Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Advanced Persistent Threats: Techniques, Solutions, Challenges, and Research Opportunities

Threats that have been primarily targeting nation states and their associated entities have expanded the target zone to include the private and corporate sectors. This class of threats, well known as advanced persistent threats (APTs), are those that every nation and well-established organization fears and wants to protect itself against. While nation-sponsored APT attacks will always be marked by their sophistication, APT attacks that have become prominent in corporate sectors do not make it any less challenging for the organizations. The rate at which the attack tools and techniques are evolving is making any existing security measures inadequate. As defenders strive to secure every endpoint and every link within their networks, attackers are finding new ways to penetrate into their target systems. With each day bringing new forms of malware, having new signatures and behavior that is close to normal, a single threat detection system would not suffice. While it requires time and patience to perform APT, solutions that adapt to the changing behavior of APT attacker(s) are required. Several works have been published on detecting an APT attack at one or two of its stages, but very limited research exists in detecting APT as a whole from reconnaissance to cleanup, as such a solution demands complex correlation and fine-grained behavior analysis of users and systems within and across networks. Through this survey paper, we intend to bring all those methods and techniques that could be used to detect different stages of APT attacks, learning methods that need to be applied and where to make your threat detection framework smart and undecipherable for those adapting APT attackers. We also present different case studies of APT attacks, different monitoring methods, and mitigation methods to be employed for fine-grained control of security of a networked system. We conclude this paper with different challenges in defending against APT and opportunities for further research, ending with a note on what we learned during our writing of this paper.
Tracking Control Design in Nonlinear Multivariable Systems: Robotic Applications

In this work, a controller design technique called linear algebra based controller (LABC) is presented. The controller is obtained following a systematic procedure that is summarized in this work. In addition, the influence of additive uncertainty on the tracking error is analyzed, and a solution using integrators is proposed. A mobile robot is used as a benchmark to test the performance of the proposed algorithms. In addition, implementation to other systems such as marine vessel is referenced. In this work, the design of controllers in continuous and discrete time is included and experimental and simulation results are shown in a Pioneer 3AT mobile robot. Comparisons are also shown with other controllers proposed in the literature.
Observables in a spatially flat Planckian universe

An evolutionary scenario of processes, especially of the thermodynamic processes, in a perpetually mass-generating Planckian model universe has been studied. This is a Milne-type flat-space, R = ct, model with steady radial mass flow as recently described by us. In this ‘Broadcasting TV’ model universe the rest-mass particles are moving practically with light speed and the neutrinos and photons, both in thermal equilibrium and decoupled states, are moving in (almost) perpendicular directions along the light cones as the frozen-in particles. Therewith the density of radiation has the same dilutional dependence as for the radially squared lowering density of rest-mass particles. In such model universe the horizon problem is avoided automatically, whereas the light-cone photons are moving relative to atomic matter along the logarithmic Archimedes spiral around the mass-generating Planckian source, covering during evolution about 22 cycles. Crucially, the cosmic ‘dark ages’ start at 33.2 times younger age and about 103 times greater densities of matter, leading to much earlier formation of stars and galaxies than in the traditional ΛCDM cosmology. In the Planckian universe the cosmological principle holds for every epoch, thus removing the anthropocentric principle. The modified critical density in the model is ρ = 8.6 ·10−31g· cm−3. The needed dark matter contribution ρν = 3 ·10−31g· cm−3 can be provided by neutrinos if their rest energy is 0.16 eV. These correspond to degenerated cooled neutrinos at velocities of the order vF = 104 km/s.
Performance evaluation of twin piezoelectric wind energy harvesters under mutual interference

This study evaluated the performance of twin adjacent galloping-based piezoelectric wind energy harvesters based on mutual interference. The relative position between the twin harvesters is crucial to their energy generation efficiency. A series of wind tunnel tests were conducted to test energy generation of the two harvesters in tandem or staggered arrangements. The optimal relative position, a streamwise center to center spacing of 1.2B (B is the width of the harvester's square prism) and a transverse center to center spacing of 1.0B, was identified. The total output power of the two harvesters placed in the optimal relative position is up to 2.2 times that of two isolated harvesters. The output power is also much larger than that of the harvesters in tandem arrangements that have been widely tested in previous studies. Therefore, it is recommended to position two adjacent harvesters in optimal relative position(s) to deliver an optimal power output.
Adaptive online distributed optimization in dynamic environments

