ppxscal/embedding_network huggingface dataset:
A Multi-Standard SDR Base Band Platform
Authors: Zhigang Luo, Wei Li, Yan Zhang, Wei Guan
Venue: Proceedings of the 2003 International Conference on Computer Networks and Mobile Computing (2003)

Software Defined Radio (SDR) is a rapidly evolving technology that will bring enormous benefits to the providers and consumers of wireless services. A multi-standard SDR base band platform for base station is proposed in the paper. The platform consists of reconfigurable and reprogrammable hardware platform which provide different standards with a common platform, the SDR software framework which control and manage the whole systems, and the protocol processing software modules which is built on reusable protocol libraries. Based on the platform, the multi-stand base station is easily developed.
From grapevine to trader: the evolution of distributed directory technology
Authors: Roger Y. M. Cheung
Venue: Proceedings of the 1992 conference of the Centre for Advanced Studies on Collaborative research - Volume 2 (1992)

Distributed directory is an essential component in a distributed processing environment that involves multiple systems connected via a LAN (local area network) and WAN (wide area network). This paper reviews the evolution of distributed directory technology from simple name-to-address mapping to sophisticated import request-to-export service mapping by examining four different kinds of directory systems: Grapevine, Global name service, ISO Directory, and Trader. The characteristics of these directory systems with respect to their directory functions are explained. The paper concludes with a discussion on the kind of directory functions most needed in a distributed processing environment.The positions expressed in this paper are solely the views of the author and are not a reflection of IBM Canada Ltd.'s position.
Efficient resource management in multimedia streaming networks
Authors: Zhi-Li Zhang, David Hung-Chang Du
Venue:  (2003)

Since multimedia applications are among the most popular applications on the Internet, it is critical to efficiently manage the resources in multimedia streaming systems. In this dissertation, we first focus on the quality assurance issue in video streaming across the best-effort Internet, and then investigate efficient resource management schemes in both wide-area networks and broadband cable networks. To address the issue of random quality degradation in streaming across the best-effort Internet, we design a practical technique, named staggered two-flow video streaming. We deliver a stored video through two flows in a staggered fashion via a VPN pipe from a central server to a proxy server. One flow containing the essential portion of the video is delivered using a novel controlled TCP (cTCP), and the other flow containing the enhanced portion of the video is transmitted using a rate-controlled RTP/UDP (rUDP). We design several application-aware flow control and adaptation approaches to control bandwidth sharing and interactions among flows. Our experiments and simulations have demonstrated the efficacy of the proposed technique in protecting essential data and significantly reducing the numbers of retransmitted/lost packets and the sizes of video prefixes required. Although Broadband Cable Networks (BCNs) is the most important form of broadband service in the US, it is still lack of effective approaches to exploit their unique characteristics for efficiently providing Video-On-Demand (VOD) service. We propose an optimal full-sharing scheduling approach to address this issue. Through analysis and simulation, we have shown that our approach has remarkable advantages in minimizing the bandwidth consumption of VOD service on BCNs. Furthermore, we design two adaptation algorithms which not only keep bandwidth consumption minimal but also significantly reduce mean service delays. We further analyze the expected bandwidth and the blocking probability of videos, and design a video assignment mechanism to maximize system profits. We also present a formal analysis of the upstream scheduling of BCNs, identify important relations among scheduling parameters, and formulate an optimal scheduling problem. We further propose several optimized scheduling algorithms to capture the unique input characteristics in the upstream scheduling and reduce request contentions.
Expanding domain-specific lexicons by term categorization
Authors: Henri Avancini, Alberto Lavelli, Bernardo Magnini, Fabrizio Sebastiani, Roberto Zanoli
Venue: Proceedings of the 2003 ACM symposium on Applied computing (2003)

We discuss an approach to the automatic expansion of domain-specific lexicons by means of term categorization, a novel task employing techniques from information retrieval (IR) and machine learning (ML). Specifically, we view the expansion of such lexicons as a process of learning previously unknown associations between terms and domains. The process generates, for each ci in a set C = {c1,..., cm} of domains, a lexicon Li1, boostrapping from an initial lexicon Li0 and a set of documents &theta; given as input. The method is inspired by text categorization (TC), the discipline concerned with labelling natural language texts with labels from a predefined set of domains, or categories. However, while TC deals with documents represented as vectors in a space of terms, we formulate the task of term categorization as one in which terms are (dually) represented as vectors in a space of documents, and in which terms (instead of documents) are labelled with domains.
Automated Assistants for Analyzing Team Behaviors
Authors: Ranjit Nair, Milind Tambe, Stacy Marsella, Taylor Raines
Venue: Autonomous Agents and Multi-Agent Systems (2004)

