pinecone/core-2020-05-10-deduplication huggingface dataset:
Predicting construction litigation outcome using particle swarm optimization

Construction claims are normally affected by a large number of complex and interrelated factors. It is highly desirable for the parties to a dispute to know with some certainty how the case would be resolved if it were taken to court. The use of artificial neural networks can be a cost-effective technique to help to predict the outcome of construction claims, on the basis of characteristics of cases and the corresponding past court decisions. In this paper, a particle swarm optimization model is adopted to train perceptrons. The approach is demonstrated to be feasible and effective by predicting the outcome of construction claims in Hong Kong in the last 10 years. The results show faster and more accurate results than its counterparts of a benching back-propagation neural network and that the PSO-based network are able to give a successful prediction rate of up to 80%. With this, the parties would be more prudent in pursuing litigation and hence the number of disputes could be reduced significantly.Department of Civil and Environmental EngineeringAuthor name used in this publication: Kwokwing ChauSeries: Lecture notes in computer science2004-2005 > Academic research: refereed > Publication in refereed journalpreprint_postprin
GBD threshold cryptography with an application to RSA key recovery

We present protocols for threshold cryptography in the GBD public-key cryptosystem. Both threshold decryption and threshold key generation are covered, in the 'honest-but-curious' setting. This shows that it is possible to perform GBD computations in a distributed manner during both key generation and decryption, without revealing the private key to any party. GBD threshold decryption is similar to El-Gamal threshold decryption. GBD threshold key generation is based on adaptations of protocols for RSA key generation by Boneh and Franklin, and Catalano et al, and includes a new protocol for efficiently computing the inverse of a shared secret modulo another shared secret.\ud
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We also show an application of GBD threshold cryptography to RSA key recovery. This is based on the use of GBD as a master cryptosystem, whose use allows generation by individual users of RSA moduli that can be factored by using the GBD private key as trapdoor information. This application requires RSA key generation to be tailored, but other operations are standard RSA. Clearly, compromise of the GBD private key would compromise all corresponding RSA private keys, so the security of the GBD master private key should be stronger than the security of the individual RSA keys, and this can be achieved using threshold methods. Finally, we point out two open problems in the RSA key recovery application
Security Requirement for Key Establishment Proof Models: Revisiting Bellare-Rogaway and Jeong-Katz-Lee Protocols

We observe that the definitions of security in the computational complexity proof models of Bellare & Rogaway (1993) and Canetti & Krawczyk (2001) require two partners in the presence of a malicious adversary to accept the same session key, which we term a key sharing requirement. We then revisit the Bellare--Rogaway three-party key distribution (3PKD) protocol and the Jeong--Katz--Lee two-party authenticated key exchange protocol TS2, which carry claimed proofs of security in the Canetti & Krawczyk (2001) model and the Bellare & Rogaway (1993) model respectively. We reveal previously unpublished flaws in these protocols where we demonstrate that both protocols fail to satisfy the definition of security in the respective models. We present a new 3PKD protocol as an improvement with a proof of security in the Canetti & Krawczyk (2001) model and a simple fix to the specification of protocol TS2. We also identify several variants of the key sharing requirement and present a brief discussion
Generating predicate rules from neural networks

Artificial neural networks (ANN) have demonstrated good predictive performance in a wide variety of practical problems. However, due to poor comprehensibility of the learned ANN, and the inability to represent explanation structures, ANNs are not considered sufficient for the general representation of knowledge. This paper details a methodology that represents the knowledge of a trained network in the form of restricted first-order logic rules, and subsequently allows user interaction by interfacing with a knowledge based reasoner
Bearing Similarity Measures for Self-Organizing Feature Maps

The neural representation of space in rats has inspired many navigation systems for robots. In particular, Self-Organizing (Feature) Maps (SOM) are often used to give a sense of location to robots by mapping sensor information to a low-dimensional grid. For example, a robot equipped with a panoramic camera can build a 2D SOM from vectors of landmark bearings. If there are four landmarks in the robot's environment, then the 2D SOM is embedded in a 2D manifold lying in a 4D space. In general, the set of observable sensor vectors form a low-dimensional Riemannian manifold in a high-dimensional space. In a landmark bearing sensor space, the manifold can have a large curvature in some regions (when the robot is near a landmark for example), making the Eulidian distance a very poor approximation of the Riemannian metric. In this paper, we present and compare three methods for measuring the similarity between vectors of landmark bearings. We also discuss a method to equip SOM with a good approximation of the Riemannian metric. Although we illustrate the techniques with a landmark bearing problem, our approach is applicable to other types of data sets
Graphics Hardware Implementation of the Parameter-Less Self Organising Map

