OpenRaiser/Intern-Atlas huggingface dataset:
Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods
Venue: arXiv cs.LG 2012
Year: 2012

A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem of inferring information - or patterns in general - about events emerging in real life based on the contents of this textual stream. We show that it is possible to extract valuable information about social phenomena, such as an epidemic or even rainfall rates, by automatic analysis of the content published in Social Media, and in particular Twitter, using Statistical Machine Learning methods. An important intermediate task regards the formation and identification of features which characterise a target event; we select and use those textual features in several linear, non-linear and hybrid inference approaches achieving a significantly good performance in terms of the applied loss function. By examining further this rich data set, we also propose methods for extracting various types of mood signals revealing how affective norms - at least within the social web's population - evolve during the day and how significant events emerging in the real world are influencing them. Lastly, we present some preliminary findings showing several spatiotemporal characteristics of this textual information as well as the potential of using it to tackle tasks such as the prediction of voting intentions.
Quantified preference logic
Venue: arXiv cs.LO 2012
Year: 2012

The logic of reason-based preference advanced in Osherson and Weinstein (2012) is extended to quantifiers. Basic properties of the new system are discussed.
Wireless Network Design Under Service Constraints
Venue: arXiv cs.SY 2012
Year: 2012

In this paper we consider the design of wireless queueing network control policies with special focus on application-dependent service constraints. In particular we consider streaming traffic induced requirements such as avoiding buffer underflows, which significantly complicate the control problem compared to guaranteeing throughput optimality only. Since state-of-the-art approaches for enforcing minimum buffer constraints in broadcast networks are not suitable for application in general networks we argue for a cost function based approach, which combines throughput optimality with flexibility regarding service constraints. New theoretical stability results are presented and various candidate cost functions are investigated concerning their suitability for use in wireless networks with streaming media traffic. Furthermore we show how the cost function based approach can be used to aid wireless network design with respect to important system parameters. The performance is demonstrated using numerical simulations.
Expurgation Exponent of Leaked Information in Privacy Amplification for Binary Sources
Venue: arXiv cs.IT 2012
Year: 2012

We investigate the privacy amplification problem in which Eve can observe the uniform binary source through a binary erasure channel (BEC) or a binary symmetric channel (BSC). For this problem, we derive the so-called expurgation exponent of the information leaked to Eve. The exponent is derived by relating the leaked information to the error probability of the linear code that is generated by the linear hash function used in the privacy amplification, which is also interesting in its own right. The derived exponent is larger than state-of-the-art exponent recently derived by Hayashi at low rate.
Analyzing the Dual-Path Peer-to-Peer Anonymous Approach
Venue: arXiv cs.NI 2012
Year: 2012

Dual-Path is an anonymous peer-to-peer approach which provides requester anonymity. This approach provides anonymity between a requester and a provider in peer-to-peer networks with trusted servers called suppernode so the provider will not be able to identify the requester and no other peers can identify the two communicating parties with certainty. Dual-Path establishes two paths for transmitting data. These paths called Request path and Response path. The first one is used for requesting data and the second one is used for sending the requested data to the requester. As Dual-Path approach is similar to Crowds approach, this article compares reliability and performance of Dual-Path and Crowds. For this purpose a simulator is developed and several scenarios are defined to compare Dual-Path and Crowds in different situations. In chapter 2 and 3 Dual-Path and Crowds approaches are briefly described. Chapter 4 is talking about simulator. Chapter 5 explains the scenarios for comparison of performance. Chapter 6 is about comparison of reliability and chapter 7 is conclusion.
Color Image Compression Algorithm Based on the DCT Blocks
Venue: arXiv cs.CV 2012
Year: 2012

This paper presents the performance of different blockbased discrete cosine transform (DCT) algorithms for compressing color image. In this RGB component of color image are converted to YCbCr before DCT transform is applied. Y is luminance component;Cb and Cr are chrominance components of the image. The modification of the image data is done based on the classification of image blocks to edge blocks and non-edge blocks, then the edge block of the image is compressed with low compression and the nonedge blocks is compressed with high compression. The analysis results have indicated that the performance of the suggested method is much better, where the constructed images are less distorted and compressed with higher factor.
Personal Safety Triggering System on Android Mobile Platform
Venue: arXiv cs.OH 2012
Year: 2012

