI using deep learning

The objective of this thesis is the automation of radiological gradings in spinal lumbar Magnetic Resonance Images (MRIs). Solving this is extremely beneficial as this in a way would help in the standardization of gradings especially for back pain research. The output of the research done in this thesis would allow extremely fast readings of clinical scans which can potentially be useful in a large scale epidemiological study of spine-related diseases and aide clinical decision making. First, we build a pipeline to automatically produce radiological gradings of spinal lumbar MRIs and also localize the predicted pathologies. We show that: (i) a Convolutional Neural Network (CNN) is able to predict multiple gradings at once, and we propose variants of the architecture including a multi-modal CNN that is able to take in both axial and sagittal or T1-weighted and T2-weighted scans; and (ii) a localization method that clearly shows pathological regions in the disc volumes using only a CNN trained for classification. The CNN is applied to a large corpus of standard clinical scan MRIs acquired from multiple machines via various scanning protocol, and is used to automatically compute intervertebral disc and vertebral body gradings for each MRI. We explore several radiological gradings: Pfirrmann grading, disc narrowing, upper/lower endplate defects, upper/lower marrow changes, spondylolisthesis, central canal stenosis, anterior/posterior disc bulging, and disc herniation. We report near human performances across all the gradings, and also visualize the evidence for these gradings localized on the original scans. Then, since a significant proportion of patients scanned in a clinical setting have follow-up scans; we show that such longitudinal scans alone can be used as a form of “free” self-supervision for training a deep network. We demonstrate this selfsupervised learning for the case of T2-weighted sagittal lumbar MRIs. This learning via self-supervision can act as a pre-training regime when labelled data is sparse. We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch. Finally, we show some preliminary results in mapping disc features learnt from radiological gradings to the Oswestry Disability Index (ODI) which is a measure of disability commonly used by back pain patients.
Defense Mechanisms Against DDoS Attacks in a Cloud Computing Environment: State-of-the-Art and Research Challenges

The salient features of cloud computing (such as on-demand self-service, resource pooling, broad network access, rapid elasticity, and measured service) are being exploited by attackers to launch the severe Distributed Denial of Service (DDoS) attack. Generally, the DDoS attacks in such an environment have been implemented by flooding a huge volume (high-rate) of malicious traffic to exhaust the victim servers’ resources. Due to this huge volume of malicious traffic, such attacks can be easily detected. Thus, attackers are getting attracted towards the low-rate DDoS attacks, slowly. Low-rate DDoS attacks are difficult to detect due to their stealthy and low-rate traffic. In the recent years, many efforts have been devoted to defend against the low-rate DDoS attacks. By utilizing the salient features of cloud computing, it becomes easy for an attacker to launch sophisticated low-rate DDoS attacks. Thus, the study of various DDoS attacks and their corresponding defense approaches becomes essential to protect the cloud infrastructure from fatal effects of DDoS attacks. This paper presents a comprehensive taxonomy of all the possible variants of cloud DDoS attacks solutions with detailed insight into the characterization, prevention, detection, and mitigation mechanisms. The paper provides a detailed discussion on essential performance metrics to evaluate various defense solutions and their behavior in a cloud environment. The purpose of this survey paper is to excite the cloud security researchers to develop effective defense solutions against the various DDoS attacks. The research gaps and challenges are found, and described in the paper while future research directions are outlined.
Fake Fingerprint Detection Biometric System Using Neural Network Algorithm

The fingerprint recognition system is the most used among various biometric methods. Biometric systems are vulnerable to fake attacks, and many studies have been done to solve it. Therefore, in this paper, we propose a fake fingerprint detection method using neural network. To evaluate the proposed method, we have evaluated the fake fingerprint detection method using various materials. Experimental results show that the detection rate for fake fingerprint is 97.6% on average, which proves an effective detection method. 
An SDN-Based Caching Decision Policy for Video Caching in Information-Centric Networking

The considerable increase of multimedia services, such as video-on-demand (VoD) services, is a significant contributor to the total Internet traffic. Software-defined networking (SDN) and information-centric networking (ICN) are two promising technologies that can be combined to facilitate video delivery and to reduce network delays. In this paper, we first formulate the caching decision problem as a 0-1 integer linear programming (ILP) problem. Second, in contrast to existing approaches that solve the formulated ILP problem by assuming all future video requests are known, we consider the impact of the time scale, which transforms the static 0-1 ILP problem into a dynamic problem. By solving the dynamic 0-1 ILP problem, we find more accurate optimal solutions compared to existing approaches. Third, since the formulated 0-1 dynamic ILP problem is NP-hard, we leverage the in-network caching of ICN and the global view of the SDN controller to propose a novel SDN-based caching decision policy. Finally, extensive evaluations are performed, and the results demonstrate that the proposed SDN-based caching decision policy provides solutions that are close to the optimum in substantially less computation time. The SDN-based caching decision policy also outperforms existing practical ICN caching decision policies in terms of the cache hit ratio and the average number of hops, which are directly related to the video delivery latency. Moreover, the SDN-based caching decision policy can substantially reduce the number of generated and broadcasted interest packets, which is a shortcoming of the current ICN.
A Sparsity-Based Adaptive Channel Estimation Algorithm for Massive MIMO Wireless Powered Communication Networks

