Query: Bootstrapping Unsupervised Bilingual Lexicon Induction

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Exploiting Similarities among Languages for Machine Translation <=SEP=> Dictionaries and phrase tables are the basis of modern statistical machine translation systems. This paper develops a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures based on large monolingual data and mapping between languages from small bilingual data. It uses distributed representation of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs.

Efficient Estimation of Word Representations in Vector Space <=SEP=> We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.

A Systematic Comparison of Various Statistical Alignment Models <=SEP=> We present and compare various methods for computing word alignments using statistical or heuristic models. We consider the five alignment models presented in Brown, Della Pietra, Della Pietra, and Mercer (1993), the hidden Markov alignment model, smoothing techniques, and refinements. These statistical models are compared with two heuristic models based on the Dice coefficient. We present different methods for combining word alignments to perform a symmetrization of directed statistical alignment models. As evaluation criterion, we use the quality of the resulting Viterbi alignment compared to a manually produced reference alignment. We evaluate the models on the German-English Verbmobil task and the French-English Hansards task. We perform a detailed analysis of various design decisions of our statistical alignment system and evaluate these on training corpora of various sizes. An important result is that refined alignment models with a first-order dependence and a fertility model yield significantly better results than simple heuristic models. In the Appendix, we present an efficient training algorithm for the alignment models presented.

Combining Bilingual and Comparable Corpora for Low Resource Machine Translation <=SEP=> Statistical machine translation (SMT) performance suffers when models are trained on only small amounts of parallel data. The learned models typically have both low accuracy (incorrect translations and feature scores) and low coverage (high out-of-vocabulary rates). In this work, we use an additional data resource, comparable corpora, to improve both. Beginning with a small bitext and corresponding phrase-based SMT model, we improve coverage by using bilingual lexicon induction techniques to learn new translations from comparable corpora. Then, we supplement the model’s feature space with translation scores estimated over comparable corpora in order to improve accuracy. We observe improvements between 0.5 and 1.7 BLEU translating Tamil, Telugu, Bengali, Malayalam, Hindi, and Urdu into English.

Part-of-Speech Tagging with Neural Networks <=SEP=> Text corpora which are tagged with part-of-speech information are useful in many areas of linguistic research. In this paper, a new part-of-speech tagging method hased on neural networks (Net-Tagger) is presented and its performance is compared to that of a llMM-tagger (Cutting et al., 1992) and a trigrambased tagger (Kempe, 1993). It is shown that the Net-Tagger performs as well as the trigram-based tagger and better than the iIMM-tagger.

Review of Ontology Matching Approaches and Challenges <=SEP=> Ontology mapping aims to solve the semantic heterogeneity problems such as ambiguous entity names, different entity granularity, incomparable categorization, and various instances of different ontologies. The mapping helps to search or query data from different sources. Ontology mapping is necessary in many applications such as data integration, ontology evolution, data warehousing, e-commerce and data exchange in various domains such as purchase order, health, music and e-commerce. It is performed by ontology matching approaches that find semantic correspondences between ontology entities. In this paper, we review state of the art ontology matching approaches. We describe the approaches according to instancebased, schema-based, instance and schema-based, usage-based, element-level, and structure-level. The analysis of the existing approaches will assist us in revealing some challenges in ontology mapping such as handling ontology matching errors, user involvement and reusing previous match operations. We explain the way of handling the challenges using new strategy in order to increase the performance.

Decision Trees: Theory and Algorithms <=SEP=> 4.

Clustering of periodic multichannel timeseries data with application to plasma fluctuations <=SEP=> A periodic datamining algorithm has been developed and used to extract distinct plasma fluctuations in multichannel oscillatory timeseries data. The technique uses the Expectation Maximisation algorithm to solve for the maximum likelihood estimates and cluster assignments of a mixture of multivariate independent von Mises distributions (EM-VMM). The performance of the algorithm shows significant benefits when compared to a periodic k-means algorithm and clustering using non-periodic techniques on several artificial datasets and real experimental data. Additionally, a new technique for identifying interesting features in multichannel oscillatory timeseries data is described (STFT-clustering). STFTclustering identifies the coincidence of spectral features overmost channels of amulti-channel array using the averaged short time Fourier transform of the signals. These features are filtered using clustering to remove noise. This method is particularly good at identifying weaker features and complements existing methods of feature extraction. Results from applying the STFT-clustering and EM-VMM algorithm to the extraction and clustering of plasma wave modes in the time series data from a helical magnetic probe array on the H-1NF heliac are presented. © 2014 Elsevier B.V. All rights reserved.

