616 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Low- and high-resource opinion summarization
Customer reviews play a vital role in the online purchasing decisions we make. The reviews
express user opinions that are useful for setting realistic expectations and uncovering important
details about products. However, some products receive hundreds or even thousands of
reviews, making them time-consuming to read. Moreover, many reviews contain uninformative
content, such as irrelevant personal experiences. Automatic summarization offers an
alternative – short text summaries capturing the essential information expressed in reviews.
Automatically produced summaries can reflect overall or particular opinions and be tailored to
user preferences. Besides being presented on major e-commerce platforms, home assistants
can also vocalize them. This approach can improve user satisfaction by assisting in making
faster and better decisions.
Modern summarization approaches are based on neural networks, often requiring thousands of
annotated samples for training. However, human-written summaries for products are expensive
to produce because annotators need to read many reviews. This has led to annotated data
scarcity where only a few datasets are available. Data scarcity is the central theme of our
works, and we propose a number of approaches to alleviate the problem. The thesis consists
of two parts where we discuss low- and high-resource data settings.
In the first part, we propose self-supervised learning methods applied to customer reviews
and few-shot methods for learning from small annotated datasets. Customer reviews without
summaries are available in large quantities, contain a breadth of in-domain specifics, and
provide a powerful training signal. We show that reviews can be used for learning summarizers
via a self-supervised objective. Further, we address two main challenges associated with
learning from small annotated datasets. First, large models rapidly overfit on small datasets
leading to poor generalization. Second, it is not possible to learn a wide range of in-domain
specifics (e.g., product aspects and usage) from a handful of gold samples. This leads to
subtle semantic mistakes in generated summaries, such as ‘great dead on arrival battery.’ We
address the first challenge by explicitly modeling summary properties (e.g., content coverage
and sentiment alignment). Furthermore, we leverage small modules – adapters – that are
more robust to overfitting. As we show, despite their size, these modules can be used to
store in-domain knowledge to reduce semantic mistakes. Lastly, we propose a simple method
for learning personalized summarizers based on aspects, such as ‘price,’ ‘battery life,’ and
‘resolution.’ This task is harder to learn, and we present a few-shot method for training a
query-based summarizer on small annotated datasets.
In the second part, we focus on the high-resource setting and present a large dataset with
summaries collected from various online resources. The dataset has more than 33,000 humanwritten
summaries, where each is linked up to thousands of reviews. This, however, makes it
challenging to apply an ‘expensive’ deep encoder due to memory and computational costs. To
address this problem, we propose selecting small subsets of informative reviews. Only these
subsets are encoded by the deep encoder and subsequently summarized. We show that the
selector and summarizer can be trained end-to-end via amortized inference and policy gradient
methods
Cognitive Machine Individualism in a Symbiotic Cybersecurity Policy Framework for the Preservation of Internet of Things Integrity: A Quantitative Study
This quantitative study examined the complex nature of modern cyber threats to propose the establishment of cyber as an interdisciplinary field of public policy initiated through the creation of a symbiotic cybersecurity policy framework. For the public good (and maintaining ideological balance), there must be recognition that public policies are at a transition point where the digital public square is a tangible reality that is more than a collection of technological widgets. The academic contribution of this research project is the fusion of humanistic principles with Internet of Things (IoT) technologies that alters our perception of the machine from an instrument of human engineering into a thinking peer to elevate cyber from technical esoterism into an interdisciplinary field of public policy. The contribution to the US national cybersecurity policy body of knowledge is a unified policy framework (manifested in the symbiotic cybersecurity policy triad) that could transform cybersecurity policies from network-based to entity-based. A correlation archival data design was used with the frequency of malicious software attacks as the dependent variable and diversity of intrusion techniques as the independent variable for RQ1. For RQ2, the frequency of detection events was the dependent variable and diversity of intrusion techniques was the independent variable. Self-determination Theory is the theoretical framework as the cognitive machine can recognize, self-endorse, and maintain its own identity based on a sense of self-motivation that is progressively shaped by the machine’s ability to learn. The transformation of cyber policies from technical esoterism into an interdisciplinary field of public policy starts with the recognition that the cognitive machine is an independent consumer of, advisor into, and influenced by public policy theories, philosophical constructs, and societal initiatives
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered.
First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes.
Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification.
Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well
Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches
Traditional networking devices support only fixed features and limited configurability.
Network softwarization leverages programmable software and hardware platforms to remove those limitations.
In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms.
This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0.
P4 is the most popular technology to implement programmable data planes.
However, programmable data planes, and in particular, the P4 technology, emerged only recently.
Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking.
The research of this thesis focuses on two open issues of programmable data planes.
First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet.
Second, it enables BIER in high-performance P4 data planes.
BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet.
The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study.
Two more peer-reviewed papers contain additional content that is not directly related to the main results.
They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts
Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review
Context: User intent modeling is a crucial process in Natural Language
Processing that aims to identify the underlying purpose behind a user's
request, enabling personalized responses. With a vast array of approaches
introduced in the literature (over 13,000 papers in the last decade),
understanding the related concepts and commonly used models in AI-based systems
is essential. Method: We conducted a systematic literature review to gather
data on models typically employed in designing conversational recommender
systems. From the collected data, we developed a decision model to assist
researchers in selecting the most suitable models for their systems.
Additionally, we performed two case studies to evaluate the effectiveness of
our proposed decision model. Results: Our study analyzed 59 distinct models and
identified 74 commonly used features. We provided insights into potential model
combinations, trends in model selection, quality concerns, evaluation measures,
and frequently used datasets for training and evaluating these models.
Contribution: Our study contributes practical insights and a comprehensive
understanding of user intent modeling, empowering the development of more
effective and personalized conversational recommender systems. With the
Conversational Recommender System, researchers can perform a more systematic
and efficient assessment of fitting intent modeling frameworks
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