Abstract: This paper presents a new technique for generating sets of synthetic classifiers to evaluate abstract-level combination methods. The sets differ in terms of both recognition rates of the individual classifiers and degree of similarity. For this purpose, each abstract-level classifier is considered as a random variable producing one class label as the output for an input pattern. From the initial set of classifiers, new slightly different sets are generated by applying specific operators, which are defined at the purpose. Finally, the sets of synthetic classifiers have been used to estimate the performance of combination methods for abstract-level classifiers. The experimental results demonstrate the effectiveness of the proposed approach.
Abstract: Trust management is one of the drawbacks in Peer-to-Peer (P2P) system. Lack of centralized control makes it difficult to control the behavior of the peers. Reputation system is one approach to provide trust assessment in P2P system. In this paper, we use fuzzy logic to model trust in a P2P environment. Our trust model combines first-hand (direct experience) and second-hand (reputation)information to allow peers to represent and reason with uncertainty regarding other peers' trustworthiness. Fuzzy logic can help in handling the imprecise nature and uncertainty of trust. Linguistic labels are used to enable peers assign a trust level intuitively. Our fuzzy trust model is flexible such that inference rules are used to weight first-hand and second-hand accordingly.
Abstract: Self-organizing map (SOM) is a well known data reduction technique used in data mining. Data visualization can reveal structure in data sets that is otherwise hard to detect from raw data alone. However, interpretation through visual inspection is prone to errors and can be very tedious. There are several techniques for the automatic detection of clusters of code vectors found by SOMs, but they generally do not take into account the distribution of code vectors; this may lead to unsatisfactory clustering and poor definition of cluster boundaries, particularly where the density of data points is low. In this paper, we propose the use of a generic particle swarm optimization (PSO) algorithm for finding cluster boundaries directly from the code vectors obtained from SOMs. The application of our method to unlabeled call data for a mobile phone operator demonstrates its feasibility. PSO algorithm utilizes U-matrix of SOMs to determine cluster boundaries; the results of this novel automatic method correspond well to boundary detection through visual inspection of code vectors and k-means algorithm.
Abstract: We depend upon explanation in order to “make sense"
out of our world. And, making sense is all the more important when
dealing with change. But, what happens if our explanations are
wrong? This question is examined with respect to two types of
explanatory model. Models based on labels and categories we shall
refer to as “representations." More complex models involving
stories, multiple algorithms, rules of thumb, questions, ambiguity we
shall refer to as “compressions." Both compressions and
representations are reductions. But representations are far more
reductive than compressions. Representations can be treated as a set
of defined meanings – coherence with regard to a representation is
the degree of fidelity between the item in question and the definition
of the representation, of the label. By contrast, compressions contain
enough degrees of freedom and ambiguity to allow us to make
internal predictions so that we may determine our potential actions in
the possibility space. Compressions are explanatory via mechanism.
Representations are explanatory via category. Managers are often
confusing their evocation of a representation (category inclusion) as
the creation of a context of compression (description of mechanism).
When this type of explanatory error occurs, more errors follow. In
the drive for efficiency such substitutions are all too often proclaimed
– at the manager-s peril..
Abstract: Extracting thematic (semantic) roles is one of the
major steps in representing text meaning. It refers to finding the
semantic relations between a predicate and syntactic constituents in a
sentence. In this paper we present a rule-based approach to extract
semantic roles from Persian sentences. The system exploits a twophase
architecture to (1) identify the arguments and (2) label them
for each predicate.
For the first phase we developed a rule based shallow parser to
chunk Persian sentences and for the second phase we developed a
knowledge-based system to assign 16 selected thematic roles to the
chunks. The experimental results of testing each phase are shown at
the end of the paper.
Abstract: In this paper, we evaluate the choice of suitable
quantization characteristics for both the decoder messages and the
received samples in Low Density Parity Check (LDPC) coded
systems using M-QAM (Quadrature Amplitude Modulation)
schemes. The analysis involves the demapper block that provides
initial likelihood values for the decoder, by relating its quantization
strategy of the decoder. A mapping strategy refers to the grouping of
bits within a codeword, where each m-bit group is used to select a
2m-ary signal in accordance with the signal labels. Further we
evaluate the system with mapping strategies like Consecutive-Bit
(CB) and Bit-Reliability (BR). A new demapper version, based on
approximate expressions, is also presented to yield a low complexity
hardware implementation.