ABSTRACT This paper presents a dynamic regret analysis on the decentralized online convex optimization problems computed over a network of agents. The goal is to distributively optimize a global function which can be decomposed into the summation of local cost functions associated with each agent. By exploiting the convexity of cost functions, previous studies have shown that the dynamic regret of a decentralized gradient-based algorithm can be upper bounded by the path-length of the comparator sequence and the connectivity in the underlying network. In this paper, we illustrate that the dynamic regret can be further improved by allowing the learner to use a class of adaptive search directions and step-sizes from estimates of first and second moments of the gradients. Specifically, we develop a new class of decentralized and stochastic algorithms based on the adaptive gradient method in the dynamic environment and show that their regret bounds for certain realistic classes of loss functions are considerably better than existing bounds. Numerical simulations verify the theoretical results and demonstrate the efficiency of the new proposed method in practice.
The Generalized Odd Lomax Generated Family of Distributions with Applications

Through this article, a new generated family of distributions under the name of "The generalized odd Lomax-G family" by adding three additional parameters to generalize any continuous baseline distribution is provided. For the generalized odd Lomax-G family main properties, stochastic orderings, entropy measures have been studied. Three special models have been discussed for the new family. By using the maximum likelihood method, The model parameters are estimated. Simulation is carried out for one of the sub-models to check the asymptotic behavior of the maximum likelihood estimates. We explained the efficiency of the new family by using four applications to the real world.
A graphical method for ranking Atanassov's intuitionistic fuzzy values using the uncertainty index and entropy

Many different types of ranking methods based on the score and accuracy functions of intuitionistic fuzzy values (IFVs) exist in the literature. The notion of knowledge bases, as in the case of rough set theory, is very handy to show that every ranking technique produces a unique classification of IFVs with a unique order among the classes. This means these rankings give rise to unique knowledge bases. Therefore, ranking of IFVs by two or more distinct techniques may produce different results. In this study, a graphical ranking method based on the uncertainty index and entropy is proposed. This approach is tested on several numerical examples existing in the literature and shown to be intuitive and convenient for applications in real‐life scenarios.
Learning Physical Human–Robot Interaction With Coupled Cooperative Primitives for a Lower Exoskeleton

Human-powered lower exoskeletons have received considerable interests from both academia and industry over the past decades, and encountered increasing applications in human locomotion assistance and strength augmentation. One of the most important aspects in those applications is to achieve robust control of lower exoskeletons, which, in the first place, requires the proactive modeling of human movement trajectories through physical human–robot interaction (pHRI). As a powerful representative tool for motion trajectories, dynamic movement primitives (DMP) have been used to model human movement trajectories. However, canonical DMP only offers a general representation of human movement trajectory and may neglects the interactive term, therefore it cannot be directly applied to lower exoskeletons which need to track human joint trajectories online, because different pilots have different trajectories and even same pilot might change his/her motion during walking. This paper presents a novel coupled cooperative primitive (CCP) strategy, which aims at modeling the motion trajectories online. Besides maintaining canonical motion primitives, we model the interaction term between the pilot and exoskeletons through impedance models, and propose a reinforcement learning method based on policy improvement and path integrals (PI2) to learn the parameters online. Experimental results on both a single degree-of-freedom platform and a HUman-powered Augmentation Lower EXoskeleton (HUALEX) system demonstrate the advantages of our proposed CCP scheme. Note to Practitioners—This paper was motivated by the problem of lower exoskeleton when it interacts with different pilots. In both military and industrial applications of lower exoskeleton for strength augmentation, a most challenge problem is how to deal with the pHRI caused by different pilots. This paper suggests a new learning-based strategy, which modeled the pilot’s motion with movement primitives and update through the pHRI between the pilot and the lower exoskeleton with online reinforcement learning method. In order to employ the proposed CCP into the real-time application, we also combine the CCP with a hierarchical control framework, and applied on a lower exoskeleton system which we built for strength augmentation application (which named as HUALEX). In the experiments of this paper, we validate the proposed CCP on different pilots with HUALEX system, the proposed CCP also achieve a good performance on the online learning and adaptation of the pilot’s gait. In the future, we will extend this algorithm for adapting complex environment in both industrial and military applications, such as in different terrains, stairs, and slopes scenarios, and so on.
Ultrashort Light Pulses in Transparent Solids: Propagation Peculiarities and Practical Applications

The peculiarities of the femtosecond filamentation in Kerr media has been studied using a set of time-resoling experimental techniques. These include the temporal self-compression of a laser pulse in the filamentation mode, repulsive and attractive interactions of filaments, and influence of the birefringence on the filamentation. The propagation of femtosecond laser pulses at the 1550-nm wavelength in c-Si is studied for the first time using methods of time-resolved transmission microscopy. The nonlinear widening of the pulse spectrum due to the Kerr- and plasma-caused self-phase modulation is recorded.
Performance of Wendelstein 7-X stellarator plasmas during the first divertor operation phase

