Simple Mapping Research Articles

Securing SDN: Hybrid autoencoder-random forest for intrusion detection and attack mitigation

Software Defined Networking (SDN) has revolutionized network administration by providing centralized management through software, enabling traffic adjustment independent of the data plane. Despite the benefits, SDN networks are prone to security threats from external sources, thus necessitating the implementation of security measures. Unfortunately, most existing efforts have been just a simple mapping of earlier solutions into the SDN environments. This paper addresses the problem of SDN security based on deep learning in a purely native SDN environment, where a Deep Learning intrusion detection module is tailored to a native SDN environment. In particular, we propose a hybrid Deep AutoEncoder with a Random Forest classifier model (DAERF) to enhance intrusion detection performance in a native SDN environment. The proposed model is incorporated into a novel adaptive framework for attack mitigation in SDN environments. The proposed framework consists of a three-layer protection mechanism for detecting and preventing attacks. It is based on entropy-based detection, hybrid machine learning in the control layer and proactive services monitoring in the application layer. Experimental results have shown that our DEARF proposed autoencoder model achieved anomaly detection rates in excess of 98% in stand-alone mode as well as when incorporated within the framework, making it highly solution for next generation SDN networks.

Z-number based neural network structured inference system

Z-number based Neural Network structured Inference System (ZNIS) with rule-base consisting of linguistic Z-terms trainable with Differential Evolution with Constraints (DEC) optimization algorithm is suggested. The inference mechanism of the multi-layered ZNIS consists of a fuzzifier, fuzzy rule base, inference engine, and output processor. Due to the use of extended fuzzy terms, each processing layer implements appropriate extended fuzzy operations, including computation of fuzzy valued rule firing strengths, fuzzy Level-2 aggregate outputs, and two consecutive Center of Gravity (COG) defuzzification procedures. The experiments with different versions of ZNIS have demonstrated that it is a universal modeling tool suitable for dealing with both approximate reasoning and functional mapping tasks. Random experiments on benchmark examples (among which are simple functional mapping, Parkinson disease, and non-linear system identification) have shown that ZNIS performance is equivalent to or better than FLS Type 2 and far superior to FLS Type 1, showing on average 2–3 times lower MSE. Along with this, the main advantages of ZNIS over other inference systems are better semantic expressing power, higher degree of perception and interpretability of the linguistic rules by humans, and a higher confidence in the reliability of achieved decision due to the transparency of the underlying decision-making mechanism.

Drug deconjugation-assisted peptide mapping by LC–MS/MS to identify conjugation sites and quantify site occupancy for antibody-drug conjugates

Antibody-drug conjugates (ADCs) are a heterogeneous mixture of conjugated species with varied drug loadings. Depending on conjugation sites, linkers and drugs can exhibit different stability as influenced by the solvent-accessibility and local charge, resulting in different ADC efficacy, pharmacokinetics, and toxicity. Conjugation site analysis is critical for ADC structural characterization to assure product quality and consistency. It enables early conjugation studies at site-specific levels, confirms the absence of unexpected products to support conjugation process development, and aids in ensuring lot-to-lot consistency for comparability studies. Peptide mapping using liquid chromatography-tandem mass spectrometry is the industry standard method for analyzing conjugation sites. However, some concerns remain for this approach as the large and hydrophobic drug moieties often result in poor MS/MS fragmentation quality and impede the identification of conjugation sites. Additionally, the ionization discrepancy between conjugated and unconjugated peptides can lead to a relatively large bias for site occupancy calculation. In this work, we present a simple drug deconjugation-assisted peptide mapping method to identify and quantify the drug conjugation for ADCs with protease-cleavable linkers. Papain-based drug deconjugation was used to remove the highly hydrophobic drug moiety, which significantly improved the quantitation accuracy of conjugation level and the fragmentation quality. Sample preparation conditions were optimized to avoid introducing artificial modifications, allowing the tracking of initial sample status and subsequent changes of quality attributes during process development and stability assessment. This method was applied to analyze thermally-stressed ADC samples to monitor changes of site-specific conjugation levels, DAR, succinimide hydrolysis of the linker, and various PTMs. We believe this is an effective and straightforward tool for conjugation site analysis while simultaneously monitoring multiple quality attributes for ADCs with protease-cleavable linkers.

