Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel robust attitude control method for small-scale unmanned helicopters based on quaternion feedback

In this paper, a novel quaternion-based nonlinear attitude control is proposed to address the attitude tracking problem of a small-scale unmanned helicopter which is subject to model uncertainties and external disturbances. The unit quaternion feedback is adopted to avoid the inherent geometrical singularity associated with orientation represen­tations which results in an problem known as "gimbal lock". The time-delay estimation (TDE) is applied to estimate the neglected model uncertainties and external disturbances. The global asymptotical stability is proven with the integrator compensation technique and simulation results on a small-scale unmanned helicopter verify the effectiveness and robustness of the proposed control scheme.
Fast and efficient network service embedding method with adaptive offloading to the edge

Next generation networks and applications have recently drawn the attention of many researchers in both academia and industry. The plethora of service orchestration, embedding and scheduling solutions being developed indicates that efficient resource utilization in the cloud and edge/fog architecture is of crucial importance in order to exploit the great economic potential of this infrastructure. In this paper we propose a novel service orchestration approach that aims for speed and quality in terms of service provisioning: our embedding algorithm instantly deploys end-to-end delay-constrained service graphs while regularly offloads the most burdened parts of the infrastructure applying a cost-aware VNF migration strategy. In this sense we propose a hybrid orchestration approach, which unites the advantages of online heuristic and offline optimizing service orchestration methods, with the goal of obtaining a system design that provides fast service placement decisions and efficient long-term operation. By an exhaustive evaluation of our orchestration system on core, cloud and edge network topologies, we show its benefits in efficiency and the collateral cost of migrations.
Detection and discrimination of obstacles to vehicle environment under convolutional neural networks

Nowadays driving safety has become an important issue. Obstacles in the driving environments is a main risk of driving. In order to meet the requirements of safety driving, we set up a novel obstacle detection and recognition method based on neural network. In our proposed method, a multi-layer neural network is established to build a 15- layer network that contains the recommended areas for vehicle obstacle and obstacle detection. The convolution neural network (CNN) is trained. To split the region of interest (ROI) completely, on the basis of the ROI algorithm, the maximum variance of the region growing algorithm is used to automatically obtain the threshold value, that also can obtain the segmentation boundary of image. Meanwhile, the morphological operation is used to smooth the boundary of image. By analyzing the accuracy and computation cost of the experimental results, the CNN network in this paper achieves great results in obstacle detection and discrimination.
Ontology-based approach for identifying the credibility domain in social Big Data

ABSTRACT The challenge of managing and extracting useful knowledge from social media data sources has attracted much attention from academics and industry. To address this challenge, semantic analysis of textual data is focused on in this paper. We propose an ontology-based approach to extract semantics of textual data and define the domain of data. In other words, we semantically analyze the social data at two levels: the entity level and the domain level. We have chosen Twitter as a social channel for the purpose of concept proof. Ontologies are used to capture domain knowledge and to enrich the semantics of tweets, by providing specific conceptual representation of entities that appear in the tweets. Case studies are used to demonstrate this approach. We experiment and evaluate our proposed approach with a public dataset collected from Twitter and from the politics domain. The ontology-based approach leverages entity extraction and concept mappings in terms of quantity and accuracy of concept identification.
YUV based automatic colour calibration for NAO robots

A challenge in real time application of NAO soccer robots is in colour calibration. Many tasks such as localisation and goal detection rely on robustness of colour calibration. In this paper a robust and accurate YUV colour space based automatic colour calibration technique is proposed. First the specific set of frames from the NAO's camera has been analysed in order to define average values for desired colour classes, namely orange, white, green and purple. Then those average values are corrected by a luminance analysis of a new frame and are passed to the K-means clustering algorithm as a set of initial means. Apart from those 4 values, set of initial means of the K-means algorithm also contains 16 values that are calculated in the following manner: the frame currently being processed is divided into 4 by 4 grid and average value from every grid will serve as an initial mean for K-means clustering. After the K-means clustering is applied to the frame so that colours of a similar type are combined into clusters. Final step of the proposed technique is the cluster classification, which is performed by measuring the distance from cluster centroids to the previously calculated average values of desired colour classes corrected by luminance analysis. The proposed colour calibration technique has been tested on white goal detection.
Joint multicast routing and OFDM resource allocation in LTE-D2D 5G cellular network

