Miracle-dz/UniFAR-FTUs huggingface dataset:
Crawler-Based Measurement of Large Scale P2P IPTV Systems: Crawler-Based Measurement of Large Scale P2P IPTV Systems

In order to gain insight on the topology characteristics of P2P IPTV systems and their online user behavior characteristics, This paper develop and deploys a multiprotocol P2P IPTV network crawler, called TVCrawler, which enables users to launch an in-depth measurement and comparative research for several well-known P2P IPTV systems, including PPLive, PPStream, and UUSee. This paper presents results from experiments and research efforts on these large-scale P2P IPTV overlay graphs. Major findings include 1) more than 50% of online users are unreachable because they lie behind NAT or firewall; 2) fluctuation range of churn increases with the increase of the population of channel, and there exists a power-law upper bound for fluctuation range of churn; 3) session length of peer follows a stretched exponential distribution; 4) while the in-degree of PPLive follows the power-law distribution with exponential cutoffs, the in-degree of PPStream appears to have multiple separate power-law regimes with different exponents, and the in-degree of UUSee approaches Weibull distribution. 5) All of measured P2P IPTV overlay graphs appear as a disassortative mixing; 6) small-world networks are ubiquitous in measured P2P IPTV systems. 7) the PPLive overlay graph is clustering, but the others are not; 8) all of measured P2P IPTV overlay graphs have robustness similar to that of a power-law graph, but PPLive is more vulnerable to malicious attacks and more robust to random failures than the other. These research and findings Will be good for designing and optimizing of P2P IPTV protocols, but also for monitoring, directing, and dominating the P2P IPTV system.
GCRT J1742-3001: A NEW RADIO TRANSIENT TOWARD THE GALACTIC CENTER

We report the detection of a new transient radio source, GCRT J1742–3001, located ∼1° from the Galactic center. The source was detected 10 times from late 2006 to 2007 May in our 235 MHz transient monitoring program with the Giant Metrewave Radio Telescope (GMRT). The radio emission brightened in about one month, reaching a peak observed flux density of ∼100 mJy on 2007 January 28, and decaying to ∼50 mJy by 2007 May when our last monitoring observation was made. Two additional faint, isolated 235 MHz detections were made in mid-2006, also with the GMRT. GCRT J1742–3001 is unresolved at each epoch, with typical resolutions of ∼20″ × 10″. No polarization information is available from the observations. Based on nondetections in observations obtained simultaneously at 610 MHz, we deduce that the spectrum of GCRT J1742–3001 is very steep, with a spectral index less than about −2. Follow-up radio observations in 2007 September at 330 MHz and 1.4 GHz, and in 2008 February at 235 MHz yielded no detections. No X-ray counterpart is detected in a serendipitous observation obtained with the X-ray telescope aboard the Swift satellite during the peak of the radio emission in early 2007. We consider the possibilities that GCRT J1742–3001 is either a new member of an existing class of radio transients, or is representative of a new class, possibly having no associated X-ray emission.
Inserting rules into recurrent neural networks

The authors present a method that incorporates a priori knowledge in the training of recurrent neural networks. This a priori knowledge can be interpreted as hints about the problem to be learned and these hints are encoded as rules which are then inserted into the neural network. The authors demonstrate the approach by training recurrent neural networks with inserted rules to learn to recognize regular languages from grammatical string examples. Because the recurrent networks have second-order connections, rule-insertion is a straightforward mapping of rules into weights and neurons. Simulations show that training recurrent networks with different amounts of partial knowledge to recognize simple grammers improves the training times by orders of magnitude, even when only a small fraction of all transitions are inserted as rules. In addition, there appears to be no loss in generalization performance. <<ETX>>
A residual-based approach to classification of remote sensing images

This paper presents a novel residual-based approach to classification of remote sensing images. The proposed approach aims at increasing the accuracy of classification methods explicitly (or implicitly) inspired to the Bayesian decision theory. In particular, an architecture composed of an ensemble of estimators is used in order to estimate the residual errors in the class conditional posterior probabilities estimated by the Bayesian classifier considered. In order to avoid overfitting of the training data, a technique based on the analysis of class conditional entropy measures of omission and commission errors is used for adaptively evaluating the number of estimators to be included in the ensemble. Experimental results obtained on two multisource and multisensor data sets (characterized by different complexities) confirm the effectiveness of the proposed system.
Specifications for Decidable Hybrid Automata and Games

