OpenRaiser/Intern-Atlas huggingface dataset:
Data Path Processing in Fast Programmable Routers
Venue: arXiv cs.NI 2004
Year: 2004

Internet is growing at a fast pace. The link speeds are surging toward 40 Gbps with the emergence of faster link technologies. New applications are coming up which require intelligent processing at the intermediate routers. Switches and routers are becoming the bottlenecks in fast communication. On one hand faster links deliver more packets every second and on the other hand intelligent processing consumes more CPU cycles at the router. The conflicting goals of providing faster but computationally expensive processing call for new approaches in designing routers. This survey takes a look at the core functionalities, like packet classification, buffer memory management, switch scheduling and output link scheduling performed by a router in its data path processing and discusses the algorithms that aim to reduce the performance bound for these operations. An important requirement for the routers is to provide Quality of Service guarantees. We propose an algorithm to guarantee QoS in Input Queued Routers. The hardware solution to speed up router operation was Application Specific Integrated Circuits (ASICs). But the inherent inflexibility of the method is a demerit as network standards and application requirements are constantly evolving, which seek a faster turnaround time to keep up with the changes. The promise of Network Processors (NP) is the flexibility of general-purpose processors together with the speed of ASICs. We will study the architectural choices for the design of Network Processors and focus on some of the commercially available NPs. There is a plethora of NP vendors in the market. The discussion on the NP benchmarks sets the normalizing platform to evaluate these NPs.
Extremal optimization for sensor report pre-processing
Venue: arXiv cs.AI 2004
Year: 2004

We describe the recently introduced extremal optimization algorithm and apply it to target detection and association problems arising in pre-processing for multi-target tracking. Here we consider the problem of pre-processing for multiple target tracking when the number of sensor reports received is very large and arrives in large bursts. In this case, it is sometimes necessary to pre-process reports before sending them to tracking modules in the fusion system. The pre-processing step associates reports to known tracks (or initializes new tracks for reports on objects that have not been seen before). It could also be used as a pre-process step before clustering, e.g., in order to test how many clusters to use. The pre-processing is done by solving an approximate version of the original problem. In this approximation, not all pair-wise conflicts are calculated. The approximation relies on knowing how many such pair-wise conflicts that are necessary to compute. To determine this, results on phase-transitions occurring when coloring (or clustering) large random instances of a particular graph ensemble are used.
Building Chinese Lexicons from Scratch by Unsupervised Short Document Self-Segmentation
Venue: arXiv cs.CL 2004
Year: 2004

Chinese text segmentation is a well-known and difficult problem. On one side, there is not a simple notion of "word" in Chinese language making really hard to implement rule-based systems to segment written texts, thus lexicons and statistical information are usually employed to achieve such a task. On the other side, any piece of Chinese text usually includes segments present neither in the lexicons nor in the training data. Even worse, such unseen sequences can be segmented into a number of totally unrelated words making later processing phases difficult. For instance, using a lexicon-based system the sequence ???(Baluozuo, Barroso, current president-designate of the European Commission) can be segmented into ?(ba, to hope, to wish) and ??(luozuo, an undefined word) changing completely the meaning of the sentence. A new and extremely simple algorithm specially suited to work over short Chinese documents is introduced. This new algorithm performs text "self-segmentation" producing results comparable to those achieved by native speakers without using either lexicons or any statistical information beyond the obtained from the input text. Furthermore, it is really robust for finding new "words", especially proper nouns, and it is well suited to build lexicons from scratch. Some preliminary results are provided in addition to examples of its employment.
Support pour la reconfiguration d'implantation dans les applications a composants Java
Venue: arXiv cs.NI 2004
Year: 2004

Nowadays, numerous component models are used for various purposes: to build applications, middleware or even operating systems. Those models commonly support structure reconfiguration, that is modification of application's architecture at runtime. On the other hand, very few allow implementation reconfiguration, that is runtime modification of the code of components building the application. In this article we present the work we performed on JULIA, a Java-based implementation of the FRACTAL component model, in order for it to support implementation reconfigurations. We show how we overcame the limitations of Java class loading mechanism to allow runtime modifications of components' implementation and interfaces. We also describe the integration of our solution with the JULIA ADL.
Gestion transactionnelle de la reprise sur erreurs dans le deploiement
Venue: arXiv cs.NI 2004
Year: 2004