This paper presents a highly parallel implementation of a\ud
new type of Self-Organising Map (SOM) using graphics hardware. The\ud
Parameter-Less SOM smoothly adapts to new data while preserving the\ud
mapping formed by previous data. It is therefore in principle highly\ud
suited for interactive use, however for large data sets the computational\ud
requirements are prohibitive. This paper will present an implementation\ud
on commodity graphics hardware which uses two forms of parallelism\ud
to signi¯cantly reduce this barrier. The performance is analysed experi-\ud
mentally and algorithmically. An advantage to using graphics hardware\ud
is that visualisation is essentially free", thus increasing its suitability\ud
for interactive exploration of large data sets
SVM Based Prediction of Bacterial Transcription Start Sites

Identifying bacterial promoters is the key to understanding gene expression. Promoters lie in tightly constrained positions relative to the transcription start site (TSS). Knowing the TSS position, one can predict promoter positions to within a few base pairs, and vice versa. As a route to promoter identification, we formally address the problem of TSS prediction, drawing on the RegulonDB database of known (mapped) Escherichia coli TSS locations. The accepted method of finding promoters (and therefore TSSs) is to use position weight matrices (PWMs). We use an alternative approach based on sup-port vector machines (SVMs). In particular, we quantify performance of several SVM models versus a PWM approach, using area under the detection-error tradeoff (DET) curve as a performance metric. SVM models are shown to out-perform the PWM at TSS prediction, and to substantially reduce numbers of false positives, which are the bane of this problem
Dynamic adaptation for Grid computing

International audienceAs Grid architectures provide resources that uctuate, applications that should be run on such environments must be able to take into account the changes that may occur. This document describes how applications can be built from components that may dynamically adapt themselves. We propose a generic framework to help the developpers of such components. In the case of a component that encapsulates a parallel code, a consistency model for the dynamic adaptation is dened. An implementation of a restricted consistency model allowed us to experiment our ideas
A cluster-based dynamic load balancing middleware protocol for grids

European Grid Conference on Advances in Grid Computing - EGC 2005; Amsterdam; Netherlands; 14 February 2005 through 16 February 2005We describe a hierarchical dynamic load balancing protocol for Grids. The Grid consists of clusters and each cluster is represented by a coordinator. Each coordinator first attempts to balance the load in its cluster and if this fails, communicates with the other coordinators to perform transfer or reception of load. This process is repetaed periodically. We show the implementation and analyze the performance and scalability of the proposed protocol
Using schema transformation pathways for data lineage tracing

With the increasing amount and diversity of information available on the Internet, there has been a huge growth in information systems that need to integrate data from distributed, heterogeneous data sources. Tracing the lineage of the integrated data is one of the problems being addressed in data warehousing research. This paper presents a data lineage tracing approach based on schema transformation pathways. Our approach is not limited to one specific data model or query language, and would be useful in any data transformation/integration framework based on sequences of primitive schema transformations
Question answering pilot task at CLEF 2004

A Pilot Question Answering Task has been activated in the Cross-Language Evaluation Forum 2004 with a twofold objective. In the first place, the evaluation of Question Answering systems when they have to answer conjunctive lists, disjunctive lists and questions with temporal restrictions. In the second place, the evaluation of systems’ capability to give an accurate self-scoring about the confidence on their answers. In this way, two measures have been designed to be applied on all these different types of questions and to reward systems that give a confidence score with a high correlation with the human assessments. The forty eight runs submitted to the Question Answering Main Track have been taken as a case of study, confirming that some systems are able to give a very accurate score and showing how the measures proposed reward this fact
Dynamic diffusion load balancing.

We consider the problem of dynamic load balancing in arbitrary (connected) networks on n nodes. Our load generation model is such that during each round, n tasks are generated on arbitrary nodes, and then (possibly after some balancing) one task is deleted from every non-empty node. Notice that this model fully saturates the resources of the network in the sense that we generate just as many new tasks per round as the network is able to delete. We show that even in this situation the system is stable, in that the total load remains bounded (as a function of n alone) over time. Our proof only requires that the underlying “communication” graph be connected. (It of course also works if we generate less than n new tasks per round, but the major contribution of this paper is the fully saturated case.) We further show that the upper bound we obtain is asymptotically tight (up to a moderate multiplicative constant) by demonstrating a corresponding lower bound on the system load for the particular example of a linear array (or path). We also show some simple negative results (i.e., instability) for work-stealing based diffusion-type algorithms in this setting
Improved Lower Bounds for Locally Decodable Codes and Private
  Information Retrieval