A numerical study of rigidity of hyperbolic splittings in simple two-dimensional maps

Chaotic hyperbolic dynamical systems enjoy a surprising degree of rigidity, a fact which is well known in the mathematics community but perhaps less so in theoretical physics circles. Low-dimensional hyperbolic systems are either conjugate to linear automorphisms, that is, dynamically equivalent to the Arnold cat map and its variants, or their hyperbolic structure is not smooth. We illustrate this dichotomy using a family of analytic maps, for which we show by means of numerical simulations that the corresponding hyperbolic structure is not smooth, thereby providing an example for a global mechanism which produces non-smooth phase space structures in an otherwise smooth dynamical system.

Minimal configuration point cloud odometry and mapping

Simultaneous Localization and Mapping (SLAM) refers to the common requirement for autonomous platforms to estimate their pose and map their surroundings. There are many robust and real-time methods available for solving the SLAM problem. Most are divided into a front-end, which performs incremental pose estimation, and a back-end, which smooths and corrects the results. A low-drift front-end odometry solution is needed for robust and accurate back-end performance. Front-end methods employ various techniques, such as point cloud-to-point cloud (PC2PC) registration, key feature extraction and matching, and deep learning-based approaches. The front-end algorithms have become increasingly complex in the search for low-drift solutions and many now have large configuration parameter sets. It is desirable that the front-end algorithm should be inherently robust so that it does not need to be tuned by several, perhaps many, configuration parameters to achieve low drift in various environments. To address this issue, we propose Simple Mapping and Localization Estimation (SiMpLE), a front-end LiDAR-only odometry method that requires five low-sensitivity configurable parameters. SiMpLE is a scan-to-map point cloud registration algorithm that is straightforward to understand, configure, and implement. We evaluate SiMpLE using the KITTI, MulRan, UrbanNav, and a dataset created at the University of Queensland. SiMpLE performs among the top-ranked algorithms in the KITTI dataset and outperformed all prominent open-source approaches in the MulRan dataset whilst having the smallest configuration set. The UQ dataset also demonstrated accurate odometry with low-density point clouds using Velodyne VLP-16 and Livox Horizon LiDARs. SiMpLE is a front-end odometry solution that can be integrated with other sensing modalities and pose graph-based back-end methods for increased accuracy and long-term mapping. The lightweight and portable code for SiMpLE is available at: https://github.com/vb44/SiMpLE.

A model for automatic agent generation based on machine learning in simulation games

With the continuous promotion of Artificial Intelligence (AI) technology in games, researchers are generally willing to pay attention to the effect of AI on game picture quality and man-machine games. However, how to generate a large number of non-player characters (NPC) in a game, especially in a simulation game, is still a problem. Even if some people use machine learning to discover solutions, most answers tend to be limited to copying finite-state machines established by programmers. This paper explores techniques, such as machine learning and maze algorithms, for 2D in-game environments to construct a model that can quickly generate NPCs with 'personality' and adapt to the environment when developing a game. In the process of model construction, the temporal difference algorithm and the depth-first algorithm are used to customize the decision structure for the NPC. At the same time, a simple node-style map generation algorithm is added to restrain NPC. Moreover, an optional neural network model gives the NPC a memory that can be updated. This model successfully realizes the rapid construction of NPC in developing some simple 2D games. In addition, NPCs have a fair amount of intelligence. It can be used during game development or while the game is running and generate a certain number of NPCs in an unfamiliar environment with differences in behavior.

