48 research outputs found
Decision-Aware Actor-Critic with Function Approximation and Theoretical Guarantees
Actor-critic (AC) methods are widely used in reinforcement learning (RL) and
benefit from the flexibility of using any policy gradient method as the actor
and value-based method as the critic. The critic is usually trained by
minimizing the TD error, an objective that is potentially decorrelated with the
true goal of achieving a high reward with the actor. We address this mismatch
by designing a joint objective for training the actor and critic in a
decision-aware fashion. We use the proposed objective to design a generic, AC
algorithm that can easily handle any function approximation. We explicitly
characterize the conditions under which the resulting algorithm guarantees
monotonic policy improvement, regardless of the choice of the policy and critic
parameterization. Instantiating the generic algorithm results in an actor that
involves maximizing a sequence of surrogate functions (similar to TRPO, PPO)
and a critic that involves minimizing a closely connected objective. Using
simple bandit examples, we provably establish the benefit of the proposed
critic objective over the standard squared error. Finally, we empirically
demonstrate the benefit of our decision-aware actor-critic framework on simple
RL problems.Comment: 44 page
Identification and Modeling Social Media Influence Pathways: a Characterization of a Disinformation Campaign Using the Flooding-the-zone Strategy via Transfer Entropy
The internet has made it easy for narratives to spread quickly and widely without regard for accuracy or the harm they may cause to society. Unfortunately, this has led to the rise of bad actors who use fake and misleading articles to spread harmful misinformation. These actors flood the information space with low-quality articles in an effort to disrupt opposing narratives, sow confusion, and discourage the pursuit of truth. In societies that prioritize free speech, maintaining control over the information space remains a persistent challenge. Achieving this requires strategic planning to protect the dissemination of information in ways that promote dialogue towards organic consensus building and protect users from undue manipulation from foreign adversarial state actors. The objective of this dissertation is to investigate how bad actors can manipulate the information space in societies that value free speech. To achieve this objective, we will define the different narratives used to flood the information space, identify the controversial elements that contribute to their spread, and analyze the actors involved in promoting these narratives and their levels of influence. To gain a deeper understanding of the dynamics that underlie information space flooding, we will examine the flow of influence from news organizations to online users across multiple social networks, and explore the formation of online communities and echo chambers that align with specific narratives. We will also investigate the role of controversiality in information and influence spread, specifically examining how controversial authors tend to be sources of influence in these networks. By addressing these objectives, we hope to provide an analysis of the ways in which bad actors can manipulate the information space. Furthermore, we aim to provide insights into how we can develop strategies to counteract these efforts and protect the integrity of the information ecosystem. Through our investigation, we hope to contribute to the growing body of research focused on understanding and addressing the challenges posed by bad actors in the information space
Constructing a meta-learner for unsupervised anomaly detection
Unsupervised anomaly detection (AD) is critical for a wide range of practical applications,
from network security to health and medical tools. Due to the diversity of problems, no single algorithm
has been found to be superior for all AD tasks. Choosing an algorithm, otherwise known as the Algorithm
Selection Problem (ASP), has been extensively examined in supervised classification problems, through the
use of meta-learning and AutoML, however, it has received little attention in unsupervised AD tasks. This
research proposes a new meta-learning approach that identifies an appropriate unsupervised AD algorithm
given a set of meta-features generated from the unlabelled input dataset. The performance of the proposed
meta-learner is superior to the current state of the art solution. In addition, a mixed model statistical analysis
has been conducted to examine the impact of the meta-learner components: the meta-model, meta-features,
and the base set of AD algorithms, on the overall performance of the meta-learner. The analysis was
conducted using more than 10,000 datasets, which is significantly larger than previous studies. Results
indicate that a relatively small number of meta-features can be used to identify an appropriate AD algorithm,
but the choice of a meta-model in the meta-learner has a considerable impac
The Challenges of Big Data - Contributions in the Field of Data Quality and Artificial Intelligence Applications
The term "big data" has been characterized by challenges regarding data volume, velocity, variety and veracity. Solving these challenges requires research effort that fits the needs of big data. Therefore, this cumulative dissertation contains five paper aiming at developing and applying AI approaches within the field of big data as well as managing data quality in big data
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
Traditional recommendation systems are faced with two long-standing
obstacles, namely, data sparsity and cold-start problems, which promote the
emergence and development of Cross-Domain Recommendation (CDR). The core idea
of CDR is to leverage information collected from other domains to alleviate the
two problems in one domain. Over the last decade, many efforts have been
engaged for cross-domain recommendation. Recently, with the development of deep
learning and neural networks, a large number of methods have emerged. However,
there is a limited number of systematic surveys on CDR, especially regarding
the latest proposed methods as well as the recommendation scenarios and
recommendation tasks they address. In this survey paper, we first proposed a
two-level taxonomy of cross-domain recommendation which classifies different
recommendation scenarios and recommendation tasks. We then introduce and
summarize existing cross-domain recommendation approaches under different
recommendation scenarios in a structured manner. We also organize datasets
commonly used. We conclude this survey by providing several potential research
directions about this field
Intelligent Sensors for Human Motion Analysis
The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems
Exploiting general-purpose background knowledge for automated schema matching
The schema matching task is an integral part of the data integration process. It is usually the first step in integrating data. Schema matching is typically very complex and time-consuming. It is, therefore, to the largest part, carried out by humans. One reason for the low amount of automation is the fact that schemas are often defined with deep background knowledge that is not itself present within the schemas. Overcoming the problem of missing background knowledge is a core challenge in automating the data integration process.