Using hybrid automata, one is able to model many types of systems and express their properties in various formal languages. Being extremely general and rich, however, the class of hybrid automata exhibits the inherent problems of automata theory: even the simplest of properties for general hybrid automata are undecidable. Therefore, one must restrict attention only to subclasses of this very general class. The literature to date has seemed to indicate that for a whole subclass of hybrid automata to have decidable properties it would either have to have very simple continuous dynamics (timed automata, piecewise affine inclusions, etc.) or decoupled discrete and continuous dynamics (o-minimal hybrid systems, initialized rectangular automata), where the continuous components of the system are reset in a memoryless manner after each discrete transition. The thesis of this dissertation is that this need not be the case! STORMED hybrid systems are introduced in this dissertation and form a subclass of hybrid automata which lie on the boundary of decidability and admit algorithms to decide properties in rich modal logics (CTL? ) without sacrificing expressiveness in their continuous dynamics or the coupling of the continuous and discrete. They do not require memoryless resets of the continuous part of the state on the discrete transitions and allow for rich nonlinear continuous trajectories. Their specifications need only satisfy geometric requirements which adhere to constraints found in common control problems such as energy depletion, time
Comparison of Soft Computing Approaches for Texture Based Land Cover Classification of Remotely Sensed Image

Texture feature is a predominant feature in land cover classification of remotely sensed images. In this study, texture features were extracted using the proposed multivariate descriptor, Multivariate Ternary Pattern (MTP). The soft classifiers such as Fuzzy k-Nearest Neighbor (Fuzzy k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM) were used along with the proposed multivariate descriptor for performing land cover classification. The experiments were conducted on IRS P6 LISS-IV data and the results were evaluated based on error matrix, classification accuracy and Kappa statistics. From the experiments, it was found that the proposed descriptor with SVM classifier gave 93.04% classification accuracy.
APPLYING AN IMAGE COLOUR APPEARANCE MODEL FOR SIMPLE SELF-LUMINOUS SCENES

Colour Appearance Models (CAM) usually assume a simple test scene of a uniform flat stimulus, background and surround. To consider the spatial information and extend the predictions of CAMs to more complex scenes, Image CAMs such as ‘iCAM’ are developed, which takes an image as the input and outputs the perceptual attributes per pixel. This paper presents the results of a study in which the performance of iCAM to predict the effects of various parameters on brightness perception, when applied to simple self-luminous scenes, is evaluated. The results show that iCAM can predict the effect of certain background luminance scenarios, however, the outcomes for unrelated stimuli do not match with the reference data. Furthermore, the impact of saturation and of stimulus size on brightness is not predicted by iCAM since both the Helmholtz-Kohlrausch and the stimulus size effect are not included in the model. Hence, a new image CAM for self-luminous scenes is needed.
Person-specific named entity recognition using SVM with rich feature sets

Purpose: The purpose of the study is to explore the potential use of nature language process (NLP) and machine learning (ML) techniques and intents to find a feasible strategy and effective approach to fulfill the NER task for Web oriented person-specific information extraction. Design/methodology/approach: An SVM-based multi-classification approach combined with a set of rich NLP features derived from state-of-the-art NLP techniques has been proposed to fulfill the NER task. A group of experiments has been designed to investigate the influence of various NLP-based features to the performance of the system, especially the semantic features. Optimal parameter settings regarding with SVM models, including kernel functions, margin parameter of SVM model and the context window size, have been explored through experiments as well. Findings: The SVM-based multi-classification approach has been proved to be effective for the NER task. This work shows that NLP-based features are of great importance in datadriven NE recognition, particularly the semantic features. The study indicates that higher order kernel function may not be desirable for the specific classification problem in practical application. The simple linear-kernel SVM model performed better in this case. Moreover, the modified SVM models with uneven margin parameter are more common and flexible, which have been proved to solve the imbalanced data problem better. Research limitations/implications: The SVM-based approach for NER problem is only proved to be effective on limited experiment data. Further research need to be conducted on the large batch of real Web data. In addition, the performance of the NER system need be tested when incorporated into a complete IE framework. Originality/value: The specially designed experiments make it feasible to fully explore the characters of the data and obtain the optimal parameter settings for the NER task, leading to a preferable rate in recall, precision and F 1 measures. The overall system performance ( F 1 value) for all types of name entities can achieve above 88.6%, which can meet the requirements for the practical application.
Analysis of admission control in SDN-based media delivery network

