Query: AntNet: Distributed Stigmergetic Control for Communications Networks

Relevant passages:
Packet Routing in Dynamically Changing Networks: A Reinforcement Learning Approach <=SEP=> This paper describes the Q-routing algorithm for packet routing, in which a reinforcement learning module is embedded into each node of a switching network. Only local communication is used by each node to keep accurate statistics on which routing decisions lead to minimal delivery times. In simple experiments involving a 36-node, irregularly connected network, Q-routing proves superior to a nonadaptive algorithm based on precomputed shortest paths and is able to route e ciently even when critical aspects of the simulation, such as the network load, are allowed to vary dynamically. The paper concludes with a discussion of the tradeo between discovering shortcuts and maintaining stable policies.

Dynamic Programming and Optimal Control <=SEP=> This is an updated version of Chapter 4 of the author’s Dynamic Programming and Optimal Control, Vol. II, 4th Edition, Athena Scientific, 2012. It includes new material, and it is substantially revised and expanded (it has more than doubled in size). The new material aims to provide a unified treatment of several models, all of which lack the contractive structure that is characteristic of the discounted problems of Chapters 1 and 2: positive and negative cost models, deterministic optimal control (including adaptive DP), stochastic shortest path models, and risk-sensitive models. Here is a summary of the new material:

1 Positive Feedback as a Search Strategy <=SEP=> A combination of distributed computation, positive feedback and constructive greedy heuristic is proposed as a new approach to stochastic optimization and problem solving. Positive feedback accounts for rapid discovery of very good solutions, distributed computation avoids premature convergence, and greedy heuristic helps the procedure to find acceptable solutions in the early stages of the search process. An application of the proposed methodology to the classical travelling salesman problem shows that the system can rapidly provide very good, if not optimal, solutions. We report on many simulation results and discuss the working of the algorithm. Some hints about how this approach can be applied to a variety of optimization problems are also given.

La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs <=SEP=> I. INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 ~I. ]:~.ECONSTRUCTION PAR LES Cubi termes sp . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 I . La r e c o n s t r u c t i o n p r o p r e m e n t dire, p. 43. 2. Le c o m p o r t e m e n t en fonc t ion du groupe , p. 46. 3. Les condui tes individuel les , p. 49. 4. Les ac t iv i t4s au t r e s que la r econs t ruc t i on , p. 51. I I [ . RECONSTRUCTION PAR Bell icosi termes natalensis . . . . . . . . . . . . . . . . . . . . . 53 I V . ~V[]~CANISME DE LA CORRELATION DES TACIIES INDIVIDUELLES ET DE LA PRETENDUE REGULATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 I . L a phase d ' incoordinat ion, p. 57. A. La s i tua t ion s t i m u l a n t e , p. 57 ; B. Les act iv i t~s incoordonn~es , p. 59 ; C. La local isa t ion des d~p6ts de t e r re , p. 60. 2. L a phase de coordinat ion, p. 61. A. La densi t6 cr i t ique des bou le t t e s de te r re et l ' o r i en ta t ion du c o m p o r t e m e n t , p. 61 ; B. La s t i g m e r g i e et les s t i m u l a t i o n s s imul tan6es , p. 62 ; C. N a t u r e des s t imu l i a g i s s a n t s u r les c o n s t r u c t e u r s : les odeurs fo rmes , p. 66 ; D. La t endance l ' un i t4 , p. 67 ; E, R61e de la m~moi re d a n s ia cons t ruc t ion , p. 67 ; F. Essa i de s y n t h ~ s e , p. 68. V. COMPORTEMENT DES TERMITES COMPARE A CELUI D'ANIMAUX SOLITAIRES. 73 VI . CARACTERISTIQUES FONDAMENTALES DU COMPORTEMENT DES INSECTES SOCIAUX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 V I I . AUTEURS CIT#.S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 L~.CENDES ~)ES PLANC~ES I A V I I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8'1

Optimization, Learning and Natural Algorithms <=SEP=> 

3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks <=SEP=> Standard 3D convolution operations require much larger amounts of memory and computation cost than 2D convolution operations. The fact has hindered the development of deep neural nets in many 3D vision tasks. In this paper, we investigate the possibility of applying depthwise separable convolutions in 3D scenario and introduce the use of 3D depthwise convolution. A 3D depthwise convolution splits a single standard 3D convolution into two separate steps, which would drastically reduce the number of parameters in 3D convolutions with more than one order of magnitude. We experiment with 3D depthwise convolution on popular CNN architectures and also compare it with a similar structure called pseudo-3D convolution. The results demonstrate that, with 3D depthwise convolutions, 3D vision tasks like classification and reconstruction can be carried out with more light-weighted neural networks while still delivering comparable performances.