Gamification Solutions to Enhance Software User Engagement - A Systematic Review <=SEP=> Gamification is the use of video-game mechanics and elements in nongame contexts to enhance user engagement and performance. The purpose of this study is to conduct a systematic review to have an in-depth investigation into the existing gamification solutions targeted at solving user engagement problems in different categories of software. We carried out this systematic review by proposing a framework of gamifying process, which is the basis for comparison of existing gamification solutions. In order to report the review, the primary studies are categorized according to the following: a) gamified software and their platforms, b) elements of the gamifying process, c) gamification solutions in each software type, d) gamification solutions for software user engagement problems, e) gamification solutions in general, and f) effects of gamification on software user engagement and performance. Based on the search procedure and criteria, a total of 78 primary studies were extracted. Most of the studies focused on educational and social software, which were developed for web or mobile platforms. We concluded that the number of studies on motivating users to use software content, solving problems in learning software, and using real identity is very limited. Furthermore, few studies have been carried out on gamifying the following software categories: productivity software, cloud storage, utility software, entertainment software, search engine software, tool software, fitness software, software engineering, information worker software, and health-care software. In addition, a large number of gamification solutions are relatively simple and require improvement. Thus, for future studies, researchers can work on the items discovered in this review; they can improve the quality of the current gamified systems by using a wide variety of game mechanics and interface elements, utilizing a combination of contextual types of rewards and giving users the ability to use received rewards “in-game” and “out-game”.

Optimal control approach for pneumatic artificial muscle with using pressure-force conversion model <=SEP=> In this paper, we propose an optimal control framework for pneumatic actuators. In particular, we consider using Pneumatic Artificial Muscle (PAM) as a part of Pneumatic-Electric (PE) hybrid actuation system. An optimal control framework can be useful for PE hybrid system to properly distribute desired torque outputs to the actuators that have different characteristics. In the optimal control framework, the standard choice to represent control cost is squared force or torque outputs. However, since the control input for PAM is pressure rather than the force or the torque, we should explicitly consider the pressure of PAM as the control cost in an objective function of the optimal control method. We show that we are able to use pressure input as the control cost for PAM by explicitly considering the model which represents a relationship between the pressure input and the force output of PAM. We demonstrate that one-DOF robot with the PE hybrid actuation system can generate pressure-optimized ball throwing movements by using the optimal control method.

Beyond Independence: Probabilistic Models for Query Approximation on Binary Transaction Data <=SEP=> We investigate the problem of generating fast approximate a nswers to queries posed to large sparse binary data sets. We focus in particular on probabilistic mode l-based approaches to this problem and develop a number of techniques that are significantly more accurate t han a baseline independence model. In particular, we introduce two techniques for building probabil ist c models from frequent itemsets: the itemset maximum entropy method, and the itemset inclusion-exclusi on model. In the maximum entropy method we treat itemsets as constraints on the distribution of the q uery variables and use the maximum entropy principle to build a joint probability model for the query at tributes online. In the inclusion-exclusion model itemsets and their frequencies are stored in a data structur e alled an ADtree that supports an efficient implementation of the inclusion-exclusion principle in orde r to answer the query. We empirically compare these two itemset-based models to direct querying of the ori ginal data, querying of samples of the original data, as well as other probabilistic models such as the indep endence model, the Chow-Liu tree model, and the Bernoulli mixture model. These models are able to handle high-dimensionality (hundreds or thousands of attributes), whereas most other work on this topic has foc used on relatively low-dimensional OLAP problems. Experimental results on both simulated and realwor d transaction data sets illustrate various fundamental tradeoffs between approximation error, model complexity, and the online time required to compute a query answer.

