ppxscal/embedding_network huggingface dataset:
Detecting Triangle Inequality Violations in Internet Coordinate Systems by Supervised Learning
Authors: Yongjun Liao, Mohamed Ali Kaafar, Bamba Gueye, François Cantin, Pierre Geurts, Guy Leduc
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

Internet Coordinates Systems (ICS) are used to predict Internet distances with limited measurements. However the precision of an ICS is degraded by the presence of Triangle Inequality Violations (TIVs). Simple methods have been proposed to detect TIVs, based e.g. on the empirical observation that a TIV is more likely when the distance is underestimated by the coordinates. In this paper, we apply supervised machine learning techniques to try and derive more powerful criteria to detect TIVs. We first show that (ensembles of) Decision Trees (DTs) learnt on our datasets are very good models for this problem. Moreover, our approach brings out a discriminative variable (called OREE), which combines the classical estimation error with the variance of the estimated distance. This variable alone is as good as an ensemble of DTs, and provides a much simpler criterion. If every node of the ICS sorts its neighbours according to OREE, we show that cutting these lists after a given number of neighbours, or when OREE crosses a given threshold value, achieves very good performance to detect TIVs.
TQL: A query language to support traceability
Authors: Jonathan I. Maletic, Michael L. Collard
Venue: Proceedings of the 2009 ICSE Workshop on Traceability in Emerging Forms of Software Engineering (2009)

A query language for traceability is proposed and presented. The language, TQL, is based in XML and supports queries across multiple artifacts and multiple traceability link types. A number of primitives are defined to allow complex queries to be constructed and executed. Example queries are presented in the context of traceability questions. The technical details of the language and issues of implementation are discussed.
Online state diagnosis of transformer windings based on time-frequency analysis
Authors: Yuying Shao, Zhushi Rao, Zhijian Jin
Venue: WSEAS Transactions on Circuits and Systems (2009)

The power transformers are the key equipments in the power system. To keep the stable operation of the transformers, it's necessary to get their state information on a real-time basis. Therefore, the efficient online monitoring system is developed which obtain the state of the transformers by the accelerometers attached on the tank. The locations of these accelerometers can be determined by the method of varying current with constant frequency. The vibration intensity change is used to initially detect the windings' state by online monitoring the vibration signal of the shocks. Then the time-frequency analysis further processes these signals in order to detect the fault information contained. The short-circuit experiments have been done on the large power transformer of 50000KVA, which finds that the time-frequency analysis can be used to more precisely detect the fault characteristics of windings. Compared with traditional off line short circuit reactance method, the time-frequency analysis is more effective.
A fair scheduling discipline for Ethernet passive optical networks
Authors: Flávio de Melo Pereira, Nelson L. S. da Fonseca, Dalton S. Arantes
Venue: Computer Networks: The International Journal of Computer and Telecommunications Networking (2009)

We propose a novel policy for scheduling upstream flows in Ethernet passive optical networks. This policy, called proportional sharing with load reservation (PSLR), provides bandwidth guarantees on a per-flow basis and redistributes the unused bandwidth among active flows in proportion to their priority level. We establish convergence conditions for the PSLR policy and show that it provides a fair service distribution among the flows. Moreover, we establish bounds for the backlog and delay on a per-flow basis, thus enabling a network to provide its users with absolute performance guarantees.
Introduction to Number Theory, 1st edition
Authors: Martin Erickson, Anthony Vazzana
Venue:  (2007)

