Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation of Connectivity and Capacity of Dynamic Spectrum Access Networks

Recent measurements on radio spectrum usage have revealed the abundance of under- utilized bands of spectrum that belong to licensed users. This necessitated the paradigm shift from static to dynamic spectrum access (DSA) where secondary networks utilize unused spectrum holes in the licensed bands without causing interference to the licensed user. However, wide scale deployment of these networks have been hindered due to lack of knowledge of expected performance in realistic environments and lack of cost-effective solutions for implementing spectrum database systems. In this dissertation, we address some of the fundamental challenges on how to improve the performance of DSA networks in terms of connectivity and capacity. Apart from showing performance gains via simulation experiments, we designed, implemented, and deployed testbeds that achieve economics of scale. We start by introducing network connectivity models and show that the well-established disk model does not hold true for interference-limited networks. Thus, we characterize connectivity based on signal to interference and noise ratio (SINR) and show that not all the deployed secondary nodes necessarily contribute towards the network's connectivity. We identify such nodes and show that even-though a node might be communication-visible it can still be connectivity-invisible. The invisibility of such nodes is modeled using the concept of Poisson thinning. The connectivity-visible nodes are combined with the coverage shrinkage to develop the concept of effective density which is used to characterize the con- nectivity. Further, we propose three techniques for connectivity maximization. We also show how traditional flooding techniques are not applicable under the SINR model and analyze the underlying causes for that. Moreover, we propose a modified version of probabilistic flooding that uses lower message overhead while accounting for the node outreach and in- terference. Next, we analyze the connectivity of multi-channel distributed networks and show how the invisibility that arises among the secondary nodes results in thinning which we characterize as channel abundance. We also capture the thinning that occurs due to the nodes' interference. We study the effects of interference and channel abundance using Poisson thinning on the formation of a communication link between two nodes and also on the overall connectivity of the secondary network. As for the capacity, we derive the bounds on the maximum achievable capacity of a randomly deployed secondary network with finite number of nodes in the presence of primary users since finding the exact capacity involves solving an optimization problem that shows in-scalability both in time and search space dimensionality. We speed up the optimization by reducing the optimizer's search space. Next, we characterize the QoS that secondary users can expect. We do so by using vector quantization to partition the QoS space into finite number of regions each of which is represented by one QoS index. We argue that any operating…
Stochastic Memory Devices for Security and Computing

With the widespread use of mobile computing and internet of things, secured communication and chip authentication have become extremely important. Hardware‐based security concepts generally provide the best performance in terms of a good standard of security, low power consumption, and large‐area density. In these concepts, the stochastic properties of nanoscale devices, such as the physical and geometrical variations of the process, are harnessed for true random number generators (TRNGs) and physical unclonable functions (PUFs). Emerging memory devices, such as resistive‐switching memory (RRAM), phase‐change memory (PCM), and spin‐transfer torque magnetic memory (STT‐MRAM), rely on a unique combination of physical mechanisms for transport and switching, thus appear to be an ideal source of entropy for TRNGs and PUFs. An overview of stochastic phenomena in memory devices and their use for developing security and computing primitives is provided. First, a broad classification of methods to generate true random numbers via the stochastic properties of nanoscale devices is presented. Then, practical implementations of stochastic TRNGs, such as hardware security and stochastic computing, are shown. Finally, future challenges to stochastic memory development are discussed.
Sparsity adaptive channel estimation based on compressed sensing for OFDM systems

Abstract A sparsity adaptive channel estimation is based on compressive sensing for OFDM systems. The proposed method is, namely, regularized sparsity adaptive matching pursuit (RSAMP) that does not need a priori knowledge of the channel sparsity compared with conventional compressed sensing algorithms, and searches the position of maximal difference to choose the supported elements, then regularizes these elements to improve the accuracy of supported set. Simulation results demonstrate that the RSAMP scheme has excellent performance and low computational complexity.
An Improved Rule-based Stemmer for Translation System from Arabic-To-Sign language