Introduction of Smart phones redefined the usage of mobile phones in the communication world. Smart phones are equipped with various sophisticated features such as Wi-Fi, GPS navigation, high resolution camera, touch screen with broadband access which helps the mobile phone users to keep in touch with the modern world. Many of these features are primarily integrated with the mobile operating system which is out of reach to public, by which the users can't manipulate those features. Google came up with an innovative operation system termed as ANDROID, which is open system architecture with customizable third party development and debugging environment which helps the user's to manipulate the features and to create their own customizable applications. In this paper, 'Emergency Based Remote Collateral Tracking System' application using Google's Android Mobile Platform is addressed. Emergency is divided into three categories: heart beat based emergency, security threats like personal safety and road accidents. This application is targeted to a person who is driving a vehicle. Heart rate monitoring device is integrated with our application to sense the heart beat of a person driving the vehicle and if there is any abnormalities in the heart beat, then our application performs a dual role. One in which, application uses a GPS to track the location information of the user and send those location information as a message via SMS, email and post it on Facebook wall Simultaneously, an emergency signal is sent to Arduino Microcontroller.This application is written in JAVA programming language which runs on Eclipse Integrated Development Kit.
Modeling the adoption and use of social media by nonprofit organizations
Venue: arXiv cs.CY 2012
Year: 2012

This study examines what drives organizational adoption and use of social media through a model built around four key factors - strategy, capacity, governance, and environment. Using Twitter, Facebook, and other data on 100 large US nonprofit organizations, the model is employed to examine the determinants of three key facets of social media utilization: 1) adoption, 2) frequency of use, and 3) dialogue. We find that organizational strategies, capacities, governance features, and external pressures all play a part in these social media adoption and utilization outcomes. Through its integrated, multi-disciplinary theoretical perspective, this study thus helps foster understanding of which types of organizations are able and willing to adopt and juggle multiple social media accounts, to use those accounts to communicate more frequently with their external publics, and to build relationships with those publics through the sending of dialogic messages.
Contour Completion Around a Fixation Point
Venue: arXiv cs.CV 2012
Year: 2012

The paper presents two edge grouping algorithms for finding a closed contour starting from a particular edge point and enclosing a fixation point. Both algorithms search a shortest simple cycle in \textit{an angularly ordered graph} derived from an edge image where a vertex is an end point of a contour fragment and an undirected arc is drawn between a pair of end-points whose visual angle from the fixation point is less than a threshold value, which is set to $π/2$ in our experiments. The first algorithm restricts the search space by disregarding arcs that cross the line extending from the fixation point to the starting point. The second algorithm improves the solution of the first algorithm in a greedy manner. The algorithms were tested with a large number of natural images with manually placed fixation and starting points. The results are promising.
AES CCMP Algorithm with N-Way Interleaved Cipher Block Chaining
Venue: arXiv cs.CR 2012
Year: 2012

Nowadays, the increased use of battery-powered mobile appliances and the urge to access time-sensitive data anytime anywhere has fuelled a high demand for wireless networks. However, wireless networks are susceptible to intrusion and security problems. There is an inherent need to secure the wireless data communication to ensure the confidentiality, authenticity, integrity and non repudiation of the data being exchanged. On the other hand, the computation and the resultant energy consumption to achieve sufficient security can be high. Encryption algorithms are generally computationally intensive, and consume a significant amount of computing resources (such as CPU time, memory, and battery power). Considering the limited resources on wireless devices, it is crucial that security protocols be implemented efficiently. This manuscript focuses on how energy consumption is impacted by the use of unoptimised AES-CCMP algorithms and proposes an optimized AES CCMP algorithm using 2-way interleaving that does not compromise the security of wireless communication sessions. There is also analysis of the performance of AES (a.k.a. Rijndael) in its AES-CCMP implementation. The 2-way interleaving technique is an optimization of the CBC-MAC that is investigated using two performance metrics (namely encryption time and throughput).
Modeling and Control of CSTR using Model based Neural Network Predictive Control
Venue: arXiv cs.AI 2012
Year: 2012

This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some comments about the optimization procedure are made. Predictive control algorithm is applied to control the concentration in a continuous stirred tank reactor (CSTR), whose parameters are optimally determined by solving quadratic performance index using the optimization algorithm. An efficient control of the product concentration in cstr can be achieved only through accurate model. Here an attempt is made to alleviate the modeling difficulties using Artificial Intelligent technique such as Neural Network. Simulation results demonstrate the feasibility and effectiveness of the NNMPC technique.
Content-based Text Categorization using Wikitology
Venue: arXiv cs.IR 2012
Year: 2012