Compressed sensing (CS) based channel estimation methods can effectively acquire channel state information for Massive MIMO wireless powered communication networks. In order to solve the problem that the existing sparsity-based adaptive matching pursuit (SAMP) channel estimation algorithm is unstable under low signal to noise ratio (SNR), an optimized adaptive matching pursuit (OAMP) algorithm is proposed in this paper. First, the channel is pre-estimated. Next, the energy entropy-based order determination is raised to optimize the reconstruction performance of the algorithm. Then, a staged adaptive variable step size method is put forward to further promote the accuracy of channel estimation. Finally, theoretical analysis and simulation results demonstrate that the proposed OAMP algorithm improves the accuracy at the expense of a small amount of time complexity, does not require a priori knowledge of sparsity and its comprehensive performance is superior to other existing channel estimation algorithms.
Energy-Efficient Security in Smart Metering Scenarios

Responsible usage of electrical energy is becoming increasingly important. Electrical energy supply and utilization have to be optimized for efficiency and comfort. Modernization of the public power supply comes into existence as so called Smart Grid. However the energy consumption has to be optimized as well. In the future, homes will be equipped with an energy management infrastructure, which monitors the energy consumption of household appliances and controls their operation. Doing so, it is possible to optimize the energy consumption and also reduce the costs, by running some appliances at nighttime where the electricity costs are low. This is convenient for appliances like a washing machine or a dryer. Such homes with intelligent energy management infrastructures are called Smart Homes. They are an important part of the smart grid. The integration of smart homes in the smart grid is done by so called Smart Metering systems. Smart meters for electrical energy, heating and water supply are capable of communicate the collected data to the providers and metering service providers. In Fig. 1 the described smart home scenario is presented. The development of energy management systems for smart homes emerges some significant challenges. At first, there are a lot of sensors needed to monitor the appliances and all relevant environmental information. Temperature, humidity, lighting conditions of each room in a house must be collected as well as energy consumption of all relevant appliances. Since it is very costly and complicated to connect all the sensors by wire, it is convenient to use wireless sensor networks (WSNs). Second, there is demand to information security. Collecting all the sensor data in a smart home, an attacker could learn personal information of the inhabitant. Manipulation of the sensor data or control messages can also lead to safety risks, e.g. when driving a heater too hot or remotely control the stove. Therefore, the Federal Office of Information Security (BSI) in Germany has published a protection profile and technical guidelines for the gateway of a smart metering system [7]. Consequently, the security requirements defined in this document should also be met by the whole smart home. In the scope of the project KASTEL [8], we develop a prototype of a real smart home. Our goal is to extend an existing living lab with a secure smart metering solution based on wireless sensor networks. In our work, we address the challenges of secure communication in WSNs. Sensor nodes have to be small and cheap, which results in resource restrictions typical for WSNs, namely little energy and low computational power. Therefore communication and security mechanisms have to be energy-efficient. For energy efficientsecurity we use a hardware-based security modules attached to standard sensor nodes to compute cryptographic functions efficiently and fast.
A Biologically-Inspired Model for a Spiking Retina

The purpose of this research is to provide potential neuroscientists and computer scientists with an artificial retina model, delivering spikes to higher-level visual tasks simulations. The architecture of our retina model is based on recent physiological studies, so that each feature is related to real retina characteristics. The model includes a linear filtering process, followed by a static non-linearity, and then a spike generation process. It thus simulates the output of the Parvocellular pathway of primate retinas, possibly for large-scale simulations. Two series of tests were performed: firstly on single cells for which ground truth is available, secondly on realistic visual scenes. The report sets the basis for further modeling. This includes nonlinear effects that could occur due to the conductance-based nature of synaptic interactions. Another future extension is modeling of the Magnocellular pathway of primate retinas. It is our conviction that building such a bio-inspired model will help in better understanding how the retina performs and also in relating observations to biological mechanisms.
Compositional Algebra of CONNECTors