An offloading scheme based on LTE-D2D is pro­posed in this paper to route the intracellular multicast traffic via a network of D2D-enabled User Equipments (UEs). The latter are ready to cooperate under the control of the eNodeB to carry and deliver the traffic. In doing so, the UEs reuse uplink resources granted by the eNodeB and thus, increasing the overall spectral efficiency while reducing the traffic load on the eNodeB. In this paper, we address the joint multicast routing and OFDM resource allocation problem in the D2D network to accomplish the offloading task. To do so, first we formulate the problem as an Integer Linear Programming (ILP) model which takes into account factors that limit spectrum reuse in addition to other LTE-D2D limitations: half-duplex operation and contiguity in resource block allocations. Then, we propose a novel scheme named Joint Multicast Routing and Wireless allocation in D2D communications (JRW-D2D-MC). The devised scheme consists of two-stage algorithm which, first, performs a pre-admittance filtering of flows that can be routed considering the current state of the network. Then, it makes use of the branch-and-cut method to solve the reduced ILP model. To evaluate effectiveness of our proposal, we implement the LTE-D2D standard in a network simulator NS-3. The results are very good in terms of flow-acceptance rate and latency.
Image classification using manifold learning based non-linear dimensionality reduction

This paper presents fast categorization or classification of images on an animal dataset using different classification algorithm in combination with manifold learning algorithms. The paper will focus on comparing the effects of different non-linear dimensionality reduction algorithms on speed and accuracy of different classification algorithms. It examines how manifold learning algorithms can improve classification speed by reducing the number of features in the vector representation of images while keeping the classification accuracy high.
EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

On-demand transport has been disrupted by Uber and other providers, which are challenging the traditional approach adopted by taxi services. Instead of using fixed passenger pricing and driver payments, there is now the possibility of adaptation to changes in demand and supply. Properly designed, this new approach can lead to desirable tradeoffs between passenger prices, individual driver profits and provider revenue. However, pricing and allocations—known as mechanisms—are challenging problems falling in the intersection of economics and computer science. In this paper, we develop a general framework to classify mechanisms in ondemand transport. Moreover, we show that data is key to optimizing each mechanism and analyze a dataset provided by a real-world on-demand transport provider. This analysis provides valuable new insights into efficient pricing and allocation in on-demand transport. Received on 09 January 2018; accepted on 30 March 2018; published on 27 June 2018
Unsupervised real-time detection of BGP anomalies leveraging high-rate and fine-grained telemetry data

Recent technology evolution of network equipment allow to continuously stream a wealth of information, pertaining to multiple protocols and layers of the stack, at a very fine spatial-grain and at furthermore high-frequency. Processing this deluge of telemetry data in real-time clearly offers new opportunities for network control and troubleshooting, but also poses serious challenges. In this demonstration, we tackle this challenge by applying streaming machine-learning techniques to the continuous flow of control and data-plane telemetry data, with the purpose of real-time detection of BGP anomalies. In particular, we implement an anomaly detection engine that leverages DenStream, an unsupervised clustering technique, and apply it to telemetry features collected from a large-scale testbed comprising tens of routers traversed by 1 Terabit/s worth of real application traffic.
Gait Dynamics in Parkinson’s Disease: Short Gait Trials “Stitched” Together Provide Different Fractal Fluctuations Compared to Longer Trials

The fractal analysis of stride-to-stride fluctuations in walking has become an integral part of human gait research. Fractal analysis of stride time intervals can provide insights into locomotor function and dysfunction, but its application requires a large number of strides, which can be difficult to collect from people with movement disorders such as Parkinson’s disease. It has recently been suggested that “stitching” together short gait trials to create a longer time series could be a solution. The objective of this study was to determine if scaling exponents from “stitched” stride time series were similar to those from continuous, longer stride time series. Fifteen young adults, fourteen older adults, and thirteen people with Parkinson’s disease walked around an indoor track in three blocks: one time 15 min, five times 3 min, and thirty times 30 s. Stride time intervals were determined from gait events recorded with instrumented insoles, and the detrended fluctuation analysis was applied to each stride time series of 512 strides. There was no statistically significant difference between scaling exponents in the three blocks, but intra-class correlation revealed very low between-blocks reliability of scaling exponents. This result challenges the premise that the stitching procedure could provide reliable information about gait dynamics, as it suggests that fractal analysis of stitched time series does not capture the same dynamics as gait recorded continuously. The stitching procedure cannot be considered as a valid alternative to the collection of continuous, long trials. Further studies are recommended to determine if the application of fractal analysis is limited by its own methodological considerations (i.e., long time series), or if other solutions exists to obtain reliable scaling exponents in populations with movement disorders.
Introducing network slice management inside M-CORD-based-5G framework