Stemming is the term used in linguistic morphology and information retrieval to describe the process for reducing inflected (or sometimes derived) words to their written word form (stem or root). For Arabic Language, several rule–based stemming algorithms were made according two types of approach: Stem based approach (light stemming) used by Darwish [10] in his stemmer Al-Stem and the well-known algorithm Khoja [11] that built his stemmer on root-based approach (heavy stemming). Many comparative studies had proved that light stemming is classified as the best technique in stemming approaches. Furthermore, results of experiments show that Light10 and Aljilayl-3 stemmers out-perform the others stemmers. In this paper we present an improved Arabic stemmer based on rules. The new stemmer is considered as hybrid approach that combines between Root-based approach and Stem-based approach; it was tested and compared with other two stemmers. Experiment results seem very encouraging and relatively better than the other stemmers.
Performance Analysis of Interior Gateway Routing Protocols across Different AS

: Routing is a vital part of today’s communication networks and without it; data can never get past its originating subnet. It requires the implementation of a set of communication rules and guidelines called Routing Protocols. Each of these protocols has different features and performance capabilities, with different architectures and route processing algorithms. The data travels between local and wide area networks via different network topologies and is handled by different protocols within and outside different Autonomous Systems (AS). A secure and scalable communication platform relies on the selection of the right communication protocols. This plays an important role in the establishment and maintenance of successful communication grids, with the dynamic nature of the Internet connections and communication between different Autonomous Systems. This research paper presents a simulation as a comparative study of the Interior Gateway Routing Protocols (IGRP) in varying scenarios. Their performance was analysed and discussed by observing the generated traffic to identify the best combination of protocols for efficient data delivery. GNS3 network simulation software was used to create and implement the scenarios, and WireShark was used to capture the data packets.
Extreme Apprenticeship Method in Teaching University-Level Mathematics

At the Department of Mathematics and Statistics in the University of Helsinki the courses Linear Algebra and Matrices I and II have traditionally been taught through lectures and take-home tasks. Although the pass rates have been satisfactory, the students’ level of understanding of the topics has not, which has been apparent in subsequent courses. The approach chosen to tackle this problem was to use the Extreme Apprenticeship method developed for teaching computer programming and implement it into teaching university-level mathematics. The courses under study in fall 2011 were among the largest courses at the department with hundreds of participants (N=361 and N=248). The results show that the students were satisfied with the new teaching method, and the passing rate did not drop even though the workload was significantly increased and the requirement level was raised. Extreme Apprenticeship method, Linear Algebra, Scaffolding, Tertiary Education
Investigation on Time Properties of Timed-pNets

Timed-pNets is a semantic model to specify the communi-cation behaviours of distributed systems. It has a tree style hierarchical structure. The leaves are timed specifications which consist of a set of logical clocks and clock relations. These logical clocks are encoded with delay variables and delay bound. In this paper we discuss how to detect time constraint conflicts and how to compute delay variables of clocks in the non-leaf nodes. Then we can check system's time properties like deadline. From our formalization of timed-pNets, we generate a system of logical clocks that can be simulated in the TimeSquare tool. We take a simple use case from ITS to simulate and check some time properties.
Interaction Driven OOAD

Traditionally, object-oriented analysis and design (OOAD) approaches have focussed on identification, analysis and specification of individual objects in a given system. Design Patterns brought a shift in this paradigm by introducing the approach of identifying design solutions that involve a group of classes/objects rather than individuals. However, the potential of this paradigm has not been fully realized yet. The aim of recent research has mainly been restricted to identification of new design patterns from successfully implemented solutions of the past. In this paper, we propose to utilize the concept of interactions among classes and objects as a key factor during problem analysis phase and as a natural way of designing object oriented systems. We look at different techniques that could be used to enable such an analysis and design process and identify open issues in those directions.
A PWM PLUS PHASE-SHIFT CONTROL BIDIRECTIONAL DC/DC CONVERTER

A PWM plus phase-shift control bidirectional DC/DC converter is proposed. In this converter, PWM control and phase-shift control are combined to reduce current stress and both conducting and switching loss, to expand ZVS range. The operation principle and analysis of the converter are explained, and ZVS condition is derived. A prototype of PWM plus phase-shift bidirectional DC/DC converter is built to verify analysis.
A Survey of Commonly Used Cryptographic Algorithms in Information Security

Progression in computing powers and parallelism technology are creating obstruction for credible security especially in electronic information transactions under cryptosystems. An enormous set of cryptographic schemes persist in which each has its own affirmative and feeble characteristics. Some schemes carry the use of long bits key and some support the use of small key. Practical cryptosystems are either symmetric or asymmetric in nature. With the fast progression of digital data exchange in electronic way, information security is becoming much more important in data storage and transmission. Network Security is the most vital component in information security because it is responsible for securing all information passed through networked computers. Cryptography is the one of the main categories of computer security that converts information from its normal form into an unreadable form by using Encryption and Decryption Techniques. This survey compares trendy encryption techniques for convinced selection of both key and cryptographic scheme.
Multi-thread Flash Translation Layer for Multi-core Solid State Drives

