35,970 research outputs found
CONSTRAINT BASED ANALYSIS OF DATABASE UPDATE PROPAGATION
Semantic and object-oriented data models provide convenient constructs for the specification of objects, relationships, and operations. The vehicle of representation is a collection of abstractions which parallel the means by which humans prefer to organize complex enterprises. These constructs inherently permit focus on one object, relationship, or operation at a time. Propagation, as a semantic construct, provides the extension of existing modeling capabilities by providing a mechanism for the specification of the update semantics between database objects. Through the analysis of constraints and the propagated actions necessary to maintain them, we attempt to do the following: 1) incorporate additional semantics into the database schema in the form of database propagation rules, 2) in the context of constraints and propagation rules, provide a model independent paradigm for determining if schemata are correct, and 3) provide a vehicle fur the explicit specification of update actions during database schema design
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
This paper aims at constructing a good graph for discovering intrinsic data
structures in a semi-supervised learning setting. Firstly, we propose to build
a non-negative low-rank and sparse (referred to as NNLRS) graph for the given
data representation. Specifically, the weights of edges in the graph are
obtained by seeking a nonnegative low-rank and sparse matrix that represents
each data sample as a linear combination of others. The so-obtained NNLRS-graph
can capture both the global mixture of subspaces structure (by the low
rankness) and the locally linear structure (by the sparseness) of the data,
hence is both generative and discriminative. Secondly, as good features are
extremely important for constructing a good graph, we propose to learn the data
embedding matrix and construct the graph jointly within one framework, which is
termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive
experiments on three publicly available datasets demonstrate that the proposed
method outperforms the state-of-the-art graph construction method by a large
margin for both semi-supervised classification and discriminative analysis,
which verifies the effectiveness of our proposed method
Coupled Deep Learning for Heterogeneous Face Recognition
Heterogeneous face matching is a challenge issue in face recognition due to
large domain difference as well as insufficient pairwise images in different
modalities during training. This paper proposes a coupled deep learning (CDL)
approach for the heterogeneous face matching. CDL seeks a shared feature space
in which the heterogeneous face matching problem can be approximately treated
as a homogeneous face matching problem. The objective function of CDL mainly
includes two parts. The first part contains a trace norm and a block-diagonal
prior as relevance constraints, which not only make unpaired images from
multiple modalities be clustered and correlated, but also regularize the
parameters to alleviate overfitting. An approximate variational formulation is
introduced to deal with the difficulties of optimizing low-rank constraint
directly. The second part contains a cross modal ranking among triplet domain
specific images to maximize the margin for different identities and increase
data for a small amount of training samples. Besides, an alternating
minimization method is employed to iteratively update the parameters of CDL.
Experimental results show that CDL achieves better performance on the
challenging CASIA NIR-VIS 2.0 face recognition database, the IIIT-D Sketch
database, the CUHK Face Sketch (CUFS), and the CUHK Face Sketch FERET (CUFSF),
which significantly outperforms state-of-the-art heterogeneous face recognition
methods.Comment: AAAI 201
Adaptive laser link reconfiguration using constraint propagation
This paper describes Harris AI research performed on the Adaptive Link Reconfiguration (ALR) study for Rome Lab, and focuses on the application of constraint propagation to the problem of link reconfiguration for the proposed space based Strategic Defense System (SDS) Brilliant Pebbles (BP) communications system. According to the concept of operations at the time of the study, laser communications will exist between BP's and to ground entry points. Long-term links typical of RF transmission will not exist. This study addressed an initial implementation of BP's based on the Global Protection Against Limited Strikes (GPALS) SDI mission. The number of satellites and rings studied was representative of this problem. An orbital dynamics program was used to generate line-of-site data for the modeled architecture. This was input into a discrete event simulation implemented in the Harris developed COnstraint Propagation Expert System (COPES) Shell, developed initially on the Rome Lab BM/C3 study. Using a model of the network and several heuristics, the COPES shell was used to develop the Heuristic Adaptive Link Ordering (HALO) Algorithm to rank and order potential laser links according to probability of communication. A reduced set of links based on this ranking would then be used by a routing algorithm to select the next hop. This paper includes an overview of Constraint Propagation as an Artificial Intelligence technique and its embodiment in the COPES shell. It describes the design and implementation of both the simulation of the GPALS BP network and the HALO algorithm in COPES. This is described using a 59 Data Flow Diagram, State Transition Diagrams, and Structured English PDL. It describes a laser communications model and the heuristics involved in rank-ordering the potential communication links. The generation of simulation data is described along with its interface via COPES to the Harris developed View Net graphical tool for visual analysis of communications networks. Conclusions are presented, including a graphical analysis of results depicting the ordered set of links versus the set of all possible links based on the computed Bit Error Rate (BER). Finally, future research is discussed which includes enhancements to the HALO algorithm, network simulation, and the addition of an intelligent routing algorithm for BP
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A practical mandatory access control model for XML databases
A practical mandatory access control (MAC) model for XML databases is presented in this paper. The
label type and label access policy can be defined according to the requirements of different applications. In order to
preserve the integrity of data in XML databases, a constraint between a read-access rule and a write-access rule in
label access policy is introduced. Rules for label assignment and propagation are presented to alleviate the workload
of label assignments. Furthermore, a solution for resolving conflicts in label assignments is proposed. Rules for
update-related operations, rules for exceptional privileges of ordinary users and the administrator are also proposed
to preserve the security of operations in XML databases. The MAC model, we proposed in this study, has been
implemented in an XML database. Test results demonstrated that our approach provides rational and scalable
performance
A Rule-Based Approach to Analyzing Database Schema Objects with Datalog
Database schema elements such as tables, views, triggers and functions are
typically defined with many interrelationships. In order to support database
users in understanding a given schema, a rule-based approach for analyzing the
respective dependencies is proposed using Datalog expressions. We show that
many interesting properties of schema elements can be systematically determined
this way. The expressiveness of the proposed analysis is exemplarily shown with
the problem of computing induced functional dependencies for derived relations.
The propagation of functional dependencies plays an important role in data
integration and query optimization but represents an undecidable problem in
general. And yet, our rule-based analysis covers all relational operators as
well as linear recursive expressions in a systematic way showing the depth of
analysis possible by our proposal. The analysis of functional dependencies is
well-integrated in a uniform approach to analyzing dependencies between schema
elements in general.Comment: Pre-proceedings paper presented at the 27th International Symposium
on Logic-Based Program Synthesis and Transformation (LOPSTR 2017), Namur,
Belgium, 10-12 October 2017 (arXiv:1708.07854
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