Wendelstein 7-X is the first comprehensively optimized stellarator aiming at good confinement with plasma parameters relevant to a future stellarator power plant. Plasma operation started in 2015 using a limiter configuration. After installing an uncooled magnetic island divertor, extending the energy limit from 4 to 80 MJ, operation continued in 2017. For this phase, the electron cyclotron resonance heating (ECRH) capability was extended to 7 MW, and hydrogen pellet injection was implemented. The enhancements resulted in the highest triple product (6.5 × 1019 keV m−3 s) achieved in a stellarator until now. Plasma conditions [Te(0) ≈ Ti(0) ≈ 3.8 keV, τE > 200 ms] already were in the stellarator reactor-relevant ion-root plasma transport regime. Stable operation above the 2nd harmonic ECRH X-mode cutoff was demonstrated, which is instrumental for achieving high plasma densities in Wendelstein 7-X. Further important developments include the confirmation of low intrinsic error fields, the observation of current-drive induced instabilities, and first fast ion heating and confinement experiments. The efficacy of the magnetic island divertor was instrumental in achieving high performance in Wendelstein 7-X. Symmetrization of the heat loads between the ten divertor modules could be achieved by external resonant magnetic fields. Full divertor power detachment facilitated the extension of high power plasmas significantly beyond the energy limit of 80 MJ.Wendelstein 7-X is the first comprehensively optimized stellarator aiming at good confinement with plasma parameters relevant to a future stellarator power plant. Plasma operation started in 2015 using a limiter configuration. After installing an uncooled magnetic island divertor, extending the energy limit from 4 to 80 MJ, operation continued in 2017. For this phase, the electron cyclotron resonance heating (ECRH) capability was extended to 7 MW, and hydrogen pellet injection was implemented. The enhancements resulted in the highest triple product (6.5 × 1019 keV m−3 s) achieved in a stellarator until now. Plasma conditions [Te(0) ≈ Ti(0) ≈ 3.8 keV, τE > 200 ms] already were in the stellarator reactor-relevant ion-root plasma transport regime. Stable operation above the 2nd harmonic ECRH X-mode cutoff was demonstrated, which is instrumental for achieving high plasma densities in Wendelstein 7-X. Further important developments include the confirmation of low intrinsic error fields, the observation of curr.
Principles for drug education in schools: Beyond the visions

The purpose of the present research was to examine the extent to which the practices of drug education in a rural Queensland high school were based on the Principles for Drug Education proposed by the School Development in Health Education Project. The Principles for Drug Education are the only nationally accepted principles that fulfil Education Queensland's criteria to provide drug programs that are consistent with accepted principles for drug education in schools. Eighty-two students from Years 9, 10 and 11, and three teachers completed questionnaires which were specifically designed for the study. The results have implications for the implementation of drug education programs in schools. Developers of drug education programs need to promote their programs in schools; schools need support in terms of teacher education and professional resources.
A Set-Membership Approach to Event-Triggered Filtering for General Nonlinear Systems Over Sensor Networks

This paper is concerned with the distributed set-membership filtering problem for a class of general discrete-time nonlinear systems under event-triggered communication protocols over sensor networks. To mitigate the communication burden, each intelligent sensing node broadcasts its measurement to the neighboring nodes only when a predetermined event-based media-access condition is satisfied. According to the interval mathematics theory, a recursive distributed set-membership scheme is designed to obtain an ellipsoid set containing the target states of interest via adequately fusing the measurements from neighboring nodes, where both the accurate estimate on Lagrange remainder and the event-based media-access condition are skillfully utilized to improve the filter performance. Furthermore, such a scheme is only dependent on neighbor information and local adjacency weights, thereby fulfilling the scalability requirement of sensor networks. In addition, an optimization algorithm is developed to minimize the trace of the estimated ellipsoid set, and the effect from the adopted event-triggered threshold is thoroughly discussed as well. Finally, a simulation example is utilized to illustrate the usefulness of the proposed distributed set-membership filtering scheme.
Theoretical contributions to Monte Carlo methods, and applications to Statistics

The first part of this thesis concerns the inference of un-normalized statistical models. We study two methods of inference based on sampling, known as Monte-Carlo MLE (Geyer, 1994), and Noise Contrastive Estimation (Gutmann and Hyvarinen, 2010). The latter method was supported by numerical evidence of improved stability, but no theoretical results had yet been proven. We prove that Noise Contrastive Estimation is more robust to the choice of the sampling distribution. We assess the gain of accuracy depending on the computational budget. The second part of this thesis concerns approximate sampling for high dimensional distributions. The performance of most samplers deteriorates fast when the dimension increases, but several methods have proven their effectiveness (e.g. Hamiltonian Monte Carlo, Langevin Monte Carlo). In the continuity of some recent works (Eberle et al., 2017; Cheng et al., 2018), we study some discretizations of the kinetic Langevin diffusion process and establish explicit rates of convergence towards the sampling distribution, that scales polynomially fast when the dimension increases. Our work improves and extends the results established by Cheng et al. for log-concave densities.
TwitSong: A current events computer poet and the thorny problem of assessment.