InVAErt networks: A data-driven framework for model synthesis and identifiability analysis

Use of generative models and deep learning for physics-based systems is currently dominated by the task of emulation. However, the remarkable flexibility offered by data-driven architectures would suggest to extend this representation to other aspects of system analysis including model inversion and identifiability. We introduce InVAErt (pronounced invert) networks, a comprehensive framework for data-driven analysis and synthesis of parametric physical systems which uses a deterministic encoder and decoder to represent the forward and inverse solution maps, a normalizing flow to capture the probabilistic distribution of system outputs, and a variational encoder designed to learn a compact latent representation for the lack of bijectivity between inputs and outputs. We formally analyze how changes in the penalty coefficients affect the stationarity condition of the loss function, the phenomenon of posterior collapse, and propose strategies for latent space sampling, since we find that all these aspects significantly affect both training and testing performance. We verify our framework through extensive numerical examples, including simple linear, nonlinear, and periodic maps, dynamical systems, and spatio-temporal PDEs.

Transition to anomalous dynamics in a simple random map.

The famous doubling map (or dyadic transformation) is perhaps the simplest deterministic dynamical system exhibiting chaotic dynamics. It is a piecewise linear time-discrete map on the unit interval with a uniform slope larger than one, hence expanding, with a positive Lyapunov exponent and a uniform invariant density. If the slope is less than one, the map becomes contracting, the Lyapunov exponent is negative, and the density trivially collapses onto a fixed point. Sampling from these two different types of maps at each time step by randomly selecting the expanding one with probability p, and the contracting one with probability 1-p, gives a prototype of a random dynamical system. Here, we calculate the invariant density of this simple random map, as well as its position autocorrelation function, analytically and numerically under variation of p. We find that the map exhibits a non-trivial transition from fully chaotic to completely regular dynamics by generating a long-time anomalous dynamics at a critical sampling probability pc, defined by a zero Lyapunov exponent. This anomalous dynamics is characterized by an infinite invariant density, weak ergodicity breaking, and power-law correlation decay.

Validation of a simple body map to measure widespread pain in urologic chronic pelvic pain syndrome: A MAPP Research Network study.

In patients with urologic chronic pelvic pain syndrome (UCPPS), the presence of widespread pain appears to identify a distinct phenotype, with a different symptom trajectory and potentially different response to treatment than patients with pelvic pain only. A 76-site body map was administered four times, at weekly intervals, to 568 male and female UCPPS participants in the MAPP Network protocol. The 76 sites were classified into 13 regions (1 pelvic region and 12 nonpelvic regions). The degree of widespread pain was scored from 0 to 12 based on the number of reported nonpelvic pain regions. This continuous body map score was regressed over other measures of widespread pain, with UCPPS symptom severity, and with psychosocial variables to measure level of association. These models were repeated using an updated body map score (0-12) that incorporated a threshold of pain ≥ 4 at each site. Body map scores showed limited variability over the 4 weekly assessments, indicating that a single baseline assessment was sufficient. The widespread pain score correlated highly with other measures of widespread pain and correlated with worsened UCPPS symptom severity and psychosocial functioning. Incorporating a pain severity threshold ≥4 resulted in only marginal increases in these correlations. These results support the use of this 13-region body map in the baseline clinical assessment of UCPPS patients. It provides reliable data about the presence of widespread pain and does not require measurement of pain severity, making it relatively simple to use for clinical purposes.

Comparison of Transfer Patterns by Emergency Medical Services in Trauma Patients in a Major U.S. City