Multi-agent teamwork is critical in a large number of agent applications, including training, education, virtual enterprises and collective robotics. The complex interactions of agents in a team as well as with other agents make it extremely difficult for human developers to understand and analyze agent-team behavior. It has thus become increasingly important to develop tools that can help humans analyze, evaluate, and understand team behaviors. However, the problem of automated team analysis is largely unaddressed in previous work. In this article, we identify several key constraints faced by team analysts. Most fundamentally, multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. In addition, effective ways of presenting the analysis to humans is critical and the presentation techniques depend on the model being presented. Finally, analysis should be independent of underlying team architecture and implementation.We also demonstrate an approach to addressing these constraints by building an automated team analyst called ISAAC for post-hoc, off-line agent-team analysis. ISAAC acquires multiple, heterogeneous team models via machine learning over teams' external behavior traces, where the specific learning techniques are tailored to the particular model learned. Additionally, ISAAC employs multiple presentation techniques that can aid human understanding of the analyses. ISAAC also provides feedback on team improvement in two novel ways: (i) It supports principled &ldquo;what-if&rdquo; reasoning about possible agent improvements; (ii) It allows the user to compare different teams based on their patterns of interactions. This paper presents ISAAC's general conceptual framework, motivating its design, as well as its concrete application in two domains: (i) RoboCup Soccer; (ii) software agent teams participating in a simulated evacuation scenario. In the RoboCup domain, ISAAC was used prior to and during the RoboCup '99 tournament, and was awarded the RoboCup Scientific Challenge Award. In the evacuation domain, ISAAC was used to analyze patterns of message exchanges among software agents, illustrating the generality of ISAAC's techniques. We present detailed algorithms and experimental results from ISAAC's application.
Link layer-based TCP optimisation for disconnecting networks
Authors: James Scott, Glenford Mapp
Venue: ACM SIGCOMM Computer Communication Review (2003)

This paper discusses a link layer approach to improving TCP performance in the face of periodic network disconnections. Network disconnections are encountered in many scenarios, including being out-of-range in a wireless network, during network handoff, and also in the case of Networked Surfaces, a novel LAN technology which provides the motivation for this work.A "smart link layer" employing repetition of selected packets at reconnection time is shown to improve TCP's utilisation of a disconnecting network to nearly 100%. This solution is also demonstrated in the context of a Networked Surface prototype, improving TCP performance for both bulk transfers and interactive traffic.The smart link layer solution is lightweight, requiring little processing and buffering only one packet per TCP connection. It is therefore easily retro-fitted to existing TCP-capable devices, without modifying the internal operation of those devices.
Cost-effective machine learning strategies for shallow parsing
Authors: Claire Cardie
Venue:  (2003)

For natural language applications to become widespread, they must be both practical and economical. Practicality demands that systems are robust and efficient enough to handle realistic input. Economy demands that systems are inexpensive to construct and maintain. This dissertation explores syntactic analysis of natural language from the perspective of practicality and economy. We adopt a corpus-based shallow parsing approach to syntactic analysis. Shallow parsing addresses practicality by avoiding difficult attachment decisions and by employing simple, efficient algorithms. Corpus-based language learning addresses economy by applying machine learning techniques to develop language processing components. In particular, we contributed a new memory-based algorithm for learning shallow syntax, called rote sequence learning. Our experiments demonstrate that rote sequence learning achieves comparable performance to other, more complex, shallow parsing methods. Moreover, rote sequence learning possesses a number of desirable properties, including simplicity, efficiency, and portability. To support rote sequence learning, we developed algorithms for pruning bad rules from the grammar, for incorporating arbitrary additional information into the grammar using statistical models, and for determining the best parse among all possible parses.Rote sequence learning addresses the practicality requirement for shallow parsing. To address economy, we investigated learning strategies that allow the machine learner to manipulate its training setting. The goal of these strategies is to reduce the cost of training by reducing the number of examples needed and/or by reducing the cost of assembling the examples. In particular, active learning allows the learner to select training examples and ask the human teacher for answers, and weakly supervised learning allows the learner to guess at answers to some of the examples on its own. In experiments with these two strategies, we discovered interesting behaviors of each. Finally, we contributed a new learning strategy, cooperative learning, that combines the best aspects of active and weakly supervised learning.
Electronic commerce software laboratory
Authors: Alfred C. Weaver
Venue: Proceedings of the 35th SIGCSE technical symposium on Computer science education (2004)