Abstract: This paper presents and evaluates a new classification
method that aims to improve classifiers performances and speed up
their training process. The proposed approach, called labeled
classification, seeks to improve convergence of the BP (Back
propagation) algorithm through the addition of an extra feature
(labels) to all training examples. To classify every new example, tests
will be carried out each label. The simplicity of implementation is the
main advantage of this approach because no modifications are
required in the training algorithms. Therefore, it can be used with
others techniques of acceleration and stabilization. In this work, two
models of the labeled classification are proposed: the LMLP
(Labeled Multi Layered Perceptron) and the LNFC (Labeled Neuro
Fuzzy Classifier). These models are tested using Iris, wine, texture
and human thigh databases to evaluate their performances.
Abstract: Alpfa-fetoprotein and its fragments may be an important vehicle for targeted delivery of radionuclides to the tumor. We investigated the effect of conditions on the labeling of biologically active synthetic peptide based on the (F-afp) with technetium-99m. The influence of the nature of the buffer solution, pH, concentration of reductant, concentration of the peptide and the reaction temperature on the yield of labeling was examined. As a result, the following optimal conditions for labeling of (F-afp) are found: pH 8.5 (phosphate and bicarbonate buffers) and pH from 1.7 to 7.0 (citrate buffer). The reaction proceeds with sufficient yield at room temperature for 30 min at the concentration of SnCl2 and (Fafp) (F-afp) is to be less than 10 mkg/ml and 25 mkg/ml, respectively. Investigations of the test drug accumulation in the tumor cells of human breast cancer were carried out. Results can be assumed that the in vivo study of the (F-afp) in experimental tumor lesions will show concentrations sufficient for imaging these lesions by SPECT.
Abstract: As the web continues to grow exponentially, the idea
of crawling the entire web on a regular basis becomes less and less
feasible, so the need to include information on specific domain,
domain-specific search engines was proposed. As more information
becomes available on the World Wide Web, it becomes more difficult
to provide effective search tools for information access. Today,
people access web information through two main kinds of search
interfaces: Browsers (clicking and following hyperlinks) and Query
Engines (queries in the form of a set of keywords showing the topic
of interest) [2]. Better support is needed for expressing one's
information need and returning high quality search results by web
search tools. There appears to be a need for systems that do reasoning
under uncertainty and are flexible enough to recover from the
contradictions, inconsistencies, and irregularities that such reasoning
involves. In a multi-view problem, the features of the domain can be
partitioned into disjoint subsets (views) that are sufficient to learn the
target concept. Semi-supervised, multi-view algorithms, which
reduce the amount of labeled data required for learning, rely on the
assumptions that the views are compatible and uncorrelated. This
paper describes the use of semi-structured machine learning approach
with Active learning for the “Domain Specific Search Engines". A
domain-specific search engine is “An information access system that
allows access to all the information on the web that is relevant to a
particular domain. The proposed work shows that with the help of
this approach relevant data can be extracted with the minimum
queries fired by the user. It requires small number of labeled data and
pool of unlabelled data on which the learning algorithm is applied to
extract the required data.
Abstract: MultiProtocol Label Switching (MPLS) is an
emerging technology that aims to address many of the existing issues
associated with packet forwarding in today-s Internetworking
environment. It provides a method of forwarding packets at a high
rate of speed by combining the speed and performance of Layer 2
with the scalability and IP intelligence of Layer 3. In a traditional IP
(Internet Protocol) routing network, a router analyzes the destination
IP address contained in the packet header. The router independently
determines the next hop for the packet using the destination IP
address and the interior gateway protocol. This process is repeated at
each hop to deliver the packet to its final destination. In contrast, in
the MPLS forwarding paradigm routers on the edge of the network
(label edge routers) attach labels to packets based on the forwarding
Equivalence class (FEC). Packets are then forwarded through the
MPLS domain, based on their associated FECs , through swapping
the labels by routers in the core of the network called label switch
routers. The act of simply swapping the label instead of referencing
the IP header of the packet in the routing table at each hop provides
a more efficient manner of forwarding packets, which in turn allows
the opportunity for traffic to be forwarded at tremendous speeds and
to have granular control over the path taken by a packet. This paper
deals with the process of MPLS forwarding mechanism,
implementation of MPLS datapath , and test results showing the
performance comparison of MPLS and IP routing. The discussion
will focus primarily on MPLS IP packet networks – by far the
most common application of MPLS today.
Abstract: Classifying data hierarchically is an efficient approach
to analyze data. Data is usually classified into multiple categories, or
annotated with a set of labels. To analyze multi-labeled data, such
data must be specified by giving a set of labels as a semantic range.
There are some certain purposes to analyze data. This paper shows
which multi-labeled data should be the target to be analyzed for
those purposes, and discusses the role of a label against a set of
labels by investigating the change when a label is added to the set of
labels. These discussions give the methods for the advanced analysis
of multi-labeled data, which are based on the role of a label against
a semantic range.