Background:Trauma patients are often unable to communicate a preferred destination hospital to EMS due to their injuries and potentially decreased level of consciousness. The destination hospital is thus determined by the EMS team transporting the patient. This study focuses on trauma admissions between Eskenazi and IUH Methodist Hospitals in downtown Indianapolis, both of which are Level 1 trauma centers within one mile of each other. We are interested in identifying differentiating factors between patients that are admitted at these two hospitals, focusing on the injury type as well as the demographic information for each patient, to determine if there are differential transport patterns.
 Methods:We performed a retrospective analysis of patient charts and transport documentation to determine basic demographic and admission data for each patient. We especially took note of the location of EMS pickup, the facility the patient was taken to, and the stated reasons for taking the patient to that specific facility. A simple Google Maps search allowed us to determine if the patient was taken to the facility closest to them, or not.
 Results:We found that among the included patients that were not taken to the nearest Level 1 trauma center, a significant majority were taken to Eskenazi rather than Methodist. Among these patients, a majority were minorities, in contrast with demographic makeup of the patients that were admitted to the closest trauma center.
 Conclusion:This study highlights how complex EMS transfer patterns are based upon diversion, traffic, individual driver preference, and other factors. There is a significant difference in transport patterns between the two hospitals that correlates with a difference in patient demographics.
 Policy Implications:This data indicates a need to monitor these numbers closely going forward, paying special attention to all possible reasons that could impact to which facility a patient is admitted.

Strategies for deep learning-based attenuation and scatter correction of brain 18 F-FDG PET images in the image domain.

Attenuation and scatter correction is crucial for quantitative positron emission tomography (PET) imaging. Direct attenuation correction (AC) in the image domain using deep learning approaches has been recently proposed for combined PET/MR and standalone PET modalities lacking transmission scanning devices or anatomical imaging. In this study, different input settings were considered in the model training to investigate deep learning-based AC in the image space. Three different deep learning methods were developed for direct AC in the image space: (i) use of non-attenuation-corrected PET images as input (NonAC-PET), (ii) use of attenuation-corrected PET images with a simple two-class AC map (composed of soft-tissue and background air) obtained from NonAC-PET images (PET segmentation-based AC [SegAC-PET]), and (iii) use of both NonAC-PET and SegAC-PET images in a Double-Channel fashion to predict ground truth attenuation corrected PET images with Computed Tomography images (CTAC-PET). Since a simple two-class AC map (generated from NonAC-PET images) can easily be generated, this work assessed the added value of incorporating SegAC-PET images into direct AC in the image space. A 4-fold cross-validation scheme was adopted to train and evaluate the different models based using 80 brain 18 F-Fluorodeoxyglucose PET/CT images. The voxel-wise and region-wise accuracy of the models were examined via measuring the standardized uptake value (SUV) quantification bias in different regions of the brain. The overall root mean square error (RMSE) for the Double-Channel setting was 0.157±0.08 SUV in the whole brain region, while RMSEs of 0.214±0.07 and 0.189±0.14 SUV were observed in NonAC-PET and SegAC-PET models, respectively. A mean SUV bias of 0.01±0.26% was achieved by the Double-Channel model regarding the activity concentration in cerebellum region, as opposed to 0.08±0.28% and 0.05±0.28% SUV biases for the network that uniquely used NonAC-PET or SegAC-PET as input, respectively. SegAC-PET images with an SUV bias of -1.15±0.54%, served as a benchmark for clinically accepted errors. In general, the Double-Channel network, relying on both SegAC-PET and NonAC-PET images, outperformed the other AC models. Since the generation of two-class AC maps from non-AC PET images is straightforward, the current study investigated the potential added value of incorporating SegAC-PET images into a deep learning-based direct AC approach. Altogether, compared with models that use only NonAC-PET and SegAC-PET images, the Double-Channel deep learning network exhibited superior attenuation correction accuracy.

An improved chaos sparrow search algorithm for UAV path planning

This study suggests an improved chaos sparrow search algorithm to overcome the problems of slow convergence speed and trapping in local optima in UAV 3D complex environment path planning. First, the quality of the initial solutions is improved by using a piecewise chaotic mapping during the population initialization phase. Secondly, a nonlinear dynamic weighting factor is introduced to optimize the update equation of producers, reducing the algorithm's reliance on producer positions and balancing its global and local exploration capabilities. In the meantime, an enhanced sine cosine algorithm optimizes the update equation of the scroungers to broaden the search space and prevent blind searches. Lastly, a dynamic boundary lens imaging reverse learning strategy is applied to prevent the algorithm from getting trapped in local optima. Experiments of UAV path planning on simple and complex maps are conducted. The results show that the proposed algorithm outperforms CSSA, SSA, and PSO algorithms with a respective time improvement of 22.4%, 28.8%, and 46.8% in complex environments and exhibits high convergence accuracy, which validates the proposed algorithm's usefulness and superiority.