Runtime Models Based on Dynamic Decision Networks: Enhancing the Decision-making in the Domain of Ambient Assisted Living Applications <=SEP=> Dynamic decision-making for self-adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) -aka quality propertiesand the costsbenefits analysis of the alternative solutions. Usually, it requires the specification of utility preferences for NFRs and decisionmaking strategies. Traditionally, these preferences have been defined at design-time. In this paper we develop further our ideas on re-assessment of NFRs preferences given new evidence found at runtime and using dynamic decision networks (DDNs) as the runtime abstractions. Our approach use conditional probabilities provided by DDNs, the concepts of Bayesian surprise and Primitive Cognitive Network Process (P-CNP), for the determination of the initial preferences. Specifically, we present a case study in the domain problem of ambient assisted living (AAL). Based on the collection of runtime evidence, our approach allows the identification of unknown situations at the design stage.

Design of a Data-Driven PID Controller <=SEP=> Since most processes have nonlinearities, controller design schemes to deal with such systems are required. On the other hand, proportional-integral-derivative (PID) controllers have been widely used for process systems. Therefore, in this paper, a new design scheme of PID controllers based on a data-driven (DD) technique is proposed for nonlinear systems. According to the DD technique, a suitable set of PID parameters is automatically generated based on input/output data pairs of the controlled object stored in the database. This scheme can adjust the PID parameters in an online manner even if the system has nonlinear properties and/or time-variant system parameters. Finally, the effectiveness of the newly proposed control scheme is evaluated on some simulation examples, and a pilot-scale temperature control system.

Blocked recursive image composition with exclusion zones <=SEP=> Photo collages are a popular and powerful storytelling mechanism. They are often enhanced with background artwork that sets the theme for the story. However, layout algorithms for photo collage creation typically do not take this artwork into account, which can result in collages where photos overlay important artwork elements. To address this, we extend our previous Blocked Recursive Image Composition (BRIC) method to allow any number of photos to be automatically arranged around preexisting exclusion zones on a canvas (exBRIC). We first generate candidate binary splitting trees to partition the canvas into regions that accommodate both photos and exclusion zones. We use a Cassowary constraint solver to ensure that the desired exclusion zones are not covered by photos. Finally, photo areas, exclusion zones and layout symmetry are evaluated to select the best candidate. This method provides flexible, dynamic and integrated photo layout with background artwork.

Data Quality and Data Cleaning: An Overview <=SEP=> Data quality is a serious concern in any data-driven enterprise, often creating misleading findings during data mining, and causing process disruptions in operational databases. The manifestations of data quality problems can be very expensive- "losing" customers, "misplacing" billions of dollars worth of equipment, misallocated resources due to glitched forecasts, and so on. Solving data quality problems typically requires a very large investment of time and energy -- often 80% to 90% of a data analysis project is spent in making the data reliable enough that the results can be trusted.In this tutorial, we present a multi disciplinary approach to data quality problems. We start by discussing the meaning of data quality and the sources of data quality problems. We show how these problems can be addressed by a multidisciplinary approach, combining techniques from management science, statistics, database research, and metadata management. Next, we present an updated definition of data quality metrics, and illustrate their application with a case study. We conclude with a survey of recent database research that is relevant to data quality problems, and suggest directions for future research.

An improved analytical IGBT model for loss calculation including junction temperature and stray inductance <=SEP=> An improved analytical model suitable for IGBT modules is proposed in this paper to calculate the power losses with high accuracy and short calculation time. In this model, the parameters varying with junction temperature of the modules, such as di/dt in the turn-on period and dv/dt in the turn-off period, are discussed and derived according to several equivalent models. In addition, the parasitic inductance in the circuit including the emitter and collector inductance in the power circuits and the gate inductance in the driving loop are considered in this model. Based on this proposed model, the simulation switching waveforms of collector currents and collector-emitter voltages are provided to verify the model. Meanwhile, the calculated power losses are confirmed to be precise by comparing with measurement results.

