1,929 research outputs found
Complexity and modeling power of insertion-deletion systems
SISTEMAS DE INSERCIÓN Y BORRADO: COMPLEJIDAD Y
CAPACIDAD DE MODELADO
El objetivo central de la tesis es el estudio de los sistemas de inserción y borrado y su
capacidad computacional. Más concretamente, estudiamos algunos modelos de
generación de lenguaje que usan operaciones de reescritura de dos cadenas. También
consideramos una variante distribuida de los sistemas de inserción y borrado en el
sentido de que las reglas se separan entre un número finito de nodos de un grafo.
Estos sistemas se denominan sistemas controlados mediante grafo, y aparecen en
muchas áreas de la Informática, jugando un papel muy importante en los lenguajes
formales, la lingüística y la bio-informática. Estudiamos la decidibilidad/
universalidad de nuestros modelos mediante la variación de los parámetros de tamaño
del vector. Concretamente, damos respuesta a la cuestión más importante
concerniente a la expresividad de la capacidad computacional: si nuestro modelo es
equivalente a una máquina de Turing o no. Abordamos sistemáticamente las
cuestiones sobre los tamaños mínimos de los sistemas con y sin control de grafo.COMPLEXITY AND MODELING POWER OF
INSERTION-DELETION SYSTEMS
The central object of the thesis are insertion-deletion systems and their computational
power. More specifically, we study language generating models that use two string
rewriting operations: contextual insertion and contextual deletion, and their
extensions. We also consider a distributed variant of insertion-deletion systems in the
sense that rules are separated among a finite number of nodes of a graph. Such
systems are refereed as graph-controlled systems. These systems appear in many
areas of Computer Science and they play an important role in formal languages,
linguistics, and bio-informatics. We vary the parameters of the vector of size of
insertion-deletion systems and we study decidability/universality of obtained models.
More precisely, we answer the most important questions regarding the expressiveness
of the computational model: whether our model is Turing equivalent or not. We
systematically approach the questions about the minimal sizes of the insertiondeletion
systems with and without the graph-control
When Stars Control a Grammar's Work
Graph-controlled insertion-deletion (GCID) systems are regulated extensions
of insertion-deletion systems. Such a system has several components and each
component contains some insertion-deletion rules. The components are the
vertices of a directed control graph. A rule is applied to a string in a
component and the resultant string is moved to the target component specified
in the rule. The language of the system is the set of all terminal strings
collected in the final component. We impose the restriction in the structure of
the underlying graph to be a star structure where there is a central, control
component which acts like a master and transmits a string (after applying one
of its rules) to one of the components specified in the (applied) rule. A
component which receives the string can process the obtained string with any
applicable rule available in it and sends back the resultant string only to the
center component. With this restriction, we obtain computational completeness
for some descriptional complexity measuresComment: In Proceedings AFL 2023, arXiv:2309.0112
AttributionLab: Faithfulness of Feature Attribution Under Controllable Environments
Feature attribution explains neural network outputs by identifying relevant
input features. How do we know if the identified features are indeed relevant
to the network? This notion is referred to as faithfulness, an essential
property that reflects the alignment between the identified (attributed)
features and the features used by the model. One recent trend to test
faithfulness is to design the data such that we know which input features are
relevant to the label and then train a model on the designed data.
Subsequently, the identified features are evaluated by comparing them with
these designed ground truth features. However, this idea has the underlying
assumption that the neural network learns to use all and only these designed
features, while there is no guarantee that the learning process trains the
network in this way. In this paper, we solve this missing link by explicitly
designing the neural network by manually setting its weights, along with
designing data, so we know precisely which input features in the dataset are
relevant to the designed network. Thus, we can test faithfulness in
AttributionLab, our designed synthetic environment, which serves as a sanity
check and is effective in filtering out attribution methods. If an attribution
method is not faithful in a simple controlled environment, it can be unreliable
in more complex scenarios. Furthermore, the AttributionLab environment serves
as a laboratory for controlled experiments through which we can study feature
attribution methods, identify issues, and suggest potential improvements.Comment: 32 pages including Appendi
Secure Time-Aware Provenance for Distributed Systems
Operators of distributed systems often find themselves needing to answer forensic questions, to perform a variety of managerial tasks including fault detection, system debugging, accountability enforcement, and attack analysis. In this dissertation, we present Secure Time-Aware Provenance (STAP), a novel approach that provides the fundamental functionality required to answer such forensic questions – the capability to “explain” the existence (or change) of a certain distributed system state at a given time in a potentially adversarial environment.
