Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time detection, isolation and monitoring of elephant flows using commodity SDN system

Operators of enterprise and carrier networks in-creasingly require real-time visibility into traffic patterns in their network, so they can do better resource management (congestion detection, dynamic routing, capacity scheduling) and security protection (detection of intrusions and volumetric attacks). Of particular interest are elephant flows that transfer large volumes, since they demand most resources and can inflict most damage. Today's techniques for detecting and monitoring elephant flows are based on software-based packet analysis or hardware-based inspection, which are either unscalable or expensive. In this paper we design, implement, and evaluate an SDN-based solution that is scalable (to tens of Gigabits-per-second) and inexpensive (built using commodity OpenFlow switches). We first develop a system architecture that judiciously combines software packet inspection with hardware flow-table counters to identify and monitor heavy flows. We then use real traffic traces taken from a campus network to tune our algorithm parameters for desired trade-off between software load and hardware table size. Finally, we prototype our solution on a commodity OpenFlow hardware switch together with open-source controller and packet inspection software, and demonstrate operation at 10Gbps in a real campus network.
Liquid pipeline leakage detection based on moving windows LS-SVM algorithm

Aiming at the problem of liquid pipeline leak detection, this paper proposed a leakage detecting method based on moving windows least square support vector machine algorithm (MWLS-SVM). Least square support vector machine algorithm (LS-SVM) classification algorithm is one of the improved algorithms of support vector machine (SVM). The main idea of the LS-SVM classification algorithm is to change the nonlinear constraint in SVM to linear constraint and to apply the sum of square errors as the empirical loss function of the training set to improve the training speed. The moving windows method is used to update the dynamic pipeline leak detection model. In this paper it also uses the negative pressure wave method for pipeline leak detection. The simulation experiment results of the liquid pipeline leakage data show that this proposed method has higher accuracy than support vector machines and neural networks methods.
FIXIDS: A high-speed signature-based flow intrusion detection system

Signature-based Network Intrusion Detection Systems (NIDS) are the state-of-the-art when it comes to precise attack detection and intrusion prevention. However, they experience critical performance problems in modern high-speed networks. At the same time, flow-based network monitoring has been investigated for high data rates. In the last years, such flow-monitoring went beyond collecting statistical information about network connections and more recent techniques are able to include selected samples of the payload of these flows. Most recently, we extended this concept to HTTP flows. We now go one step further and combine IPFIX-based flow monitoring with NIDS. We developed IPFIX-based Signature-based Intrusion Detection System (FIXIDS), a system that exploits the recently introduced HTTP related flow Information Elements (IEs) to do signature-based flow intrusion detection in high-speed networks on commodity hardware. FIXIDS makes use of HTTP intrusion signatures from the widely used Snort NIDS and applies them to incoming IPFIX Flows. In the experimental evaluation, we are able to show a performance gain of a factor of three compared to Snort while maintaining the same detection ratio.
OxInflammation: From Subclinical Condition to Pathological Biomarker

Inflammation is a complex systemic response evolved to cope with cellular injury, either due to infectious agents or, in general, with sporadic events challenging tissue integrity and function. Researchers involved in different fields have the tendency to look at the inflammatory response with different angles, according to their specific interest. Established its complexity, one of the most evident features of the inflammatory response is the generation of a pro-oxidative environment due to the production of high fluxes of pro-oxidant species. This production begins locally, close to the sites of tissue damage or infection, but eventually becomes a chronic challenge for the organism, if the inflammatory response is not properly controlled. In this review, we focus on this specific aspect of chronic, low-level sub-clinical inflammatory response. We propose the term “OxInflammation” as a novel operative term describing a permanent pro-oxidative feature that interact, in a positive feed-back manner, to a not yet clinically detectable inflammatory process, leading in a long run (chronically) to a systemic/local damage, as a consequence of the cross talk between inflammatory, and oxidative stress mediators. Therefore, it could be useful to analyze inflammatory markers in pathologies where there is an alteration of the redox homeostasis, although an inflammatory status is not clinically evident.
Classification of human poses and orientations with deep learning

Within the scope of this study, we aim to classify human poses and orientations from the group activity images using deep learning. In the framework that we developed, the detection, pose and orientation classification steps are performed in a cascade fashion. Firstly, people in the images are detected, then, the detected people are classified as belonging to one of the classes “standing”, “sitting on an object” and “sitting on the ground” and finally classified into one of the eight different orientations of these three pose classes. To this end, an end-to-end trainable deep learning framework is used. The experimental evaluation show that the trained Convolutional Neural Network model produces successful results.
Some relations between unscented and cubature kalman filters