One of the oldest branches of mathematics, number theory is a vast field devoted to studying the properties of whole numbers. Offering a flexible format for a one- or two-semester course, Introduction to Number Theory uses worked examples, numerous exercises, and two popular software packages to describe a diverse array of number theory topics. This classroom-tested, student-friendly text covers a wide range of subjects, from the ancient Euclidean algorithm for finding the greatest common divisor of two integers to recent developments that include cryptography, the theory of elliptic curves, and the negative solution of Hilberts tenth problem. The authors illustrate the connections between number theory and other areas of mathematics, including algebra, analysis, and combinatorics. They also describe applications of number theory to real-world problems, such as congruences in the ISBN system, modular arithmetic and Eulers theorem in RSA encryption, and quadratic residues in the construction of tournaments. The book interweaves the theoretical development of the material with Mathematica and Maple calculations while giving brief tutorials on the software in the appendices. Highlighting both fundamental and advanced topics, this introduction provides all of the tools to achieve a solid foundation in number theory.
Efficient influence maximization in social networks
Authors: Wei Chen, Yajun Wang, Siyu Yang
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. In this paper, we study the efficient influence maximization from two complementary directions. One is to improve the original greedy algorithm of [5] and its improvement [7] to further reduce its running time, and the second is to propose new degree discount heuristics that improves influence spread. We evaluate our algorithms by experiments on two large academic collaboration graphs obtained from the online archival database arXiv.org. Our experimental results show that (a) our improved greedy algorithm achieves better running time comparing with the improvement of [7] with matching influence spread, (b) our degree discount heuristics achieve much better influence spread than classic degree and centrality-based heuristics, and when tuned for a specific influence cascade model, it achieves almost matching influence thread with the greedy algorithm, and more importantly (c) the degree discount heuristics run only in milliseconds while even the improved greedy algorithms run in hours in our experiment graphs with a few tens of thousands of nodes. Based on our results, we believe that fine-tuned heuristics may provide truly scalable solutions to the influence maximization problem with satisfying influence spread and blazingly fast running time. Therefore, contrary to what implied by the conclusion of [5] that traditional heuristics are outperformed by the greedy approximation algorithm, our results shed new lights on the research of heuristic algorithms.
Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary
Authors: Oriol Pujol, David Masip
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence (2009)

This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points - points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention.
Continuous obstructed nearest neighbor queries in spatial databases
Authors: Yunjun Gao, Baihua Zheng
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

In this paper, we study a novel form of continuous nearest neighbor queries in the presence of obstacles, namely continuous obstructed nearest neighbor (CONN) search. It considers the impact of obstacles on the distance between objects, which is ignored by most of spatial queries. Given a data set P, an obstacle set O, and a query line segment q in a two-dimensional space, a CONN query retrieves the nearest neighbor of each point on q according to the obstructed distance, i.e., the shortest path between them without crossing any obstacle. We formulate CONN search, analyze its unique properties, and develop algorithms for exact CONN query processing, assuming that both P and O are indexed by conventional data-partitioning indices (e.g., R-trees). Our methods tackle the CONN retrieval by performing a single query for the entire query segment, and only process the data points and obstacles relevant to the final result, via a novel concept of control points and an efficient quadratic-based split point computation algorithm. In addition, we extend our solution to handle the continuous obstructed k-nearest neighbor (COkNN) search, which finds the k (≥1)nearest neighbors to every point along q based on obstructed distances. A comprehensive experimental evaluation using both real and synthetic datasets has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms.
Recommendations Using Information from Selected Sources with the ISIRES Methodology
Authors: Silvana Aciar, Josefina López Herrera, Josep Lluis de la Rosa
Venue: Proceeding of the 2006 conference on Artificial Intelligence Research and Development (2006)

Recommender systems have traditionally made use of the all variety of sources to obtain the suitable information to make recommendations. There are costs associated with the use of information sources those costs are an important determinant in the choice of which information sources are finally used. For example recommendation can be better if the recommender knows where is the suitable information to predict user's preferences to offer products. Sources that provide in-formation that is timely, accurate and relevant are expected to be used more often than sources that provide irrelevant information. This paper shows how the precision of the recommendations using either Collaborative Filtering (CF) or Content-Base Filtering (CBF) increases by selecting the most relevant information sources based on their intrinsic characteristics.
Adaptive Backstepping Fuzzy Control for a Class of Nonlinear Systems
Authors: Yih-Guang Leu, Jian-You Lin
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

By using a nonlinear parametric fuzzy identifier, an adaptive backstepping controller is proposed for a class of nonlinear systems. The nonlinear parametric fuzzy identifier is capable of automatically learning its membership functions. Since the fuzzy identifier is highly nonlinear, the derivative computation burden is enormous. Thus, this paper uses an estimation technique to effectively alleviate the derivative computation burden, and demonstrates the applicability of the proposed scheme by using computer simulation.
Online exploration in least-squares policy iteration
Authors: Lihong Li, Michael L. Littman, Christopher R. Mansley
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (MDPs), where compact function approximation has to be used. In this paper, we provide a practical solution to exploring large MDPs by integrating a powerful exploration technique, Rmax, into a state-of-the-art learning algorithm, least-squares policy iteration (LSPI). This approach combines the strengths of both methods, and has shown its effectiveness and superiority over LSPI with two other popular exploration rules in several benchmark problems.
Verification of the Lyee requirement
Authors: Osamu Arai, Hamido Fujita
Venue: Proceeding of the 2006 conference on New Trends in Software Methodologies, Tools and Techniques: Proceedings of the fifth SoMeT_06 (2006)