Temporal Segmentation of Egocentric Videos <=SEP=> The use of wearable cameras makes it possible to record life logging egocentric videos. Browsing such long unstructured videos is time consuming and tedious. Segmentation into meaningful chapters is an important first step towards adding structure to egocentric videos, enabling efficient browsing, indexing and summarization of the long videos. Two sources of information for video segmentation are (i) the motion of the camera wearer, and (ii) the objects and activities recorded in the video. In this paper we address the motion cues for video segmentation. Motion based segmentation is especially difficult in egocentric videos when the camera is constantly moving due to natural head movement of the wearer. We propose a robust temporal segmentation of egocentric videos into a hierarchy of motion classes using a new Cumulative Displacement Curves. Unlike instantaneous motion vectors, segmentation using integrated motion vectors performs well even in dynamic and crowded scenes. No assumptions are made on the underlying scene structure and the method works in indoor as well as outdoor situations. We demonstrate the effectiveness of our approach using publicly available videos as well as choreographed videos. We also suggest an approach to detect the fixation of wearer's gaze in the walking portion of the egocentric videos.

Compact and power-efficient 5 GHz full-duplex design utilizing the 180° ring hybrid coupler <=SEP=> This paper presents a compact and power-efficient 5 Ghz in-band full-duplex (FD) design in ANSYS HFSS using the 180-degree ring hybrid coupler. The proposed design achieves an excellent isolation of 57dB by taking advantage of destructive interference between two radiating antennas attached to the coupler, leading to a large reduction in self-interference. The design is passive and hence overcomes additional power requirement for adaptive channel estimation. In addition, it has a very workable physical size for the desired frequency of operation. The proposed FD design is therefore compact and power-efficient, which can be used in mobile devices, such as cell phones or tablet/phablet devices for a more flexible and greater contention of scarce RF resources.

TAaaS: Trustworthy Authentication as a Service Based on Trusted Path <=SEP=> Authentication as a Service (AaaS) provides on-demand delivery of multi-factor authentication (MFA). However, current AaaS has left out of consideration the trustworthiness of user inputs at client evices and the risk of privacy exposure at the AaaS providers. To solve these concerns, we present TAaaS, Trustworthy Authentication as a Service, which offers a trusted path-based MFA service to the service provider in the cloud. TAaaS leverages the hypervisor-based trusted path to ensure the trustworthiness of user inputs, and addresses privacy concerns in the cloud by storing only the irreversible user account information. We implement two end-to-end prototypes and evaluate our work to show its feasibility and security.

OFDM : Basic Concepts , Scope & its Applications <=SEP=> Orthogonal frequency division multiplexing (OFDM) is a special case of multicarrier transmission where a single DataStream is transmitted over a number of lower rate subcarriers. In July 1998, the IEEE standardization group decided to select OFDM as the basis for their new 5-GHz standard aiming a range of data stream from 6 up to 54 Mbps. This new standard is the first one to use OFDM in packetbased communications. In wireless communication, concept of parallel transmission of symbols is used to achieve high throughput and better transmission quality. Orthogonal Frequency Division Multiplexing (OFDM) is one of the techniques for parallel transmission. The idea of OFDM is to split the total transmission bandwidth into a number of orthogonal subcarriers in order to transmit the symbols using these subcarriers in parallel. In this paper we will discuss the basics of OFDM techniques, role of OFDM in this era, its benefits and losses and also some of its application. Keywords— Orthogonal frequency division multiplexing(OFDM); BER; ISI; PAPR; DVB; DAB.