The CONNECT project aims to develop a novel network infrastructure to allow heterogeneous networked systems to freely communicate with each other via on-the-fly synthesis of emergent connectors. The role of Work Package 2 (WP2) is to investigate the foundations and verification methods for composable connectors, so that support is provided for composition of networked systems, whilst enabling automated learning, reasoning and synthesis. In the second year, we focused our attention on providing an underpinning for a framework capable of supporting the dimensions of interest to the project, and the generalisation of assume-guarantee properties beyond probabilistic safety properties, in addition to supporting the automated learning of assumptions. In this deliverable, we report on work carried out in two streams, compositional theory of connector behaviours and compositional assume-guarantee verification for probabilistic automata, which we are beginning to bring together. At the theory level, we first considered several probabilistic models, focusing on their interactive behaviour and asynchronous parallel composition. We then conducted a survey of the different formalisms of component models from the viewpoint of whether they supported a number of operators (e.g. parallel composition, conjunction and quotient) and a refinement relation. Based on this, we concluded that interface automata were one of the most amenable formalisms for basing an algebra of CONNECTors upon. We have formulated a preliminary (non-quantitative) connector algebra for resolving protocol mismatches based on interface automata. A software tool IAAnalyzer was implemented to allow users to create interface automata models using the connector algebra. In order to address the need for a quantitative extension, we have proposed a specification theory based on a probabilistic extension of interface automata. In the second stream, we continued our work on compositional assume-guarantee verification for probabilistic automata, extending the framework to allow, as assumptions and guarantees, a broader class of quantitative multi-objective properties which include probabilistic liveness and expected rewards. We also proposed a novel learning technique based on the L* algorithm, which automatically generates probabilistic (safety) assumptions using the results of queries executed by a probabilistic model checker. These two techniques have been implemented for PRISM, a well-known probabilistic model checker. Finally, we have laid the foundations for a comprehensive framework capable of modelling the quantitative (in particular the probabilistic) behaviour of arbitrary components and associated compositional assume-guarantee proof rules. Connectors arise as certain types of components. This framework is intended to integrate the two streams of work.
A Predictive Modeling for Detecting Fraudulent Automobile Insurance Claims

Fraudulent automobile insurance claims are not only a loss for insurance companies, but also for their policyholders. The goal of this research is to develop, first, a decision-making algorithm to classify whether a claim is classified as fraudulent or not; and, second, what types of variables should be focused to detect fraudulent claims. To achieve this goal, highly accurate prediction models are built by discovering important sets of features via variable selection algorithms, which can in turn help prevent future loss. In this research, parametric and nonparametric statistical learning algorithms are considered to reduce uncertainty and increase the chances of detecting the appropriate claims. An important set of features for a model is determined by measuring variable importance based on the observed characteristics of a claim via a cross-validation and by testing improvement of the performance at which automobile fraudulent claims are accurately classified using Akaike Information Criterion. We could achieve accuracy above 95% with a set of features selected via a cross-validation. This research would offer some benefit to the insurance industry for their fraud detection research in order to prevent insurance abuse from escalating any further.
An exploration of improving sampling within Monte Carlo ray tracing using adaptive blue noise.

In this report we demonstrate that strategically choosing sampling points with an intelligent use of adaptive blue noise sampling methods can drastically reduce the computation time required in the rendering process. We explore the state of the art in blue noise sample generation and explore new ways it can be used within the rendering process. Monte Carlo ray tracing is a vastly adopted image syntheses technique used ubiquitously across commercial and academic applications. It is capable of creating very high fidelity physically plausible images with computation that it unrestricted by dimensionality unlike most analytical approaches. Although capable of producing a high quality images Monte Carlo ray tracing often still needs to compute millions, if not billions of samples to produce a fully converged, noise free image. Tracing these samples comes at a cost and can lead to large computation times. Although we can reduce the cost of tracing rays with more optimized acceleration structures or naively throwing more hardware at the problem these are overshadowed by the improved quality gained via improved strategic sampling. Strategic sample placement has been proved to improve convergence rate of Monte Carlo ray tracing requiring fewer samples, and therefore decrease computation required to produce comparable results in quality. We explore the current literature on sampling methodologies and compare their implementation, performance and limitations and show that their sampling quality is inferior to adaptive blue noise. We will focus on applying the use of adaptive blue noise sampling within four dimensions of the rendering pipeline specifically. Firstly, we present a technique for generating primary ray samples that adaptively samples the image plane. We use a blue noise algorithm that adapts based on pre-existing information about the scene to increase the sampling frequency within areas of interest. Secondly we look at filter importance sampling, a technique that seems to be becoming ever more popular in rendering, and how we can use adaptive blue noise to generate higher quality sampling distributions than what is possible with the currently used methods of importance sampling. Next we will explore importance sampling BxDFs by generating samples over the hemi- sphere. Finally we will conclude with a brief discussion on some future ideas about direct light sampling of arbitrarily defined mesh lights. A challenge that is faced by all professional rendering software as efficient sampling methodologies are not well defined. Although not all our results in this report are entirely conclusive we believe that we have brought attention and provided promising results that build the foundations to an under researched area of knowledge that could help solve practical rendering problems faced by professional graphics.
Mechanical design and dynamics system identification of two-section Flapping Wing Aircraft