With the development of the networks and the Internet, the problems of automated deployment on broad scale became increasingly crucial. Software deployment is a complex process covering several activities going from the configuration to the retirement of a software product. During the execution of a deployment process, exceptions can be met which put the site in an incoherent state. To solve them, we propose an approach based on transactional concepts which describes the actions to be undertaken when an exceptional situation is met during the deployment process. The approach guaranties the respect of the site's consistency by preserving part of the work already carried out by the process. This article presents our approach and an experimentation made in an academic deployment system.
Principles for Digital Preservation
Venue: arXiv cs.DL 2004
Year: 2004

The immense investments in creating and disseminating digitally represented information have not been accompanied by commensurate effort to ensure the longevity of information of permanent interest. Asserted difficulties with long-term digital preservation prove to be largely underestimation of what technology can provide. We show how to clarify prominent misunderstandings and sketch a 'Trustworthy Digital Object (TDO)' method that solves all the published technical challenges.
Randomized Initialization of a Wireless Multihop Network
Venue: arXiv cs.DC 2004
Year: 2004

Address autoconfiguration is an important mechanism required to set the IP address of a node automatically in a wireless network. The address autoconfiguration, also known as initialization or naming, consists to give a unique identifier ranging from 1 to $n$ for a set of $n$ indistinguishable nodes. We consider a wireless network where $n$ nodes (processors) are randomly thrown in a square $X$, uniformly and independently. We assume that the network is synchronous and two nodes are able to communicate if they are within distance at most of $r$ of each other ($r$ is the transmitting/receiving range). The model of this paper concerns nodes without the collision detection ability: if two or more neighbors of a processor $u$ transmit concurrently at the same time, then $u$ would not receive either messages. We suppose also that nodes know neither the topology of the network nor the number of nodes in the network. Moreover, they start indistinguishable, anonymous and unnamed. Under this extremal scenario, we design and analyze a fully distributed protocol to achieve the initialization task for a wireless multihop network of $n$ nodes uniformly scattered in a square $X$. We show how the transmitting range of the deployed stations can affect the typical characteristics such as the degrees and the diameter of the network. By allowing the nodes to transmit at a range $r= \sqrt{\frac{(1+\ell) \ln{n} \SIZE}{πn}}$ (slightly greater than the one required to have a connected network), we show how to design a randomized protocol running in expected time $O(n^{3/2} \log^2{n})$ in order to assign a unique number ranging from 1 to $n$ to each of the $n$ participating nodes.
Inside-Outside Estimation Meets Dynamic EM
Venue: arXiv cs.CL 2004
Year: 2004

We briefly review the inside-outside and EM algorithm for probabilistic context-free grammars. As a result, we formally prove that inside-outside estimation is a dynamic-programming variant of EM. This is interesting in its own right, but even more when considered in a theoretical context since the well-known convergence behavior of inside-outside estimation has been confirmed by many experiments but apparently has never been formally proved. However, being a version of EM, inside-outside estimation also inherits the good convergence behavior of EM. Therefore, the as yet imperfect line of argumentation can be transformed into a coherent proof.
An Analysis of the Skype Peer-to-Peer Internet Telephony Protocol
Venue: arXiv cs.NI 2004
Year: 2004

Skype is a peer-to-peer VoIP client developed by KaZaa in 2003. Skype claims that it can work almost seamlessly across NATs and firewalls and has better voice quality than the MSN and Yahoo IM applications. It encrypts calls end-to-end, and stores user information in a decentralized fashion. Skype also supports instant messaging and conferencing. This report analyzes key Skype functions such as login, NAT and firewall traversal, call establishment, media transfer, codecs, and conferencing under three different network setups. Analysis is performed by careful study of Skype network traffic.
Towards Reliable Network Wide Broadcast in Mobile Ad Hoc Networks
Venue: arXiv cs.NI 2004
Year: 2004