Graduated Consistency-Regularized Optimization for Multi-graph Matching <=SEP=> Graph matching has a wide spectrum of computer vision applications such as finding feature point correspondences across images. The problem of graph matching is generally NP-hard, so most existing work pursues suboptimal solutions between two graphs. This paper investigates a more general problem of matching N attributed graphs to each other, i.e. labeling their common node correspondences such that a certain compatibility/affinity objective is optimized. This multigraph matching problem involves two key ingredients affecting the overall accuracy: a) the pairwise affinity matching score between two local graphs, and b) global matching consistency that measures the uniqueness and consistency of the pairwise matching results by different sequential matching orders. Previous work typically either enforces the matching consistency constraints in the beginning of iterative optimization, which may propagate matching error both over iterations and across different graph pairs; or separates score optimizing and consistency synchronization in two steps. This paper is motivated by the observation that affinity score and consistency are mutually affected and shall be tackled jointly to capture their correlation behavior. As such, we propose a novel multigraph matching algorithm to incorporate the two aspects by iteratively approximating the global-optimal affinity score, meanwhile gradually infusing the consistency as a regularizer, which improves the performance of the initial solutions obtained by existing pairwise graph matching solvers. The proposed algorithm with a theoretically proven convergence shows notable efficacy on both synthetic and public image datasets.

Mental Stress Assessment of ECG Signal using Statistical Analysis of Bio-Orthogonal Wavelet Coefficients <=SEP=> It is observed that the stress level is function of various statistical parameters like standard deviation, entropy, energy,, mean, Covariance and power of the ECG signals of two states i.e. normal state of mind and stressed state of mind. Further, it is observed that the features extracted are directly from the ECG in frequency domain using db4 wavelet. However, db4 introduces some error on account of db4 wavelet shape. This error in turn amplifies while measuring the features as mentioned above. In order to reduce this error, we propose a Bior 3.9 wavelet family to decompose the ECG signal. The decomposed ECG signal is now analyzed statistically to extract the above feature. Further, in the existing work, all the ECGs are taken by using the 12 leads method. This factor also adds some undue stress to the person under scanner. And this is time consuming too. Therefore, to reduce this complexity, we propose to analyze all above features by using a two lead ECG. Further, a back propagation neural network is trained using the above features as input neurons in order to classify the stress level.

A Better Comparison Summary of Credit Scoring Classification <=SEP=> The credit scoring aim is to classify the customer credit as defaulter or non-defaulter. The credit risk analysis is more effective with further boosting and smoothing of the parameters of models. The objective of this paper is to explore the credit score classification models with an imputation technique and without imputation technique. However, data availability is low in case of without imputation because of missing values depletion from the large dataset. On the other hand, imputation based dataset classification accuracy with linear method of ANN is better than other models. The comparison of models with boosting and smoothing shows that error rate is better metric than area under curve (AUC) ratio. It is concluded that artificial neural network (ANN) is better alternative than decision tree and logistic regression when data availability is high in dataset. Keywords—Credit score data mining; classification; artifical neural network; imputation

A New Travelling Wave Antenna in Microstrip <=SEP=> The radiation characteristics of the first higher order mode of microstrip lines are investigated. As a result, a simple travelling wave antenna element is scribed, having a larger bandwidth compared with resonator antennas. A method to excite the first higher order mode is shown. A single antenna element is treated theoretically and experimentally, and an array of four antenna elements is demonstrated.

How to Predict Mood? Delving into Features of Smartphone-Based Data <=SEP=> Smartphones are increasingly utilized in society and enable scientists to record a wide range of behavioral and environmental information. These information, referred to as Unobtrusive Ecological Momentary Assessment Data, might support prediction procedures regarding the mood level of users and simultaneously contribute to an enhancement of therapy strategies. In this paper, we analyze how the mood level of healthy clients is affected by unobtrusive measures and how this kind of data contributes to the prediction performance of various statistical models (Bayesian methods, Lasso procedures, etc.). We conduct analyses on a non-user and a user level. We then compare the models by utilizing introduced performance measures. Our findings indicate that the prediction performance increases when considering individual users. However, the implemented models only perform slightly better than the introduced mean model. Indicated by feature selection methods, we assume that more meaningful variables regarding the outcome can potentially increase prediction performance.

