11,173 research outputs found
Type-Constrained Representation Learning in Knowledge Graphs
Large knowledge graphs increasingly add value to various applications that
require machines to recognize and understand queries and their semantics, as in
search or question answering systems. Latent variable models have increasingly
gained attention for the statistical modeling of knowledge graphs, showing
promising results in tasks related to knowledge graph completion and cleaning.
Besides storing facts about the world, schema-based knowledge graphs are backed
by rich semantic descriptions of entities and relation-types that allow
machines to understand the notion of things and their semantic relationships.
In this work, we study how type-constraints can generally support the
statistical modeling with latent variable models. More precisely, we integrated
prior knowledge in form of type-constraints in various state of the art latent
variable approaches. Our experimental results show that prior knowledge on
relation-types significantly improves these models up to 77% in link-prediction
tasks. The achieved improvements are especially prominent when a low model
complexity is enforced, a crucial requirement when these models are applied to
very large datasets. Unfortunately, type-constraints are neither always
available nor always complete e.g., they can become fuzzy when entities lack
proper typing. We show that in these cases, it can be beneficial to apply a
local closed-world assumption that approximates the semantics of relation-types
based on observations made in the data
Reciprocal Recommender System for Learners in Massive Open Online Courses (MOOCs)
Massive open online courses (MOOC) describe platforms where users with
completely different backgrounds subscribe to various courses on offer. MOOC
forums and discussion boards offer learners a medium to communicate with each
other and maximize their learning outcomes. However, oftentimes learners are
hesitant to approach each other for different reasons (being shy, don't know
the right match, etc.). In this paper, we propose a reciprocal recommender
system which matches learners who are mutually interested in, and likely to
communicate with each other based on their profile attributes like age,
location, gender, qualification, interests, etc. We test our algorithm on data
sampled using the publicly available MITx-Harvardx dataset and demonstrate that
both attribute importance and reciprocity play an important role in forming the
final recommendation list of learners. Our approach provides promising results
for such a system to be implemented within an actual MOOC.Comment: 10 pages, accepted as full paper @ ICWL 201
Towards formal models of psychopathological traits that explain symptom trajectories
BACKGROUND: A dominant methodology in contemporary clinical neuroscience is the use of dimensional self-report questionnaires to measure features such as psychological traits (e.g., trait anxiety) and states (e.g., depressed mood). These dimensions are then mapped to biological measures and computational parameters. Researchers pursuing this approach tend to equate a symptom inventory score (plus noise) with some latent psychological trait. MAIN TEXT: We argue this approach implies weak, tacit, models of traits that provide fixed predictions of individual symptoms, and thus cannot account for symptom trajectories within individuals. This problem persists because (1) researchers are not familiarized with formal models that relate internal traits to within-subject symptom variation and (2) rely on an assumption that trait self-report inventories accurately indicate latent traits. To address these concerns, we offer a computational model of trait depression that demonstrates how parameters instantiating a given trait remain stable while manifest symptom expression varies predictably. We simulate patterns of mood variation from both the computational model and the standard self-report model and describe how to quantify the relative validity of each model using a Bayesian procedure. CONCLUSIONS: Ultimately, we would urge a tempering of a reliance on self-report inventories and recommend a shift towards developing mechanistic trait models that can explain within-subject symptom dynamics
Charge Symmetry Breaking and QCD
Charge symmetry breaking (CSB) in the strong interaction occurs because of
the difference between the masses of the up and down quarks. The use of
effective field theories allows us to follow this influence of confined quarks
in hadronic and nuclear systems. The progress in observing and understanding
CSB is reviewed with particular attention to the recent successful observations
of CSB in measurements involving the production of a single neutral pion and to
the related theoretical progress.Comment: 41 pages, 10 figures, for Nov. 2006 edition Annual Review of Nuclear
and Particle Physic
Measuring Accuracy of Triples in Knowledge Graphs