MapMarker: Extraction of Postal Addresses and Associated Information for General Web Pages <=SEP=> Address information is essential for people’s daily life. People often need to query addresses of unfamiliar location through Web and then use map services to mark down the location for direction purpose. Although both address information and map services are available online, they are not well combined. Users usually need to copy individual address from a Web site and paste it to another Web site with map services to locate its direction. Such copy and paste operations have to be repeated if multiple addresses are listed on a single page such as public school list or apartment list. Furthermore, associated information with individual address has to be copied and included on each marker for better comprehension. Our research is devoted to automate the above process and make the combination an easier task for users. The main techniques applied here include postal address extraction and associated information extraction. We apply sequence labeling algorithm based on Conditional Random Fields (CRFs) to train models for address extraction. Meanwhile, using the extracted addresses as landmarks, we apply pattern mining to identify the boundaries of address blocks and extract associated information with each individual address. The experimental result shows high F-score at 91% for postal address extraction and 87% accuracy for associated information extraction.

Revising the Structural Framework for Marketing Management <=SEP=> Special thanks to Robert Skipper and Aaron Hyman for their assistance on an earlier version of this manuscript. Also thanks to Shaun McQuitty, Robin Peterson, Chuck Pickett, Kevin Shanahan, and the Journal of Business Research editors and reviewers, for their helpful comments. An earlier version of this manuscript won the Shaw Award for best paper presented at 2001 Society for Marketing Advances conference. An abridged version of this manuscript has been accepted for publication in Journal of Business Research.

End-to-End Learning of Task-Oriented Dialogs <=SEP=> In this thesis proposal, we address the limitations of conventional pipeline design of taskoriented dialog systems and propose end-toend learning solutions. We design neural network based dialog system that is able to robustly track dialog state, interface with knowledge bases, and incorporate structured query results into system responses to successfully complete task-oriented dialog. In learning such neural network based dialog systems, we propose hybrid offline training and online interactive learning methods. We introduce a multi-task learning method in pre-training the dialog agent in a supervised manner using task-oriented dialog corpora. The supervised training agent can further be improved via interacting with users and learning online from user demonstration and feedback with imitation and reinforcement learning. In addressing the sample efficiency issue with online policy learning, we further propose a method by combining the learning-from-user and learningfrom-simulation approaches to improve the online interactive learning efficiency.

Learning model-based strategies in simple environments with hierarchical q-networks <=SEP=> Recent advances in deep learning have allowed artificial agents to rival human-level performance on a wide range of complex tasks; however, the ability of these networks to learn generalizable strategies remains a pressing challenge. This critical limitation is due in part to two factors: the opaque information representation in deep neural networks and the complexity of the task environments in which they are typically deployed. Here we propose a novel Hierarchical Q-Network (HQN), motivated by theories of the hierarchical organization of the human prefrontal cortex, that attempts to identify lower dimensional patterns in the value landscape that can be exploited to construct an internal model of rules in simple environments. We draw on combinatorial games, where there exists a single optimal strategy for winning that generalizes across other features of the game, to probe the strategy generalization of the HQN and other reinforcement learning (RL) agents using variations of Wythoff’s game. Traditional RL approaches failed to reach satisfactory performance on variants of Wythoff’s Game; however, the HQN learned heuristic-like strategies that generalized across changes in board configuration. More importantly, the HQN allowed for transparent inspection of the agent’s internal model of the game following training. Our results show how a biologically inspired hierarchical learner can facilitate learning abstract rules to promote robust and flexible action policies in simplified training environments with clearly delineated optimal strategies. 1 ar X iv :1 80 1. 06 68 9v 1 [ cs .A I] 2 0 Ja n 20 18