Fast Quasi-Optimal Power Flow of Flexible DC Traction Power Systems

This paper proposes a quasi-optimal power flow (OPF) algorithm for flexible DC traction power systems (TPSs). Near-optimal OPF solutions can be solved with high computational efficiency by quasi-OPF. Unlike conventional OPF algorithms, quasi-OPF does not utilize mathematical optimization algorithms but adopts a new methodology. First, we adopt a new modeling method and successfully reveal the physical meaning of OPF solutions in flexible DC TPSs. Then, by converting the physical meaning of OPF solutions into mathematical expressions, a simple mapping from the power flow solution to the near-optimal OPF solution is obtained, and the quasi-OPF algorithm is designed based on power flow and this mapping. Since calculating power flow is computationally cheap and the mapping is based on simple arithmetic, the quasi-OPF algorithm can solve OPF with much less execution time, achieving subsecond level calculation and a speed-up of 57 times compared to the primal-dual interior point method. The effectiveness is verified by mathematical proofs and a case study with Beijing Metro Line 13. This study provides insight into the physical meaning of OPF solutions and is a powerful tool for flexible DC TPSs to analyze the effects of coordinated control, design real-time coordinated control strategies, and solve operational problems in planning.

A Simple Atmospheric Delay Mapping Function for Near-Nadir Altimeters

A Simple Atmospheric Delay Mapping Function for Near-Nadir Altimeters

An image encryption algorithm based on a compound-coupled chaotic system

Image encryption based on chaotic systems and DNA encoding is the focus of current research. However, existing image encryption algorithms lack attention with regard to the application efficiency of scrambling and diffusing operations based on DNA encoding and chaos, resulting in more redundancy and low efficiency. In practical applications, when chaotic systems are simulated on digital platforms, their chaotic performance will be weakened because of the presence of dynamic degradation. In addition, many chaotic systems have simple control parameters, making their behavior easily predictable. These flaws weaken the security and efficiency of cryptographic systems. For the purpose of solving the above problems, a new simultaneous confusion-diffusion image encryption algorithm (SCD-IEA) is presented that fully considers application efficiency and security. Firstly, a compound-coupled chaotic system (CCCS) is used to solve the problems listed above. Following that, a novel image encryption method is proposed using DNA coding and CCCS based on simple logistic and sine mappings. The algorithm improves application efficiency and performs synchronous scrambling diffusion encryption based on DNA coding. Simulation and security analysis results show that our SCD-IEA contributes to a higher level of security and superior performance than some representative methods.

Deep kinematic inference affords efficient and scalable control of bodily movements.

Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging. We introduce an active inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems.

„bei / beider Neige“. Ortlose Sprache in Gedichten Paul Celans

The extraordinary richness of multilingualism in Paul Celan’s poetry has often been remarked. However, ‘multilingualism’ might not be the best word, since Celan’s one-of-a-kind conjunc-ture of languages and meanings/ambiguity rather undermines language categories and especial-ly the notion of national languages altogether. In the poem “Kermorvan” for example, knowing the words’ multilingual etymologies and applying language-structures from Hebrew establishes meaningful connections among them. Combining multilingualism and ambiguity, Celan’s Sprachigkeit (languality ) exposes the dynamics and movement of language itself, including the histories, contacts and ambivalences of words and idioms. In this way, his insistent claim to write in one language corrects the idea of exchangeable, co-existing languages towards an un-derstanding of their interconnectedness, which might best be described by David Gramling’s term semiodiversity. In “Bei Wein und Verlorenheit”, the word “Neige” incorporates such an idea of the larger movements of language. It is used similarly to a tilted image to adhere to at least three languages and numerous meanings in a snowball-like principle. The new view of (multi-)lingualism shown in the poem and in Celan’s poetic utterances replaces not only ideas of a simple mapping between things and words (de Saussure) or metaphors and meanings in language. It also replaces the understanding of a general translatability among all languages of the world. Last but not least, in the specific discourse on Jewish language, it resists the notion of a general foreignness towards all languages which has traditionally been projected onto Jews, by positioning itself inside the larger movements of language itself.

