Miracle-dz/UniFAR-FTUs huggingface dataset:
CoherentPaaS - A Coherent and Rich PaaS with a Common Programming Model

CloudWave will revolutionise modern cloud infrastructures and tools by enabling agile development and delivery of adaptive cloud services which dynamically adjust to changes in their environment so as to optimise service quality and resource utilization. In doing so, CloudWave will advance the competitiveness of the EU economy by providing a powerful foundation, based on open standards, for creating innovative Future Internet services, realising the full promise of cloud computing. CloudWave will tangibly deliver (1) an open architecture and standards-based reference implementation of an advanced cloud software stack, with novel capabilities for adaptation across all cloud layers; and (2) tools and methods for agile development of reliable and adaptable cloud services, facilitated by the new stack. Technologically, CloudWave will advance the state-of-art along three dimensions: •Execution Analytics: A new framework where specialised algorithms dynamically analyse cloud infrastructure and application behaviour, seamlessly integrate data pertaining to physical and virtual resources and IoT elements, and provide consolidated feedback to drive service evolution and adaptation •Coordinated Adaptation: A new software technology where cloud services, cloud infrastructure and end-user devices exploit Execution Analytics to collaboratively and automatically undertake complex adaptation actions across the cloud stack, ensuring quality of service and effective utilization of ICT resources •Feedback-Driven Development: A new agile approach for developing cloud applications, where developers exploit Execution Analytics to incrementally determine and evolve application features, extensions and optimizations, based on observed user needs Using three industry case studies, we will demonstrate how the CloudWave technology supports emerging Future Internet services and provides high and quantifiable improvements in service delivery quality, productivity, reliability and cost. Coordinator: IBM ISRAEL SCIENCE AND TECHNOLOGY LTD Partners: ATOS SPAIN SA Cloudmore OÜ IBM ISRAEL SCIENCE AND TECHNOLOGY LTD INTEL PERFORMANCE LEARNING SOLUTIONS LIMITED SAP AG TECHNION ISRAEL INSTITUTE OF TECHNOLOGY TELECOM ITALIA S.p.A UNIVERSITA DEGLI STUDI DI MESSINA UNIVERSITAET DUISBURG-ESSEN UNIVERSITAET ZUERICH VALADIS SYSTEM SA
Multimodal Biometric Systems - A Roadmap toImprove Performance and Accuracy

Today with rapid developments in electronically wired information society, reliable and accurate control mechanism for identification and verification of individuals has become an important issue in several applications. Personal identification using biometrics has emerged as a promising component of secure authentication system. Single modality based biometrics recognition systems are not very robust while combining information from various biometric modalities provides better performance. By fusing several different types of complementary biometric traits together, multi-biometric systems are able to represent and discriminate subjects effectively. Since combining multiple biometric modalities can alleviate many of problems faced by unimodal systems, multimodal biometrics becomes a focused field of research. Considering the importance of information fusion in multimodel system, the main aim of this paper is to focus on the different levels of fusion. Finally, the paper concludes with a discussion on the issues that should be taken into account in deployment of multimodal biometric person authentication systems. Keywords—Biometrics; Identifiaction; Authentication; Fusion; Unimodal; Multimodal.
A Literature Review of Historical and Detection Analysis of Botnets Forensics

In 1988 Botnet was originally developed only as a virtual individual on IRC channels (Internet Relay Chat) while the owner was busy doing activities elsewhere. Furthermore, Botnet incarnated as a frightening specter on the global Internet network with the impact of damage and anxiety on government institutions, private companies, education, health, and even households. According to FBI and cybersecurity agencies estimating losses, the growth and number of attacks using Botnet have reached a worrying point. Therefore, a literature review is needed to uncover all the important spectrums in the Botnet. Hence, this paper will present the two most important spectrums in the Botnet: Historical and Forensic Analysis Botnet. The history of Botnet spectrum was originally developed in 1988 and it was used by operators while they were on IRC channels until the 2006 era the emergence of the ZeuS Botnet that had made global losses of approximately $110 billion globally. On the spectrum of Forensic Analysis, it is divided into three important aspects: Forensic Analysis Botnet from Software aspect, Forensic Analysis Botnet from Detection aspect, and Botnet Forensic Analysis from Behavior aspect.
Deep Probabilistic Matrix Factorization Framework for Online Collaborative Filtering