We prove new lower bounds for locally decodable codes and private information
retrieval. We show that a 2-query LDC encoding n-bit strings over an l-bit
alphabet, where the decoder only uses b bits of each queried position of the
codeword, needs code length m = exp(Omega(n/(2^b Sum_{i=0}^b {l choose i})))
Similarly, a 2-server PIR scheme with an n-bit database and t-bit queries,
where the user only needs b bits from each of the two l-bit answers, unknown to
the servers, satisfies t = Omega(n/(2^b Sum_{i=0}^b {l choose i})). This
implies that several known PIR schemes are close to optimal. Our results
generalize those of Goldreich et al. who proved roughly the same bounds for
linear LDCs and PIRs. Like earlier work by Kerenidis and de Wolf, our classical
lower bounds are proved using quantum computational techniques. In particular,
we give a tight analysis of how well a 2-input function can be computed from a
quantum superposition of both inputs.Comment: 12 pages LaTeX, To appear in ICALP '0
Dynamical frustration in ANNNI model and annealing

Zero temperature quench in the Axial Next Nearest Neighbour Ising (ANNNI)
model fails to bring it to its ground state for a certain range of values of
the frustration parameter $\kappa$, the ratio of the next nearest neighbour
antiferromagnetic interaction strength to the nearest neighbour one. We apply
several annealing methods, both classical and quantum, and observe that the
behaviour of the residual energy and the order parameter depends on the value
of $\kappa$ strongly. Classical or thermal annealing is found to be adequate
for small values of $\kappa$.
  However, neither classical nor quantum annealing is effective at values of
$\kappa$ close to the fully frustrated point $\kappa=0.5$, where the residual
energy shows a very slow algebraic decay with the number of MCS.Comment: 6 pages,10 figures, to be published in Proceedings of " The
  International Workshop on Quantum annealing and other Optimization Methods
Finding Exponential Product Formulas of Higher Orders

In the present article, we review a continual effort on generalization of the
Trotter formula to higher-order exponential product formulas. The exponential
product formula is a good and useful approximant, particularly because it
conserves important symmetries of the system dynamics. We focuse on two
algorithms of constructing higher-order exponential product formulas. The first
is the fractal decomposition, where we construct higher-order formulas
recursively. The second is to make use of the quantum analysis, where we
compute higher-order correction terms directly. As interludes, we also have
described the decomposition of symplectic integrators, the approximation of
time-ordered exponentials, and the perturbational composition.Comment: 22 pages, 9 figures. To be published in the conference proceedings
  ''Quantum Annealing and Other Optimization Methods," eds. B.K.Chakrabarti and
  A.Das (Springer, Heidelberg
Learning rules from multisource data for cardiac monitoring

This paper aims at formalizing the concept of learning rules from multisource data in a cardiac monitoring context. Our method has been implemented and evaluated on learning from data describing cardiac behaviors from different viewpoints, here electrocardiograms and arterial blood pressure measures. In order to cope with the dimensionality problems of multisource learning, we propose an Inductive Logic Programming method using a two-step strategy. Firstly, rules are learned independently from each sources. Secondly, the learned rules are used to bias a new learning process from the aggregated data. The results show that the the proposed method is much more efficient than learning directly from the aggregated data. Furthermore, it yields rules having better or equal accuracy than rules obtained by monosource learning.status: publishe
Relation mining over a corpus of scientific literature

The amount of new discoveries (as published in the scientific \ud
literature) in the area of Molecular Biology is currently growing at an \ud
exponential rate. This growth makes it very difficult to filter the most \ud
relevant results, and the extraction of the core information, for inclusion \ud
in one of the knowledge resources being maintained by the research community, becomes very expensive. Therefore, there is a growing interest \ud
in text processing approaches that can deliver selected information from \ud
scientific publications, which can limit the amount of human intervention \ud
normally needed to gather those results. \ud
This paper presents and evaluates an approach aimed at automating \ud
the process of extracting semantic relations (e.g. interactions between \ud
genes and proteins) from scientific literature in the domain of Molecular \ud
Biology. The approach, using a novel dependency-based parser, is based \ud
on a complete syntactic analysis of the corpus
Up and down the number line: Modelling collaboration in contrasting school and home environments

This paper is concerned with user modelling issues such as adaptive educational environments, adaptive information retrieval, and support for collaboration. The HomeWork project is examining the use of learner modelling strategies within both school and home environments for young children aged 5 – 7 years. The learning experience within the home context can vary considerably from school especially for very young learners, and this project focuses on the use of modelling which can take into account the informality and potentially contrasting learning styles experienced within the home and school
Between-Source modelling for likelihood ratio computation in forensic biometric recognition

The final publication is available at Springer via http://dx.doi.org/10.1007/11527923_112Proceedings of 5th International Conference, AVBPA 2005, Hilton Rye Town (NY)In this paper, the use of biometric systems in forensic applications is reviewed. Main differences between the aim of commercial biometric systems and forensic reporting are highlighted, showing that commercial biometric systems are not suited to directly report results to a court of law. We propose the use of a Bayesian approach for forensic reporting, in which the forensic scientist has to assess a meaningful value, in the form of a likelihood ratio (LR). This value assist the court in their decision making in a clear way, and can be computed using scores coming from any biometric system, with independence of the biometric discipline. LR computation in biometric systems is reviewed, and statistical assumptions regarding estimations involved in the process are addressed. The paper is focused in handling small sample size effects in such estimations, presenting novel experiments using a fingerprint and a voice biometric system.This work has been partially supported by the Spanish MCYT projects TIC03 09068-C02-01 and TIC2000 1669-C04-01
The external network problem with edge- or arc-connectivity requirements.