A novel partially observable Markov decision process (POMDP) based admission control scheme is presented for SDN-based media delivery network. The performance bottleneck of service nodes on the application layer, and the service ability limit of transmission links between nodes and users are comprehensively considered, then the POMDP is used to model the admission control process of the service system. The randomized policy is applied to optimize system performance, and the policy-gradient iteration algorithm is used to compute the optimal admission control policy. Numerical experiment illustrates the efficiency of the proposed approach.
The integrated properties of the molecular clouds from the JCMT CO(3–2) High-Resolution Survey

We define the molecular cloud properties of the Milky Way first quadrant using data from the JCMT CO(3–2) High-Resolution Survey. We apply the Spectral Clustering for Interstellar Molecular Emission Segmentation (SCIMES) algorithm to extract objects from the full-resolution data set, creating the first catalogue of molecular clouds with a large dynamic range in spatial scale. We identify more than 85000 clouds with two clear sub-samples: ∼35500 well-resolved objects and ∼540 clouds with well-defined distance estimations. Only 35 per cent of the catalogued clouds (as well as the total flux encompassed by them) appear enclosed within the Milky Way spiral arms. The scaling relationships between clouds with known distances are comparable to the characteristics of the clouds identified in previous surveys. However, these relations between integrated properties, especially from the full catalogue, show a large intrinsic scatter (∼0.5 dex), comparable to other cloud catalogues of the Milky Way and nearby galaxies. The mass distribution of molecular clouds follows a truncated-power-law relationship over three orders of magnitude in mass with a form dN/dM ∝ M−1.7 with a clearly defined truncation at an upper mass of M0∼3×106M⊙⁠, consistent with theoretical models of cloud formation controlled by stellar feedback and shear. Similarly, the cloud population shows a power-law distribution of size with dN/dR ∝ R−2.8 with a truncation at R0 = 70 pc.
Large-Scale Analysis of Microtubule Pharmacology via Phosphoproteomics and High-Content Imaging

The dynamic instability of microtubules is a critical phenomenon that regulates several biological processes. Chemical perturbation of microtubules disrupts these processes and is known to stimulate phosphorylation of downstream substrates. Despite decades of research into microtubule pharmacology, unanswered questions remain regarding the relationship between anti-tubulin drugs and downstream signaling and phenotypes. The following work addresses and answers three outstanding questions regarding the biology of anti-tubulin drugs. It first addresses an important metric for anti-tubulin drug efficacy – site occupancy – and relates classic drug-induced phenotypes to drug occupancy of the taxane site on microtubules in live cells and tissues. The data presented suggest that, when considering site occupancy, the spindle-assembly checkpoint is relatively insensitive to microtubule-stabilizing drugs. Next, after probing the relationship between site occupancy and drug-relevant phenotypes, work is described which utilized mass spectrometry-based phosphoproteomics to elucidate differences in phospho-regulation induced by microtubule-stabilizing and destabilizing drugs. Analysis of this data revealed a large wave of rapid phosphorylation that was common to both classes of drug and highlighted the prominent activity of a protein phosphatase stimulated by microtubule destabilization. Furthermore, the data demonstrated a surprising increase of JNK activity without additional JNK activation in response to microtubule stabilization. Lastly, after comparing drug effects on phospho-signaling in an averaged cell population, work is presented which involved high-content analysis at the single-cell level to elicit information regarding cell heterogeneity in anti-tubulin drug responses. This required development of a novel approach to quantify microtubule dynamics, namely EB3 comet counting in segmented single cells. The data revealed drug-induced singlecell heterogeneity with respect to microtubule dynamics upon treatment with a panel of drugs. Moreover, this occurred in the presence of a pan-efflux pump inhibitor, suggesting that drug-induced heterogeneity can occur independently of drug pump action. This biological finding, along with those mentioned above, is likely important for understanding the therapeutic efficacy of these drugs in cancer chemotherapy.
Online security analysis based on database method for electrical power system

Online security analysis (OSA) is one of the important functions of dispatching automation for modern power systems. In this paper, a new method of OSA based on a database is presented. The fundamental principal of this method is that a large amount of real time calculation would be substituted by a small amount of processing data which are obtained from the OSA database. This database is established by means of organizing the offline calculation data. The results of finite and reasonable offline calculation are taken as an OSA database. Then the theory of OSA using the database and its algorithm are developed. The application of this method to two practical power systems shows that it is a new way to carry out OSA in large power systems and has a wide spectrum in power system real time applications. <<ETX>>
Analysis and Modelling of Dynamically Variable Topology of Low Earth Orbit Satellite Networks

