Miracle-dz/UniFAR-FTUs huggingface dataset:
The influence of cognitive conflict on the result of collaborative editing in Wikipedia

Different levels of cognitive conflict widely exist in the process of collaborative editing, affecting the result of editing. This can be seen especially in Wikipedia, the free-content encyclopedia edited by users collaboratively. Here, we used the method of exploratory case study to explore the influence of wiki-based cognitive conflict on the result of collaborative editing. Page quality was considered as the result of co-editing. By measuring cognitive conflict and calculating page quality of ‘Hong Kong MRT’ featured article, we found that different levels of conflict had different influence on page quality and the influence was changing with time variation. Our findings are concluded into four propositions, which highlight the role of cognitive conflict in affecting page quality. This paper provides the foundation for further revising and develops the theory by using the methods of verification research and statistical analysis.
Model-Based Count Series Clustering for Bike Sharing System Usage Mining: A Case Study with the Vélib’ System of Paris

Today, more and more bicycle sharing systems (BSSs) are being introduced in big cities. These transportation systems generate sizable transportation data, the mining of which can reveal the underlying urban phenomenon linked to city dynamics. This article presents a statistical model to automatically analyze the trip data of a bike sharing system. The proposed solution partitions (i.e., clusters) the stations according to their usage profiles. To do so, count series describing the stations’s usage through departure/arrival counts per hour throughout the day are built and analyzed. The model for processing these count series is based on Poisson mixtures and introduces a station scaling factor that handles the differences between the stations’s global usage. Differences between weekday and weekend usage are also taken into account. This model identifies the latent factors that shape the geography of trips, and the results may thus offer insights into the relationships between station neighborhood type (its amenities, its demographics, etc.) and the generated mobility patterns. In other words, the proposed method brings to light the different functions in different areas that induce specific patterns in BSS data. These potentials are demonstrated through an in-depth analysis of the results obtained on the Paris Velib’ large-scale bike sharing system.
Chapter 2 Big Data Analytics

PCA Principal components analysis 3Vs Volume, velocity, and variety IDC International Data Corporation KDD Knowledge discovery in databases SVM Support vector machine SSE Sum of squared errors GLADE Generalized linear aggregates distributed engine BDAF Big data architecture framework CBDMASP Cloud-based big data mining and analyzing services platform SODSS Service-oriented decision support system HPCC High performance computing cluster system BI&I Business intelligence and analytics DBMS Database management system MSF Multiple species flocking GA Genetic algorithm SOM Self-organizing map MBP Multiple back-propagation YCSB Yahoo cloud serving benchmark HPC High performance computing EEG Electroencephalography
Interactive haptic deformation of dynamic soft objects

Adding haptic feedback in deformable objects is attractive in many applications, such as computer games, interactive cartoon design, and virtual prototyping. In this paper, we propose an interactive haptic deformation approach incorporating the dynamic simulation of mass-spring systems and flexible control of free-form deformation in the touch-enabled modeling of soft-object deformation. Through distributing mass, spring and damping coefficients of the object to the bounded Bezier volume lattice, the deformations of the object in response to the haptic avatar follow the physical laws and acquire high deformation working rate. Both homogenous and inhomogenous materials are simulated, and anchor nodes of haptic input are specified to create amazing effects during the interactive haptic deformation. The experimental results have shown the intuitive and flexible control of the proposed approach.
Analysis of product codes for frequency-hopped spread-spectrum with partial band interference

A jamming game is considered. A transmitter encodes the data, in a product code (PC), and transmits it in a slow frequency-hopped spread-spectrum (FHSS) system. A hostile jammer attempts to block correct packet reception by adjusting the fraction of the total band in which it distributes the jam power, /spl rho/. Two cases are considered. In the first case, the transmitter declares his strategy and the jammer uses this information to select his best jamming fraction. In the second case, the jammer declares his strategy first and the transmitter uses this information to select his best rates. Results are obtained using asymptotic analysis of a product code using singly extended Reed-Solomon (RS) row and column codes whose size grows large. This asymptotic analysis leads to good choices of the row and column rates for the PC in both cases.
Algorithmes de sélection d'utilisateurs à complexité réduite pour les systèmes MIMO multi-utilisateurs

For MISO multi-users downlink wireless communication system with precoding at the transmission, it is important to decrease the feedback rate. In this paper, we study different algorithms and criterions in order to significantly reduced the amount of feedback data. We associate the classical norm criterion with a criterion based on the users orthogonality. Without cooperation between the users, we only allow users that are orthogonal with the others to feedback their channel information to the base station. We calculate the theoretical thresholds and finally we evaluate the performance of the different criterions using numerical simulation.
Short-term stent coverage of second-generation zotarolimus-eluting durable polymer stents: Onyx one-month optical coherence tomography study