Ending the war between Sales & Marketing. <=SEP=> Sales departments tend to believe that marketers are out of touch with what's really going on in the marketplace. Marketing people, in turn, believe the sales force is myopic--too focused on individual customer experiences, insufficiently aware of the larger market, and blind to the future. In short, each group undervalues the other's contributions. Both stumble (and organizational performance suffers) when they are out of sync. Yet few firms seem to make serious overtures toward analyzing and enhancing the relationship between these two critical functions. Curious about the misalignment between Sales and Marketing, the authors interviewed pairs of chief marketing officers and sales vice presidents to capture their perspectives. They looked in depth at the relationship between Sales and Marketing in a variety of companies in different industries. Their goal was to identify best practices that could enhance the joint performance and increase the contributions of these two functions. Among their findings: The marketing function takes different forms in different companies at different product life cycle stages. Marketing's increasing influence in each phase of an organization's growth profoundly affects its relationship with Sales. The strains between Sales and Marketing fall into two main categories: economic (a single budget is typically divided, between Sales and Marketing, and not always evenly) and cultural (the two functions attract very different types of people who achieve success by spending their time in very different ways). In this article, the authors describe the four types of relationships Sales and Marketing typically exhibit. They provide a diagnostic to help readers assess their companies' level of integration, and they offer recommendations for more closely aligning the two functions.

The Enigmatic temporal pole: a review of findings on social and emotional processing. <=SEP=> The function of the anterior-most portion of the temporal lobes, the temporal pole, is not well understood. Anatomists have long considered it part of an extended limbic system based on its location posterior to the orbital frontal cortex and lateral to the amygdala, along with its tight connectivity to limbic and paralimbic regions. Here we review the literature in both non-human primates and humans to assess the temporal pole's putative role in social and emotional processing. Reviewed findings indicate that it has some role in both social and emotional processes, including face recognition and theory of mind, that goes beyond semantic memory. We propose that the temporal pole binds complex, highly processed perceptual inputs to visceral emotional responses. Because perceptual inputs remain segregated into dorsal (auditory), medial (olfactory) and ventral (visual) streams, the integration of emotion with perception is channel specific.

Short-Text Clustering using Statistical Semantics <=SEP=> Short documents are typically represented by very sparse vectors, in the space of terms. In this case, traditional techniques for calculating text similarity results in measures which are very close to zero, since documents even the very similar ones have a very few or mostly no terms in common. In order to alleviate this limitation, the representation of short-text segments should be enriched by incorporating information about correlation between terms. In other words, if two short segments do not have any common words, but terms from the first segment appear frequently with terms from the second segment in other documents, this means that these segments are semantically related, and their similarity measure should be high. Towards achieving this goal, we employ a method for enhancing document clustering using statistical semantics. However, the problem of high computation time arises when calculating correlation between all terms. In this work, we propose the selection of a few terms, and using these terms with the Nystr\"om method to approximate the term-term correlation matrix. The selection of the terms for the Nystr\"om method is performed by randomly sampling terms with probabilities proportional to the lengths of their vectors in the document space. This allows more important terms to have more influence on the approximation of the term-term correlation matrix and accordingly achieves better accuracy.

A Novel Metal-Plate Monopole Antenna for DTV Application <=SEP=> In this paper, a novel metal-plate monopole antenna is presented for indoor digital television (DTV) signal coverage in the 468-880MHz band. The proposed antenna consists of two orthorhombic metal-plates with symmetrical bevels, a top-uploaded metal-plate and a ground plane. Simulation results show that the antenna achieves a bandwidth ranging from 468MHz to 880MHz which covers the DTV band (470-860MHZ) and shows stable radiation patterns in three coordinate planes.

QUO VADIS , DYNAMIC CAPABILITIES ? ACONTENT-ANALYTICREVIEWOFTHECURRENTSTATEOF KNOWLEDGE AND RECOMMENDATIONS FOR FUTURE RESEARCH <=SEP=> Although the dynamic capabilities perspective has become one of the most frequently used theoretical lenses in management research, critics have repeatedly voiced their frustration with this literature, particularly bemoaning the lack of empirical knowledge and the underspecification of the construct of dynamic capabilities. But research on dynamic capabilities has advanced considerably since its early years, in which most contributions to this literature were purely conceptual. A plethora of empirical studies as well as further theoretical elaborations have shed substantial light on a variety of specific, measurable factors connected to dynamic capabilities. Our article starts out by analyzing these studies to develop a meta-framework that specifies antecedents, dimensions, mechanisms, moderators, and outcomes of dynamic capabilities identified in the literature to date. This framework provides a comprehensive and systematic synthesis of the dynamic capabilities perspective that reflects the richness of the research while at the same time unifying it into a cohesive, overarching model. Such an analysis has not yet been undertaken; no comprehensive framework with this level of detail has previously been presented for dynamic capabilities. Our analysis shows where research has made the most progress and where gaps and unresolved tensions remain. Based on this analysis, we propose a forward-looking research agenda that outlines directions for future research.

