ppxscal/embedding_network huggingface dataset:
A Novel Mobility Model from a Heterogeneous Military MANET Trace
Authors: Xiaofeng Lu, Yung-Chih Chen, Ian Leung, Zhang Xiong, Pietro Liò
Venue: Proceedings of the 7th international conference on Ad-hoc, Mobile and Wireless Networks (2008)

In this paper we describe our analysis of a real trace and propose a mobility model. The trace data we used for this study was collected from a military experiment carried out in Lakehurst, N.J., U.S.A. The structure of these entities in the trace is novel, say layered and heterogeneous--some nodes moved on the ground whilst some hovered in the sky. Evaluation results show our mobility model well and truly captures some aspects of the spatial and temporal characteristics of the real traces. This mobility model can be used to generate synthetic traces with different travel schedules. Such experiments are costly to be realized in real world scenarios
Impact of Drifter Deployment on the Quality of Ocean Sensing
Authors: Konstantinos P. Ferentinos, Niki Trigoni, Silvia Nittel
Venue: GeoSensor Networks: Second International Conference, GSN 2006, Boston, MA, USA, October 1-3, 2006, Revised Selected and Invited Papers (2008)

Traditional means of observing the ocean, like fixed moorings and radar systems, are expensive to deploy and provide coarse-grained data measurements of ocean currents and waves. In this paper, we explore the use of an inexpensive wireless mobile ocean sensor network as an alternative flexible infrastructure for fine-grained ocean monitoring. Surface drifters are designed specifically to move passively with the flow of water on the ocean surface and they are able to acquire sensor readings and GPS-generated positions at regular intervals. We view the fleet of drifters as a wireless ad-hoc sensor network with two types of nodes: i) a few powerful drifters with satellite connectivity, acting as mobile base-stations, and ii) a large number of low-power drifters with short-range acoustic or radio connectivity. We study connectivity and uniformity properties of the ad-hoc mobile sensor network. We investigate the effect of deployment strategy. The objective of this paper is to address the following challenge: how can we trade the usage of resources (e.g. number of drifters, and number of basestations vs. communication range) and which deployment strategy should be chosen (e.g. grid-like, star-like, etc.) to minimize energy costs, whilst satisfying application requirements for network connectivity and sensing density. Using simulation and real dataset, we investigate the effects of deploying drifters with regard to the following questions: i) where/when should drifters be placed initially? ii) how many drifters should initially be deployed?, iii) the effect of the number of basestations (drifters with satellite connectivity) on the overall network connectivity, and iv) the optimal communication range of the basic drifters. Our empirical study provides useful insights on how to design distributed routing and in-network processing algorithms tailored for ocean-monitoring sensor networks.
WYSINWYX: What You See Is Not What You eXecute
Authors: G. Balakrishnan, T. Reps, D. Melski, T. Teitelbaum
Venue: Verified Software: Theories, Tools, Experiments: First IFIP TC 2/WG 2.3 Conference, VSTTE 2005, Zurich, Switzerland, October 10-13, 2005, Revised Selected Papers and Discussions (2005)

What You See Is Not What You eXecute: computers do not execute source-code programs; they execute machine-code programs that are generated from source code. Not only can the WYSINWYX phenomenon create a mismatch between what a programmer intends and what is actually executed by the processor, it can cause analyses that are performed on source code to fail to detect certain bugs and vulnerabilities. This issue arises regardless of whether one's favorite approach to assuring that programs behave as desired is based on theorem proving, model checking, or abstract interpretation.
An Algorithm for Orienting Graphs Based on Cause-Effect Pairs and Its Applications to Orienting Protein Networks
Authors: Alexander Medvedovsky, Vineet Bafna, Uri Zwick, Roded Sharan
Venue: Proceedings of the 8th international workshop on Algorithms in Bioinformatics (2008)

We consider a graph orientation problem arising in the study of biological networks. Given an undirected graph and a list of ordered source-target pairs, the goal is to orient the graph so that a maximum number of pairs will admit a directed path from the source to the target. We show that the problem is NP-hard and hard to approximate to within a constant ratio. We then study restrictions of the problem to various graph classes, and provide an O(logn) approximation algorithm for the general case. We show that this algorithm achieves very tight approximation ratios in practice and is able to infer edge directions with high accuracy on both simulated and real network data.
Design of Design Methodology for Autonomous Robots
Authors: Eli Kolberg, Yoram Reich, Ilya Levin
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