Recently, significant interest is shown to creation rather inexpensive global systems communications on base of Low-Earth-Orbit Satellite Networks (LEOSN). One of problems of design and creation LEOSN is development of the stream control methods and estimation it's efficiency in such networks. The given problem is complicated, that the topology of the satellite networks varies in time. It essentially hinders the analytical decision of the given problem. An effective way of overcoming of these difficulties is simulation modeling. For realization of research experiments on learning the information streams routing algorithms in LEOSN a special program complex SANET was developed. In the given paper principles of development of LEOSN simulation models and architecture of the manager by the process of a simulation modeling of the unit are considered. Methods of promotion of modeling time and architecture of a simulator complex offered in the article allow to boost essentially efficiency of simulation analysis and to ensure simulation modeling of the satellite networks consisting of several hundreds space vehicles.
A Comparison of Semantic Annotation Systems for Text-Based Web Documents

The Semantic Web promises new as well as extended applications, such as concept searching, custom Web page generation, and question-answering systems. Semantic annotation is a key component for the realization of the Semantic Web. The volume of existing and new documents on the Web makes manual annotation problematic. Semi-automatic semantic annotation systems, which we call platforms because of their extensibility and composability of services, have been designed to alleviate this burden for text-based Web documents. These semantic annotation platforms provide services supporting annotation, including ontology and knowledge base access and storage, information extraction, programming interfaces, and end-user interfaces. This chapter defines a framework for examining semantic annotation platform differences based on platform characteristics, surveys several recent platform implementations, defines a classification scheme based on information extraction method used, and discusses general platform architecture.
A STUDY OF RECENT RESEARCH TRENDS OF PROXY SERVER

A proxy server is a kind of buffer between your computer and the Internet resources we are accessing (Web sites, FTP archives etc.). The data we request come to the proxy first, and only then it transmits the data to us. Any web site in the world can track your movements through its pages and monitor your reading interests using our IP address, a unique ID assigned to each computer on the Internet. Depending on the policies of the Internet resource, we might not be able to get access to the information we need. Using only our IP address and the information about our operating system, a Web site can automatically exploit security holes in our system using some not-very-complex, ready-made, free hacking programs. A proxy server is a computer that offers a computer network service to allow clients to make indirect network connections to other network services. A client connects to the proxy server, then requests a connection, file, or other resource available on a different server. The proxy provides the resource either by connecting to the specified server or by serving it from a cache. In some cases, the proxy may alter the client's request or the server's response for various purposes. In this paper discuss a proxy server, need to use a proxy server, purpose, different types of proxies server, advantages, purpose of proxy server, performance etc.
The Relationship between Factors of Quality Models and Attributes of Process Models: Represented through Venn diagram

There are certain different software engineering tools which have been rapidly applied on various scenarios of Testing, Output, Relationship and other developing phases of any product. The Quality Models have the ability to implement their Factors and the Process Models can judge the requirements of any project in any phase of during its development. By synchronizing both of these main tools together, it can be easy for the developer to achieve the minimum requirements in a project to get the maximum out of it with having the surety of its working. The combination of these tools can form such a pattern which can be useful for the full functionality of a product to its development within the limitations.
Research and Survey on Algorithms of Blind Signal Separation Technology

Blind signal separation(BSS)is a useful technology which can recover the unknown source signals from the received signals.It has become a new research hot spot of neural network and signal processing.We first introduced the development of BSS and then introduced the BSS's math models which containe linear instant model,linear convolutive model and nonlinear model.We also analysed the algorithms' principal and characteristic of every model and introduced blind signal extraction technology which is closed to BSS.Finally,we pointed out the research direction and wide foreground of BSS.
A 0.18-μm CMOS selective receiver front-end for UWB applications

This paper addresses the problem of 5-6-GHz WLAN interferer rejection in a direct-conversion receiver front-end for multi-band orthogonal frequency division multiplexing (OFDM) ultra-wideband (UWB) applications. The IC, realized in a 0.18-μm CMOS technology, comprises a single-ended voltage-voltage feedback low-noise amplifier (LNA) and a quadrature mixer. The LNA employs a double-peak single-notch network in the output load, amplifying UWB groups #1 and #3, while rejecting WLAN interferes in the 5-6-GHz frequency range. The mixer, based on a merged quadrature topology, also realizes a second-order low-pass filtering. Fabricated dies have been bonded on PCB for characterization. The front-end, drawing 10 mA from 1.8 V, achieves a 1-dB gain desensitization with a -6.5-dBm interferer power at 5.5 GHz. Other measured performances are 5.2-dB and 7.7-dB minimum and maximum noise figure (NF), -3.5-dBm minimum IIP3 and +34.5-dBm minimum in-band IIP2 and +21-dBm out-of-band IIP2.