NAND flash memory-based solid state drives (SSDs) have many advantages compared with Hard Disk Drives (HDDs), such as high I/O operations per second (IOPS), low power, and small form factor. However, NAND flash memory needs a flash translation layer (FTL) to manage its limitations such as erase before write and a limit of cell durability. To overcome these limitations, intensive FTL algorithms have been proposed considering the performance and wear leveling. However, with an increase in the popularity of the latest multi-core processors, it is important to study how multi-thread FTL can improve SSD performance. Therefore, in this paper, we propose a novel multi-thread FTL that considers a critical region to improve the performance of NAND flash memory by taking advantage of multi-core processors
Area statistical method of image target object based on chain code technology

This article elaborated the extracting process of target object boundary in the image using the chain code and introduced the principle and steps of the line segment table method which based on the chain code technology calculating the area of enclosed area.It proposed a new area computing method in the foundation of chain code technology and compared the two area calculating methods through the experiments.The result indicates that the new method has the characteristics which the design concept simple and easy to realize.
Software-Defined Networking Reviewed Model

Software-defined Networking (SDN) is one new promising development in between Cloud Computing and Networking industries. As, SDN is quite new, there are many challenges in implementing it in traditional networking environment. In this paper, my aim is to describe current SDN implementation challenges in detail and proposing a reviewed model for SDN, where many of these challenges are solved.
Predicció de la decisió de compra basada en la detecció d’emocions amb EEG i Machine Learning

There is currently a growing interest on neuromarketing techniques, which allow discovering the unconscious reactions of the public to commercial stimuli. Several studies have analysed the brain activity searching relationship with the attraction level given a commercial advert, or with the aim to discover which brain areas are activated by stimuli retained in memory. However, the study of audiovisual advertising perception by means of electroencephalography (EEG) has not been conducted, to our knowledge, with the approach of emotional states and the arousal-valence model. The main goal of this project is to study the real effect of audiovisual advertising in the instantaneous emotional state of spectators, and how this affects to decision making. For this, a completely specific research framework has been proposed which includes, first, the creation of a 43-video database labeled by emotional intention. Then, we have done an estimation of the emotional state based on EEG signal processing, using alpha and beta waves to measure different values of arousal and valence. Experiments have been performed with 40 volunteers who have rated videos according to their subjective emotional intention, their real emotional effect and the purchase intention they associate, while their brain signal was captured. The obtained data has been processed with Machine Learning tools to find whether a relationship exists between EEG signal, written feedback from the subjects and purchase intention.
Gaze Interaction With Vibrotactile Feedback: Review and Design Guidelines

Vibrotactile feedback is widely used in mobile devices because it provides a discreet and private feedback channel. Gaze-based interaction, on the other hand, is useful in various applications due to its unique capability to convey the focus of interest. Gaze input is naturally available as people typically look at things they operate, but feedback from eye movements is primarily visual. Gaze interaction and the use of vibrotactile feedback have been two parallel fields of human–computer interaction research with a limited connection. Our aim was to build this connection by studying the temporal and spatial mechanisms of supporting gaze input with vibrotactile feedback. The results of a series of experiments showed that the temporal distance between a gaze event and vibrotactile feedback should be less than 250 ms to ensure that the input and output are perceived as connected. The effectiveness of vibrotactile feedback was largely independent of the spatial body location of vibrotactile actuators. In comparison to other modalities, vibrotactile feedback performed equally to auditory and visual feedback. Vibrotactile feedback can be especially beneficial when other modalities are unavailable or difficult to perceive. Based on the findings, we present design guidelines for supporting gaze interaction with vibrotactile feedback.
Modes of convection in the magnetotail

The flow of plasma in the Earth’s magnetotail cannot reach a steady state, since adiabatic convection would lead to exceedingly high pressure of the associated magnetic flux tubes closer to the Earth, the so-called pressure catastrophe. The natural way to avoid the pressure catastrophe is to significantly reduce the flux tube volume by reconnection, and observations show a near-Earth reconnection line typically around 20–25 Earth radii down tail. Earthward flows from this reconnection line are rather bursty and typically seen outside of 10 Earth radii. At this point they are strongly braked by the here dominant dipolar magnetic field. The pressure gradients piled up by the flow lead to the substorm current wedge, and possibly other substorm phenomena observed in the Earth’s ionosphere. When more and more flux tubes are piled up, the dipolarization front moves tailward and finally shuts off near-Earth reconnection.
Exploiting Truly Fast Hardware in Data Mining