This thesis is driven by the question of how computers can generate poetry, and how that poetry can be evaluated. We survey existing work on computer-generated poetry and interdisciplinary work on how to evaluate this type of computer-generated creative product. We perform experiments illuminating issues in evaluation which are specific to poetry. Finally, we produce and evaluate three versions of our own generative poetry system, TwitSong, which generates poetry based on the news, evaluates the desired qualities of the lines that it chooses, and, in its final form, can make targeted and goal-directed edits to its own work. While TwitSong does not turn out to produce poetry comparable to that of a human, it represents an advancement on the state of the art in its genre of computer-generated poetry, particularly in its ability to edit for qualities like topicality and emotion.
GENETIC SUSCEPTIBILITY FOR LYMPHEDEMA SECONDARY TO BREAST CANCER TREATMENT: AN INVESTIGATION OF THE CONNEXIN GENES

Secondary lymphedema is the accumulation of protein-rich fluid in the interstitial spaces of the extremities. It typically occurs as a result of a trauma or infection in the lymphatic system. This is a significant public health issue because lymphedema has emerged as one of the most debilitating consequences of breast cancer treatment and currently no model exists to predict who will be affected. The aim of this study was to examine genes that may increase the susceptibility to developing secondary lymphedema following breast cancer surgery and/or radiation. Perometry and bioelectrical impedance spectrometry (BIS) were also used to examine clinical and subclinical swelling in individuals. This is a case-control study that sequenced connexin genes of 70 women with secondary lymphedema and over 100 control participants without lymphedema. Connexins form gap junction channels that facilitate communication between cells. The connexins that were sequenced include connexin 47 (GJA12), connexin 37 (GJA4), connexin 40 (GJA5), and exon 2 of connexin 43 (GJA1). Four missense mutations and one synonymous substitution were identified in connexin 47. The mutations were not found to be polymorphic in control individuals. The identification of connexin 47 mutations is compelling and warrants further research to determine if and how this gene increases the risk of secondary lymphedema after breast cancer treatment. These findings could have implications for prevention, management, and early diagnosis of breast cancer-associated secondary lymphedema.
Data assimilation method to de-noise and de-filter particle image velocimetry data

We present a variational data assimilation method in order to improve the accuracy of velocity fields $\tilde{\boldsymbol{v}}$ , that are measured using particle image velocimetry (PIV). The method minimises the space–time integral of the difference between the reconstruction $\boldsymbol{u}$ and $\tilde{\boldsymbol{v}}$ , under the constraint, that $\boldsymbol{u}$ satisfies conservation of mass and momentum. We apply the method to synthetic velocimetry data, in a two-dimensional turbulent flow, where realistic PIV noise is generated by computationally mimicking the PIV measurement process. The method performs optimally when the assimilation integration time is of the order of the flow correlation time. We interpret these results by comparing them to one-dimensional diffusion and advection problems, for which we derive analytical expressions for the reconstruction error.
Robust static output feedback H∞-controller design for three degree of freedom integrated bus lateral, yaw, roll dynamics model

In this paper, a three-degree-of freedom (3 DOF) integrated vehicle lateral, yaw, roll dynamics model with optimal control design have been proposed to improve the bus lateral stability and handling performance. First, a 3 DOF vehicle model for a passenger bus is introduced. The 3 DOF model dynamics include the vehicle steering wheel angle, forward speed, yaw motion, sideslip angle, lateral acceleration and the rolling motion. Then, the presented 3 DOF model is used to design the robust static output feedback H ∞ controller for both nominal system and uncertain system. The proposed controller is designed to improve the bus lateral stability and handling performance by controlling the yaw rate during normal driving and maneuvers. For the robust static output feedback H ∞ controller, the norm bounded uncertainty is considered to simulate the variation of vehicle forward velocity uncertainty. The robust controller is designed to check the lateral stability of the bus at different forward velocity and at different velocity uncertainty. The controllers are synthesized within the H ∞ control approach and the controllers’ design conditions are given within the Linear Matrix Inequalities (LMIs) framework. Numerical simulations have been carried out to demonstrate the effectiveness of the proposed controllers. The obtained simulation results show that the designed nominal and robust controllers enhance the lateral stability of the bus by reducing the amplitude of the yaw rate, lateral acceleration and rolling motion. Hence, the improvements in bus lateral stability and handling performance are achieved.