Introduction To date the early strut coverage with the second-generation durable-polymer ONYX zotarolimus-eluting stent (O-ZES) is unknown. Aim Optical coherence tomography (OCT) assessed the strut coverage of O-ZES at thirty-day follow-up. Material and methods OCT was performed after implantation and at 1-month follow-up in 15 patients treated with O-ZES. Results Mean patient age was 67 ±7 years (73% males). The clinical presentation consisted of acute coronary syndromes (n = 13) and stable coronary disease (n = 2). Four (26%) patients had diabetes. OCT analysis was performed at baseline and 1-month follow-up in all stents. 378 cross-sections with 3582 struts were assessed at baseline and 3661 at follow-up. At follow-up, 88% struts were covered by tissue with a median thickness 37.91 μm (IQR: 22.32–64.15). Median in-stent area obstruction by neointima was 2.64% (IQR: 1.70–4.84). From the total stent covered area, 92.3% showed complete strut coverage. Homogeneous tissue was observed in 74% of cases. There were no differences in minimal lumen area (5.07 ±1.08 mm2 vs. 4.81 ±0.94 mm2, p = 0.125) or minimal stent area (4.95 ±1.22 mm2 vs. 4.92 ±0.99 mm2) at baseline and at follow-up. There were no differences in the rate of strut malapposition (4.3% vs. 5.7%, p = 0.417). For all stents, malapposition volume was 47.9 mm3 at baseline and 51.7 mm3 at follow-up, giving the late acquired stent malapposition volume of 3.8 mm3. Conclusions The second-generation durable polymer O-ZES showed favorable vessel healing at 30-day OCT follow-up.
A NOVEL TECHNIQUE TO DETECT INTRUSION IN MANET

In this paper a novel technique has been proposed for intrusion detection in MANET, where agents are fired from a node for each node randomly and detect the defective nodes. Detection is based on triangular encryption technique (TE)[9,10], and AODV[1,2,3,8] is taken as routing protocol. The scheme is an ‘Agent’ based intrusion detection system. This technique is applied on two types of defective nodes namely packet sinking and black hole attack. For simulation purpose we have taken NS2 (2.33) and three type of parameters are considered. These are number of nodes, percentage of node mobility and type of defective nodes. For analysis purpose 20, 30, 30, 40, 50 and 60 nodes are taken with variability. Percentage of defectiveness as 10%, 20%, 30% and 40%. Packet sink and black hole attack are considered as defectiveness of nodes. We have considered generated packets, forward packets, average delay and drop packets as comparisons and performance analysis parameters.
Hardware implementation of Radial Basis Function Neural Network based on sigma-delta modulation

A digital hardware implementation of Radial Basis Function Neural Network (RBFNN) based on sigma-delta modulated bit-streams is presented. Through the change of feedback coefficient in the framework of traditional sigma-delta modulator, a new limiting amplifier modulator (LAM) is fabricated, and the approximation to Gauss kernel function can be achieved by the combination of several LAMs with different coefficients. The bit-stream neurons with Gauss kernel function and a whole feed-forward artificial neural network is implemented on field programmable gate array (FPGA). Thus a nonlinear function approximation problem can be solved by the neural networks presented.
Modal Phase-Matched Second Harmonic Generation in Asymmetric Silicon Slot Waveguide Coated with a Nonlinear Polymer

In this paper, an asymmetric silicon slot waveguide coated with a nonlinear polymer is proposed to obtain efficient second harmonic generation (SHG) from mid-infrared (λ = 3.1 μm) to near-infrared (λ = 1.55 μm) wavelengths. The required phase matching is fulfilled between fundamental mode at fundamental frequency and thirdorder mode at SHG by carefully designing the waveguide dimensions. A conversion efficiency of 24.7% is predicted for a low pump power of 10 mW which is comparable to the previously reported results. Our concept can be extended to other second-order nonlinear processes like sum and difference frequency generation.
Teaching Business Informatics with Web 2.0 Technologies

In this paper we report on a collaborative learning approach for teaching Business Process Management (BPM) with a focus on technologies and concepts of the Web 2.0. We explore the virtual continent – the cloud – for web service interfaces and applications related to e-Business in a broader context by following the core definitions of the Web 2.0. In doing so, we strive for an interdisciplinary challenge and investigate an application based on a historical idea. By following the evolution of the Web until we arrive at what is known as the Web as platform. With this metaphor in mind, we aim at interlinking the evolution of the Web and its applications with traditional aspects of computer science.
OpenVPN: Building and Integrating Virtual Private Networks: Learn how to build secure VPNs using this powerful Open Source application

