1,720,971 research outputs found
A Neuro-Symbolic Artificial Intelligence Network Intrusion Detection System
No full textEver-changing cyber threats require strong and flexible network security solutions. This paper suggests a new method to improve the performance of detecting both known and unknown attacks using a neuro-symbolic artificial intelligence (NSAI) network intrusion detection system (NIDS). Deep neural networks (DNN) learn complex network data patterns, which create a detailed overview of cyber-attack characteristics. Symbolic logic integration into the DNN allows for model training guidance by applying penalties when the DNN fails to differentiate between malicious and benign network traffic. This improves our model’s adaptability to new attacks and overcomes traditional signature-based NIDS limitations. By testing our NSAI NIDS on a large cyber dataset that includes novel attack scenarios, we show that it delivers an improvement in how accurately it detects attacks compared to traditional DNN methods. While our system maintains its high accuracy in recognizing known attacks, it outperforms conventional NIDS in discovering unknown attacks. This work improves cybersecurity by introducing a new way to detect both known and unknown network intrusions by combining DNNs with symbolic logic
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Knowledge Engineering for Hybrid Intelligence
No full textHybrid Intelligence (HI) is a rapidly growing field aiming at creating collaborative systems where humans and intelligent machines cooperate in mixed teams towards shared goals. A clear characterization of the tasks and knowledge exchanged by the agents in HI applications is still missing, hampering both standardization and reuse when designing new HI systems. Knowledge Engineering (KE) methods have been used to solve such issue through the formalization of tasks and roles in knowledge-intensive processes. We investigate whether KE methods can be applied to HI scenarios, and specifically whether common, reusable elements such as knowledge roles, tasks and subtasks can be identified in contexts where symbolic, subsymbolic and human-in-the-loop components are involved. We first adapt the well-known CommonKADS methodology to HI, and then use it to analyze several HI projects and identify common tasks. The results are (i) a high-level ontology of HI knowledge roles, (ii) a set of novel, HI-specific tasks and (iii) an open repository to store scenarios1 - allowing reuse, validation and design of existing and new HI applications
Advancing data sharing and reusability for restricted access data on the Web: Introducing the DataSet-Variable Ontology
Full text linkIn response to the increasing volume of research data being gener-
ated, more and more data portals have been designed to facilitate
data findability and accessibility. However, a significant portion
of this data remains confidential or restricted due to its sensitive
nature, such as patient data or census microdata. While maintaining
confidentiality prohibits its public release, the emergence of portals
supporting rich metadata can help enable researchers to at least
discover the existence of restricted access data, empowering them
to assess the suitability of the data before requesting access.
Existing standards, such as CSV on the Web and RDF Data Cube,
have been adopted to facilitate data management, integration, and
re-use of data on the Web. However, the current landscape still
lacks adequate standards not only to effectively describe restricted
access data while preserving confidentiality but also to facilitate its
discovery. In this work, we investigate the relationship between the
structural, statistical, and semantic elements of restricted access
tabular data, and we explore how such relationship can be formally
modeled in a way that is Findable, Accessible, Interoperable, and
Reusable. We introduce the DataSet-Variable Ontology (DSV), that
by combining CSV on the Web and RDF Data Cube standards,
leveraging semantic technologies and Linked Data principles, and
introducing variable-level metadata, aims to capture high-quality
metadata to support the management and re-use of restricted access
data on the Web. As evaluation, we conducted a case study where
we applied DSV to four different datasets from different statistical
governmental agencies. We employed a set of competency questions
to assess the ontology’s ability to support knowledge discovery and
data exploration
Knowledge-enhanced Agents for Interactive Text Games
Full text linkCommunication via natural language is a key aspect of machine intelligence,
and it requires computational models to learn and reason about world concepts,
with varying levels of supervision. Significant progress has been made on
fully-supervised non-interactive tasks, such as question-answering and
procedural text understanding. Yet, various sequential interactive tasks, as in
text-based games, have revealed limitations of existing approaches in terms of
coherence, contextual awareness, and their ability to learn effectively from
the environment. In this paper, we propose a knowledge-injection framework for
improved functional grounding of agents in text-based games. Specifically, we
consider two forms of domain knowledge that we inject into learning-based
agents: memory of previous correct actions and affordances of relevant objects
in the environment. Our framework supports two representative model classes:
reinforcement learning agents and language model agents. Furthermore, we devise
multiple injection strategies for the above domain knowledge types and agent
architectures, including injection via knowledge graphs and augmentation of the
existing input encoding strategies. We experiment with four models on the 10
tasks in the ScienceWorld text-based game environment, to illustrate the impact
of knowledge injection on various model configurations and challenging task
settings. Our findings provide crucial insights into the interplay between task
properties, model architectures, and domain knowledge for interactive contexts.Comment: Published at K-CAP '2
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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