In this dissertation, the task of matching semantic models, so-called ontologies, with the help of external background knowledge is investigated in-depth in Part I. Throughout this thesis, the focus lies on large, general-purpose resources since domain-specific resources are rarely available for most domains. Besides new knowledge resources, this thesis also explores new strategies to exploit such resources.
A technical base for the development and comparison of matching systems is presented in Part II. The framework introduced here allows for simple and modularized matcher development (with background knowledge sources) and for extensive evaluations of matching systems.
One of the largest structured sources for general-purpose background knowledge are knowledge graphs which have grown significantly in size in recent years. However, exploiting such graphs is not trivial. In Part III, knowledge graph em- beddings are explored, analyzed, and compared. Multiple improvements to existing approaches are presented.
In Part IV, numerous concrete matching systems which exploit general-purpose background knowledge are presented. Furthermore, exploitation strategies and resources are analyzed and compared. This dissertation closes with a perspective on real-world applications
Reflektierte algorithmische Textanalyse. Interdisziplinäre(s) Arbeiten in der CRETA-Werkstatt
The Center for Reflected Text Analytics (CRETA) develops interdisciplinary mixed methods for text analytics in the research fields of the digital humanities. This volume is a collection of text analyses from specialty fields including literary studies, linguistics, the social sciences, and philosophy. It thus offers an overview of the methodology of the reflected algorithmic analysis of literary and non-literary texts
Advanced Process Monitoring for Industry 4.0
This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and âextreme dataâ conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes
Graphs behind data: A network-based approach to model different scenarios
openAl giorno dâoggi, i contesti che possono beneficiare di tecniche di estrazione della conoscenza a partire dai dati grezzi sono aumentati drasticamente. Di conseguenza, la definizione di modelli capaci di rappresentare e gestire dati altamente eterogenei è un argomento di ricerca molto dibattuto in letteratura. In questa tesi, proponiamo una soluzione per affrontare tale problema. In particolare, riteniamo che la teoria dei grafi, e piĂš nello specifico le reti complesse, insieme ai suoi concetti ed approcci, possano rappresentare una valida soluzione. Infatti, noi crediamo che le reti complesse possano costituire un modello unico ed unificante per rappresentare e gestire dati altamente eterogenei. Sulla base di questa premessa, mostriamo come gli stessi concetti ed approcci abbiano la potenzialitĂ di affrontare con successo molti problemi aperti in diversi contesti. âNowadays, the amount and variety of scenarios that can benefit from techniques for extracting and managing knowledge from raw data have dramatically increased. As a result, the search for models capable of ensuring the representation and management of highly heterogeneous data is a hot topic in the data science literature. In this thesis, we aim to propose a solution to address this issue. In particular, we believe that graphs, and more specifically complex networks, as well as the concepts and approaches associated with them, can represent a solution to the problem mentioned above. In fact, we believe that they can be a unique and unifying model to uniformly represent and handle extremely heterogeneous data. Based on this premise, we show how the same concepts and/or approach has the potential to address different open issues in different contexts. âINGEGNERIA DELL'INFORMAZIONEopenVirgili, Luc