This book is a comprehensive guide to using OpenVPN for building both secure VPNs. The book is written in a very friendly style that makes this complex topic easy and a joy to read. It first covers basic VPN concepts, then moves to introduce basic OpenVPN configurations, before covering advanced uses of OpenVPN. This book is for both experienced and new OpenVPN users. Network administrators and any one who is interested in building secure VPNs using OpenVPN. It presumes basic knowledge of Linux, but no knowledge of VPNs is required. All basic VPN and relevant security concepts are covered.
Natural Language Allomorphy in mo_lex

Linguistically, formalizations of natural language allomorphy should meet at least two adequacy criteria: The non-monotonic ("elsewhere") nature of allomorphy has to be captured and there must be a natural way to model phonological aspects of allomorphy. In this paper I present mo_lex, a formalism that meets both criteria by using violable and ranked finite-state constraints operating over strings of typed feature structures. Modifying an approach by Karttunen(199 8) it is shown that the formalism is equivalent in generative power to finite-state transducers, and thus inherits the well-known formal properties of these devices, like computational tractability and a straightforward approach to parsing. It is superior in this respect to other approaches to allomorphy which are problematic both with respect to generative capacity and parsing.
A Predicate Encryption Scheme Supporting Multiparty Cloud Computation

Multiparty cloud computation can deal with the problem that the traditional homomorphic encryption can't operation on the ciphertexts generated by different users, and the final result is computed by cloud server. The trivial multiparty cloud computation doesn't take the access control to the final result into consideration. In reality, we perhaps do not want all parties to gain the result. In this paper we construct a predicate encryption with multiplicative homomorphic property for the class of inner-product predicates. The scheme can also support multiparty cloud computation. By use of predicate encryption, we can realize the fine-grained access control. At last, we prove that our scheme is secure under IND-AH-CPA.
Effectiveness Of Genetic Algorithm In Reactive Protocols For MANET

Mobile Ad-hoc networks (MANET) consist of mobile platforms which are free to move arbitrarily. These are self-organizing and adaptive networks. These networks allow spontaneous formation and deformation of mobile networks. Shortest path problem in MANETS asks for the computation of path from source to destination node that minimizes the sum of total cost associated with the path. Several traditional algorithms like Bellman ford Algorithm, Dijkstra’s Algorithm are developed to find the shortest path. These algorithms are not suitable for MANETS, but are suitable onlyfor wired networks. For wireless networks, biologically inspired algorithms like Genetic Algorithm work efficiently. Genetic Algorithm techniques have been recently adopted in the networking arena to design efficient network protocols. On the other hand, multipath routing protocols have been considered far more robust than the single path routing protocols in MANETs because the topology changes in MANETs are quite frequent. A few researchers have also demonstrated the effectiveness of GA in MANETs. However, it is demonstrated only in the context of single path routing protocols. This paper aims to explore the effectiveness of GA in multipath routing protocols to improve the MANETs performance. Thorough analysis on efficiency is incorporated, demonstrating the feasibility and effectiveness of the proposed architecture.
Invariance constraints for improving generalization in probabilistic neural networks

Probabilistic neural networks can approximate class conditional densities in optimal (Bayesian) pattern classifiers. In natural pattern recognition applications, the size of the training set is always limited, making the approximation task difficult. Invariance constraints can significantly simplify the task of density approximation. A technique is presented for learning invariant representations, based on a statistical approach to ground invariance. An iterative method is developed formally for computing the maximum likelihood estimate to the parameters of an invariant mixture model. The method can be interpreted as a competitive training strategy for a radial basis function (RBF) network. It can be used for self-organizing formation of both invariant templates and features. <<ETX>>
Efficient and Reliable Filesystem Snapshot Distribution