Abstract: There exists an injective, information-preserving function
that maps a semantic network (i.e a directed labeled network)
to a directed network (i.e. a directed unlabeled network). The edge
label in the semantic network is represented as a topological feature
of the directed network. Also, there exists an injective function that
maps a directed network to an undirected network (i.e. an undirected
unlabeled network). The edge directionality in the directed network
is represented as a topological feature of the undirected network.
Through function composition, there exists an injective function that
maps a semantic network to an undirected network. Thus, aside from
space constraints, the semantic network construct does not have any
modeling functionality that is not possible with either a directed
or undirected network representation. Two proofs of this idea will
be presented. The first is a proof of the aforementioned function
composition concept. The second is a simpler proof involving an
undirected binary encoding of a semantic network.
Abstract: In this work, we present a novel active learning approach
for learning a visual object detection system. Our system
is composed of an active learning mechanism as wrapper around
a sub-algorithm which implement an online boosting-based learning
object detector. In the core is a combination of a bootstrap procedure
and a semi automatic learning process based on the online boosting
procedure. The idea is to exploit the availability of classifier during
learning to automatically label training samples and increasingly
improves the classifier. This addresses the issue of reducing labeling
effort meanwhile obtain better performance. In addition, we propose
a verification process for further improvement of the classifier.
The idea is to allow re-update on seen data during learning for
stabilizing the detector. The main contribution of this empirical study
is a demonstration that active learning based on an online boosting
approach trained in this manner can achieve results comparable or
even outperform a framework trained in conventional manner using
much more labeling effort. Empirical experiments on challenging data
set for specific object deteciton problems show the effectiveness of
our approach.
Abstract: Radiolabeled cyclic RGD peptides targeting integrin αvβ3 are reported as promising agents for the early diagnosis of metastatic tumors. With an aim to improve tumor uptake and retention of the peptide, cyclic RGD peptide dimer E[c (RGDfK)] 2 (E = Glutamic acid, f = phenyl alanine, K = lysine) coupled to the bifunctional chelator DOTA was custom synthesized and radiolabelled with 68Ga. Radiolabelling of cyclic RGD peptide dimer with 68Ga was carried out using HEPES buffer and biological evaluation of the complex was done in nude mice bearing HT29 tumors.
Abstract: The globe Sustainability has become the subject of international attention, the key reason is that global climate change. Climate and disasters around the abnormal frequency multiplier, the global temperature of the catastrophe and disaster continue to occur throughout the world, as well as countries around the world. Currently there are many important international conferences and policy, it is a "global environmental sustainability " and "living human health " as the goal of development, including the APEC 2007 meeting to "climate Clean Energy" as the theme Sydney Declaration, 2008 World Economic Forum's "Carbon - promote Cool Earth energy efficiency improvement project", the EU proposed "Green Idea" program, the Japanese annual policy, "low-carbon society, sustainable eco-city environment (Eco City) "And from 2009 to 2010 to promote the "Eco-Point" to promote green energy and carbon reduction products .And the 2010 World Climate Change Conference (COP16 United Nations Climate Change Conference Copenhagen), the world has been the subject of Negative conservative "Environmental Protection ", "save energy consumption, " into a positive response to the "Sustainable " and" LOHAS", while Taiwan has actively put forward eco-cities, green building, green building materials and other related environmental response Measures, especially green building construction environment that is the basis of factors, the most widely used application level, and direct contact with human health and the key to sustainable planet. "Sustainable development "is a necessary condition for continuation of the Earth, "healthy and comfortable" is a necessary condition for the continuation of life, and improve the "quality" is a necessary condition for economic development, balance between the three is "to enhance the efficiency of ", According to the World Business Council for Sustainable Development (WBCSD) for the "environmental efficiency "(Eco-Efficiency) proposed: " the achievement of environmental efficiency, the price to be competitive in the provision of goods or services to meet people's needs, improve living Quality at the same time, the goods or services throughout the life cycle. Its impact on the environment and natural resource utilization and gradually reduced to the extent the Earth can load. "whichever is the economy "Economic" and " Ecologic". The research into the methodology to obtain the Taiwan Green Building Material Labeling product as the scope of the study, by investigating and weight analysis to explore green building environmental load (Ln) factor and the Green Building Quality (Qn) factor to Establish green building environmental efficiency assessment model (GBM Eco-Efficiency). And building materials for healthy green label products for priority assessment object, the object is set in the material evidence for the direct response to the environmental load from the floor class-based, explicit feedback correction to the Green Building environmental efficiency assessment model, "efficiency " as a starting point to achieve balance between human "health "and Earth "sustainable development of win-win strategy. The study is expected to reach 1.To establish green building materials and the quality of environmental impact assessment system, 2. To establish value of GBM Eco-Efficiency model, 3. To establish the GBM Eco-Efficiency model for application of green building material feedback mechanisms.