The unscented Kalman filter (UKF) and the cubature Kalman filter (CKF) are the popular nonlinear Gaussian sigma point filters. UKF propagates the sigma points by function evaluations using the unscented transformation (UT) and it is at first glance very different from CKF which is based on the spherical-radial cubature rule. In this paper, some relations between UKF and CKF are analyzed from the perspective of Mahalanobis distance ellipsoid. Two examples are given to demonstrate why UKF and CKF can achieve nearly the same accuracy.
Dissecting Privacy Risks in Biomedical Data

The decreasing costs of molecular profiling has fueled the biomedical research community with a plethora of new types of biomedical data, enabling a breakthrough towards a more precise and personalized medicine. However, the release of these intrinsically highly sensitive data poses a new severe privacy threat. While biomedical data is largely associated with our health, there also exist various correlations between different types of biomedical data, along the temporal dimension, and also in-between family members. However, so far, the security community has focused on privacy risks stemming from genomic data, largely overlooking the manifold interdependencies between other biomedical data. In this paper, we present a generic framework for quantifying the privacy risks in biomedical data taking into account the various interdependencies between data (i) of different types, (ii) from different individuals, and (iii) at different time. To this end, we rely on a Bayesian network model that allows us to take all aforementioned dependencies into account and run exact probabilistic inference attacks very efficiently. Furthermore, we introduce a generic algorithm for building the Bayesian network, which encompasses expert knowledge for known dependencies, such as genetic inheritance laws, and learns previously unknown dependencies from the data. Then, we conduct a thorough inference risk evaluation with a very rich dataset containing genomic and epigenomic data of mothers and children over multiple years. Besides effective probabilistic inference, we further demonstrate that our Bayesian network model can also serve as a building block for other attacks. We show that, with our framework, an adversary can efficiently identify the parent-child relationships based on methylation data with a success rate of 95%.
Model of partially coherent combining and propagation of 2D array of laser beams

The aim of paper is to develop the analytical tools for evaluation of possibility and properties of laser beams combining in real conditions, particularly for propagation on medium and long distances in atmosphere. The concepts of coherent vs incoherent laser beam combing were briefly described. The review of novel methods of beam combining was given. The advantages and drawbacks of both methods were analyzed in a case of diffraction limited optical systems. The model of partial coherence was implemented to analyze array of laser beams propagation in atmosphere.
Managing approximation errors in quantum programs

We address the problem of distributing approximation errors in large-scale quantum programs. It has been known for some time that when compiling quantum algorithms for a fault-tolerant architecture, some operations must be approximated as they cannot be implemented with arbitrary accuracy by the underlying gate set. This leads to approximation errors which often can be grouped along subroutines that the given quantum algorithm is composed of. Typically, choices can be made as to how to distribute approximation errors so that the overall error is kept beneath a user- or application-defined threshold. These choices impact the resource footprint of the fault-tolerant implementation. We develop an automatic approximation error management module to tackle the resulting optimization problems. The module is based on annealing and can be integrated into any quantum software framework. Using the benchmark of simulating an Ising model with transverse field, we provide numerical results to quantify the benefits and trade-offs involved in our approach.
Touch and see: Physical interactions stimulating patterns in artificial cephalopod skin

Cephalopods (e.g., octopus, squid and cuttlefish) employ their colour-changing skin for rapid active camouflage and signalling in complex visual environments. This is achieved through the collective embodied intelligence of chromatophores: pigment organs which stretch under electrical stimulation to affect local skin colouration, and are also responsive to physical stimulation. In this study, we present an experimental investigation of touch-responsive bioinspired artificial cephalopod skin fabricated from soft dielectric elastomer, a material that has the potential to mimic the actuation of biological chromatophore cells in both speed and optical modulation. We measure the behaviour of an interacting cyclic network of such artificial chromatophores, using local strain as the control input that drives cell actuation. By applying simple local feedback rules analogous to cellular automata, we demonstrate that physical stimulation can generate a variety of travelling wave-like patterns that mimic those seen in biological cephalopod skins.
Function of Connexins in the Interaction between Glial and Vascular Cells in the Central Nervous System and Related Neurological Diseases