Congestion control in constrained Internet of Things networks

AbstractThe Internet of Things (IoT) is a growing technology that remotely connects multiple devices (ranging across many fields and applications) over the Internet. The scalability of an IoT network mandates a reliable transport infrastructure. Traditional transport control protocol (TCP) control protocol is unsuitable for such domain, mainly due to energy and power consumption reasons. A lighter version of TCP, light weight IP (lwIP) provides a promising solution for current and projected future scalable IoT infrastructures. However, the original lwIP is just a simple mapping of the protocol, without insight into the IoT specific requirements. This paper examines the lwIP congestion control mechanism and addresses its shortcomings. In particular, a detailed examination is devoted to the various metrics such as retransmission time‐outs and its back‐off epochs, the congestion window behaviour and progress in the absence (and presence) of congestion. In particular, we propose a set of novel algorithms to address both the IoT constraints nature (light‐weight) as well as keeping up with scalability in IoT network size and performance. A detailed simulation study has been conducted to endorse the viability of our proposed set of algorithms for next‐generation IoT networks.

Sound map of urban areas recorded by smart devices: case study at Okayama and Kurashiki

This paper presents a sound map to visualize environmental sounds collected using a cloud sensing approach, i.e. participatory sensing and opportunistic sensing. Our sound map have two major extra functions than a simple noise map which visualized sound levels. The first one is introduction of visualization of sound-type icons on the noise map to enrich information. The second one is animating a transition of sound levels on noise map to visible the change according to the time in a day as a simulation. To demonstrate our sound map and develop the functions, we carried out several experiments. First, we developed a sound collection system to simultaneously collect physical sounds, their statistics, and their subjective impression. Then, we conducted sound collection experiments using the developed system in Okayama city and Kurashiki city. We analyze the data to focus on the difference between the cities and the differences between the usual and the unusual days. Furthermore, the collected data included subjective impressions by participants. We try to predict bustle situations in sightseeing areas by two approaches; the first is using Gaussian Mixture Models, and the second is using i-vector, which is well-known approach in the speaker identification tasks.

LGNN: a novel linear graph neural network algorithm.

The emergence of deep learning has not only brought great changes in the field of image recognition, but also achieved excellent node classification performance in graph neural networks. However, the existing graph neural network framework often uses methods based on spatial domain or spectral domain to capture network structure features. This process captures the local structural characteristics of graph data, and the convolution process has a large amount of calculation. It is necessary to use multi-channel or deep neural network structure to achieve the goal of modeling the high-order structural characteristics of the network. Therefore, this paper proposes a linear graph neural network framework [Linear Graph Neural Network (LGNN)] with superior performance. The model first preprocesses the input graph, and uses symmetric normalization and feature normalization to remove deviations in the structure and features. Then, by designing a high-order adjacency matrix propagation mechanism, LGNN enables nodes to iteratively aggregate and learn the feature information of high-order neighbors. After obtaining the node representation of the network structure, LGNN uses a simple linear mapping to maintain computational efficiency and obtain the final node representation. The experimental results show that the performance of the LGNN algorithm in some tasks is slightly worse than that of the existing mainstream graph neural network algorithms, but it shows or exceeds the machine learning performance of the existing algorithms in most graph neural network performance evaluation tasks, especially on sparse networks.