ppxscal/embedding_network huggingface dataset:
Formal Grammars of Early Language
Authors: Shuly Wintner, Alon Lavie, Brian Macwhinney
Venue: Languages: From Formal to Natural: Essays Dedicated to Nissim Francez on the Occasion of His 65th Birthday (2009)

We propose to model the development of language by a series of formal grammars, accounting for the linguistic capacity of children at the very early stages of mastering language. This approach provides a testbed for evaluating theories of language acquisition, in particular with respect to the extent to which innate, language-specific mechanisms must be assumed. Specifically, we focus on a single child learning English and use the CHILDES corpus for actual performance data. We describe a set of grammars which account for this child's utterances between the ages 1;8.02 and 2;0.30. The coverage of the grammars is carefully evaluated by extracting grammatical relations from induced structures and comparing them with manually annotated labels.
Knowledge-Based Recommendation: Technologies and Experiences from Projects
Authors: Alexander Felfernig, Klaus Isak, Christian Russ
Venue: Proceeding of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy (2006)

Recommender applications are used to support intuitive and personalized product and service selection processes for customers and sales representatives. In this paper we present a knowledge-based recommender environment which assists users in the identification of appropriate solutions from complex product and service assortments. We show how intelligent query relaxation, personalization and intuitive knowledge acquisition support the implementation of customer-friendly sales dialogues and report experiences gained in industrial recommender projects.
Queuing model based on scheduling strategies affect local network services
Authors: Zakaria Suliman Zubi, Ismaeel H. Al-Dubar
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

This paper is a comprehensive study of some queuing scheduling strategies that operates on a local network. The study addresses the most convenient strategies used in operating systems. It indicates a new queue model based on some scheduling strategies that affect a local network services. The model was propose by us and entitled as Queuing Model Based on Scheduling Strategies in Local Network Services (QMBSSLNS). The aim of this model is to accelerate the arriving jobs in a local queue. Jobs sent through a local network suffers always from delays caused by several reasons, one of these reasons we believe is the scheduling strategies used. We are going to investigate the most common strategies used in queuing jobs on local network and some interesting properties including the arrival theorem, exact aggregation and insensitivity. This will motivate us to represent a particular system features such as batch arrivals and departures and finite capacity queues in a visual mode. Some scheduling strategies such as first come first serve (FCFS), last come first serve (LCFS), overall throughput, resource path, average queue length and average waiting times will be indicating as well. Average Performance measures will be evaluating based on Generic Cell Rate Algorithm (GCRA) algorithm and Markov process. A framework form will be implemented to improve the model, this framework will simulate the job priority, the queue length distribute, average responding, job state, resource path and throughput. Performance evaluation often involves the use of queuing network models. QMBSSLNS has been also proposing to analyze and schedule queuing job on a local network structure in a comfortable and easy-to-use environment.
Using Co-Lab to build System Dynamics models: Students' actions and on-line tutorial advice
Authors: Crescencio Bravo, Wouter R. van Joolingen, Ton de Jong
Venue: Computers Education (2009)

Modeling offers a promising form of constructivist learning for students. By making and executing models of dynamic systems in a computer environment, students are stimulated to learn about the specific domain that is modeled as well as about the process of modeling in general. However, learning by modeling also leads to characteristic student mistakes, based on a combination of faulty domain knowledge and insufficient modeling skills. In this article, we describe a method of generating advice to students during their modeling process. The on-line advice system was informed by our observations of a teacher who gave advice via a textual communication tool to students building models with a System Dynamics model editor. The first version of the on-line advice system was evaluated in two ways: first, three teachers evaluated the advice the system generated for students' final solutions; second, we analyzed the advice the system provided as it was used by a sample of students who were building a physics model. These evaluations showed that the overall approach, including matching a student solution to a family of reference solutions together with the other mechanisms of the advice system, is valid. However, they also highlighted the difficulty of building 'intelligent' support to help students to improve their models and gain modeling expertise. The article concludes with a discussion of our current efforts to improve the advice system based on the lessons learnt, which suggest extension of the range of solution representations and of the operations of the advice method.
Molding technology for improvement on dimensional accuracy in micro metal injection molding
Authors: Kenji Okubo, Shigeo Tanaka, Hiroshi Ito
Venue: Microsystem Technologies (2009)