A major computational burden, while performing document clustering, is the calculation of similarity measure between a pair of documents. Similarity measure is a function that assign a real number between 0 and 1 to a pair of documents, depending upon the degree of similarity between them. A value of zero means that the documents are completely dissimilar whereas a value of one indicates that the documents are practically identical. Traditionally, vector-based models have been used for computing the document similarity. The vector-based models represent several features present in documents. These approaches to similarity measures, in general, cannot account for the semantics of the document. Documents written in human languages contain contexts and the words used to describe these contexts are generally semantically related. Motivated by this fact, many researchers have proposed semantic-based similarity measures by utilizing text annotation through external thesauruses like WordNet (a lexical database). In this paper, we define a semantic similarity measure based on documents represented in topic maps. Topic maps are rapidly becoming an industrial standard for knowledge representation with a focus for later search and extraction. The documents are transformed into a topic map based coded knowledge and the similarity between a pair of documents is represented as a correlation between the common patterns. The experimental studies on the text mining datasets reveal that this new similarity measure is more effective as compared to commonly used similarity measures in text clustering.
2.5K-Graphs: from Sampling to Generation
Venue: arXiv cs.SI 2012
Year: 2012

Understanding network structure and having access to realistic graphs plays a central role in computer and social networks research. In this paper, we propose a complete, and practical methodology for generating graphs that resemble a real graph of interest. The metrics of the original topology we target to match are the joint degree distribution (JDD) and the degree-dependent average clustering coefficient ($\bar{c}(k)$). We start by developing efficient estimators for these two metrics based on a node sample collected via either independence sampling or random walks. Then, we process the output of the estimators to ensure that the target properties are realizable. Finally, we propose an efficient algorithm for generating topologies that have the exact target JDD and a $\bar{c}(k)$ close to the target. Extensive simulations using real-life graphs show that the graphs generated by our methodology are similar to the original graph with respect to, not only the two target metrics, but also a wide range of other topological metrics; furthermore, our generator is order of magnitudes faster than state-of-the-art techniques.
A Survey of Recent View-based 3D Model Retrieval Methods
Venue: arXiv cs.CV 2012
Year: 2012

Extensive research efforts have been dedicated to 3D model retrieval in recent decades. Recently, view-based methods have attracted much research attention due to the high discriminative property of multi-views for 3D object representation. In this report, we summarize the view-based 3D model methods and provide the further research trends. This paper focuses on the scheme for matching between multiple views of 3D models and the application of bag-of-visual-words method in 3D model retrieval. For matching between multiple views, the many-to-many matching, probabilistic matching and semisupervised learning methods are introduced. For bag-of-visual-words application in 3D model retrieval, we first briefly review the bag-of-visual-words works on multimedia and computer vision tasks, where the visual dictionary has been detailed introduced. Then a series of 3D model retrieval methods by using bag-of-visual-words description are surveyed in this paper. At last, we summarize the further research content in view-based 3D model retrieval.
Information-theoretic Dictionary Learning for Image Classification
Venue: arXiv cs.CV 2012
Year: 2012

We present a two-stage approach for learning dictionaries for object classification tasks based on the principle of information maximization. The proposed method seeks a dictionary that is compact, discriminative, and generative. In the first stage, dictionary atoms are selected from an initial dictionary by maximizing the mutual information measure on dictionary compactness, discrimination and reconstruction. In the second stage, the selected dictionary atoms are updated for improved reconstructive and discriminative power using a simple gradient ascent algorithm on mutual information. Experiments using real datasets demonstrate the effectiveness of our approach for image classification tasks.
On Money as a Means of Coordination between Network Packets
Venue: arXiv cs.DS 2012
Year: 2012

In this work, we apply a common economic tool, namely money, to coordinate network packets. In particular, we present a network economy, called PacketEconomy, where each flow is modeled as a population of rational network packets, and these packets can self-regulate their access to network resources by mutually trading their positions in router queues. Every packet of the economy has its price, and this price determines if and when the packet will agree to buy or sell a better position. We consider a corresponding Markov model of trade and show that there are Nash equilibria (NE) where queue positions and money are exchanged directly between the network packets. This simple approach, interestingly, delivers improvements even when fiat money is used. We present theoretical arguments and experimental results to support our claims.
OntoAna: Domain Ontology for Human Anatomy
Venue: arXiv cs.AI 2012
Year: 2012