With NSF support we developed a hands-on laboratory to augment our CS453 Electronic Commerce Technologies course. We conducted weekly lab sessions that covered HTML, CSS, JavaScript, Perl, CGI, SQL, ASP, and Flash programming. Each topic was covered over a period of one to three weeks and each topic was supported by 10 to 26 individual programming exercises. Four teaching assistants provided real-time support during the laboratory period. Answers to all exercises were accessible on-line so that no student would be needlessly delayed by any particular problem. In this paper we discuss the laboratory's design goals, topical content, and the students' performance, reactions, and suggestions for improvement.
Predictor-directed data prefetching for pointer-based applications
Authors: Suleyman Sair
Venue:  (2003)

CPU speeds double approximately every eighteen months, while main memory speeds double only about every ten years. These diverging rates imply an impending &ldquo;Memory Wall&rdquo;, in which memory accesses dominate program performance. An effective way to reduce the observed latency of memory accesses is data prefetching. Data prefetching involves predicting which data the processor will use in the near future and then bring that data into storage locations closer to the execution engine (e.g., caches, dedicated prefetch buffers etc.). Meanwhile, applications are constantly becoming more complex and demanding. This is reflected in programs through the use of various data structures and constructs provided by the programming language. While caches are extremely successful in handling accesses with good locality, the remaining accesses result in costly cache misses. For these reasons, we performed a study that maps the miss stream to several fundamental access types inherent to programming constructs. The results of this study show that pointer-based applications with accesses to linked data structures present the biggest challenge to the memory subsystem. Consequently, they have the biggest potential for speedup when their adverse effects on program performance are removed by data prefetching. For these reasons, in this thesis, we focus on data prefetching techniques targeting pointer-based applications which have irregular access patterns. With a better understanding of the kinds of accesses that cause cache misses, we have developed three prefetching techniques that can handle all these cases. The first technique enhances traditional stream buffer designs by following an address prediction stream instead of a fixed stride as originally proposed. This improves the stream buffers ability to target pointer-based programs. We also explore utilizing a hardware pointer cache to predict the values of pointer loads in order to break down long dependence chains caused by recurrent pointer accesses. This allows overlapping the execution of dependent instructions with the actual memory access, providing significant benefits. Another technique, Speculative Precomputation, attempts to prefetch data ahead of its use, by running a shortened version of the program on a spare multi-threaded hardware context. The final scheme we propose builds upon speculative Precomputation and uses the pointer cache for the pointer loads in speculative threads. Furthermore, we add control flow instructions to the speculative threads to guide them down the correct traversal path when multiple next transitions are possible. Our results show considerable benefits when these techniques are utilized together, complementing each other.
Using register-diversified corpora for general language studies
Authors: Douglas Biber
Venue: Computational Linguistics (1993)

The present study summarizes corpus-based research on linguistic characteristics from several different structural levels, in English as well as other languages, showing that register variation is inherent in natural language. It further argues that, due to the importance and systematicity of the linguistic differences among registers, diversified corpora representing a broad range of register variation are required as the basis for general language studies.First, the extent of cross-register differences are illustrated from consideration of individual grammatical and lexical features; these register differences are also important for probabilistic part-of-speech taggers and syntactic parsers, because the probabilities associated with grammatically ambiguous forms are often markedly different across registers. Then, corpus-based multidimensional analyses of English are summarized, showing that linguistic features from several structural levels function together as underlying dimensions of variation, with each dimension defining a different set of linguistic relations among registers. Finally, the paper discusses how such analyses, based on register-diversified corpora, can be used to address two current issues in computational linguistics: the automatic classification of texts into register categories and cross-linguistic comparisons of register variation.
Generating spreadsheet-like tools from strong attribute grammars
Authors: João Saraiva, Doaitse Swierstra
Venue: Proceedings of the 2nd international conference on Generative programming and component engineering (2003)

This paper presents techniques for the formal specification and efficient incremental implementation of spreadsheet-like tools. The spreadsheets are specified by strong attribute grammars. In this style of attribute grammar programming every single inductive computation is expressed within the attribute grammar formalism. Well-known attribute grammar techniques are used to reason about such grammars. For example, ordered scheduling algorithms can be used to statically guarantee termination of the attribute grammars and to derive efficient implementations. A strong attribute grammar for a spreadsheet is defined and the first incremental results are presented.
Ontology-Based Medical Image Annotation with Description Logics
Authors: Bo Hu, Srinandan Dasmahapatra, Paul Lewis, Nigel Shadbolt
Venue: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (2003)