This study aims to develop processing techniques to improve dimensional accuracy of fine metal parts produced by metal injection molding. Micro dumbbell specimens were molded by the micro injection molding machine, which monitor the cavity pressure during injection molding. Among various injection molding conditions, the influences of injection volume on the cavity pressure, and dimension of both green compacts and sintered parts were investigated. For dimension control, the importance of monitoring cavity pressure at each shot was verified.
A New Message Recognition Protocol with Self-recoverability for Ad Hoc Pervasive Networks
Authors: Ian Goldberg, Atefeh Mashatan, Douglas R. Stinson
Venue: Proceedings of the 7th International Conference on Applied Cryptography and Network Security (2009)

We examine the problem of message recognition by reviewing the definitions and the security model in the literature. In particular, we examine the Jane Doe protocol, which was proposed by Lucks et al., more closely and note its inability to recover in case of a certain adversarial disruption. Our paper saves this well-studied protocol from its unrecoverable state when such adversarial disruption occurs. We propose a new message recognition protocol, which is based on the Jane Doe protocol, and incorporate the resynchronization technique within the protocol itself. That is, without having to provide a separate resynchronization procedure, we overcome the recoverability problem of the Jane Doe protocol. Moreover, we enumerate all possible attacks against the new protocol and show that none of the attacks can occur. We further prove the security of the new protocol and its ability to self-recover once the disruption has stopped.
Building a Local Hybrid Map from Sensor Data Fusion
Authors: Zoe Falomir, Juan Carlos Peris, M. Teresa Escrig
Venue: Proceeding of the 2006 conference on Artificial Intelligence Research and Development (2006)

A local hybrid (qualitative + quantitative) map is developed in this paper. The concave and convex corners of the environment and a distance perception pattern, provided by a fuzzy-qualitative sensor fusion process, are included in the map. This map is the base for solving the robot localization, path planning and navigation problem by using qualitative reasoning models. According to the model described in this paper, the size of the representation of the world is given only by the complexity of the environment.
Use of abstract categories in data clustering algorithms
Authors: William L. Hafner
Venue:  (2008)

This research was motivated to address the problem of solving the difficulty of distinguishing data anomalies and patterns of information within the enormous volume of data generated in audit logs, and to approach or achieve real-time analysis of audit log data in applications and operating systems.The research question was if the employment of data clustering techniques that reduce the volume of audit data, would enable data analysts to approach or achieve real time intrusion detection due to the reduction in audit data. The approach used in this study was to use data mining clustering techniques to develop abstract categories from the audit log data, that can be used with the pattern matching techniques already in use or under experimental development. The results of this study clearly demonstrate that the suffix tree algorithm is the fastest in terms of real time processing speed, the most accurate, in terms of producing the fewest false positives, and the best at reducing audit data file size.The objectives of this research were to identify and test a group of algorithms for speed and accuracy in searching audit log data, as well as to determine their ability to distinguish anomalies and patterns. While researchers have developed a number of different algorithms for intrusion detection and pattern matching in databases, the approaches used in these algorithms have not been previously tested to determine their comparability in terms of speed of processing and the accuracy of the results.The methodology that was used in this study involves the assessment of data mining clustering techniques to develop abstract categories from the audit log data that can be used with pattern matching techniques already in existence. The methodology established criteria for the selection of cluster algorithms, resulting in the selection of K-Means, Single Pass, Fuzzy c-means, and Suffix Tree clustering algorithms for analysis. The test procedure was to measure the execution time of the algorithms in clustering datasets. The number of the false positives in the answer for the query is compared against the execution time. The findings determined which of the algorithms produced the greatest accuracy in the shortest amount of time, which indicates which approach is most effective and advanced. A superiority of the suffix tree algorithm was found, followed by the k-means, single pass, and fuzzy c-means.
Personalized web browsing experience
Authors: Barbara Rita Barricelli, Marco Padula, Paolo Luigi Scala
Venue: Proceedings of the 20th ACM conference on Hypertext and hypermedia (2009)

In this paper we propose a Web-based software system that allows the realization of a highly personalized Web browsing experience. The system is based on two relevant elements: the Intelligent Agent and the pCard document. The Intelligent Agent analyzes the Web sites visited by the users collecting information useful to her/his characterization and crosses such information with the personal preferences expressed by the user her/himself. The user profile is stored in a document written in pCard format that we have defined as an evolution of the vCard format.
Optimal stochastic routing in low duty-cycled wireless sensor networks
Authors: Dongsook Kim, Mingyan Liu
Venue: Proceedings of the 4th Annual International Conference on Wireless Internet (2008)