Network-Wide Broadcast (NWB) is a common operation in Mobile Ad hoc Networks (MANETs) used by routing protocols to discover routes and in group communication operations. NWB is commonly performed via flooding, which has been shown to be expensive in dense MANETs because of its high redundancy. Several efforts have targeted reducing the redundancy of floods. In this work, we target another problem that can substantially impact the success of NWBs: since MAC level broadcasts are unreliable, it is possible for critical rebroadcasts to be lost, leading to a significant drop in the node coverage. This is especially true under heavy load and in sparse topologies. We show that the techniques that target reducing the overhead of flooding, reduce its inherent redundancy and harm its reliability. In addition, we show that static approaches are more vulnerable to this problem. We then present a selective rebroadcast approach to improve the robustness of NWBs. We show that our approach leads to considerable improvement in NWB coverage relative to a recently proposed solution to this problem, with a small increase in overhead. The proposed approaches do not require proactive neighbor discovery and are therefore resilient to mobility. Finally, the solution can be added to virtually all NWB approaches to improve their reliability.
Finite Domain Bounds Consistency Revisited
Venue: arXiv cs.AI 2004
Year: 2004

A widely adopted approach to solving constraint satisfaction problems combines systematic tree search with constraint propagation for pruning the search space. Constraint propagation is performed by propagators implementing a certain notion of consistency. Bounds consistency is the method of choice for building propagators for arithmetic constraints and several global constraints in the finite integer domain. However, there has been some confusion in the definition of bounds consistency. In this paper we clarify the differences and similarities among the three commonly used notions of bounds consistency.
Multidimensional data classification with artificial neural networks
Venue: arXiv cs.NE 2004
Year: 2004

Multi-dimensional data classification is an important and challenging problem in many astro-particle experiments. Neural networks have proved to be versatile and robust in multi-dimensional data classification. In this article we shall study the classification of gamma from the hadrons for the MAGIC Experiment. Two neural networks have been used for the classification task. One is Multi-Layer Perceptron based on supervised learning and other is Self-Organising Map (SOM), which is based on unsupervised learning technique. The results have been shown and the possible ways of combining these networks have been proposed to yield better and faster classification results.
A Statistical Framework for Efficient Monitoring of End-to-End Network Properties
Venue: arXiv cs.NI 2004
Year: 2004

Network service providers and customers are often concerned with aggregate performance measures that span multiple network paths. Unfortunately, forming such network-wide measures can be difficult, due to the issues of scale involved. In particular, the number of paths grows too rapidly with the number of endpoints to make exhaustive measurement practical. As a result, there is interest in the feasibility of methods that dramatically reduce the number of paths measured in such situations while maintaining acceptable accuracy. In previous work we proposed a statistical framework to efficiently address this problem, in the context of additive metrics such as delay and loss rate, for which the per-path metric is a sum of (possibly transformed) per-link measures. The key to our method lies in the observation and exploitation of significant redundancy in network paths (sharing of common links). In this paper we make three contributions: (1) we generalize the framework to make it more immediately applicable to network measurements encountered in practice; (2) we demonstrate that the observed path redundancy upon which our method is based is robust to variation in key network conditions and characteristics, including link failures; and (3) we show how the framework may be applied to address three practical problems of interest to network providers and customers, using data from an operating network. In particular, we show how appropriate selection of small sets of path measurements can be used to accurately estimate network-wide averages of path delays, to reliably detect network anomalies, and to effectively make a choice between alternative sub-networks, as a customer choosing between two providers or two ingress points into a provider network.
Assembly and Disassembly Planning by using Fuzzy Logic & Genetic Algorithms
Venue: arXiv cs.RO 2004
Year: 2004

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of theoptimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies.
Collective Intelligence Quanitifed for Computer-Mediated Group Problem Solving
Venue: arXiv cs.CY 2004
Year: 2004

Collective Intelligence (CI) is the ability of a group to exhibit greater intelligence than its individual members. Expressed by the common saying that "two minds are better than one," CI has been a topic of interest for social psychology and the information sciences. Computer mediation adds a new element in the form of distributed networks and group support systems. These facilitate highly organized group activities that were all but impossible before computer mediation. This paper presents experimental findings on group problem solving where a distributed software system automatically integrates input from many humans. In order to quantify Collective Intelligence, we compare the performance of groups to individuals when solving a mathematically formalized problem. This study shows that groups can outperform individuals on difficult but not easy problems, though groups are slower to produce solutions. The subjects are 57 university students. The task is the 8-Puzzle sliding tile game.
ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System
Venue: arXiv cs.CR 2004
Year: 2004

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.
Swarming around Shellfish Larvae
Venue: arXiv cs.AI 2004
Year: 2004