The connectivity of a communications network can often be enhanced if the nodes are able, at some expense, to form links using an external network. In this paper, we consider the problem of how to obtain a prescribed level of connectivity with a minimum number of nodes connecting to the external network.\ud
Let D = (V,A) be a digraph. A subset X of vertices in V may be chosen, the so-called external vertices. An internal path is a normal directed path in D; an external path is a pair of internal paths p1=v1 ⋯ vs, p2=w1 ⋯ wt in D such that vs and w1 are external vertices ( the idea is that v1 can contact wt along this path using an external link from vt to w1 ). Then (D,X) is externally-k-arc-strong if for each pair of vertices u and v in V, there are k arc-disjoint paths ( which may be internal or external ) from u to v.\ud
We present polynomial algorithms that, given a digraph D and positive integer k, will find a set of external vertices X of minimum size subject to the requirement that (D,X) must be externally-k-arc-strong
Managing student expectations online

In contrast to other studies of students in online environments, which examine the skills and attitudes that students bring to an online university learning environment, we are interested in the expectations with which students come to online university study. Four expectational barriers, which arise from students&rsquo; background and cultural history, are identified as being: who is responsible for learning, who is responsible for student interaction with content, who is responsible for the use of appropriate learning strategies and who is responsible for required ancillary skills. There is a discussion of how these barriers arise and how one might attempt to manage the students&rsquo; expectations and ameliorate their effects.<br /
Classifying relational data with neural networks

We introduce a novel method for relational learning with neural networks. The contributions of this paper are threefold. First, we introduce the concept of relational neural networks: feedforward networks with some recurrent components, the structure of which is determined by the relational database schema. For classifying a single tuple, they take as inputs the attribute values of not only the tuple itself, but also of sets of related tuples. We discuss several possible architectures for such networks. Second, we relate the expressiveness of these networks to the 'aggregation vs. selection' dichotomy in current relational learners, and argue that relational neural networks can learn non-trivial combinations of aggregation and selection, a task beyond the capabilities of most current relational learners. Third, we present and motivate different possible training strategies for such networks. We present experimental results on synthetic and benchmark data sets that support our claims and yield insight in the behaviour of the proposed training strategies.status: publishe
Logical Bayesian networks and their relation to other probabilistic logical models

Acceptance rate: 51%Logical Bayesian Networks (LBNs) have recently been introduced as another language for knowledge based model construction of Bayesian networks, besides existing languages such as Probabilistic Relational Models (PRMs) and Bayesian Logic Programs (BLPs). The original description of LBNs introduces them as a variant of BLPs and discusses the differences with BLPs but still leaves room for a deeper discussion of the relationship between LBNs and BLPs. Also the relationship to PRMs was not treated in much detail.status: publishe
Polymorphism and danger susceptibility of system call DASTONs

We have proposed a metaphor â€œDAnger Susceptible daTa codONâ€? (DASTON) in data subject to processing by Danger Theory (DT) based Artificial Immune System (DAIS). The DASTONs are data chunks or data point sets that actively take part to produce â€œdangerâ€?; here we abstract â€œdangerâ€? as required outcome. To have closer look to the metaphor, this paper furthers  biological abstractions for DASTON. Susceptibility of DASTON is important parameter for generating dangerous outcome. In biology, susceptibility of a host to pathogenic activities (potentially dangerous activities) is related to polymorphism. Interestingly, results of experiments conducted for system call DASTONs are in close accordance to biological theory of polymorphism and susceptibility. This shows that computational data (system calls in this case) exhibit biological properties when processed with DT point of view
Approximation results for the weighted P4 partition problem

International audienceWe present several new standard and differential approximation results for the P4-partition problem using the Hassin and Rubinstein' algorithm (Information Processing Letters, 63: 63-67, 1997). Those results concern both minimization and maximization versions of the problem. However, the main point of this paper lies on the establishment it does of the robustness of this algorithm, in the sense that this latter provides good quality solutions, whatever version of the problem is addressed, whatever approximation framework is considered in order to evaluate the approximate solutions