Neuronal signaling together with synapse activity in the central nervous system requires a precisely regulated microenvironment. Recently, the blood-brain barrier is considered as a “neuro-glia-vascular unit,” a structural and functional compound composed of capillary endothelial cells, glial cells, pericytes, and neurons, which plays a pivotal role in maintaining the balance of the microenvironment in and out of the brain. Tight junctions and adherens junctions, which function as barriers of the blood-brain barrier, are two well-known kinds in the endothelial cell junctions. In this review, we focus on the less-concerned contribution of gap junction proteins, connexins in blood-brain barrier integrity under physio-/pathology conditions. In the neuro-glia-vascular unit, connexins are expressed in the capillary endothelial cells and prominent in astrocyte endfeet around and associated with maturation and function of the blood-brain barrier through a unique signaling pathway and an interaction with tight junction proteins. Connexin hemichannels and connexin gap junction channels contribute to the physiological or pathological progress of the blood-brain barrier; in addition, the interaction with other cell-cell-adhesive proteins is also associated with the maintenance of the blood-brain barrier. Lastly, we explore the connexins and connexin channels involved in the blood-brain barrier in neurological diseases and any programme for drug discovery or delivery to target or avoid the blood-brain barrier.
Controllability of edge dynamics on directed multi-agent systems

In this paper, the controllability problem is investigated for the directed multi-agent system with both edge dynamics and node dynamics. The system with edge dynamics is addressed by associating each edge of the network with a state variable. Firstly, we propose a new class of dynamic-edge multi-agent system based on the first-order integrator model and the rationality of this system is illustrated by the example of a power network system. Then, by discussing the relationship between λ and 0 and the relationship between λ and decay rate respectively, the controllability problem is investigated for the directed multi-agent system with both node dynamics and edge dynamics. With PBH criterion, necessary and sufficient conditions are finally derived for the controllability of the system.
A multi-view face database from Turkish TV series

Facial image processing and analysis has numerous applications. Recently, with deep learning based approaches, a significant performance improvement has been obtained. An important factor affecting the performance of the deep learning-based systems is availability of large amount of data. Therefore, recently, a couple of large-scale face datasets that contain still images of subjects have become publicly available. However, a video face database that contains both a large number of subjects as well as a large number of samples per subject has not been available, yet. In this study, to fulfill this need, Turkish TV series have been used, and preparation of a multi-view face sequence dataset has been focused. In the study, state-of-the-art off-the-shelf tools have been utilized for face detection, face alignment, and face recognition and the steps we have followed to collect the database are presented. Our goal is, upon completion of the processes, to reach a face database of at least 3000 subjects, each having at least 100 video sequences. In the database, along with the identity labels, we also plan to generate age and gender labels. We believe that this database would become an invaluable resource for automatic facial image processing and analysis research, especially, for the approaches that aim at exploiting dynamic features.
Poster abstract: Themis: A data-driven approach to bot detection

We propose Themis, a bot detection approach based on the inference of the structure of time varying IP-to-IP communication with the Stochastic Block Model (SBM). Themis uses the inferred structure to detect and quantify abnormal behavior of individual hosts. The novelty of our approach is the use of probabilistic inference directly on host interactions to model normality. The challenges of our approach are the adaptation of the inference process to obtain usable outputs in a dynamic system, and the specification of abnormal behavior with respect to the inferred structure. Themis identifies infected hosts with accuracy larger 95 % and compares favorably against state of the art botnet detection mechanisms.
The p38 MAPK Inhibitor SB203580 Abrogates Tumor Necrosis Factor-Induced Proliferative Expansion of Mouse CD4+Foxp3+ Regulatory T Cells

There is now compelling evidence that tumor necrosis factor (TNF) preferentially activates and expands CD4+Foxp3+ regulatory T cells (Tregs) through TNF receptor type II (TNFR2). However, it remains unclear which signaling transduction pathway(s) of TNFR2 is required for the stimulation of Tregs. Previously, it was shown that the interaction of TNF–TNFR2 resulted in the activation of a number of signaling pathways, including p38 MAPK, NF-κB, in T cells. We thus examined the role of p38 MAPK and NF-κB in TNF-mediated activation of Tregs, by using specific small molecule inhibitors. The results show that treatment with specific p38 MAPK inhibitor SB203580, rather than NF-κB inhibitors (Sulfasalazine and Bay 11-7082), abrogated TNF-induced expansion of Tregs in vitro. Furthermore, upregulation of TNFR2 and Foxp3 expression in Tregs by TNF was also markedly inhibited by SB203580. The proliferative expansion and the upregulation of TNFR2 expression on Tregs in LPS-treated mice were mediated by TNF–TNFR2 interaction, as shown by our previous study. The expansion of Tregs in LPS-treated mice were also markedly inhibited by in vivo treatment with SB203580. Taken together, our data clearly indicate that the activation of p38 MAPK is attributable to TNF/TNFR2-mediated activation and proliferative expansion of Tregs. Our results also suggest that targeting of p38 MAPK by pharmacological agent may represent a novel strategy to up- or downregulation of Treg activity for therapeutic purposes.
Generating person images based on attributes