This dissertation makes the following contributions. First, we propose the STAP model, to explicitly represent time and state changes. The STAP model allows consistent and complete explanations of system state (and changes) in dynamic environments. Second, we show that it is both possible and practical to efficiently and scalably maintain and query provenance in a distributed fashion, where provenance maintenance and querying are modeled as recursive continuous queries over distributed relations. Third, we present security extensions that allow operators to reliably query provenance information in adversarial environments. Our extensions incorporate tamper-evident properties that guarantee eventual detection of compromised nodes that lie or falsely implicate correct nodes. Finally, the proposed research results in a proof-of-concept prototype, which includes a declarative query language for specifying a range of useful provenance queries, an interactive exploration tool, and a distributed provenance engine for operators to conduct analysis of their distributed systems. We discuss the applicability of this tool in several use cases, including Internet routing, overlay routing, and cloud data processing
SoK: Cryptographically Protected Database Search
Protected database search systems cryptographically isolate the roles of
reading from, writing to, and administering the database. This separation
limits unnecessary administrator access and protects data in the case of system
breaches. Since protected search was introduced in 2000, the area has grown
rapidly; systems are offered by academia, start-ups, and established companies.
However, there is no best protected search system or set of techniques.
Design of such systems is a balancing act between security, functionality,
performance, and usability. This challenge is made more difficult by ongoing
database specialization, as some users will want the functionality of SQL,
NoSQL, or NewSQL databases. This database evolution will continue, and the
protected search community should be able to quickly provide functionality
consistent with newly invented databases.
At the same time, the community must accurately and clearly characterize the
tradeoffs between different approaches. To address these challenges, we provide
the following contributions:
1) An identification of the important primitive operations across database
paradigms. We find there are a small number of base operations that can be used
and combined to support a large number of database paradigms.
2) An evaluation of the current state of protected search systems in
implementing these base operations. This evaluation describes the main
approaches and tradeoffs for each base operation. Furthermore, it puts
protected search in the context of unprotected search, identifying key gaps in
functionality.
3) An analysis of attacks against protected search for different base
queries.
4) A roadmap and tools for transforming a protected search system into a
protected database, including an open-source performance evaluation platform
and initial user opinions of protected search.Comment: 20 pages, to appear to IEEE Security and Privac
Image Understanding by Hierarchical Symbolic Representation and Inexact Matching of Attributed Graphs
We study the symbolic representation of imagery information by a powerful global representation scheme in the form of Attributed Relational Graph (ARG), and propose new techniques for the extraction of such representation from spatial-domain images, and for performing the task of image understanding through the analysis of the extracted ARG representation. To achieve practical image understanding tasks, the system needs to comprehend the imagery information in a global form. Therefore, we propose a multi-layer hierarchical scheme for the extraction of global symbolic representation from spatial-domain images. The proposed scheme produces a symbolic mapping of the input data in terms of an output alphabet, whose elements are defined over global subimages. The proposed scheme uses a combination of model-driven and data-driven concepts. The model- driven principle is represented by a graph transducer, which is used to specify the alphabet at each layer in the scheme. A symbolic mapping is driven by the input data to map the input local alphabet into the output global alphabet. Through the iterative application of the symbolic transformational mapping at different levels of hierarchy, the system extracts a global representation from the image in the form of attributed relational graphs. Further processing and interpretation of the imagery information can, then, be performed on their ARG representation. We also propose an efficient approach for calculating a distance measure and finding the best inexact matching configuration between attributed relational graphs. For two ARGs, we define sequences of weighted error-transformations which when performed on one ARG (or a subgraph of it), will produce the other ARG. A distance measure between two ARGs is defined as the weight of the sequence which possesses minimum total-weight. Moreover, this minimum-total weight sequence defines the best inexact matching configuration between the two ARGs. The global minimization over the possible sequences is performed by a dynamic programming technique, the approach shows good results for ARGs of practical sizes. The proposed system possesses the capability to inference the alphabets of the ARG representation which it uses. In the inference phase, the hierarchical scheme is usually driven by the input data only, which normally consist of images of model objects. It extracts the global alphabet of the ARG representation of the models. The extracted model representation is then used in the operation phase of the system to: perform the mapping in the multi-layer scheme. We present our experimental results for utilizing the proposed system for locating objects in complex scenes
- …