In many data mining applications, the size of the database is not only extremely large, it is also growing rapidly. Even for relatively simple searches, the time required to move the data off magnetic media, cross the system bus into main memory, copy into processor cache, and then execute code to perform a search is prohibitive. We are proposing that a significant portion of the data mining task (i.e., the portion that examines the bulk of the raw data) be implemented in fast hardware, close to the magnetic media on which it is stored. Furthermore, this hardware can be replicated allowing mining tasks to be performed in parallel, thus providing further speedup for the overall mining application. In this paper, we describe a general framework under which this can be accomplished, and identify a number of important research questions that must be addressed for it to be practical.
Error Analysis Framework for Automatic Summarization

We propose an error analysis framework for automatic summarization. The framework presented herein incorporates five problems that cause automatic summarization systems to produce errors and three metrics for quality. We classify errors in automatic summaries into 15 categories comprising a combination of the three quality metrics and five problems. We also present a method to classify automatic-summary errors into these categories. Using our error analysis framework, we analyze the errors in an automatic summary produced by our system and present the results. We use these results to refine our system and then show that the quality of the automatic summary is improved. The error analysis framework that we propose is demonstrably useful for improving the quality of an automatic summarization system.
A Pricing Model for Big Personal Data

Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent problem has thus arisen in the data market: How to price Big Personal Data fairly and reasonably. This paper proposes a pricing model for Big Personal Data based on tuple granularity, with the help of comparative analysis of existing data pricing models and strategies. This model is put forward to implement positive rating and reverse pricing for Big Personal Data by investigating data attributes that affect data value, and analyzing how the value of data tuples varies with information entropy, weight value, data reference index, cost, and other factors. The model can be adjusted dynamically according to these parameters. With increases in data scale, reductions in its cost, and improvements in its quality, Big Personal Data users can thereby obtain greater benefits.
Acoustic Communication In Insects And Anurans Common Problems And Diverse Solutions

Thank you for downloading acoustic communication in insects and anurans common problems and diverse solutions. Maybe you have knowledge that, people have search hundreds times for their favorite books like this acoustic communication in insects and anurans common problems and diverse solutions, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some harmful bugs inside their laptop.
A novel co-training object tracking algorithm based on online semi-supervised boosting

The self-training based discriminative tracking methods use the classification results to update the classifier itself. However, these methods easily suffer from the drifting issue because the classification errors are accumulated during tracking. To overcome the disadvantages of self-training based tracking methods, a novel co-training tracking algorithm, termed Co-SemiBoost, is proposed based on online semi-supervised boosting. The proposed algorithm employs a new online co-training framework, where unlabeled samples are used to collaboratively train the classifiers respectively built on two feature views. Moreover, the pseudo-labels and weights of unlabeled samples are iteratively predicted by combining the decisions of a prior model and an online classifier. The proposed algorithm can effectively improve the discriminative ability of the classifier, and is robust to occlusions, illumination changes, etc. Thus the algorithm can better adapt to object appearance changes. Experimental results on several challenging video sequences show that the proposed algorithm achieves promising tracking performance.
P2P-based Semantic Web Service Discovery Model

With the development of the research on Web service becoming more and more profound,the importance of Web service has been recognized gradually.Presently,the UDDI registry model of Web service is still a centralized architecture,so the application of Web service suffers from many defects such as bottlenecks,single points of failure,the lower-efficiency of discovery and the restraint of the capacity of the peers which can be registered in the UDDI.In addition,the description of Web service which is described by the present Web service standard is just only syntax-level,not semantic-level.This affects the efficiency and accuracy of the Web service extremely.In this paper,it proposes a model that offers the P2P-Based semantic Web service discovery for solving these problems.
A Survey on Infrastructure as Service Layer based Security Models for Cloud

Cloud security is been a center of attraction of all the researchers in the recent years. In this paper a survey is done on cloud security issues which are focused on Infrastructure as a Service (IaaS) layer. The virtualization in cloud is provided by infrastructure as a service layer and in this paper security issues related to virtualization is discussed.