We study a routing problem in wireless sensor networks where sensors are duty-cycled. When sensors alternate between on and off modes, delay encountered in packet delivery due to loss in connectivity can become a critical problem, and how to achieve delay-optimality is non-trivial. For instance, when sensors' sleep schedules are uncoordinated, it is not immediately clear whether a sensor with data to transmit should wait for a particular neighbor (who may be on a short route) to become available/active before transmission, or simply transmit to an available/active neighbor to avoid waiting. To obtain some insight into this problem, in this paper we formulate the above problem as an optimal stochastic routing problem, where the randomness in the system comes from random duty cycling, as well as the uncertainty in packet transmission due to channel variations. Similar framework has been used in prior work which results in optimal routing algorithms that are sample-path dependent, also referred to as opportunistic in some cases. We show such algorithms are no longer optimal when duty cycling is introduced. We first develop and analyze an optimal centralized stochastic routing algorithm for randomly duty-cycled wireless sensor network, and then simplify the algorithm when local sleep/wake states of neighbors are available. We further develop a distributed algorithm utilizing local sleep/wake states of neighbors which performs better than some existing distributed algorithms such as ExOR.
Rhetorical end-user programming
Authors: Christopher Bogart
Venue: Proceedings of the 2008 IEEE Symposium on Visual Languages and Human-Centric Computing (2008)

The study of rhetoric has long been a route to empowerment for people, by helping them share their ideas and inspire cooperation from others. Is it possible for designers of programming environments to piggyback on that success and allow people without computer science training to leverage their human communication skills more fully when programming? I hope with this research to take a step towards answering that question.
Multilingual Indexing Based on Ontologies
Authors: Catherine Roussey, Sylvie Calabretto, Farah Harrathi, Mohamed Gammoudi
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

This article deals with multilingual document indexing. We propose an indexing method based on several stages. First of all the most important terms of the document are extracted using general characteristics of languages and statistical methods. Thus, term extraction stages can be applied to any document whatever the document language is. Secondly, our indexing method uses a multilingual ontology in order to find the most relevant concept representing the document content. Our method can be applied to a multilingual corpus containing document written in different languages. This indexing procedure is part of a Multilingual Document System untitled SyDoM, which manages XML documents.
Automatic quality assessment of content created collaboratively by web communities: a case study of wikipedia
Authors: Daniel Hasan Dalip, Marcos André Gonçalves, Marco Cristo, Pável Calado
Venue: Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009)

The old dream of a universal repository containing all the human knowledge and culture is becoming possible through the Internet and the Web. Moreover, this is happening with the direct collaborative, participation of people. Wikipedia is a great example. It is an enormous repository of information with free access and edition, created by the community in a collaborative manner. However, this large amount of information, made available democratically and virtually without any control, raises questions about its relative quality. In this work we explore a significant number of quality indicators, some of them proposed by us and used here for the first time, and study their capability to assess the quality of Wikipedia articles. Furthermore, we explore machine learning techniques to combine these quality indicators into one single assessment judgment. Through experiments, we show that the most important quality indicators are the easiest ones to extract, namely, textual features related to length, structure and style. We were also able to determine which indicators did not contribute significantly to the quality assessment. These were, coincidentally, the most complex features, such as those based on link analysis. Finally, we compare our combination method with state-of-the-art solution and show significant improvements in terms of effective quality prediction.
Localized minimum-latency broadcasting in multi-radio multi-rate wireless mesh networks
Authors: Junaid Qadir, Chun Tung Chou, Archan Misra, JooGhee Lim
Venue: Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (2008)

We address the problem of minimizing the worst-case broadcast delay in “multi-radio multi-channel multi-rate wireless mesh networks” (MR2-MC WMN) in a distributed and localized fashion. Efficient broadcasting in such networks is especially challenging due to the desirability of exploiting the “wireless broadcast advantage” (WBA), the interface-diversity, the channel-diversity and the rate-diversity offered by these networks. We propose a framework that calculates a set of forwarding nodes and transmission rate at these forwarding nodes irrespective of the broadcast source. Thereafter, a forwarding tree is constructed taking into consideration the source of broadcast. Our broadcasting algorithms are distributed and utilize locally available information. We present a detailed performance evaluation of our distributed and localized algorithm and demonstrate that our algorithm can greatly improve broadcast performance by exploiting the rate, interface and channel diversity of MR2-MC WMNs and match the performance of centralized algorithms proposed in literature while utilizing only limited two-hop neighborhood information.
Guest Editor's Introduction: Software Measurement
Authors: Stan Rifkin
Venue: IEEE Software (2009)

Two articles address aspects of software measurement. One finds some unifying concepts among the alternative ways to measure function points and presents a common model. The other proposes rules of thumb to employ when specifying and buying software; it therefore also offers advice to bidders.
Cooperative caching for homogeneous wireless sensor networks
Authors: T. P. Sharma, R. C. Joshi, Manoj Misra
Venue: International Journal of Communication Networks and Distributed Systems (2009)