As living data growing and evolving rapidly, traditional machine learning algorithms are hard to update models when dealing with new training data. When new data arrives, traditional collaborative filtering methods have to train their model from scratch. It is expensive for them to retrain a model and update their parameters. Compared with traditional collaborative filtering, the online collaborative filtering is effective to update the models instantly when new data arrives. But the cold start and data sparsity remain major problems for online collaborative filtering. In this paper, we try to utilize the convolutional neural network to extract user/item features from user/item side information to address these problems. First, we proposed a deep bias probabilistic matrix factorization (DBPMF) model by utilizing the convolutional neural network to extract latent user/item features and adding the bias into probabilistic matrix factorization to track user rating behavior and item popularity. Second, we constrain user-specific and item-specific feature vectors to further improve the performance of the DBPMF. Third, we update two models by an online learning algorithm. The extensive experiments for three datasets (MovieLens100K, MovieLens1M, and HetRec2011) show that our methods have a better performance than baseline approaches.
A methodology for rapid hypersonic flow predictions via surrogate modeling with machine learning and deep learning

Generating and parsing through large amounts of wind tunnel, flight test, or computational fluid dynamics (CFD) data can prove to be expensive. This makes, for example, the optimization of aerothermal hypersonic components, which may contain a large number of independent variables, challenging. Having a surrogate model to quickly and accurately approximate the data can help with the optimal design process. A lower order model can be used instead of or in conjunction with a higher order model to model a system with less computational effort. Typically, additional assumptions are made to make a lower order model. These have the benefit of being faster to execute or simpler to solve but come at the cost of reduced accuracy. Here, an effort is made to construct lower order/surrogate models, that are built and operate with exactly the same assumptions as the higher order model, in the form of machine learning and deep learning-based surrogate models. In tandem with this effort, the goal is to have a function evaluation time significantly smaller than the higher order method. This can increase the number of variables the designer can consider during the optimal design process or reduce the time required to design a component. Potential methodologies using machine learning (ML) and deep learning as surrogate models are used to evaluate the flow field and subsequently the performance of a 2-D hypersonic compression ramp. Gradient Boosting, Neural Networks, and Random Forest regressors are used as the surrogate models. With the flow field predicted, performance characteristics can be calculated such as: lift, drag, moment, pressure distribution, shear, heat transfer, etc. Thus, multiple phenomena can be considered during the design process. The nine flow parameters being predicted are: pressure, total pressure, density, dynamic viscosity, Mach number, temperature, total temperature, and velocities in the principal axes. CFD simulations using a commercial package are used to generate the training data for the regressors. The predictors are measured on how accurately the simulation data can be predicted.
Blind Image Deblurring using the l0 Gradient Prior

Many blind image deblurring methods rely on unnatural image priors that are explicitly designed to restore salient image structures, necessary to estimate the blur kernel. In this article, we analyze the blur kernel estimation method introduced by Pan and Su in 2013 that uses an ℓ 0 prior on the gradient image. We present deconvolution results using the estimated blur kernels. Our experiments show the eﬀectiveness of the method as well as some of its shortcomings.
Non-rigid Shape Registration using Curvature Information

This paper addresses a registration problem for an industrial control application: it meets the need to registrate a model on an image of a flexible object. We propose a non-rigid shape registration approach that deals with a great disparity of the number of points in the model and in the manufactured object. We have developed a method based on a classical minimization process combining a distance term and a regularization term. We observed that, even if the control points fall on the object boundary, the registration failed on high curvature points. In this paper we add a curvature-based term in order to improve the registration on object extremities. We validate our approach on a real industrial application. The addition of this curvature term reduces by two the error of the inner boundaries location on the previously problematic cases of our database.
Cooperative Routing Problem for Ground Vehicle and Unmanned Aerial Vehicle: The Application on Intelligence, Surveillance, and Reconnaissance Missions

In this paper, we investigate a novel two-echelon ground vehicle and its mounted unmanned aerial vehicle cooperative routing problem (2E-GUCRP) for intelligence, surveillance, and reconnaissance (ISR) missions. In a typical cooperative system with ground vehicle (GV) and unmanned aerial vehicle (UAV), the UAV is launched from the GV, automatically flies to the designated target and return to the GV before powering off. Meanwhile, acting as a mobile platform, the GV can carry the UAV moving in a larger region and charge or exchange the UAV’s battery. The objective is to design efficient GV and UAV routes to minimize the total mission time while meeting the operational constraints. A mixed integer programming (MIP) model, which could be solved by commercial software (e.g. CPLEX), is developed to formulate the problem. In order to quickly solve the medium-scale or above problems, two heuristics are designed. The random instances and practical case based on the road network in Changsha, China, are used for experiments. The computational results indicate that, compared to the mode of two-echelon vehicle routing problem (2E-VRP), the cooperative GV and UAV can complete the ISR missions in a much shorter time through utilizing the proposed 2E-GUCRP mode. Furthermore, discussion of the practical instance points out that the increase in efficiency is related to the speed relationship between the GV and the UAV.
Polyp Shape Recovery using Vascular Border from Single Colonoscopy Image