We present a methodology for deriving design methodology for autonomous robots. We designed this methodology in the context of a robotics course in high schools. The motivation for designing this new methodology was improving the robots' robustness and reliability and preparing students for becoming better designers. The new methodology proved to be highly successful in designing top quality robots. In the methodology design, we explored and adapted design methods to the specific designers, the nature of the product, the environment, the product needs, and the design context goals. At the end of this comprehensive design, we selected a synergetic integration of six methods to compose the methodology for this product context: conceptual design, fault tolerant design, atomic requirements, using fuzzy logic for the control of robotics systems, creative thinking method, and microprogramming design.
Comparing Two Models for Terrorist Group Detection: GDM or OGDM?
Authors: Fatih Ozgul, Zeki Erdem, Hakan Aksoy
Venue: Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics (2008)

Since discovery of organization structure of offender groups leads the investigation to terrorist cells or organized crime groups, detecting covert networks from crime data are important to crime investigation. Two models, GDM and OGDM, which are based on another representation model - OGRM are developed and tested on eighty seven known offender groups where nine of them were terrorist cells. GDM, which is basically depending on police arrest data and "caught together" information, performed well on terrorist groups, whereas OGDM, which uses a feature matching on year-wise offender components from arrest and demographics data, performed better on non-terrorist groups. OGDM uses a terror crime modus operandi ontology which enabled matching of similar crimes.
Extraction of Partially Occluded Elliptical Objects by Modified Randomized Hough Transform
Authors: Kwangsoo Hahn, Youngjoon Han, Hernsoo Hahn
Venue: Proceedings of the 30th annual German conference on Advances in Artificial Intelligence (2007)

Ellipse detection is very important in computer vision, object recognition, feature selection and so on. This paper proposes a new ellipse detection method using local information of edge points. It merges line segments using modified randomized Hough transform (RHT) that belongs to same ellipse. It is fast, correct and robust to noise because it detects ellipse using line segments that are constructed by local information of edge points. The proposed method in this paper can not apply only ellipse detection but line, circle and partially occluded elliptical object.
Lecomps5: A Framework for the Automatic Building of Personalized Learning Sequences
Authors: Carla Limongelli, Filippo Sciarrone, Marco Temperini, Giulia Vaste
Venue: Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society (2008)

In the context of distance learning, Adaptive Web-based Educational System focus on personalizationand adaptation, that is on "learner's satisfaction". In this paper we address the other side of the coin, that is the "teacher's satisfaction" problem, which is quite seldom taken into account in educational systems. We present a new version of the Lecomps5 Web-based Educational System, a system capable of providing personalization and adaptation on the basis of learner's knowledge, learning styles and learning progresses. In this new version, a framework provides the teacher with an easy and flexible tool for managing learning material, expressing different didactic strategies and sequencing personalized courses by means of an embedded planner. Such functionalities are supported by the system basing on evaluations of learner's knowledge, learning styles, and learning progresses. We report on a first controlled experiment, we made to evaluate the "teacher's satisfaction".
Erlang --- Software for a Concurrent World
Authors: Joe Armstrong
Venue: Proceedings of the 21st European conference on ECOOP 2007: Object-Oriented Programming (2007)

This talk is about Erlang and Concurrency Oriented Programming. We start with a short history of Erlang and of shared state and message passing concurrency. We argue that it is impossible to make fault-tolerant systems using mutable shared state concurrency models. We explain the thinking behind what has become known as "Erlang style concurrency" and show the relation to Concurrency Oriented Programming. We take a brief detour and talk about the commercial spread of Erlang, highlighting some of the more successful products and companies based on Erlang. We talk about the general problem of programming multicore computers and show how the goal of achieving factor N speedups on N-core processors with no change to the code, is being realised.
Definition Extraction with Balanced Random Forests
Authors: Łukasz Kobyliński, Adam Przepiórkowski
Venue: Proceedings of the 6th international conference on Advances in Natural Language Processing (2008)

We propose a novel machine learning approach to the task of identifying definitions in Polish documents. Specifics of the problem domain and characteristics of the available dataset have been taken into consideration, by carefully choosing and adapting a classification method to highly imbalanced and noisy data. We evaluate the performance of a Random Forest-based classifier in extracting definitional sentences from natural language text and give a comparison with previous work.
The implementation of coding in optical telecommunication systems
Authors: Natasa Zivic, Nenad Radenkovic
Venue: Proceedings of the 6th WSEAS International Conference on Applied Electromagnetics, Wireless and Optical (2008)

A review of the most common methods for electronic mitigation of optical and electrical impairments in modern high-speed and/or long-haul optical transmission systems is presented. Among them, the newest "third-generation" FEC techniques with turbo-codes and iterative decoding can be considered as the most serious candidates for the future optical transmission applications. In this paper, the results of a few recent and successful projects in which different FEC or other electronic methods are used, are also reported.
Topic 7 Peer-to-Peer Computing
Authors: Alberto Montresor, Fabrice Fessant, Dick Epema, Spyros Voulgaris
Venue: Proceedings of the 13th European international conference on Parallel Processing (2007)