Network slicing has been considered as one of the fundamental technologies in the fifth generation (5G) mobile networks. Slicing mechanism enables virtual networks while providing customized services on demand. There are several papers attempting to narrow down the architectural requirement for 5G networks. Many of them consider the aspect of slice creation as a central point, sometimes strengthening slices with slice isolation. The aim of this paper is to focus more on network slice management, slicing of transport network (TN) and challenges to elaborate technological options and enablers, irrespective of the slicing mechanism. Our proposed framework creates, manages and associates the slice to user equipment (UE) upon the request made by different applications. Proposed framework exploits the key features offered by CORD including OpenStack and ONOS as well as modules based on M-CORD such as vBBU, NSSF and vEPC for establishing a network slice scenario.
One-dimensional data augmentation using a wasserstein generative adversarial network with supervised signal

In recent years, Generative Adversarial Network (GAN) is widely applied in many domains; however, there is still some difficulties in training the network, which are mainly caused by mode collapse, vanishing gradient of generator and indirect assessment criteria of generated samples. In this paper, the supervised signal is introduced into Wasserstein Generative Adversarial Network (WGAN) on the application of one-dimensional data augmentation to alleviate this difficulty. In the proposed method, besides generating fake samples, a well trained generative model is implemented to reconstruct the real samples , whose input data are latent space samples obtained from autoencoder (AE). In addition, the mode collapse can be prevented by the new model through ensuring that the supervised signal grounded in all the available training data. The performance of our method is verified based on parameters of electronic equipment and stock index systematically and quantitatively, and the superiority of the algorithm is demonstrated by the experiment results both in convergence rate and the quality of samples compared with WGAN and VAEGAN.
Comparison of data center traffic division policies using SDN

It is estimated that Internet traffic will triple in five years, which will increase server response time. One way to reduce such time is to balance the load on replicated servers using SDN switches. However, existing SDN switches have performance limitations that influence the performance of the load balacing. This work compares five load balancing policies: round robin, random, txbytes (transmitted bytes), cpuq-load (CPU usage and number of open connections), and load/load-prev (load forecast with switch statistics). These policies consider limitations such as the cost to retrieve network statistics and to install new rules. The results show that the txbytes and cpuq-load policies outperformed the others. On the other hand, the load-prev policy proved to be promising when adjusted for traffic.
A QUALIDADE NA SAÚDE COMPREENDIDA SOB A ÓTICA DOS SEUS SINAIS: UM ESTUDO DE CASO EM UMA CLÍNICA DE TRATAMENTO ONCOLÓGICO

Cada cliente constroi uma experiencia de servico que pode ser decomposta por uma serie de outras pequenas experiencias. Cada uma delas transmite inumeras mensagens que impactam a forma como os clientes se sentem em relacao ao servico entregue. Essas mensagens podem ser compreendidas como varios “Sinais da Qualidade” que sao percebidos pelos clientes durante e apos a entrega do servico. Tendo como referencia a importância destes sinais para entrega de um servico de qualidade, este trabalho busca compreender como os Sinais da Qualidade sao construidos em organizacoes de saude que notadamente trabalham com elevados padroes de qualidade. Nesta direcao, o trabalho analisou diversas acoes realizadas pelos gestores e funcionarios de uma clinica de tratamento oncologico de destaque setorial para criacao destes sinais. O estudo apontou a importância dos Sistemas de Gestao da Qualidade para a criacao de sinais funcionais, importantes para a confiabilidade dos servicos. Entretanto, constatou-se que a criacao de sinais mecânicos e humanos foram frutos de iniciativas da empresa que transpassaram, em grande parte, os requisitos dos Sistemas de Gestao implementados.
Integrating Rotations Using Nonunit Quaternions

In this letter, we propose and demonstrate a method for integration of rotations using nonunit quaternions. Unit quaternions are commonly used to represent rotation, in which case the rotation operation involves the conjugate of the unit quaternion. However, a redundant mapping can be defined from all nonzero quaternions to the set of rotation matrices, ${\mathrm{SO}(3)}$, based on the more general rotation operation involving the quaternion inverse. From this we show that the well-known formula that maps angular velocity to the derivative of a unit quaternion actually represents the minimum-norm solution within a set of solutions for the derivative of a nonunit quaternion. This fact enables efficient, singularity free, numerical integration of rotations over long intervals. The approach inherently preserves the structure of ${\mathrm{SO}(3)}$ during the integration with any standard routine or ordinary differential equation (ODE) solver package without employing specially designed geometric integration schemes, exponential updates, or the many quaternion length enforcement techniques found in the literature. We demonstrate the accuracy of this approach compared to other common methods applied to integrate a known angular velocity function and a classic Lagrange top.
Effects of gain nonlinearities in an optically injected gain lever semiconductor laser

The effects of gain compression on the modulation dynamics of an optically injected gain lever semiconductor laser are studied. Calculations reveal that the gain compression is not necessarily a drawback affecting the laser dynamics. With a practical injection strength, a high gain lever effect and a moderate compression value allow us to theoretically predict a modulation bandwidth four times higher than the free-running one without a gain lever, which is of paramount importance for the development of directly modulated broadband optical sources compatible with short-reach communication links.
Assessing Grounding Frequency using Ship Traffic and Waterway Complexity