The shape and size of a colonic polyp is a biomarker that correlates with its risk of malignancy and guides its clinical management. It is the most accurate method for detecting polyps of all sizes, and it allows biopsy of lesions and resection of most polyps, and it is considered nowadays as the gold standard for colon screening. However, there are still open challenges to overcome, such as the reduction of the missing rate. Colonoscopy images usually consist of nonrigid objects such as a polyp, and no approaches have been proposed to recovery shape and absolute size from a single image. Hence, it is a challenging topic to reconstruct polyp shape using computer vision technique. This paper proposes a polyp shape retrieval method based on Shape from Shading (SFS), and this research contributes to mitigating constraint for applying SFS to the single colonoscopy image using vascular border information. Experiments confirmed that the proposed method recovered approximate polyp shapes.
Volumetric Video Capture using Unsynchronized, Low-cost Cameras

Volumetric video can be used in virtual and augmented reality applications to show detailed animated performances by human actors. In this paper, we describe a volumetric capture system based on a photogrammetry cage with unsynchronized, low-cost cameras which is able to generate high-quality geometric data for animated avatars. This approach requires, inter alia, a subsequent synchronization of the captured videos.
Energy-Spectrum Efficiency Trade-Off in Energy Harvesting Cooperative Cognitive Radio Networks

This paper addresses energy efficiency (EE)-spectrum efficiency (SE) trade-off in an energy harvesting (EH) cooperative cognitive radio network operated in a frame structure comprising of spectrum sensing (SS) and cooperation-transmission mode. The secondary users (SUs) are clustered into two groups and function in a way that when the SUs in Group I participate in cooperative SS, the SU nodes in Group II harvest energy from the radio frequency signal of primary user (PU). If PU is detected to be in transmit mode, the SU nodes in Group II cooperate in PUs transmission while the SUs in Group I perform EH. All the SU nodes transmit data when PU is detected idle. The objective is to maximize EE and SE via joint optimization of SS time, final decision threshold, self and cooperative transmission power gains. Mathematical analysis is done to solve the problem of EE-SE trade-off under the constraints of SS reliability, SU power budget, PU cooperative SE and fairness in SE for each SU. Numerical results highlight about 19% and 14% gain in normalized EE and SE values, respectively, compared to the existing work and the issue of EE-SE trade-off is clearly investigated.
How Robust is Robust ML? Evaluating Models of Classifier Evasion in PDF Malware Detection.

Machine learning (ML) techniques are increasingly common in security applications, such as malware and intrusion detection. However, ML models are often susceptible to evasion attacks, in which an adversary makes changes to the input (such as malware) in order to avoid being detected. A conventional approach to evaluate ML robustness to such attacks, as well as to design robust ML, is by considering simplified feature-space models of attacks, where the attacker changes ML features directly to effect evasion, while minimizing or constraining the magnitude of this change. We investigate the effectiveness of this approach to designing robust ML in the face of attacks that can be realized in actual malware (realizable attacks). We demonstrate that in the context of structure-based PDF malware detection, such techniques appear to have limited effectiveness, but they are effective with content-based detectors. In either case, we show that augmenting the feature space models with conserved features (those that cannot be unilaterally modified without compromising malicious functionality) significantly improves performance. Finally, we show that feature space models enable generalized robustness when faced with a variety of realizable attacks, as compared to classifiers which are tuned to be robust to a specific realizable attack.
Semi-Analytic Nonparametric Bayesian Inference for Spike-Spike Neuronal Connectivity

Estimating causal connectivity between spiking neurons from measured spike sequences is one of the main challenges of systems neuroscience. In this paper we introduce two nonparametric Bayesian methods for spike-membrane and spikespike causal connectivity based on Gaussian process regression. For spike-spike connectivity, we derive a new semi-analytic variational approximation of the response functions of a non-linear dynamical model of interconnected neurons. This semi-analytic method exploits the tractability of GP regression when the membrane potential is observed. The resulting posterior is then marginalized analytically in order to obtain the posterior of the response functions given the spike sequences alone. We validate our methods on both simulated data and real neuronal recordings.
Conversation Modeling with Neural Network

The ability to process, understand and interact in natural language carries high importance for building a Intelligent system, as it will greatly affect the way of communicating with the system. Deep Neural Networks (DNNs) have achieved excellent performance for many of machine learning problems and are widely accepted for applications in the field of computer vision and supervised learning. Although DNNs work well with availability of large labeled training set, it cannot be used to map complex structures like sentences end-to-end. Existing approaches for conversational modeling are domain specific and require handcrafted rules. This paper proposes a simple approach based on use of neural networks’ recently proposed sequence to sequence framework. The proposed model generates reply by predicting sentence using chained probability for given sentence(s) in conversation. This model is trained end-to-end on large data set. Proposed approach uses Attention to focus text generation on intent of conversation as well as beam search to generate optimum output with some diversity.Primary findings show that model shows common sense reasoning on movie transcript data set. *Corresponding author: E-mail: jivan.y. patil@gmail.com; Patil and Potdar; AJRCOS, 1(3): 1-10, 2018; Article no.AJRCOS. 42930
Towards an Efficient Verification Method for Monotonicity Properties of Chemical Reaction Networks