In many scenarios, data generated at sensor nodes in wireless sensor networks must be stored temporarily in a network for subsequent use. An individual sensor node's storage capacity is too small to hold it and may soon overflow. Therefore, we propose an effective caching strategy based on cooperation among various sensor nodes to form a larger cumulative cache. As part of the complete caching scheme, a novel token-based cache admission control scheme is devised, which ensures proximity of cached data closer to sink(s). A cache discovery mechanism to efficiently fetch a copy of data items is developed. A single-copy cache coherency scheme is developed to avoid sink from fetching stale data and also avoids undesired data replication. Further, a least utility based item-replacement policy is developed to evict data items with the least utility in case of desired replacement. Simulation results show significant improvements in network energy savings and data availability when cooperative caching is used.
Distributed and Incremental Clustering Based on Weighted Affinity Propagation
Authors: Xiangliang Zhang, Cyril Furtlehner, Michèle Sebag
Venue: Proceeding of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium (2008)

A new clustering algorithm Affinity Propagation (AP) is hindered by its quadratic complexity. The Weighted Affinity Propagation (WAP) proposed in this paper is used to eliminate this limitation, support two scalable algorithms. Distributed AP clustering handles large datasets by merging the exemplars learned from subsets. Incremental AP extends AP to online clustering of data streams. The paper validates all proposed algorithms on benchmark and on real-world datasets. Experimental results show that the proposed approaches offer a good trade-off between computational effort and performance.
Searching Protein 3-D Structures in Linear Time
Authors: Tetsuo Shibuya
Venue: Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology (2009)

Finding similar structures from 3-D structure databases of proteins is becoming more and more important issue in the post-genomic molecular biology. To compare 3-D structures of two molecules, biologists mostly use the RMSD (root mean square deviation) as the similarity measure. We propose new theoretically and practically fast algorithms for the fundamental problem of finding all the substructures of structures in a structure database of chain molecules (such as proteins), whose RMSDs to the query are within a given constant threshold. We first propose a breakthrough linear-expected-time algorithm for the problem, while the previous best-known time complexity was O(Nlogm), where N is the database size and m is the query size. For the expected time analysis, we propose to use the random-walk model (or the ideal chain model) as the model of average protein structures. We furthermore propose a series of preprocessing algorithms that enable faster queries. We checked the performance of our linear-expected-time algorithm through computational experiments over the whole PDB database. According to the experiments, our algorithm is 3.6 to 28 times faster than previously known algorithms for ordinary queries. Moreover, the experimental results support the validity of our theoretical analyses.
Assessing the costs of sampling methods in active learning for annotation
Authors: Robbie Haertel, Eric Ringger, Kevin Seppi, James Carroll, Peter McClanahan
Venue: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers (2008)

Traditional Active Learning (AL) techniques assume that the annotation of each datum costs the same. This is not the case when annotating sequences; some sequences will take longer than others. We show that the AL technique which performs best depends on how cost is measured. Applying an hourly cost model based on the results of an annotation user study, we approximate the amount of time necessary to annotate a given sentence. This model allows us to evaluate the effectiveness of AL sampling methods in terms of time spent in annotation. We acheive a 77% reduction in hours from a random baseline to achieve 96.5% tag accuracy on the Penn Treebank. More significantly, we make the case for measuring cost in assessing AL methods.
WiSeKit: A Distributed Middleware to Support Application-Level Adaptation in Sensor Networks
Authors: Amirhosein Taherkordi, Quan Le-Trung, Romain Rouvoy, Frank Eliassen
Venue: Proceedings of the 9th IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems (2009)

Applications for Wireless Sensor Networks (WSNs) are being spread to areas in which the contextual parameters modeling the environment are changing over the application lifespan. Whereas software adaptation has been identified as an effective approach for addressing context-aware applications, the existing work on WSNs fails to support context-awareness and mostly focuses on developing techniques to reprogram the whole sensor node rather than reconfiguring a particular portion of the sensor application software. Therefore, enabling adaptivity in the higher layers of a WSN architecture such as the middleware and application layers, beside the consideration in the lower layers, becomes of high importance. In this paper, we propose a distributed component-based middleware approach, named WiSeKit, to enable adaptation and reconfiguration of WSN applications. In particular, this proposal aims at providing an abstraction to facilitate development of adaptive WSN applications. As resource availability is the main concern of WSNs, the preliminary evaluation shows that our middleware approach promises a lightweight, fine-grained and communication-efficient model of application adaptation with a very limited memory and energy overhead.