1,339 research outputs found
Sensitive and Scalable Online Evaluation with Theoretical Guarantees
Multileaved comparison methods generalize interleaved comparison methods to
provide a scalable approach for comparing ranking systems based on regular user
interactions. Such methods enable the increasingly rapid research and
development of search engines. However, existing multileaved comparison methods
that provide reliable outcomes do so by degrading the user experience during
evaluation. Conversely, current multileaved comparison methods that maintain
the user experience cannot guarantee correctness. Our contribution is two-fold.
First, we propose a theoretical framework for systematically comparing
multileaved comparison methods using the notions of considerateness, which
concerns maintaining the user experience, and fidelity, which concerns reliable
correct outcomes. Second, we introduce a novel multileaved comparison method,
Pairwise Preference Multileaving (PPM), that performs comparisons based on
document-pair preferences, and prove that it is considerate and has fidelity.
We show empirically that, compared to previous multileaved comparison methods,
PPM is more sensitive to user preferences and scalable with the number of
rankers being compared.Comment: CIKM 2017, Proceedings of the 2017 ACM on Conference on Information
and Knowledge Managemen
Microdissection of human chromosomes by a laser microbeam
A laser microbeam apparatus, based on an excimer laser pumped dye laser is used to microdissect human chromosomes and to isolate a single chromosome slice
Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data
User-generated data is crucial to predictive modeling in many applications.
With a web/mobile/wearable interface, a data owner can continuously record data
generated by distributed users and build various predictive models from the
data to improve their operations, services, and revenue. Due to the large size
and evolving nature of users data, data owners may rely on public cloud service
providers (Cloud) for storage and computation scalability. Exposing sensitive
user-generated data and advanced analytic models to Cloud raises privacy
concerns. We present a confidential learning framework, SecureBoost, for data
owners that want to learn predictive models from aggregated user-generated data
but offload the storage and computational burden to Cloud without having to
worry about protecting the sensitive data. SecureBoost allows users to submit
encrypted or randomly masked data to designated Cloud directly. Our framework
utilizes random linear classifiers (RLCs) as the base classifiers in the
boosting framework to dramatically simplify the design of the proposed
confidential boosting protocols, yet still preserve the model quality. A
Cryptographic Service Provider (CSP) is used to assist the Cloud's processing,
reducing the complexity of the protocol constructions. We present two
constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of
homomorphic encryption, garbled circuits, and random masking to achieve both
security and efficiency. For a boosted model, Cloud learns only the RLCs and
the CSP learns only the weights of the RLCs. Finally, the data owner collects
the two parts to get the complete model. We conduct extensive experiments to
understand the quality of the RLC-based boosting and the cost distribution of
the constructions. Our results show that SecureBoost can efficiently learn
high-quality boosting models from protected user-generated data
Implicitly Constrained Semi-Supervised Least Squares Classification
We introduce a novel semi-supervised version of the least squares classifier.
This implicitly constrained least squares (ICLS) classifier minimizes the
squared loss on the labeled data among the set of parameters implied by all
possible labelings of the unlabeled data. Unlike other discriminative
semi-supervised methods, our approach does not introduce explicit additional
assumptions into the objective function, but leverages implicit assumptions
already present in the choice of the supervised least squares classifier. We
show this approach can be formulated as a quadratic programming problem and its
solution can be found using a simple gradient descent procedure. We prove that,
in a certain way, our method never leads to performance worse than the
supervised classifier. Experimental results corroborate this theoretical result
in the multidimensional case on benchmark datasets, also in terms of the error
rate.Comment: 12 pages, 2 figures, 1 table. The Fourteenth International Symposium
on Intelligent Data Analysis (2015), Saint-Etienne, Franc
Validity Arguments for Diagnostic Assessment Using Automated Writing Evaluation
Two examples demonstrate an argument-based approach to validation of diagnostic assessment using automated writing evaluation (AWE). Criterion ®, was developed by Educational Testing Service to analyze students’ papers grammatically, providing sentence-level error feedback. An interpretive argument was developed for its use as part of the diagnostic assessment process in undergraduate university English for academic purposes (EAP) classes. The Intelligent Academic Discourse Evaluator (IADE) was developed for use in graduate EAP university classes, where the goal was to help students improve their discipline-specific writing. The validation for each was designed to support claims about the intended purposes of the assessments. We present the interpretive argument for each and show some of the data that have been gathered as backing for the respective validity arguments, which include the range of inferences that one would make in claiming validity of the interpretations, uses, and consequences of diagnostic AWE-based assessments
Towards End-to-end Video-based Eye-Tracking
Estimating eye-gaze from images alone is a challenging task, in large parts
due to un-observable person-specific factors. Achieving high accuracy typically
requires labeled data from test users which may not be attainable in real
applications. We observe that there exists a strong relationship between what
users are looking at and the appearance of the user's eyes. In response to this
understanding, we propose a novel dataset and accompanying method which aims to
explicitly learn these semantic and temporal relationships. Our video dataset
consists of time-synchronized screen recordings, user-facing camera views, and
eye gaze data, which allows for new benchmarks in temporal gaze tracking as
well as label-free refinement of gaze. Importantly, we demonstrate that the
fusion of information from visual stimuli as well as eye images can lead
towards achieving performance similar to literature-reported figures acquired
through supervised personalization. Our final method yields significant
performance improvements on our proposed EVE dataset, with up to a 28 percent
improvement in Point-of-Gaze estimates (resulting in 2.49 degrees in angular
error), paving the path towards high-accuracy screen-based eye tracking purely
from webcam sensors. The dataset and reference source code are available at
https://ait.ethz.ch/projects/2020/EVEComment: Accepted at ECCV 202
Achieving Generalizable Robustness of Deep Neural Networks by Stability Training
We study the recently introduced stability training as a general-purpose
method to increase the robustness of deep neural networks against input
perturbations. In particular, we explore its use as an alternative to data
augmentation and validate its performance against a number of distortion types
and transformations including adversarial examples. In our image classification
experiments using ImageNet data stability training performs on a par or even
outperforms data augmentation for specific transformations, while consistently
offering improved robustness against a broader range of distortion strengths
and types unseen during training, a considerably smaller hyperparameter
dependence and less potentially negative side effects compared to data
augmentation.Comment: 18 pages, 25 figures; Camera-ready versio
Combining active learning and semi-supervised learning techniques to extract protein interaction sentences
Background: Protein-protein interaction (PPI) extraction has been a focal point of many biomedical research and database curation tools. Both Active Learning and Semi-supervised SVMs have recently been applied to extract PPI automatically. In this paper, we explore combining the AL with the SSL to improve the performance of the PPI task. Methods: We propose a novel PPI extraction technique called PPISpotter by combining Deterministic Annealing-based SSL and an AL technique to extract protein-protein interaction. In addition, we extract a comprehensive set of features from MEDLINE records by Natural Language Processing (NLP) techniques, which further improve the SVM classifiers. In our feature selection technique, syntactic, semantic, and lexical properties of text are incorporated into feature selection that boosts the system performance significantly. Results: By conducting experiments with three different PPI corpuses, we show that PPISpotter is superior to the other techniques incorporated into semi-supervised SVMs such as Random Sampling, Clustering, and Transductive SVMs by precision, recall, and F-measure. Conclusions: Our system is a novel, state-of-the-art technique for efficiently extracting protein-protein interaction pairs.X116sciescopu
A novel essential splice site variant in SPTB in a large hereditary spherocytosis family
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