Software development in general lacks simplicity and richness in expressing appropriately the requirement, as well related supportive tools. Despite the advances in this field, the solutions have still not overcome and the proposed way of thinking is far from resolving the problems on software development and maintenance. Recently, a new promising methodology, called Lyee, has been proposed. It aims to automatically, generate programs from user requirement. Program structure in terms of Lyee was formalized. However, when requirement and operation are not correct, Lyee program may not perform correctly. Especially, when requirements are reactive type, the validation mechanism have not proposed yet to show correctness or appropriateness of the running requirement For this purpose, the properties that requirement and operation should have, been described as a proposition in terms of temporal logic and verified by such logic.
A Distributed Argumentation Framework using Defeasible Logic Programming
Authors: Matthias Thimm, Gabriele Kern-Isberner
Venue: Proceeding of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008 (2008)

Defeasible Logic Programming (DeLP) by García and Simari is an approach to realise non-monotonic reasoning via dialectical argumentation. We extend their approach by explicitly supporting distributed entities in the argumentation process on a structural basis. This makes the modelling of distributed argumentation systems like a jury court by using DeLP techniques possible. In this framework possibly many different agents with different opinions argue with each other on a given logical subject. We compare our framework with general DeLP and present the results.
Development of the Integrated Animation Studio for Cyber Theater
Authors: Hiroshi Matsuda, Yoshiaki Shindo
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

To improve and to aid the courses of computer-based education in high school, we have developed new educational support tool named Cyber Theater (CT). CT provides the capability of easy making the 3DCG animation of pupil's story by using script language named Cyber Theater Scenario Language 2 (CTSL2). Furthermore, reducing a cost of animation stories, we have developed an integrated animation studio for CT to high school students. The main part of the integrated animation studio is a scenario production editor (CTSL Script Writer). We can call the browser (Cyber Theater), gallery (CT Gallery) and camera work function (Camera Work Tracker) from the editor. In this paper, we report the detail of the integrated animation studio for CT and experimental lesson using the integrated animation studio for Cyber Theater. As a result of our experiment, we are convinced that our system can supply the suitable environment creating 3DCG animation to the high school students.
Brief paper: Active disturbance rejection control for high pointing accuracy and rotation speed
Authors: Shunli Li, Xu Yang, Di Yang
Venue: Automatica (Journal of IFAC) (2009)

This paper presents a design method proposed to design an active disturbance rejection control for the antenna pointing control of a large flexible satellite system. A simplified dynamics model is established using Euler-Lagrange and used to analyze the dynamic stability of the antenna system. The capturing strategy of the antenna is configured and the inner and outer loops of the active disturbance rejection control are then designed to improve pointing accuracy and rotation speed. The design of the active disturbance rejection control is verified through numerical simulation. Simulation results indicate that the proposed method can be used to achieve high pointing accuracy and rotation speed.
Project Portfolio Management Tools & Techniques
Authors: Parviz F. Rad, Ginger Levin
Venue:  (2007)

Does your organization want to achieve success in prioritizing projects systematically, deliberately, and logically? Project Portfolio Management Tools and Techniques is written to demonstrate how to elevate your organizations project management thinking to the level beyond managing individual projects in a standalone fashion. This book is for those executives and other project professionals who strive to have a formalized system of authorizing the right projects and abandoning the wrong projects, who desire to spend resources in the most efficient manner, and who want to have an actionable strategic plan for improving organizational project management sophistication. Project Portfolio Management Tools and Techniques deals with the full spectrum of project portfolio management (PPM) functions, from selecting projects through formalized portfolio management processes to facilitating the successful execution of projects through creating a formalized, project-friendly environment. This book will aid you in the implementation of a PPM system, assist in gaining the necessary commitment from executive management, and provide guidelines for the modification of operational practices. Get ahead of the game by seeing a comprehensive project portfolio management model that can help you establish yours successfully!
Uncertainty modeling for database design using intuitionistic and rough set theory
Authors: Theresa Beaubouef, Frederick E. Petry
Venue: Journal of Intelligent Fuzzy Systems: Applications in Engineering and Technology (2009)