Today, we can find many search engines which provide us with information which is more operational in nature. None of the search engines provide domain specific information. This becomes very troublesome to a novice user who wishes to have information in a particular domain. In this paper, we have developed an ontology which can be used by a domain specific search engine. We have developed an ontology on human anatomy, which captures information regarding cardiovascular system, digestive system, skeleton and nervous system. This information can be used by people working in medical and health care domain.
A New Algorithm for the Subtraction Games
Venue: arXiv cs.GT 2012
Year: 2012

Subtraction games is a class of combinatorial games. It was solved since the Sprague-Grundy Theory was put forward. This paper described a new algorithm for subtraction games. The new algorithm can find win or lost positions in subtraction games. In addition, it is much simpler than Sprague-Grundy Theory in one pile of the games.
Effects of delayed immune-response in tumor immune-system interplay
Venue: arXiv cs.CE 2012
Year: 2012

Tumors constitute a wide family of diseases kinetically characterized by the co-presence of multiple spatio-temporal scales. So, tumor cells ecologically interplay with other kind of cells, e.g. endothelial cells or immune system effectors, producing and exchanging various chemical signals. As such, tumor growth is an ideal object of hybrid modeling where discrete stochastic processes model agents at low concentrations, and mean-field equations model chemical signals. In previous works we proposed a hybrid version of the well-known Panetta-Kirschner mean-field model of tumor cells, effector cells and Interleukin-2. Our hybrid model suggested -at variance of the inferences from its original formulation- that immune surveillance, i.e. tumor elimination by the immune system, may occur through a sort of side-effect of large stochastic oscillations. However, that model did not account that, due to both chemical transportation and cellular differentiation/division, the tumor-induced recruitment of immune effectors is not instantaneous but, instead, it exhibits a lag period. To capture this, we here integrate a mean-field equation for Interleukins-2 with a bi-dimensional delayed stochastic process describing such delayed interplay. An algorithm to realize trajectories of the underlying stochastic process is obtained by coupling the Piecewise Deterministic Markov process (for the hybrid part) with a Generalized Semi-Markovian clock structure (to account for delays). We (i) relate tumor mass growth with delays via simulations and via parametric sensitivity analysis techniques, (ii) we quantitatively determine probabilistic eradication times, and (iii) we prove, in the oscillatory regime, the existence of a heuristic stochastic bifurcation resulting in delay-induced tumor eradication, which is neither predicted by the mean-field nor by the hybrid non-delayed models.
A novel approach for e-payment using virtual password system
Venue: arXiv cs.CR 2012
Year: 2012

In today's world of E-Commerce everything comes online like Music, E-Books, Shopping all most everything is online. If you are using some service or buying things online then you have to pay for that. For that you have to do Net Banking or you have to use Credit card which will do online payment for you. In today's environment when everything is online, the service you are using for E-Payment must be secure and you must protect your banking information like debit card or credit card information from possible threat of hacking. There were lots way to threat like Key logger, Forgery Detection, Phishing, Shoulder surfing. Therefore, we reveal our actual information of Bank and Credit Card then there will be a chance to lose data and same credit card and hackers can use banking information for malicious purpose. In this paper we discuss available E-Payment protocols, examine its advantages and delimitation's and shows that there are steel needs to design a more secure E-Payment protocol. The suggested protocol is based on using hash function and using dynamic or virtual password, which protects your banking or credit card information from possible threat of hacking when doing online transactions.
Coordination Level Modeling and Analysis of Parallel Programs using Petri Nets
Venue: arXiv cs.DC 2012
Year: 2012

In the last fifteen years, the high performance computing (HPC) community has claimed for parallel programming environments that reconciles generality, higher level of abstraction, portability, and efficiency for distributed-memory parallel computing platforms. The Hash component model appears as an alternative for addressing HPC community claims for fitting these requirements. This paper presents foundations that will enable a parallel programming environment based on the Hash model to address the problems of "debugging", performance evaluation and verification of formal properties of parallel program by means of a powerful, simple, and widely adopted formalism: Petri nets.
Performance Tuning Of J48 Algorithm For Prediction Of Soil Fertility
Venue: arXiv cs.LG 2012
Year: 2012

Data mining involves the systematic analysis of large data sets, and data mining in agricultural soil datasets is exciting and modern research area. The productive capacity of a soil depends on soil fertility. Achieving and maintaining appropriate levels of soil fertility, is of utmost importance if agricultural land is to remain capable of nourishing crop production. In this research, Steps for building a predictive model of soil fertility have been explained. This paper aims at predicting soil fertility class using decision tree algorithms in data mining . Further, it focuses on performance tuning of J48 decision tree algorithm with the help of meta-techniques such as attribute selection and boosting.