Ship traffic is the factor that presents in almost all of the existing grounding risk models. It is considered to be one of the main factors affecting the expected frequency of ship groundings. This is mostly accepted by experts as common sense. However, there is no research available on the actual dependency between ship traffic and grounding accidents. In this paper, we conduct a study aimed at determining the statistical dependency between the density and distribution of traffic, the number and frequency of grounding accidents and the dependency between the complexity of waterways and an actual accident. For this purpose we utilise statistical analysis of maritime traffic, obtained from Automatic Identification System (AIS) data and grounding accidents, enhanced with the expert elicitation techniques delivering the waterway complexity index. The sea area under investigation is the Gulf of Finland. The results show statistical dependency between frequency of grounding and waterway complexity as well as the traffic distribution. However, the study does not reveal any significant dependency between grounding and traffic density.
An improved FCM method for image segmentation based on wavelet transform and particle swarm

Fuzzy C-Means (FCM) algorithm is one of the most commonly used image segmentation algorithms. It has the advantages of unsupervised, easy calculation, soft segmentation and so on. However, for the image containing noise, it will be more obviously disturbed. At the same time, it is sensitive to the initial value and easy to fall into the local minimum. Aiming at solving above problems, a new FCM algorithm is proposed, which combines wavelet transform and improved FCM algorithm. Firstly, the high frequency and low frequency coefficients of different scales are obtained by using the wavelet transform to decompose the image. The Anisotropic Diffusion is used to denoise the decomposed high frequency coefficients. Then, the processed coefficients are reconstructed by wavelet to get the processed images. Finally, the particle swarm optimization algorithm is used to update the FCM cluster centers to get the global optimal value. The experimental results show that the proposed algorithm can better suppress the influence of noise and has better robustness.
The new approach to the analysis of linear control systems

This paper introduces the σ-entropy of an input signal. The considered approach to the analysis of linear control systems is based on using the σ-entropy norm, which is defined as the maximum of the ratio of L2 norm of the system output to that of the input wherein the σ-entropy of input signal does not exceed a given value s.
Telephony Denial of Service defense at data plane (TDoSD@DP)

The Session Initiation Protocol (SIP) is an application-layer control protocol used to establish and terminate calls that are deployed globally. A flood of SIP INVITE packets sent by an attacker causes a Telephony Denial of Service (TDoS) incident, during which legitimate users are unable to use telephony services. Legacy TDoS defense is typically implemented as network appliances and not sufficiently deployed to enable early detection. To make TDoS defense more widely deployed and yet affordable, this paper presents TDoSD@DP where TDoS detection and mitigation is programmed at the data plane so that it can be enabled on every switch port and therefore serves as distributed SIP sensors. With this approach, the damage is isolated at a particular switch and bandwidth saved by not sending attack packets further upstream. Experiments have been performed to track the SIP state machine and to limit the number of active SIP session per port. The results show that TDoSD@DP was able to detect and mitigate ongoing INVITE flood attack, protecting the SIP server, and limiting the damage to a local switch. Bringing the TDoS defense function to the data plane provides a novel data plane application that operates at the SIP protocol and a novel approach for TDoS defense implementation.
Stream4Flow: Real-time IP flow host monitoring using Apache Spark

In this paper, we present Stream4Flow, a framework for cyber situational awareness based on Apache Spark Streaming. We demonstrate utilization of Stream4Flow for real-time IP flow host monitoring in a large campus network. Contemporary IP flow analysis systems are not designed for the continuous host monitoring. Gaining the detailed overview of each host is not straightforward with these systems due to connection-based paradigm and performance challenges. We show that distributed stream processing is a natural solution for detailed IP flow host monitoring. Moreover, we describe a real-time host monitoring workflow in data streams in detail and present advantages of flow-based host monitoring in Apache Spark including real-time host profiling, dynamic level of detail and granularity.
Sketch classification with deep learning models

Sketch classification problem is challenging due to several reasons, such as absence of color and texture information, lack of detailed information of objects, and the quality, which depends on drawing ability of the person. In this study, sketch classification problem is addressed by using deep convolutional neural network models. Specifically, the effect of domain adaptation is examined, when fine-tuning the convolutional neural networks for sketch classification. By employing domain adaptation, the classification accuracy is increased by around 3%. The proposed system, which utilizes VGG-16 network model and performs two-stage fine-tuning, outperforms the previous state-of-the-art approaches on the TU Berlin sketch dataset by reaching 79,72% accuracy.