Linux is an portable operating system kernel devised by Linus Torvalds and it can be used in conjunction with other userspace utilities such as GNU to build a free and open-source operating system for a multitude of target applications. While Linux-based operating systems have made significant progress on the servers and embedded systems, there is still much room for improvement for workstations and laptops.Up to now Linux-based operating system deployment has been error prone, time-consuming process and usually specific to a particular distribution of Linux. Linux-based operating systems also have a reputation of being overly complex to set up for a novice computer user and even though there are now laptops available with pre-installed Ubuntu [1], installing Linux-based operating system on arbitrary device is troublesome due to lack of native support for certain hardware components.In this thesis Butterknife, a B-tree file system (Btrfs) and Linux Containers (LXC) based provisioning suite is presented. Butterknife can be used to significantly reduce deployment time of customized Linuxbased operating system. Butterknife makes use of LXC to prepare a template of the root filesystem and Btrfs snapshotting to save state of the template. Btrfs send/receive mechanism is then used to transfer the root filesystem to the target machine. Post-deployment scripts are then used to configure the root filesystem for particular deployment, optionally retaining hostname, domain membership, configuration management keys etc. Current implementation of Butterknife uses HTTP(S) and multicast for transport, and various peer-to-peer scenarios are discussed in the Section 6 – Conclusions and Future Work.In addition to provisioning, Butterknife makes use of Btrfs incremental snapshots to implement differential upgrades. This approach is especially attractive for mobile devices, embedded systems and Internet of Things, where software upgrades have to be delivered in a guaranteed manner. Butterknife brings additional value to already existing ecosystem by bridging gap between stock installation medium and configuration management.
Feedback-Directed Optimizations in GCC with Estimated Edge Profiles from Hardware Event Sampling

Traditional feedback-directed optimization (FDO) uses static instrumentation to collect profiles. This method has shown good application performance gains, but is not commonly used in practice due to the high runtime overhead of profile collection, the tedious dual-compile usage model, and difficulties in generating representative training data sets. In this paper, we show that edge frequency estimates can be successfully constructed with heuristics using profile data collected by sampling of hardware events, incurring low runtime overhead (e.g., less then 2%), and requiring no instrumentation, yet achieving competetive performance gains. Our initial results show a 3-4% performance gain on the SPEC C benchmarks.
0 0 1 1 2 2 3 3 ≡ ... A A AB Operations : A Local iteration Global aggregation

Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradientdescent based approaches. We analyze the convergence rate of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.
Freight trains , nocturnal vibration and noise , and their physiological effects during sleep

There has been much previous research examining the effects of environmental noise on sleep, but the specific influence of vibration exposure has largely been neglected and primarily limited to cross-sectional field studies. Within the EU CargoVibes project, we experimentally investigated physiological reactions to freight train vibration and noise exposure during sleep. Polysomnography was used to determine the impact on sleep architecture, in terms of macrostructure and for eventrelated autonomic and cortical arousals. The work aimed to examine the effects on sleep of vibration amplitude, number of events, and the interaction of vibration and noise. Nights with 36 events and high vibration were found to result in greatest sleep fragmentation, more changes to lighter sleep stage, shorter continuous time in slow wave sleep and earlier awakenings than the control, and increased wakefulness and reduced rapid eye movement sleep relative to the 20 train night. The likelihood of the occurrence of event-related awakenings, arousals and changes in sleep depth all increased with vibration amplitude. Heart rate response was greater during exposure to high than moderate vibration. Comparison between first and second half of the night indicated that cardiac reactions sensitized to repeated vibration exposure. Vibration in residential environments from railway freight contributes towards sleep fragmentation which may have implications for health outcomes. Guidelines to prevent adverse health outcomes for freight lines where there is a risk for vibration exposure therefore need to consider not only noise but also the added impact of vibration.
Why simulating evolutionary processes is just as interesting as applying them

Evolutionary algorithms are very efficient tools to find a near-optimum solution in many cases. Until now they have been mostly used to find results but in this article we argue that evolutionary algorithms can also be used to simulate the evolution of complex systems. We model complex systems as networks in which agents are connected by edges if they interact with each other. It is known that many networks of this kind exhibit stable properties despite the dynamic processes they are subject to. We show here how evolutionary processes on complex systems can be modeled with a new kind of evolutionary algorithm which we have presented in [8]. We will show that some evolutionary processes within this framework yield networks with stable properties in reasonable time. An understanding of what kind of evolutionary processes will produce what kind of network properties in what time is vital to transfer evolutionary processes to technical ad-hoc networks in order to improve their flexibility and stability in quickly changing environments.
Inspecting DNS Flow Traffic for Purposes of Botnet Detection

The goal of this report is to examine DNS IP flow traffic and its relation to the botnet presence in the observed network. We focus on the DNS traffic generated by the botnets in general and inspect existing botnet detection methods using DNS data. DNS traffic in the form of IP flow data is studied and the possibility to use DNS detection methods on the IP flow data is discussed. An analysis of DNS traffic from backbone network is presented and directions for future work are provided.