Is Tom Cruise Threatened ? Using Netflix Prize Data to Examine the Long Tail of Electronic Commerce <=SEP=> We analyze a large data set from Netflix, the leading online movie rental company, to shed new light on the causes and consequences of the Long Tail effect, which suggests that on the Internet, over time, consumers will increasingly shift away from hit products and toward niche products. We examine the aggregate level demand as well as demand at the individual consumer level and we find that the consumption of both the hit and the niche movies decreased over time when the popularity of the movies is ranked in absolute terms (e.g., the top/bottom 10 titles). However, we also observe that the active product variety has increased dramatically over the study period. To separate out the demand diversification effect from the shift in consumer preferences, we propose to measure the popularity of movies in relative terms by dynamically adjusting for the current product variety (e.g., the top/bottom 1% of titles). Using this alternative definition of popularity, we find that the demand for the hits rises, while the demand for the niches still falls. We conclude that new movie titles appear much faster than consumers discover them. Finally, we find no evidence that niche titles satisfy consumer tastes better than hit titles and that a small number of heavy users are more likely to venture into niches than light users.

Improvasher: A Real-Time Mashup System for Live Musical Input <=SEP=> In this paper we present Improvasher a real-time musical accompaniment system which creates an automatic mashup to accompany live musical input. Improvasher is built around two music processing modules, the first, a performance following technique, makes beat-synchronous predictions of chroma features from a live musical input. The second, a music mashup system, determines the compatibility between beat-synchronous chromagrams from different pieces of music. Through the combination of these two techniques, a real-time predictive mashup can be generated towards a new form of automatic accompaniment for interactive musical performance.

Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection <=SEP=> This paper presents an intelligent traffic control system to pass emergency vehicles smoothly. Each individual vehicle is equipped with special radio frequency identification (RFID) tag (placed at a strategic location), which makes it impossible to remove or destroy. We use RFID reader, NSK EDK-125-TTL, and PIC16F877A system-on-chip to read the RFID tags attached to the vehicle. It counts number of vehicles that passes on a particular path during a specified duration. It also determines the network congestion, and hence the green light duration for that path. If the RFID-tag-read belongs to the stolen vehicle, then a message is sent using GSM SIM300 to the police control room. In addition, when an ambulance is approaching the junction, it will communicate to the traffic controller in the junction to turn ON the green light. This module uses ZigBee modules on CC2500 and PIC16F877A system-on-chip for wireless communications between the ambulance and traffic controller. The prototype was tested under different combinations of inputs in our wireless communication laboratory and experimental results were found as expected.

CAD-based pose estimation for random bin-picking of multiple objects using a RGB-D camera <=SEP=> In this paper, we propose a CAD-based 6-DOF pose estimation design for random bin-picking of multiple different objects using a Kinect RGB-D sensor. 3D CAD models of objects are constructed via a virtual camera, which generates a point cloud database for object recognition and pose estimation. A voxel grid filter is suggested to reduce the number of 3D point cloud of objects for reducing computing time of pose estimation. A voting-scheme method was adopted for the 6-DOF pose estimation a swell as object recognition of different type objects in the bin. Furthermore, an outlier filter is designed to filter out bad matching poses and occluded ones, so that the robot arm always picks up the upper object in the bin to increase pick up success rate. A series of experiments on a Kuka 6-axis robot revels that the proposed system works satisfactorily to pick up all random objects in the bin. The average recognition rate of three different type objects is 93.9% and the pickup success rate is 89.7%.

Linear codes over Z4+uZ4: MacWilliams identities, projections, and formally self-dual codes <=SEP=> Linear codes are considered over the ring Z 4 + uZ 4 , a non-chain extension of Z 4. Lee weights, Gray maps for these codes are defined and MacWilliams identities for the complete, symmetrized and Lee weight enumer-ators are proved. Two projections from Z 4 + uZ 4 to the rings Z 4 and F 2 + uF 2 are considered and self-dual codes over Z 4 +uZ 4 are studied in connection with these projections. Finally three constructions are given for formally self-dual codes over Z 4 + uZ 4 and their Z 4-images together with some good examples of formally self-dual Z 4-codes obtained through these constructions.