The mechanical design and system identification of the Flapping Wing Aircraft (FWA) has always been a problem. In this paper, we design a two-section FWA through the study of bird flight, where the wing structure is divided into the inner wing and outer wing, which can imitate the flight of birds. When flapping upward, the wings can be folded to reduce resistance, and when flapping downward, the wings can be expanded to increase lift. This flying method can improve the flight efficiency. The aircraft we designed has achieved flexible flight under human operation. Aiming at the complex problem of dynamic modeling of FWA, this paper proposes a linear system model by analyzing the force of the aircraft.We use the collected flight data to train the system model. The training of the model uses the gradient descent method to minimize the loss function, and the accuracy of the model is verified with the test data. The experimental results show that the system model can accurately calculate the acceleration, velocity, angle velocity and orientation of the aircraft based on the current control input.
Multi-Sensor 3D Scanner For Postural Analysis

A postura é geralmente definida como a posição relativa dos elemen- tos do corpo. A postura ideal é aquela em que as articulações estão sob uma quantidade mı́nima de estresse, mas nunca são alcançadas devido a fatores indi- viduais. É importante medir essa falta de equilı́brio para minimizar seus efeitos a longo prazo. Este artigo apresenta a criação de um scanner 3D multifuncional para análise postural que permite a medição de distâncias e ângulos entre pon- tos anatômicos do corpo de forma semi-automática, adicionando aplicações que somente informações 3D podem agregar, como a medição do volume cor- poral para calcular o percentual de gordura corporal e os perı́metros das partes do corpo. Este equipamento foi construı́do usando 2 sensores RGB-D combina- dos com a biblioteca de reconstrução ReconstructMe e ao software MeshLab, criando um sistema interativo e fácil de usar. A aplicação desenvolvida foi uti- lizada em testes de análise postural de um manequim seguindo o protocolo de avaliação postural SAPO e comparada com o software original SAPO (mesmo do protocolo), indicando boa correspondência entre os métodos.
Eine ereignisorientierte Simulation von Aktivitätenketten zur Parkstandswahl

Die Beschreibung des ruhenden Verkehrs wirft fuer die vorhandenen Modelleder Verkehrsplanung eine Reihe von grundsaetzlichen Fragen auf. Die Beschreibung der raeumlichen und zeitlichen Ungleichgewichte, die zu Ueberlastungen im ruhenden Verkehr fuehren, erfordern, dass ein geeignetes Modell zumindest zwei Anforderungen erfuellen muss: a) Abbildung der Entscheidungen der Verkehrsteilnehmer unter Beruecksichtigung ihrer inneren Logik und b) Abbildung der Wechselwirkungen zwischen den Verkehrsteilnehmern im Strassenraum unter Einschluss der Konkurrenz um den Parkraum. Die Arbeit beschreibt einen Modellansatz, der diese Anforderungen erfuellt. Der Ansatz vereinigt ein Aktivitaetenkettenmodell mit einer Verkehrsflusssimulation zu einer ereignisorientierten Simulation des taeglichen Verkehrsverhaltens. Die Verwendbarkeit des Ansatzes wird durch eine erste ausfuehrlich beschriebene Implementierung nachgewiesen und an Hand eines Beispielnetzes getestet. Empirische Ergebnisse zum Parkstandswahlverhalten werden vorgestellt. Die Ergebnisse einer Suchzeitbefragung, als auch einer mathematischen Modellierung des Entscheidungsverhaltens ergeben, dass im ruhenden Verkehr verschiedene Marktsegmente unterschieden werden muessen. Die Vernachlaessigung der Unterschiede im Verhalten zwischen den Marktsegmenten ist nicht zulaessig.
Sandpiles and Synthesizers: Listening to the Discrete Laplacian

There are many visual representations of the dynamical system determined by the action of the discrete Laplace operator of a graph. In this paper, I present an auditory representation which translates information about the evolving system into pitch, volume, and timbre data of notes played on a synthesizer. I have implemented the method on square lattices and recordings of the resulting pieces of music are available online.