Distributed systems have experienced a shift of scale in the past few years. This evolution has generated an interest in peer-to-peer systems and resulted in much interesting work. Peer-to-peer systems are characterized by their potential to scale due to their fully decentralized nature. They are self-organizing, adapting automatically to peer arrivals and departures, and are highly resilient to failures. They rely on a symmetric communication model where peers act both as servers and clients. As the peer-to-peer concepts and technologies become more mature, many distributed services and applications relying on this model are envisaged in the context of large-scale distributed and parallel systems. This topic examines peer-to-peer technologies, applications, and systems, and also identifies key research issues and challenges. Twenty-six papers were submitted to the track and we accepted six. We organized two sessions, the first devoted to the problem of query management in structured and unstructured overlay networks, the second containing a broader selection of topics.
Ontology-based test generation for multiagent systems
Authors: Cu D. Nguyen, Anna Perini, Paolo Tonella
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

This paper investigates software agents testing, and in particular how to automate test generation. We propose a novel approach, which takes advantage of agent interaction ontologies that define content semantic of agent interactions to: (i) generate test inputs; (ii) guide the exploration of the input space during generation; and, (iii) verify messages exchanged among agents with respect to the defined interaction ontology. We integrated the proposed approach into a testing framework, called eCAT, which can generate and evolve test cases automatically, and run them continuously.
Neural Network for Modeling Esthetic Selection
Authors: Tamás (Tom) Gedeon
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

Some real world problems require significant human interaction for labeling the data, which is very expensive. Worse, in some cases, the exercise of human judgement is inherently subjective and contextual, and so the entire labeling must be done in one session, which may be too long. Our domain is the automatic generation of Mondrian-like images with an interactive interface for the user to select images. We use back-propagation neural networks to learn an approximation of a viewer's aesthetic using 2 category labelled data (images liked/disliked). We construct a data set for training in a sequential fashion related to the interactive art appreciation task, and produce an output profile which well approximates a regression task, even trained on classification data. Analysis of the learned network produces some surprises, with the discovery of some input contributions which are unexpected to the user.
A Neural Model for Delay Correction in a Distributed Control System
Authors: Ana Antunes, Fernando Morgado Dias, Alexandre Mota
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Distributed control systems frequently suffer from variable sampling to actuation delay. This delay can degrade the control quality of such systems. In some distributed control systems it is possible to know, at control time, the value of the delay. The work reported in this paper proposes to build a model of the behavior of the system in the presence of the variable delay and to use this model to compensate the control signal in order to avoid the delay effect. This model can be used as a compensator that can easily be added to an existing control system that does not account for the sampling to actuation delay effect. The compensator can be applied to distributed systems using online or off-line scheduling policies provided that the sampling to actuation delay can be evaluated. The effectiveness of the neural network delay compensator is evaluated using a networked control system with a pole-placement controller.
Neural Model-Based Segmentation of Image Motion
Authors: Lucia Maddalena, Alfredo Petrosino
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

Besides enabling the segmentation of video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient. We propose a novel model for image sequences based on self organization through artificial neural networks, that is used both for background modeling, allowing to handle scenes containing moving backgrounds or gradual illumination variations, and for stopped foreground modeling, helping in distinguishing between moving and stopped foreground regions and leading to an initial segmentation of scene objects. Experimental results are presented for real video sequences.
Integrated data modeling in high-throughput proteomics
Authors: John H. Miller
Venue:  (2007)

The purpose of this research project is to investigate the work flow in high-throughput quantitative proteomics. After data collection on complex protein mixtures subjected to proteolysis, liquid chromatography (LC) and mass spectrometry (MS), a long data reduction procedure beings that involves protein identification, protein abundance estimation, biological interpretation of differentially abundant proteins. The data reduction procedure contains many steps that are the subject of ongoing research in bioinformatics. This thesis research addresses the following issues: (1) protein database redundancy, (2) peptides from proteolysis that are found in more than one database entry, (3) separation of biological effects on protein abundance from variance due to instrument and processing effects, (4) data mining to relate observed global changes in an organisms proteome to biological processes perturbed by treatments.
MR-PDP: Multiple-Replica Provable Data Possession
Authors: Reza Curtmola, Osama Khan, Randal Burns, Giuseppe Ateniese
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems (2008)