Millimeter-Wave Compact EBG Structure for Mutual Coupling Reduction Applications <=SEP=> A new millimeter-wave (MMW), electromagnetic band-gap (EBG) structure is presented. The proposed EBG structure without the use of metallic vias or vertical components is formed by etching two slots and adding two connecting bridges to a conventional uniplanar EBG unit-cell. The transmission characteristics of the proposed EBG structure are measured. Results show that the proposed EBG structure has a wide bandgap around the 60 GHz band. The size of the proposed EBG unit-cell is 78% less than a conventional uniplanar EBG, and 72% less than a uniplanar-compact EBG (UC-EBG) operating at the same frequency band. Moreover, and despite the fabrication limitations at the 60 GHz band, the proposed EBG unit-cell provides at least 12% more size reduction than any other planar EBG structures at microwave frequencies. Its enhanced performance and applicability to reduce mutual coupling in antenna arrays are then investigated. Results show a drastic decrease in the mutual coupling level. This EBG structure can find its applications in MMW wireless communication systems.

Alternative activation of macrophages <=SEP=> The classical pathway of interferon-γ-dependent activation of macrophages by T helper 1 (TH1)-type responses is a well-established feature of cellular immunity to infection with intracellular pathogens, such as Mycobacterium tuberculosis and HIV. The concept of an alternative pathway of macrophage activation by the TH2-type cytokines interleukin-4 (IL-4) and IL-13 has gained credence in the past decade, to account for a distinctive macrophage phenotype that is consistent with a different role in humoral immunity and repair. In this review, I assess the evidence in favour of alternative macrophage activation in the light of macrophage heterogeneity, and define its limits and relevance to a range of immune and inflammatory conditions.

EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks <=SEP=> Decision support systems have been utilised since 1960, providing physicians with fast and accurate means towards more accurate diagnoses and increased tolerance when handling missing or incomplete data. This paper describes the application of neural network models for classification of electroencephalogram (EEG) signals. Decision making was performed in two stages: initially, a feature extraction scheme using the wavelet transform (WT) has been applied and then a learning-based algorithm classifier performed the classification. The performance of the neural model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals

ProM: The Process Mining Toolkit <=SEP=> Nowadays, all kinds of information systems store detailed information in logs. Process mining has emerged as a way to analyze these systems based on these detailed logs. Unlike classical data mining, the focus of process mining is on processes. First, process mining allows us to extract a process model from an event log. Second, it allows us to detect discrepancies between a modeled process (as it was envisioned to be) and an event log (as it actually is). Third, it can enrich an existing model with knowledge derived from an event log. This paper presents our tool ProM, which is the world-leading tool in the area of process mining.

AnySee: Peer-to-Peer Live Streaming <=SEP=> Efficient and scalable live-streaming overlay construction has become a hot topic recently. In order to improve the performance metrics, such as startup delay, source-to-end delay, and playback continuity, most previous studies focused on intra-overlay optimization. Such approaches have drawbacks including low resource utilization, high startup and source-to-end delay, and unreasonable resource assignment in global P2P networks. Anysee is a peer-to-peer live streaming system and adopts an inter-overlay optimization scheme, in which resources can join multiple overlays, so as to (1) improve global resource utilization and distribute traffic to all physical links evenly; (2) assign resources based on their locality and delay; (3) guarantee streaming service quality by using the nearest peers, even when such peers might belong to different overlays; and (4) balance the load among the group members. We compare the performance of our design with existing approaches based on comprehensive trace driven simulations. Results show that AnySee outperforms previous schemes in resource utilization and the QoS of streaming services. AnySee has been implemented as an Internet based live streaming system, and was successfully released in the summer of 2004 in CERNET of China. Over 60,000 users enjoy massive entertainment programs, including TV programs, movies, and academic conferences. Statistics prove that this design is scalable and robust, and we believe that the wide deployment of AnySee will soon benefit many more Internet users.