scholarly journals Directed Reachability for Infinite-State Systems

2021 ◽  
pp. 3-23
Author(s):  
Michael Blondin ◽  
Christoph Haase ◽  
Philip Offtermatt
Keyword(s):  
Petri Nets ◽  
Program Analysis ◽  
Infinite Graphs ◽  
Reachability Problem ◽  
Graph Exploration ◽  
Concurrent Program ◽  
Domain Specific ◽  
Novel Approach ◽  
Distance Oracles ◽  
Analysis And Synthesis

AbstractNumerous tasks in program analysis and synthesis reduce to deciding reachability in possibly infinite graphs such as those induced by Petri nets. However, the Petri net reachability problem has recently been shown to require non-elementary time, which raises questions about the practical applicability of Petri nets as target models. In this paper, we introduce a novel approach for efficiently semi-deciding the reachability problem for Petri nets in practice. Our key insight is that computationally lightweight over-approximations of Petri nets can be used as distance oracles in classical graph exploration algorithms such as $$\mathsf {A}^{*}$$ A ∗ and greedy best-first search. We provide and evaluate a prototype implementation of our approach that outperforms existing state-of-the-art tools, sometimes by orders of magnitude, and which is also competitive with domain-specific tools on benchmarks coming from program synthesis and concurrent program analysis.

scholarly journals DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang
Keyword(s):  
Deep Learning ◽  
Complete Information ◽  
Specific Information ◽  
Multimodal Integration ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Input Feature ◽  
Novel Approach ◽  
Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

scholarly journals Application Layer Packet Processing Using PISA Switches

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8010
Author(s):  
Ismail Butun ◽  
Yusuf Tuncel ◽  
Kasim Oztoprak
Keyword(s):  
Streaming Data ◽  
The Novel ◽  
Application Layer ◽  
Domain Specific ◽  
Switch Architecture ◽  
Novel Approach ◽  
Data Processor ◽  
Application Identification ◽  
The Cost ◽  
Telecommunication Operators

This paper investigates and proposes a solution for Protocol Independent Switch Architecture (PISA) to process application layer data, enabling the inspection of application content. PISA is a novel approach in networking where the switch does not run any embedded binary code but rather an interpreted code written in a domain-specific language. The main motivation behind this approach is that telecommunication operators do not want to be locked in by a vendor for any type of networking equipment, develop their own networking code in a hardware environment that is not governed by a single equipment manufacturer. This approach also eases the modeling of equipment in a simulation environment as all of the components of a hardware switch run the same compatible code in a software modeled switch. The novel techniques in this paper exploit the main functions of a programmable switch and combine the streaming data processor to create the desired effect from a telecommunication operator perspective to lower the costs and govern the network in a comprehensive manner. The results indicate that the proposed solution using PISA switches enables application visibility in an outstanding performance. This ability helps the operators to remove a fundamental gap between flexibility and scalability by making the best use of limited compute resources in application identification and the response to them. The experimental study indicates that, without any optimization, the proposed solution increases the performance of application identification systems 5.5 to 47.0 times. This study promises that DPI, NGFW (Next-Generation Firewall), and such application layer systems which have quite high costs per unit traffic volume and could not scale to a Tbps level, can be combined with PISA to overcome the cost and scalability issues.

Layered Region Based Flow-Sensitive Demand-Driven Alias Analysis

2014 ◽  
Vol 577 ◽  
pp. 917-920
Author(s):  
Long Pang ◽  
Xiao Hong Su ◽  
Pei Jun Ma ◽  
Ling Ling Zhao
Keyword(s):  
Program Analysis ◽  
Control Flow ◽  
Alias Analysis ◽  
Reachability Problem ◽  
Analysis Algorithm ◽  
Context Free Language ◽  
Context Free ◽  
Free Language ◽  
Demand Driven Analysis

The pointer alias is indispensable for program analysis. Comparing to point-to set, it’s more efficient to formulate the alias as the context free language (CFL) reachability problem. However, the precision is limited to flow-insensitivity. To solve this problem, we propose a flow sensitive, demand-driven analysis algorithm for answering may-alias queries. First the partial single static assignment is used to discriminate the address-taken pointers. Then the order of control flow is encoded in the level linearization code to ease comparison. Finally, the query of alias in demand driven is converted into the search of CFL reachability with feasible flows. The experiments demonstrate the effectiveness of the proposed approach.

Mining Domain Terminologies Using Search Engine's Query Log

2021 ◽  
Vol 20 (6) ◽  
pp. 1-32
Author(s):  
Weijian Ni ◽  
Tong Liu ◽  
Qingtian Zeng ◽  
Nengfu Xie
Keyword(s):  
Language Processing ◽  
Heterogeneous Network ◽  
Transductive Learning ◽  
Query Log ◽  
Domain Specific ◽  
Novel Approach ◽  
Commercial Search Engine ◽  
Domain Expertise ◽  
Traditional Approaches ◽  
Domain Independent

Domain terminologies are a basic resource for various natural language processing tasks. To automatically discover terminologies for a domain of interest, most traditional approaches mostly rely on a domain-specific corpus given in advance; thus, the performance of traditional approaches can only be guaranteed when collecting a high-quality domain-specific corpus, which requires extensive human involvement and domain expertise. In this article, we propose a novel approach that is capable of automatically mining domain terminologies using search engine's query log—a type of domain-independent corpus of higher availability, coverage, and timeliness than a manually collected domain-specific corpus. In particular, we represent query log as a heterogeneous network and formulate the task of mining domain terminology as transductive learning on the heterogeneous network. In the proposed approach, the manifold structure of domain-specificity inherent in query log is captured by using a novel network embedding algorithm and further exploited to reduce the need for the manual annotation efforts for domain terminology classification. We select Agriculture and Healthcare as the target domains and experiment using a real query log from a commercial search engine. Experimental results show that the proposed approach outperforms several state-of-the-art approaches.

A Novel Approach for Domain Specific Lucky Web Search

2010 ◽  
Vol 5 (5) ◽  
pp. 72-80 ◽  
Author(s):  
Debajyoti Mukhopadhyay ◽  
Sukanta Sinha
Keyword(s):  
Web Search ◽  
Domain Specific ◽  
Novel Approach

INTENT RECOGNITION IN MULTI-AGENT SYSTEMS: COW HERDING

2014 ◽  
Vol 28 (05) ◽  
pp. 1459006
Author(s):  
NAJLA AHMAD ◽  
ARVIN AGAH
Keyword(s):  
Conceptual Framework ◽  
Recognition System ◽  
Plan Recognition ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Intent Recognition ◽  
Domain Specific ◽  
Novel Approach ◽  
Multi Agent

In a multi-agent system, an agent may utilize its idle time to assist other agents in the system. Intent recognition is proposed to accomplish this with minimal communication. An agent performing recognition observes the tasks other agents are performing and, unlike the much studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. A conceptual framework is proposed for intent recognition systems. An implementation of the conceptual framework is tested and evaluated. We hypothesize that using intent recognition in a multi-agent system increases utility (where utility is domain specific) and decreases the amount of communication. We test our hypotheses using the domain of Cow Herding, where agents attempt to herd cow agents into team corrals. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. In our results, we find that intent recognition agents communicate fewer times than plan recognition agents. In addition, unlike plan recognition, when agents use the novel approach of intent recognition, they select unobserved actions to perform. Intent recognition agents were also able to outperform plan recognition agents by consistently scoring more points in the Cow Herding domain. This research shows that under certain conditions, an intent recognition system is more efficient than a plan recognition system. The advantage of intent recognition over plan recognition becomes more apparent in complex domains.

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