This paper introduces the intuitionistic rough set and intuitionistic rough relational and rough object oriented database models. Rough set, fuzzy set, and intuitionistic set uncertainty management are discussed and compared, and the model based on intuitionistic and rough sets developed here is applied to databases. The intuitionistic rough set database models draw benefits from both the rough set and intuitionistic techniques, providing greater management of uncertainty for databases applications in a less than certain world.
Language Learning Outside the Classroom Using Handhelds with Knowledge Management
Authors: Li Hui Gan, Hiroaki Ogata, Makoto Wada, Yasuko Oishi, Takahito Ueda, Yoneo Yano
Venue: Proceeding of the 2007 conference on Supporting Learning Flow through Integrative Technologies (2007)

Advancement of wireless and mobile technology has enabled additional platforms to support learning in a single learning space. Current trend integrates usage of mobile terminals such as smart PDA (personal digital assistant) in a learning system. Such devices can communicate with wireless mobile terminal and realize the intelligence exchange with its surroundings [1]. A LOCH (Language Learning Outside the Classroom) system named One Day Trip is developed to assist overseas students to learn Japanese in real life situations with the help of a PDA. This paper proposes the implementation with Knowledge Management (KM) concepts in the system to aid the teacher and learners in the learning environment.
Analysis of the Effects of XLFrames in a Network
Authors: Dinil Mon Divakaran, Eitan Altman, Georg Post, Ludovic Noirie, Pascale Vicat-Blanc Primet
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

The phenomenal increase in network capacity to hundreds and thousands of Gbits/s in the core as well as Gbits/s at the access, is soon to witness stupendous amounts of packets that have to be processed and switched at amplifying line rates. Looking into the future, we address the need for the integration of packets of larger size, called XLFrames (XLFs), into the future Internet framework. This paper analyses the effects of introducing XLFs in a network that has both packets and XLFs. We evaluate the gains in terms of processing power and throughput. As we observe that XLFs have an impact on loss rate and fairness, we study how, with minimal efforts at routers while keeping the existing protocols (TCP/UDP, IP), XLFs may integrate in the current scenario.
Dynamic information source selection for intrusion detection systems
Authors: Martin Rehak, Eugen Staab, Michal Pechoucek, Jan Stiborek, Martin Grill, Karel Bartos
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

Our work presents a mechanism designed for the selection of the optimal information provider in a multi-agent, heterogeneous and unsupervised monitoring system. The self-adaptation mechanism is based on the insertion of a small set of prepared challenges that are processed together with the real events observed by the system. The evaluation of the system response to these challenges is used to select the optimal information source. Our algorithm uses the concept of trust to identify the best source and to optimize the number of challenges inserted into the system. The mechanism is designed for intrusion/fraud detection systems, which are frequently deployed as part of online transaction processing (banking, telecommunications or process monitoring systems). Our approach features unsupervised adjustment of its configuration and dynamic adaptation to the changing environment, which are both vital for these domains.
Literature mining on pharmacokinetics numerical data: A feasibility study
Authors: Zhiping Wang, Seongho Kim, Sara K. Quinney, Yingying Guo, Stephen D. Hall, Luis M. Rocha, Lang Li
Venue: Journal of Biomedical Informatics (2009)

A feasibility study of literature mining is conducted on drug PK parameter numerical data with a sequential mining strategy. Firstly, an entity template library is built to retrieve pharmacokinetics relevant articles. Then a set of tagging and extraction rules are applied to retrieve PK data from the article abstracts. To estimate the PK parameter population-average mean and between-study variance, a linear mixed meta-analysis model and an E-M algorithm are developed to describe the probability distributions of PK parameters. Finally, a cross-validation procedure is developed to ascertain false-positive mining results. Using this approach to mine midazolam (MDZ) PK data, an 88% precision rate and 92% recall rate are achieved, with an F-score=90%. It greatly out-performs a conventional data mining approach (support vector machine), which has an F-score of 68.1%. Further investigate on 7 more drugs reveals comparable performances of our sequential mining approach.