Attribute based person image generation is a problem which considers generating realistic person images using attributes like pose, gender, clothes, whether a bag is present or not etc. and it has wide variety of applications on computer vision. Realization of that generation process is quite difficult due to several reasons such as foreground/background, partial occlusion, stance of a person, camera angle and distance, complex relationships between attributes, unbalanced and poor quality data etc. Synthetic images are generated in related works which have worked on relatively easier datasets using less attributes with more complex models for more specific purposes. In this work, a more controversial goal was set up and a model named DCGAN-C is developed based on conditional generative adversarial networks and it can produce sythetic person images with both multi-class and multi-labels. Consequently, both quantitative and qualitative experiments were performed on the PA-100K dataset and the performance of the model was demonstrated.
Design and Optimization of Distribution Routes Using Evolutionary Strategy and Type-1 Singleton Neuro-Fuzzy Systems

This paper presents a new proposal to design and to optimize the routes by hybrid intelligent model using evolutionary strategies and Adaptive Network Fuzzy Inference System (ANFIS). The hybridization uses an evolutionary computing model to create random a priori routes in the first step. The model serves as the inputs to the singleton type-1 ANFIS system that provides the knowledge and changes the connections between customers or sub-routes for distances by approximation. The generated routes solved by ANFIS model in the second phase will be optimized in the last phase of the proposal with the evolutionary strategy. The goal of this proposal is to provide a simple way to optimize routes without a hard mathematical model that produces an optimal or sub-optimal solution without an optimization function reducing the complexities of classical approaches.
A Tutorial on Bayesian Optimization

Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian machine learning technique, Gaussian process regression, and then uses an acquisition function defined from this surrogate to decide where to sample. In this tutorial, we describe how Bayesian optimization works, including Gaussian process regression and three common acquisition functions: expected improvement, entropy search, and knowledge gradient. We then discuss more advanced techniques, including running multiple function evaluations in parallel, multi-fidelity and multi-information source optimization, expensive-to-evaluate constraints, random environmental conditions, multi-task Bayesian optimization, and the inclusion of derivative information. We conclude with a discussion of Bayesian optimization software and future research directions in the field. Within our tutorial material we provide a generalization of expected improvement to noisy evaluations, beyond the noise-free setting where it is more commonly applied. This generalization is justified by a formal decision-theoretic argument, standing in contrast to previous ad hoc modifications.
Vulnerability Detection on Mobile Applications Using State Machine Inference

Although the importance of mobile applications grows every day, recent vulnerability reports argue the application's deficiency to meet modern security standards. Testing strategies alleviate the problem by identifying security violations in software implementations. This paper proposes a novel testing methodology that applies state machine learning of mobile Android applications in combination with algorithms that discover attack paths in the learned state machine. The presence of an attack path evidences the existence of a vulnerability in the mobile application. We apply our methods to real-life apps and show that the novel methodology is capable of identifying vulnerabilities.
Face Recognition Using Map Discriminant on YCbCr Color Space

This paper presents face recognition using maximum a posteriori (MAP) discriminant on YCbCr color space. The YCbCr color space is considered in order to cover the skin information of face image on the recognition process. The proposed method is employed to improve the recognition rate and equal error rate (EER) of the gray scale based face recognition. In this case, the face features vector consisting of small part of dominant frequency elements which is extracted by non-blocking DCT is implemented as dimensional reduction of the raw face images. The matching process between the query face features and the trained face features is performed using maximum a posteriori (MAP) discriminant. From the experimental results on data from four face databases containing 2268 images with 196 classes show that the face recognition YCbCr color space provide better recognition rate and lesser EER than those of gray scale based face recognition which improve the first rank of grayscale based method result by about 4%. However, it requires three times more computation time than that of grayscale based method.