787 research outputs found
Neuronavigational approach for orbital neurofibroma excision: a case report
Orbital neurofibromas are uncommon in adults, accounting for approximately 1%-3% of all space occupying lesions of the orbit. The complex anatomy of the orbital region, with the pronounced vulnerability of its neurovascular structures, requires particular surgical precautions. Neuronavigation, as a high-tech device for intraoperative safety, represents a valuable option for the confined orbital space. However, the application of neuronavigation in orbital surgery has been rarely reported. The authors present a case report of a 32-year-old female with an isolated localized neurofibroma surgically approached by intraoperative navigation and a review of the literature
Mining Network Events using Traceroute Empathy
In the never-ending quest for tools that enable an ISP to smooth
troubleshooting and improve awareness of network behavior, very much effort has
been devoted in the collection of data by active and passive measurement at the
data plane and at the control plane level. Exploitation of collected data has
been mostly focused on anomaly detection and on root-cause analysis. Our
objective is somewhat in the middle. We consider traceroutes collected by a
network of probes and aim at introducing a practically applicable methodology
to quickly spot measurements that are related to high-impact events happened in
the network. Such filtering process eases further in- depth human-based
analysis, for example with visual tools which are effective only when handling
a limited amount of data. We introduce the empathy relation between traceroutes
as the cornerstone of our formal characterization of the traceroutes related to
a network event. Based on this model, we describe an algorithm that finds
traceroutes related to high-impact events in an arbitrary set of measurements.
Evidence of the effectiveness of our approach is given by experimental results
produced on real-world data.Comment: 8 pages, 7 figures, extended version of Discovering High-Impact
Routing Events using Traceroutes, in Proc. 20th International Symposium on
Computers and Communications (ISCC 2015
Unveiling Web Fingerprinting in the Wild Via Code Mining and Machine Learning
Abstract
Fueled by advertising companies' need of accurately tracking users and their online habits, web fingerprinting practice has grown in recent years, with severe implications for users' privacy. In this paper, we design, engineer and evaluate a methodology which combines the analysis of JavaScript code and machine learning for the automatic detection of web fingerprinters.
We apply our methodology on a dataset of more than 400, 000 JavaScript files accessed by about 1, 000 volunteers during a one-month long experiment to observe adoption of fingerprinting in a real scenario. We compare approaches based on both static and dynamic code analysis to automatically detect fingerprinters and show they provide different angles complementing each other. This demonstrates that studies based on either static or dynamic code analysis provide partial view on actual fingerprinting usage in the web. To the best of our knowledge we are the first to perform this comparison with respect to fingerprinting.
Our approach achieves 94% accuracy in small decision time. With this we spot more than 840 fingerprinting services, of which 695 are unknown to popular tracker blockers. These include new actual trackers as well as services which use fingerprinting for purposes other than tracking, such as anti-fraud and bot recognition
Generating Mathematical Derivations with Large Language Models
The derivation of mathematical results in specialised fields using Large
Language Models (LLMs) is an emerging research direction that can help identify
models' limitations, and potentially support mathematical discovery. In this
paper, we leverage a symbolic engine to generate derivations of equations at
scale, and investigate the capabilities of LLMs when deriving goal equations
from premises. Specifically, we employ in-context learning for GPT and
fine-tune a range of T5 models to compare the robustness and generalisation of
pre-training strategies to specialised models. Empirical results show that
fine-tuned FLAN-T5-large (MathT5) outperforms GPT models on all static and
out-of-distribution test sets in terms of absolute performance. However, an
in-depth analysis reveals that the fine-tuned models are more sensitive to
perturbations involving unseen symbols and (to a lesser extent) changes to
equation structure. In addition, we analyse 1.7K equations and over 200
derivations to highlight common reasoning errors such as the inclusion of
incorrect, irrelevant, and redundant equations, along with the tendency to skip
derivation steps. Finally, we explore the suitability of existing metrics for
evaluating mathematical derivations finding evidence that, while they capture
general properties such as sensitivity to perturbations, they fail to highlight
fine-grained reasoning errors and essential differences between models.
Overall, this work demonstrates that training models on synthetic data can
improve their mathematical capabilities beyond larger architectures.Comment: 13 page
Unification-based Reconstruction of Multi-hop Explanations for Science Questions
This paper presents a novel framework for reconstructing multi-hop
explanations in science Question Answering (QA). While existing approaches for
multi-hop reasoning build explanations considering each question in isolation,
we propose a method to leverage explanatory patterns emerging in a corpus of
scientific explanations. Specifically, the framework ranks a set of atomic
facts by integrating lexical relevance with the notion of unification power,
estimated analysing explanations for similar questions in the corpus.
An extensive evaluation is performed on the Worldtree corpus, integrating
k-NN clustering and Information Retrieval (IR) techniques. We present the
following conclusions: (1) The proposed method achieves results competitive
with Transformers, yet being orders of magnitude faster, a feature that makes
it scalable to large explanatory corpora (2) The unification-based mechanism
has a key role in reducing semantic drift, contributing to the reconstruction
of many hops explanations (6 or more facts) and the ranking of complex
inference facts (+12.0 Mean Average Precision) (3) Crucially, the constructed
explanations can support downstream QA models, improving the accuracy of BERT
by up to 10% overall.Comment: Accepted at EACL 202
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