Abstract: In this study, a Loop Back Algorithm for component
connected labeling for detecting objects in a digital image is
presented. The approach is using loop back connected component
labeling algorithm that helps the system to distinguish the object
detected according to their label. Deferent than whole window
scanning technique, this technique reduces the searching time for
locating the object by focusing on the suspected object based on
certain features defined. In this study, the approach was also
implemented for a face detection system. Face detection system is
becoming interesting research since there are many devices or
systems that require detecting the face for certain purposes. The input
can be from still image or videos, therefore the sub process of this
system has to be simple, efficient and accurate to give a good result.
Abstract: We demonstrate a nonfaradaic electrochemical impedance spectroscopy measurement of biochemically modified gold plated electrodes using a two-electrode system. The absence of any redox indicator in the impedance measurements provide more precise and accurate characterization of the measured bioanalyte at molecular resolution. An equivalent electrical circuit of the electrodeelectrolyte interface was deduced from the observed impedance data of saline solution at low and high concentrations. The detection of biomolecular interactions was fundamentally correlated to electrical double-layer variation at modified interface. The investigations were done using 20mer deoxyribonucleic acid (DNA) strands without any label. Surface modification was performed by creating mixed monolayer of the thiol-modified single-stranded DNA and a spacer thiol (mercaptohexanol) by a two-step self-assembly method. The results clearly distinguish between the noncomplementary and complementary hybridization of DNA, at low frequency region below several hundreds Hertz.
Abstract: The similarity comparison of RNA secondary
structures is important in studying the functions of RNAs. In recent
years, most existing tools represent the secondary structures by
tree-based presentation and calculate the similarity by tree alignment
distance. Different to previous approaches, we propose a new method
based on maximum clique detection algorithm to extract the maximum
common structural elements in compared RNA secondary structures.
A new graph-based similarity measurement and maximum common
subgraph detection procedures for comparing purely RNA secondary
structures is introduced. Given two RNA secondary structures, the
proposed algorithm consists of a process to determine the score of the
structural similarity, followed by comparing vertices labelling, the
labelled edges and the exact degree of each vertex. The proposed
algorithm also consists of a process to extract the common structural
elements between compared secondary structures based on a proposed
maximum clique detection of the problem. This graph-based model
also can work with NC-IUB code to perform the pattern-based
searching. Therefore, it can be used to identify functional RNA motifs
from database or to extract common substructures between complex
RNA secondary structures. We have proved the performance of this
proposed algorithm by experimental results. It provides a new idea of
comparing RNA secondary structures. This tool is helpful to those
who are interested in structural bioinformatics.
Abstract: The vertex connectivity of a graph is the smallest number of vertices whose deletion separates the graph or makes it trivial. This work is devoted to the problem of vertex connectivity test of graphs in a distributed environment based on a general and a constructive approach. The contribution of this paper is threefold. First, using a preconstructed spanning tree of the considered graph, we present a protocol to test whether a given graph is 2-connected using only local knowledge. Second, we present an encoding of this protocol using graph relabeling systems. The last contribution is the implementation of this protocol in the message passing model. For a given graph G, where M is the number of its edges, N the number of its nodes and Δ is its degree, our algorithms need the following requirements: The first one uses O(Δ×N2) steps and O(Δ×logΔ) bits per node. The second one uses O(Δ×N2) messages, O(N2) time and O(Δ × logΔ) bits per node. Furthermore, the studied network is semi-anonymous: Only the root of the pre-constructed spanning tree needs to be identified.
Abstract: This study was carried out to investigate lamb mortalities relating to ewes' breed and some managemental factors on 250 pregnant ewes (190-Rahmani, 30-Ossimi and 30-Romanov) at Mehallet Mousa, Animal Production Research Station, Kafr El- Sheikh Province, Egypt. These animals divided into five groups according to the managemental factors used. The results revealed that the lamb mortality was higher in Ossimi breed and lower in Romanov one. In addition, the highest lamb mortality occurred among lambs for unsupplemented ewes, for those had body condition score two and for lambs which born outdoor. Moreover, the lamb survivability was increased by the parity of ewes. From this study it can be concluded that the lamb mortality depends on ewes' body condition score, parity, lambing system (indoor or outdoor), nutrition during pregnancy period and selected breed. In addition, the most important period for lamb survival is the first week of age.