The collection of wild larvae seed as a source of raw material is a major sub industry of shellfish aquaculture. To predict when, where and in what quantities wild seed will be available, it is necessary to track the appearance and growth of planktonic larvae. One of the most difficult groups to identify, particularly at the species level are the Bivalvia. This difficulty arises from the fact that fundamentally all bivalve larvae have a similar shape and colour. Identification based on gross morphological appearance is limited by the time-consuming nature of the microscopic examination and by the limited availability of expertise in this field. Molecular and immunological methods are also being studied. We describe the application of computational pattern recognition methods to the automated identification and size analysis of scallop larvae. For identification, the shape features used are binary invariant moments; that is, the features are invariant to shift (position within the image), scale (induced either by growth or differential image magnification) and rotation. Images of a sample of scallop and non-scallop larvae covering a range of maturities have been analysed. In order to overcome the automatic identification, as well as to allow the system to receive new unknown samples at any moment, a self-organized and unsupervised ant-like clustering algorithm based on Swarm Intelligence is proposed, followed by simple k-NNR nearest neighbour classification on the final map. Results achieve a full recognition rate of 100% under several situations (k =1 or 3).
The Combination of Paradoxical, Uncertain, and Imprecise Sources of Information based on DSmT and Neutro-Fuzzy Inference
Venue: arXiv cs.AI 2004
Year: 2004

The management and combination of uncertain, imprecise, fuzzy and even paradoxical or high conflicting sources of information has always been, and still remains today, of primal importance for the development of reliable modern information systems involving artificial reasoning. In this chapter, we present a survey of our recent theory of plausible and paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT) in the literature, developed for dealing with imprecise, uncertain and paradoxical sources of information. We focus our presentation here rather on the foundations of DSmT, and on the two important new rules of combination, than on browsing specific applications of DSmT available in literature. Several simple examples are given throughout the presentation to show the efficiency and the generality of this new approach. The last part of this chapter concerns the presentation of the neutrosophic logic, the neutro-fuzzy inference and its connection with DSmT. Fuzzy logic and neutrosophic logic are useful tools in decision making after fusioning the information using the DSm hybrid rule of combination of masses.
ScotGrid: A Prototype Tier 2 Centre
Venue: arXiv cs.AR 2004
Year: 2004

ScotGrid is a prototype regional computing centre formed as a collaboration between the universities of Durham, Edinburgh and Glasgow as part of the UK's national particle physics grid, GridPP. We outline the resources available at the three core sites and our optimisation efforts for our user communities. We discuss the work which has been conducted in extending the centre to embrace new projects both from particle physics and new user communities and explain our methodology for doing this.
Preemptive Multi-Machine Scheduling of Equal-Length Jobs to Minimize the Average Flow Time
Venue: arXiv cs.DS 2004
Year: 2004

We study the problem of preemptive scheduling of n equal-length jobs with given release times on m identical parallel machines. The objective is to minimize the average flow time. Recently, Brucker and Kravchenko proved that the optimal schedule can be computed in polynomial time by solving a linear program with O(n^3) variables and constraints, followed by some substantial post-processing (where n is the number of jobs.) In this note we describe a simple linear program with only O(mn) variables and constraints. Our linear program produces directly the optimal schedule and does not require any post-processing.
Negotiating over Bundles and Prices Using Aggregate Knowledge
Venue: arXiv cs.MA 2004
Year: 2004

Combining two or more items and selling them as one good, a practice called bundling, can be a very effective strategy for reducing the costs of producing, marketing, and selling goods. In this paper, we consider a form of multi-issue negotiation where a shop negotiates both the contents and the price of bundles of goods with his customers. We present some key insights about, as well as a technique for, locating mutually beneficial alternatives to the bundle currently under negotiation. The essence of our approach lies in combining historical sales data, condensed into aggregate knowledge, with current data about the ongoing negotiation process, to exploit these insights. In particular, when negotiating a given bundle of goods with a customer, the shop analyzes the sequence of the customer's offers to determine the progress in the negotiation process. In addition, it uses aggregate knowledge concerning customers' valuations of goods in general. We show how the shop can use these two sources of data to locate promising alternatives to the current bundle. When the current negotiation's progress slows down, the shop may suggest the most promising of those alternatives and, depending on the customer's response, continue negotiating about the alternative bundle, or propose another alternative. Extensive computer simulation experiments show that our approach increases the speed with which deals are reached, as well as the number and quality of the deals reached, as compared to a benchmark. In addition, we show that the performance of our system is robust to a variety of changes in the negotiation strategies employed by the customers.