The interpretation of medical evidence is normally presented in terms of a controlled, but diversely expressed specialist vocabulary and natural language phrases. Such informally expressed data require human intervention to ascertain its relevance in any specific case. In order to facilitate machine-based reasoning about the evidence gathered, additional interpretive semantics must be attached to the data; a shift from amerely data-intensive approach to a semantics-rich model of evidence. In this paper, we present a system to formally annotate medical images captured to aid the diagnosis and management of breast cancer, that enables a series of semantics-based operations to be performed. Our approach is grounded upon an imaging ontology specifying the domain knowledge and a Description Logic (DL) taxonomic inferential engine responsible for semantics-based reasoning and image retrieval.
High performance distributed real-time commit protocol
Authors: Biao Qin, Yunsheng Liu
Venue: Journal of Systems and Software (2003)

In a distributed real-time database system, the only way to ensure transaction atomicity is to investigate and develop a real-time atomic commit protocol. This paper presents the model of distributed real-time transaction and analyses all kinds of dependencies because of data conflicts access. Based on this model, we propose an optimistic real-time commit protocol, double space commit (2SC), which is specifically designed for the high-performance distributed real-time transaction. 2SC allows a non-healthy transaction to lend its held data to the transactions in its commit dependency set. When the prepared transaction aborts, only the transactions in its abort dependency set are aborted while the transactions in its commit dependency set will execute as normal. The two properties of 2SC can reduce the data inaccessibility and the priority inversion that is inherent in distributed real-time commit processing. Extensive simulation experiments have been performed to compare the performance of the 2SC with that of other protocols such as the base protocol, the permits reading of modified prepared-data for timeliness [IEEE Transactions on Parallel and Distributed Systems 11 (2) (2000) 160-181] and the deadline-driven conflict resolution [The Computer Journal 42 (8) (1999) 674-692]. The simulation results show that 2SC has the best performance. Furthermore, it is easy to incorporate in current concurrency control protocols.
Investigating the Effects of Store-Brand Introduction on Retailer Demand and Pricing Behavior
Authors: Pradeep K. Chintagunta, André Bonfrer, Inseong Song
Venue: Management Science (2002)

Researchers have recently been interested in studying the drivers of store-brand success as well as factors that motivate retailers to introduce store brands. In this paper, we study the effects of the introduction of a store-brand into a particular product category. Specifically, we are interested in the effect of store-brand introduction on the demand as well as on the supply side. On the demand side, we investigate the changes in preferences for the national brands and price elasticities in the category. On the supply side, we study the effects of the new entrant on the interactions between the national brand manufacturers and the retailer introducing the store brand, including how these interactions influence the retailer's pricing behavior. In doing so, we are also able to test whether the observed data are consistent with some of the commonly used assumptions regarding retailer pricing behavior. For the demand specification we use a random coefficients logit model that allows for consumer heterogeneity. The model parameters are estimated using aggregate data while explicitly accounting for endogeneity in retail prices.Our empirical results obtained from the oats product category based on store-level data from a multistore retail chain indicate that the store-brand introduction generates notable changes within the category. The store-brand introduction coincides with an increase in the retailer's margins for the national brand. We find that the preferences for the national brand are relatively unaffected by the introduction of the store-brand. While consumers are, in general, more price sensitive (in terms of elasticities) than they were prior to store-brand introduction, a statistical test of the differences inmean price elasticities across stores and between the two regimes fails to reject the hypothesis of no change in these elasticities. Elasticities in specific stores however, do increase after the store brand is introduced. We also find that there is considerable heterogeneity in the preferences for the store-brand. On the supply side, we test several forms of manufacturer-retailer interactions to identify retailer pricing behavior most consistent with the data. Our results indicate that the data reject several, commonly imposed, forms of interactions. In examining the nature of manufacturer interactions with the retailer, we find that the manufacturer of the national brand appears to take a softer stance in its interactions with the retailer subsequent to store-brand entry. This finding is consistent with academic research and with articles in the popular press which suggest that the store brand enhances the retailer's bargaining ability vis-Ã -vis the manufacturers of the national brands. We also provide results from a second product category frozen pasta) that are largely consistent with those found in the oats category.
On large intersecting subfamilies of uniform setfamilies
Authors: Richard A. Duke, Paul Erdős, Vojtěch Rödl
Venue: Random Structures Algorithms (2003)

In an earlier work [R. A. Duke and V. Rödl, The Erdös-Ko-Rado theorem for small families, J Combin Theory Ser A 65(2) (1994), 246-251] it was shown that for t a fixed positive integer and κ a real constant, 0 < κ < 1/2, if n is sufficiently large each family A of ⌊κn⌋-element subsets of [n] of size N (linear in n) contains a t-intersecting subfamily of size at least (1 - o(1)) κ N. Here we consider the case when t, the intersection size, is no longer bounded, specifically t = ⌊τn⌋ for 0 < τ < κ. We show that for sufficiently large n and N each family of this type contains an r-wise t-intersecting subfamily of size at least N1-γ, and that, apart for the size of γ this result is the best possible.