An increasing amount of large-scale knowledge graphs have been constructed in recent years. Those graphs are often created from text-based extraction, which could be very noisy. So far, cleaning knowledge graphs are often carried out by human experts and thus very inefficient. It is necessary to explore automatic methods for identifying and eliminating erroneous information. In order to achieve this, previous approaches primarily rely on internal information i.e. the knowledge graph itself. In this paper, we introduce an automatic approach, Triples Accuracy Assessment (TAA), for validating RDF triples (source triples) in a knowledge graph by finding consensus of matched triples (among target triples) from other knowledge graphs. TAA uses knowledge graph interlinks to find identical resources and apply different matching methods between the predicates of source triples and target triples. Then based on the matched triples, TAA calculates a confidence score to indicate the correctness of a source triple. In addition, we present an evaluation of our approach using the FactBench dataset for fact validation. Our findings show promising results for distinguishing between correct and wrong triples
Perioperative care of the geriatric patient for noncardiac surgery
Adults age 65 and over are the fastest growing segment of the population in the United States and around the world. As the size of this population expands, the number of older adults referred for surgical procedures will continue to increase. Due to the physiologic changes of aging and the increased frequency of comorbidities, older adults are at increased risk for adverse outcomes, and perioperative care is inherently more complex than in younger individuals. In this review, we discuss the physiologic changes of aging relevant to the surgical patient, comprehensive preoperative assessment, and postoperative management of common complications in older adults in order to promote optimal clinical outcomes both perioperatively and long-term
Keystroke Inference Using Smartphone Kinematics
The use of smartphones is becoming ubiquitous in modern society, these very personal devices store large amounts of personal information and we use these devices to access everything from our bank to our social networks, we communicate using these devices in both open one-to-many communications and in more closed, private one-to-one communications. In this paper we have created a method to infer what is typed on a device purely from how the device moves in the user’s hand. With very small amounts of training data (less than the size of a tweet) we are able to predict the text typed on a device with accuracies of up to 90%. We found no effect on this accuracy from how fast users type, how comfortable they are using smartphone keyboards or how the device was held in the hand. It is trivial to create an application that can access the motion data of a phone whilst a user is engaged in other applications, the accessing of motion data does not require any permission to be granted by the user and hence represents a tangible threat to smartphone users
'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems
An examination of object recognition challenge leaderboards (ILSVRC,
PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small
differences amongst themselves in terms of error rate/mAP. To better
differentiate the top performers, additional criteria are required. Moreover,
the (test) images, on which the performance scores are based, predominantly
contain fully visible objects. Therefore, `harder' test images, mimicking the
challenging conditions (e.g. occlusion) in which humans routinely recognize
objects, need to be utilized for benchmarking. To address the concerns
mentioned above, we make two contributions. First, we systematically vary the
level of local object-part content, global detail and spatial context in images
from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12.
Second, we propose an object-part based benchmarking procedure which quantifies
classifiers' robustness to a range of visibility and contextual settings. The
benchmarking procedure relies on a semantic similarity measure that naturally
addresses potential semantic granularity differences between the category
labels in training and test datasets, thus eliminating manual mapping. We use
our procedure on the PPSS-12 dataset to benchmark top-performing classifiers
trained on the ILSVRC-2012 dataset. Our results show that the proposed
benchmarking procedure enables additional differentiation among
state-of-the-art object classifiers in terms of their ability to handle missing
content and insufficient object detail. Given this capability for additional
differentiation, our approach can potentially supplement existing benchmarking
procedures used in object recognition challenge leaderboards.Comment: Extended version of our ACCV-2016 paper. Author formatting modifie
Evaluating Maintainability Prejudices with a Large-Scale Study of Open-Source Projects
Exaggeration or context changes can render maintainability experience into
prejudice. For example, JavaScript is often seen as least elegant language and
hence of lowest maintainability. Such prejudice should not guide decisions
without prior empirical validation. We formulated 10 hypotheses about
maintainability based on prejudices and test them in a large set of open-source
projects (6,897 GitHub repositories, 402 million lines, 5 programming
languages). We operationalize maintainability with five static analysis
metrics. We found that JavaScript code is not worse than other code, Java code
shows higher maintainability than C# code and C code has longer methods than
other code. The quality of interface documentation is better in Java code than
in other code. Code developed by teams is not of higher and large code bases
not of lower maintainability. Projects with high maintainability are not more
popular or more often forked. Overall, most hypotheses are not supported by
open-source data.Comment: 20 page
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