Many storage systems rely on replication to increase the availability and durability of data on untrusted storage systems. At present, such storage systems provide no strong evidence that multiple copies of the data are actually stored. Storage servers can collude to make it look like they are storing many copies of the data, whereas in reality they only store a single copy. We address this shortcoming through multiple-replica provable data possession (MR-PDP): A provably-secure scheme that allows a client that stores treplicas of a file in a storage system to verify through a challenge-response protocol that (1) each unique replica can be produced at the time of the challenge and that (2) the storage system uses t times the storage required to store a single replica. MR-PDP extends previous work on data possession proofs for a single copy of a file in a client/server storage system [4]. Using MR-PDP to store t replicas is computationally much more efficient than using a single-replica PDP scheme to store t separate, unrelated files (e.g., by encrypting each file separately prior to storing it). Another advantage of MR-PDP is that it can generate further replicas on demand, at little expense, when some of the existing replicas fail.
Scheduling for Server Farms: Approaches and Open Problems
Authors: Mor Harchol-Balter
Venue: Proceedings of the SPEC international workshop on Performance Evaluation: Metrics, Models and Benchmarks (2008)

Server farms are ubiquitous in applications ranging from Web server farms to high-performance supercomputing systems to call centers. The popularity of the server farm architecture is understandable, as it allows for increased performance, while being cost-effective and easily scalable.Given the prevalence of server farms, it is surprising that even at this late date so little is understood regarding their performance as compared with their single-server counterpart, particularly with respect to scheduling. Part of the problem is that there are at least three disjoint communities studying scheduling in server farms, including the SIGMETRICS community, the INFORMS community, and the SPAA/STOC/FOCS community, all of which have different approaches and goals. One of our goals in this talk is to make researchers aware of results in these different communities.
Technical Section: GPU-based rendering of curved polygons using simplicial coverings
Authors: A. J. Rueda, J. Ruiz de Miras, F. R. Feito
Venue: Computers and Graphics (2008)

In this work, we describe a new algorithm for rendering polygons defined by cubic Bezier curve segments in current GPUs. Unlike other approaches, our algorithm has a simple preprocessing that does not require computing tessellations, and can be implemented in GPU as a geometry shader. The polygon is decomposed into a set of simplices which are individually rasterized into the stencil buffer to recreate the shape that is finally rendered in the frame buffer. Each simplex is rasterized using a fragment shader that evaluates the implicit equation of the Bezier curve to discard the pixels that fall outside it. The proposed method is simple, fast, robust and general, as it can handle curved polygons with holes, several components or self-intersections.
Discriminative cue integration for medical image annotation
Authors: Tatiana Tommasi, Francesco Orabona, Barbara Caputo
Venue: Pattern Recognition Letters (2008)

Automatic annotation of medical images is an increasingly important tool for physicians in their daily activity. Hospitals nowadays produce an increasing amount of data. Manual annotation is very costly and prone to human mistakes. This paper proposes a multi-cue approach to automatic medical image annotation. We represent images using global and local features. These cues are then combined using three alternative approaches, all based on the support vector machine algorithm. We tested our methods on the IRMA database, and with two of the three approaches proposed here we participated in the 2007 ImageCLEFmed benchmark evaluation, in the medical image annotation track. These algorithms ranked first and fifth, respectively among all submission. Experiments using the third approach also confirm the power of cue integration for this task.
The Salsa20 Family of Stream Ciphers
Authors: Daniel J. Bernstein
Venue: New Stream Cipher Designs: The eSTREAM Finalists (2008)

Salsa20 is a family of 256-bit stream ciphers designed in 2005 and submitted to eSTREAM, the ECRYPT Stream Cipher Project. Salsa20 has progressed to the third round of eSTREAM without any changes. The 20-round stream cipher Salsa20/20 is consistently faster than AES and is recommended by the designer for typical cryptographic applications. The reduced-round ciphers Salsa20/12 and Salsa20/8 are among the fastest 256-bit stream ciphers available and are recommended for applications where speed is more important than confidence. The fastest known attacks use ≈ 2153 simple operations against Salsa20/7, ≈ 2249 simple operations against Salsa20/8, and ≈ 2255 simple operations against Salsa20/9, Salsa20/10, etc. In this paper, the Salsa20 designer presents Salsa20 and discusses the decisions made in the Salsa20 design.
Content Adaptation in Mobile Multimedia System for M-Learning
Authors: Adriana Reveiu, Ion Smeureanu, Marian Dardala
Venue: Proceedings of the 2008 7th International Conference on Mobile Business (2008)

The goal of the paper is to present an architecture designed for mobile multimedia content adaptation, applied to the newest solutions for instruction and learning, generically named mobile learning or mlearning. The field of research is situated at the intersection of mobile tools and e-learning and the research has as a goal to provide the opportunity to access computer resources from any location and to adapt the multimedia content to the specific features of mobile devices, communication environment, users’ preferences and users’ knowledge. The systems developed based on the architecture proposed in this paper will be able to provide powerful facilities for searching and support for instruction effectiveness, for performances evaluation, are time and location independent. The technical solutions proposed follow to use the potential of mobile devices to become important tools in education field.