One of the main goals of systems biology is to understand the behaviour of (bio)chemical reaction networks, which can be very complex and difficult to analyze. Often, dynamical properties of reaction networks are studied by performing simulations based on the Ordinary Differential Equations (ODEs) models of the reactions’ kinetics. For some kinds of dynamical properties (e.g. robustness) simulations have to be repeated many times by varying the initial concentration of some components of interest. In this work, we propose sufficient conditions that guarantee the existence of monotonicity relationships between the variation of the initial concentration of an “input” biochemical species and the concentration (at all times) of an “output” species involved in the same reaction network. Our sufficient conditions allow monotonicity properties to be verified efficiently by exploring a dependency graph constructed on the set of species of the reaction network. Once established, monotonicity allows us to drastically restrict the number of simulations required to prove dynamical properties of the chemical reaction network.
Classification of brain tumors using personalized deep belief networks on MRImages: PDBN-MRI

As of late, various studies used deep learning methods on brain tumor data. There are two principle tasks used for brain tumor classifications: feature extraction and brain tumor classification. The motive of this study is to analyze various images of the BraTS 2015 dataset by using deep learning algorithms and to classify the brain tumors to different types of regions: whole, core and enhanced. We address the issue by using deep belief networks, a kind of neural network that detects features in images and classifies them. This paper introduces a three-step framework for classifying multiclass radiography images. The first step utilizes a de-noising technique based on vanilla data preprocessing to remove noise and insignificant features of the images. For learning the unlabelled features we used unsupervised deep belief network (DBN) in the second step. In the small-scale DBNs have demonstrated significant potential but when scaling to large networks the computational cost of training the restricted Boltzmann machine is a major issue. Discriminative feature subsets obtained in the first two steps serve as inputs into classifiers in the third step for evaluations. Our goal is a machine capable of recognizing a brain tumor’s type; we define a probabilistic model that classifies brain tumors to different types of regions having as input the best preprocessing vanilla (Image size 256×256). Using the BraTS data to train the deep belief networks proves an accuracy of 91.6% on its classifications.
Communication-Efficient Distributed Dual Coordinate Ascent for Machine Learning in General Tree Networks

Due to the size of data and the limited data storage space in a single local computer, data can often be stored in a distributed manner. In order to use the distributed big data in machine learning, performing large-scale machine learning from the distributed data through communication networks is inevitable. In this paper, we investigate the impact of network communication constraints on the convergence speed of distributed machine learning optimization algorithms. Firstly, we study the convergence rate of the distributed dual coordinate ascent in a general tree structured network, since every connected communication network can have a spanning tree, and a tree network can be understood as the generalization of a star network. Secondly, by considering network communication delays, we optimize the network-constrained dual coordinate ascent to maximize its convergence speed in terms of operation time. Through numerical experiments, we demonstrate that under different network communication delays, the delay-dependent number of local and global iterations in distributed dual coordinated ascent can play a significant role in the achievement of maximum convergence speed.
Cyber Risk in IoT Systems

In this paper we present an understanding of cyber risks in the Internet of Things (IoT), we explain why it is important to understand what IoT cyber risks are and how we can use risk assessment and risk management approaches to deal with these challenges. We introduce the most effective ways of doing Risk assessment and Risk Management of IoT risk. As part of our research, we also developed methodologies to assess and manage risk in this emerging environment. This paper will take you through our research and we will explain: what we mean by the IoT; what we mean by risk and risk in the IoT; why risk assessment and risk management are important; the IoT risk management for incident response and recovery; what open questions on IoT risk assessment and risk management remain.
A Deep Learning Method With Filter Based Feature Engineering for Wireless Intrusion Detection System

In recent years, the increased use of wireless networks for the transmission of large volumes of information has generated a myriad of security threats and privacy concerns; consequently, there has been the development of a number of preventive and protective measures including intrusion detection systems (IDS). Intrusion detection mechanisms play a pivotal role in securing computer and network systems; however, for various IDS, the performance remains a major issue. Moreover, the accuracy of existing methodologies for IDS using machine learning is heavily affected when the feature space grows. In this paper, we propose a IDS based on deep learning using feed forward deep neural networks (FFDNNs) coupled with a filter-based feature selection algorithm. The FFDNN-IDS is evaluated using the well-known NSL-knowledge discovery and data mining (NSL-KDD) dataset and it is compared to the following existing machine learning methods: support vectors machines, decision tree, K-Nearest Neighbor, and Naïve Bayes. The experimental results prove that the FFDNN-IDS achieves an increase in accuracy in comparison to other methods.