A four-electrode low frequency impedance spectroscopy measurement system using the AD5933 measurement chip. <=SEP=> This paper presents the design of a four electrode impedance measurement circuit dedicated to bioimpedance embedded applications. It extends the use of the AD5933 measurement chip to allow it to operate in a four electrode configuration in order to limit the effects of the parasitic impedances between the medium under test and the electrodes. The circuit has shown a good measurement accuracy on various test circuits. In association with a four microband electrode system it has been successfully used to characterize small physiological samples (50 μl) with conductivities ranging from 0.14 to 1.2 S m(-1). It can be used as an alternative bioimpedance measurement approach for embedded applications operating in the four electrode configuration.

Highlighting interventions and user differences: informing adaptive information visualization support <=SEP=> There is increasing evidence that the effectiveness of information visualization techniques can be impacted by the particular needs and abilities of each user. This suggests that it is important to investigate information visualization systems that can dynamically adapt to each user. In this paper, we address the question of how to adapt. In particular, we present a study to evaluate a variety of visual prompts, called "interventions", that can be performed on a visualization to help users process it. Our results show that some of the tested interventions perform better than a condition in which no intervention is provided, both in terms of task performance as well as subjective user ratings. We also discuss findings on how intervention effectiveness is influenced by individual differences and task complexity.

A RFID case-based logistics resource management system for managing order-picking operations in warehouses <=SEP=> In the supply chain, a warehouse is an essential component for linking the chain partners. It is necessary to allocate warehouse resources efficiently and effectively to enhance the productivity and reduce the operation costs of the warehouse. Therefore, warehouse management systems (WMSs) have been developed for handling warehouse resources and monitoring warehouse operations. However, it is difficult to update daily operations of inventory level, locations of forklifts and stock keeping units (SKUs) in realtime by using the bar-code-based or manual-based warehouse management systems. In this paper, RFID technology is adopted to facilitate the collection and sharing of data in a warehouse. Tests are performed for evaluating the reading performance of both the active and passive RFID apparatus. With the help of the testing results, the efficient radio frequency cover ranges of the readers are examined for formulating a radio frequency identification case-based logistics resource management system (R-LRMS). The capabilities of R-LRMS are demonstrated in GSL Limited. Three objectives are achieved: (i) a simplification of RFID adoption procedure, (ii) an improvement in the visibility of warehouse operations and (iii) an enhancement of the productivity of the warehouse. The successful case example proved the feasibility of R-LRMS in real working practice. 2008 Elsevier Ltd. All rights reserved.

Deep Representation Learning with Target Coding <=SEP=> We consider the problem of learning deep representation when target labels are available. In this paper, we show that there exists intrinsic relationship between target coding and feature representation learning in deep networks. Specifically, we found that distributed binary code with error correcting capability is more capable of encouraging discriminative features, in comparison to the 1-of-K coding that is typically used in supervised deep learning. This new finding reveals additional benefit of using error-correcting code for deep model learning, apart from its well-known error correcting property. Extensive experiments are conducted on popular visual benchmark datasets.

Autoencoders, Unsupervised Learning, and Deep Architectures <=SEP=> To better understand deep architectures and unsupervised learning, uncluttered by hardware details, we develop a general autoencoder framework for the comparative study of autoencoders, including Boolean autoencoders. We derive several results regarding autoencoders and autoencoder learning, including results on learning complexity, vertical and horizontal composition, and fundamental connections between critical points and clustering. Possible implications for the theory of deep architectures are discussed.

Confocal Microwave Imaging for Breast Cancer Detection: Delay-Multiply-and-Sum Image Reconstruction Algorithm <=SEP=> A new image reconstruction algorithm, termed as delay-multiply-and-sum (DMAS), for breast cancer detection using an ultra-wideband confocal microwave imaging technique is proposed. In DMAS algorithm, the backscattered signals received from numerical breast phantoms simulated using the finite-difference time-domain method are time shifted, multiplied in pair, and the products are summed to form a synthetic focal point. The effectiveness of the DMAS algorithm is shown by applying it to backscattered signals received from a variety of numerical breast phantoms. The reconstructed images illustrate improvement in identification of embedded malignant tumors over the delay-and-sum algorithm. Successful detection and localization of tumors as small as 2 mm in diameter are also demonstrated.

