415 research outputs found
Effect of Tuned Parameters on a LSA MCQ Answering Model
This paper presents the current state of a work in progress, whose objective
is to better understand the effects of factors that significantly influence the
performance of Latent Semantic Analysis (LSA). A difficult task, which consists
in answering (French) biology Multiple Choice Questions, is used to test the
semantic properties of the truncated singular space and to study the relative
influence of main parameters. A dedicated software has been designed to fine
tune the LSA semantic space for the Multiple Choice Questions task. With
optimal parameters, the performances of our simple model are quite surprisingly
equal or superior to those of 7th and 8th grades students. This indicates that
semantic spaces were quite good despite their low dimensions and the small
sizes of training data sets. Besides, we present an original entropy global
weighting of answers' terms of each question of the Multiple Choice Questions
which was necessary to achieve the model's success.Comment: 9 page
Towards an OpenSource Logger for the Analysis of RPA Projects
Process automation typically begins with the observation of
humans conducting the tasks that will be eventually automated. Sim ilarly, successful RPA projects require a prior analysis of the undergo ing processes which are being executed by humans. The process of col lecting this type of information is known as user interface (UI) logging
since it records the interaction against a UI. Main RPA platforms (e.g.,
Blueprism and UIPath) incorporate functionalities that allow the record ing of these UI interactions. However, the records that these platforms
generate lack some functionalities that large-scale RPA projects require.
Besides, they are only understandable by the proper RPA platforms.
This paper presents an extensible and multi-platform OpenSource UI
logger that generate UI logs in a standard format. This system collects
information from all the computers it is running on and sends it to a
central server for its processing. Treatment of the collected information
will allow the creation of an enriched UI log which can be used, among
others purposes, for smart process analysis, machine learning training,
the creation of RPA robots, or, being more general, for task mining .Ministerio de EconomĂa y Competitividad TIN2016-76956-C3-2-R (POLOLAS)Junta de AndalucĂa CEI-12-TIC021Centro para el Desarrollo Tecnol´ogico Industrial (CDTI) P011-19/E0
Privacy-Preserving Similarity-Based Text Retrieval
Article No.: 4</p
Transcriptional landscape of the human and fly genomes: Nonlinear and multifunctional modular model of transcriptomes
Regions of the genome not coding for proteins or not involved in cis-acting regulatory activities are frequently viewed as lacking in functional value. However, a number of recent large-scale studies have revealed significant regulated transcription of unannotated portions of a variety of plant and animal genomes, allowing a new appreciation of the widespread transcription of large portions of the genome. High-resolution mapping of the sites of transcription of the human and fly genomes has provided an alternative picture of the extent and organization of transcription and has offered insights for biological functions of some of the newly identified unannotated transcripts. Considerable portions of the unannotated transcription observed are developmental or cell-type-specific parts of protein-coding transcripts, often serving as novel, alternative 5′ transcriptional start sites. These distal 5′ portions are often situated at significant distances from the annotated gene and alternatively join with or ignore portions of other intervening genes to comprise novel unannotated protein-coding transcripts. These data support an interlaced model of the genome in which many regions serve multifunctional purposes and are highly modular in their utilization. This model illustrates the underappreciated organizational complexity of the genome and one of the functional roles of transcription from unannotated portions of the genome. Copyright 2006, Cold Spring Harbor Laboratory Press © 2006 Cold Spring Harbor Laboratory Press
Nonlinear transmission through a tapered fiber in rubidium vapor
Sub-wavelength diameter tapered optical fibers surrounded by rubidium vapor
can undergo a substantial decrease in transmission at high atomic densities due
to the accumulation of rubidium atoms on the surface of the fiber. Here we
demonstrate the ability to control these changes in transmission using light
guided within the taper. We observe transmission through a tapered fiber that
is a nonlinear function of the incident power. This effect can also allow a
strong control beam to change the transmission of a weak probe beam.Comment: 10 pages, 4 figure
Machine Learning in Automated Text Categorization
The automated categorization (or classification) of texts into predefined
categories has witnessed a booming interest in the last ten years, due to the
increased availability of documents in digital form and the ensuing need to
organize them. In the research community the dominant approach to this problem
is based on machine learning techniques: a general inductive process
automatically builds a classifier by learning, from a set of preclassified
documents, the characteristics of the categories. The advantages of this
approach over the knowledge engineering approach (consisting in the manual
definition of a classifier by domain experts) are a very good effectiveness,
considerable savings in terms of expert manpower, and straightforward
portability to different domains. This survey discusses the main approaches to
text categorization that fall within the machine learning paradigm. We will
discuss in detail issues pertaining to three different problems, namely
document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey
Computational Indistinguishability between Quantum States and Its Cryptographic Application
We introduce a computational problem of distinguishing between two specific
quantum states as a new cryptographic problem to design a quantum cryptographic
scheme that is "secure" against any polynomial-time quantum adversary. Our
problem, QSCDff, is to distinguish between two types of random coset states
with a hidden permutation over the symmetric group of finite degree. This
naturally generalizes the commonly-used distinction problem between two
probability distributions in computational cryptography. As our major
contribution, we show that QSCDff has three properties of cryptographic
interest: (i) QSCDff has a trapdoor; (ii) the average-case hardness of QSCDff
coincides with its worst-case hardness; and (iii) QSCDff is computationally at
least as hard as the graph automorphism problem in the worst case. These
cryptographic properties enable us to construct a quantum public-key
cryptosystem, which is likely to withstand any chosen plaintext attack of a
polynomial-time quantum adversary. We further discuss a generalization of
QSCDff, called QSCDcyc, and introduce a multi-bit encryption scheme that relies
on similar cryptographic properties of QSCDcyc.Comment: 24 pages, 2 figures. We improved presentation, and added more detail
proofs and follow-up of recent wor
Modeling and Inferring Cleavage Patterns in Proliferating Epithelia
The regulation of cleavage plane orientation is one of the key mechanisms driving
epithelial morphogenesis. Still, many aspects of the relationship between local
cleavage patterns and tissue-level properties remain poorly understood. Here we
develop a topological model that simulates the dynamics of a 2D proliferating
epithelium from generation to generation, enabling the exploration of a wide
variety of biologically plausible cleavage patterns. We investigate a spectrum
of models that incorporate the spatial impact of neighboring cells and the
temporal influence of parent cells on the choice of cleavage plane. Our findings
show that cleavage patterns generate “signature” equilibrium
distributions of polygonal cell shapes. These signatures enable the inference of
local cleavage parameters such as neighbor impact, maternal influence, and
division symmetry from global observations of the distribution of cell shape.
Applying these insights to the proliferating epithelia of five diverse
organisms, we find that strong division symmetry and moderate neighbor/maternal
influence are required to reproduce the predominance of hexagonal cells and low
variability in cell shape seen empirically. Furthermore, we present two distinct
cleavage pattern models, one stochastic and one deterministic, that can
reproduce the empirical distribution of cell shapes. Although the proliferating
epithelia of the five diverse organisms show a highly conserved cell shape
distribution, there are multiple plausible cleavage patterns that can generate
this distribution, and experimental evidence suggests that indeed plants and
fruitflies use distinct division mechanisms
Embellishing Text Search Queries to Protect User Privacy
Users of text search engines are increasingly wary that their activities may disclose confidential information about their business or personal profiles. It would be desirable for a search engine to perform document retrieval for users while protecting their intent. In this paper, we identify the privacy risks arising from semantically related search terms within a query, and from recurring highspecificity query terms in a search session. To counter the risks, we propose a solution for a similarity text retrieval system to offer anonymity and plausible deniability for the query terms, and hence the user intent, without degrading the system’s precision-recall performance. The solution comprises a mechanism that embellishes each user query with decoy terms that exhibit similar specificity spread as the genuine terms, but point to plausible alternative topics. We also provide an accompanying retrieval scheme that enables the search engine to compute the encrypted document relevance scores from only the genuine search terms, yet remain oblivious to their distinction from the decoys. Empirical evaluation results are presented to substantiate the effectiveness of our solution. 1
The Effect of Mindfulness-based Programs on Cognitive Function in Adults: A Systematic Review and Meta-analysis
Mindfulness-based programs (MBPs) are increasingly utilized to improve mental health. Interest in the putative effects of MBPs on cognitive function is also growing. This is the first meta-analysis of objective cognitive outcomes across multiple domains from randomized MBP studies of adults. Seven databases were systematically searched to January 2020. Fifty-six unique studies (n = 2,931) were included, of which 45 (n = 2,238) were synthesized using robust variance estimation meta-analysis. Meta-regression and subgroup analyses evaluated moderators. Pooling data across cognitive domains, the summary effect size for all studies favored MBPs over comparators and was small in magnitude (g = 0.15; [0.05, 0.24]). Across subgroup analyses of individual cognitive domains/subdomains, MBPs outperformed comparators for executive function (g = 0.15; [0.02, 0.27]) and working memory outcomes (g = 0.23; [0.11, 0.36]) only. Subgroup analyses identified significant effects for studies of non-clinical samples, as well as for adults aged over 60. Across all studies, MBPs outperformed inactive, but not active comparators. Limitations include the primarily unclear within-study risk of bias (only a minority of studies were considered low risk), and that statistical constraints rendered some p-values unreliable. Together, results partially corroborate the hypothesized link between mindfulness practices and cognitive performance. This review was registered with PROSPERO [CRD42018100904]
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