A Framework for Smart Servicescape : A Case of Smart Home Service Experience <=SEP=> The rapid development of IoT technology has accelerated the growth of smart services. Despite the proliferation of smart services, academic research is still in its early stage particularly in terms of service experience and service design. Concerning a service experience viewpoint, it is essential to consider the context and environment of smart services, namely “smart servicescape,” as this can influence users’ entire experience. Moreover, the smart servicescape will have different characteristics due to the convergence of online and offline connected environments. With this background, this study aimed to propose a framework for the smart servicescape by identifying new dimensions that reflect the characteristics of smart services. Accordingly, an initial analytic framework of service experience blueprint was established on the basis of the conventional servicescape and service blueprinting. Twenty movie clips on smart home services officially produced by ICT corporations were collected, were analyzed through grounded theory, and were classified according to the analytic framework. Through a series of qualitative analysis, the framework structure was improved to make it more suitable for the smart servicescape. Finally, this study proposed a framework for the smart servicescape derived from the smart home service experience blueprint. The values of this framework can be identified in two aspects: (1) by identifying new dimensions to reflect the characteristics of smart services such as Smart device, Datascape, and Connected scape; and (2) by suggesting the structure of the service experience blueprint infused with the perspective of service experience, which consists of service encounters and the servicescape.

Decision-Theoretic User Interface Generation <=SEP=> For decades, researchers have debated the pros and cons of adaptive user interfaces with enthusiastic AI practitioners often confronting skeptical HCI experts (Shneiderman & Maes, 1997). This paper summarizes the SUPPLE project’s six years of work analyzing the characteristics of successful adaptive interfaces and developing efficient algorithms for their automatic generation (Gajos & Weld, 2004; Gajos et al., 2005; Gajos & Weld, 2005; Gajos et al., 2006; Gajos, Wobbrock, & Weld, 2007; Gajos et al., 2008; Gajos, Wobbrock, & Weld, 2008). We conclude that personalized user interfaces, which are adapted to a person’s devices, tasks, preferences and abilities, can improve user satisfaction and performance. Further, we demonstrate that automatic generation of these interfaces is computationally feasible. We developed two systems which autonomously construct a broad range of personalized adaptive interfaces: 1) SUPPLE/ARNAULD uses decision-theoretic optimization to automatically generate user interfaces adapted to target device, usage patterns, and a learned model of user preferences, and 2) SUPPLE++ first performs a one-time assessment of a person’s motor abilities and then automatically generates user interfaces adapted to that user’s abilities. Our experiments show that these automatically generated interfaces significantly improve speed, accuracy and satisfaction of users with motor impairments compared to manufacturers’ defaults. We also provide the first characterization of the design space of adaptive interfaces and demonstrate how such interfaces can significantly improve the quality and efficiency of daily interactions for typical users.

Efficient High-Quality Volume Rendering of SPH Data <=SEP=> High quality volume rendering of SPH data requires a complex order-dependent resampling of particle quantities along the view rays. In this paper we present an efficient approach to perform this task using a novel view-space discretization of the simulation domain. Our method draws upon recent work on GPU-based particle voxelization for the efficient resampling of particles into uniform grids. We propose a new technique that leverages a perspective grid to adaptively discretize the view-volume, giving rise to a continuous level-of-detail sampling structure and reducing memory requirements compared to a uniform grid. In combination with a level-of-detail representation of the particle set, the perspective grid allows effectively reducing the amount of primitives to be processed at run-time. We demonstrate the quality and performance of our method for the rendering of fluid and gas dynamics SPH simulations consisting of many millions of particles.

A survey of event detection techniques in online social networks <=SEP=> The online social networks (OSNs) have become an important platform for detecting real-world event in recent years. These real-world events are detected by analyzing huge social-stream data available on different OSN platforms. Event detection has become significant because it contains substantial information which describes different scenarios during events or crisis. This information further helps to enable contextual decision making, regarding the event location, content and the temporal specifications. Several studies exist, which offers plethora of frameworks and tools for detecting and analyzing events used for applications like crisis management, monitoring and predicting events in different OSN platforms. In this paper, a survey is done for event detection techniques in OSN based on social text streams—newswire, web forums, emails, blogs and microblogs, for natural disasters, trending or emerging topics and public opinion-based events. The work done and the open problems are explicitly mentioned for each social stream. Further, this paper elucidates the list of event detection tools available for the researchers.