37,782 research outputs found
Student Success Prediction in MOOCs
Predictive models of student success in Massive Open Online Courses (MOOCs)
are a critical component of effective content personalization and adaptive
interventions. In this article we review the state of the art in predictive
models of student success in MOOCs and present a categorization of MOOC
research according to the predictors (features), prediction (outcomes), and
underlying theoretical model. We critically survey work across each category,
providing data on the raw data source, feature engineering, statistical model,
evaluation method, prediction architecture, and other aspects of these
experiments. Such a review is particularly useful given the rapid expansion of
predictive modeling research in MOOCs since the emergence of major MOOC
platforms in 2012. This survey reveals several key methodological gaps, which
include extensive filtering of experimental subpopulations, ineffective student
model evaluation, and the use of experimental data which would be unavailable
for real-world student success prediction and intervention, which is the
ultimate goal of such models. Finally, we highlight opportunities for future
research, which include temporal modeling, research bridging predictive and
explanatory student models, work which contributes to learning theory, and
evaluating long-term learner success in MOOCs
Autonomous Wireless Systems with Artificial Intelligence
This paper discusses technology and opportunities to embrace artificial
intelligence (AI) in the design of autonomous wireless systems. We aim to
provide readers with motivation and general AI methodology of autonomous agents
in the context of self-organization in real time by unifying knowledge
management with sensing, reasoning and active learning. We highlight
differences between training-based methods for matching problems and
training-free methods for environment-specific problems. Finally, we
conceptually introduce the functions of an autonomous agent with knowledge
management
Intelligent Wireless Communications Enabled by Cognitive Radio and Machine Learning
The ability to intelligently utilize resources to meet the need of growing
diversity in services and user behavior marks the future of wireless
communication systems. Intelligent wireless communications aims at enabling the
system to perceive and assess the available resources, to autonomously learn to
adapt to the perceived wireless environment, and to reconfigure its operating
mode to maximize the utility of the available resources. The perception
capability and reconfigurability are the essential features of cognitive radio
while modern machine learning techniques project great potential in system
adaptation. In this paper, we discuss the development of the cognitive radio
technology and machine learning techniques and emphasize their roles in
improving spectrum and energy utility of wireless communication systems. We
describe the state-of-the-art of relevant techniques, covering spectrum sensing
and access approaches and powerful machine learning algorithms that enable
spectrum- and energy-efficient communications in dynamic wireless environments.
We also present practical applications of these techniques and identify further
research challenges in cognitive radio and machine learning as applied to the
existing and future wireless communication systems
Predicting Student Performance Based on Online Study Habits: A Study of Blended Courses
Online tools provide unique access to research students' study habits and
problem-solving behavior. In MOOCs, this online data can be used to inform
instructors and to provide automatic guidance to students. However, these
techniques may not apply in blended courses with face to face and online
components. We report on a study of integrated user-system interaction logs
from 3 computer science courses using four online systems: LMS, forum, version
control, and homework system. Our results show that students rarely work across
platforms in a single session, and that final class performance can be
predicted from students' system use.Comment: Published in the International Conference on Educational Data Mining
(EDM 2018
A Survey on Food Computing
Food is very essential for human life and it is fundamental to the human
experience. Food-related study may support multifarious applications and
services, such as guiding the human behavior, improving the human health and
understanding the culinary culture. With the rapid development of social
networks, mobile networks, and Internet of Things (IoT), people commonly
upload, share, and record food images, recipes, cooking videos, and food
diaries, leading to large-scale food data. Large-scale food data offers rich
knowledge about food and can help tackle many central issues of human society.
Therefore, it is time to group several disparate issues related to food
computing. Food computing acquires and analyzes heterogenous food data from
disparate sources for perception, recognition, retrieval, recommendation, and
monitoring of food. In food computing, computational approaches are applied to
address food related issues in medicine, biology, gastronomy and agronomy. Both
large-scale food data and recent breakthroughs in computer science are
transforming the way we analyze food data. Therefore, vast amounts of work has
been conducted in the food area, targeting different food-oriented tasks and
applications. However, there are very few systematic reviews, which shape this
area well and provide a comprehensive and in-depth summary of current efforts
or detail open problems in this area. In this paper, we formalize food
computing and present such a comprehensive overview of various emerging
concepts, methods, and tasks. We summarize key challenges and future directions
ahead for food computing. This is the first comprehensive survey that targets
the study of computing technology for the food area and also offers a
collection of research studies and technologies to benefit researchers and
practitioners working in different food-related fields.Comment: Accepted by ACM Computing Survey
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Accelerating Large Scale Knowledge Distillation via Dynamic Importance Sampling
Knowledge distillation is an effective technique that transfers knowledge
from a large teacher model to a shallow student. However, just like massive
classification, large scale knowledge distillation also imposes heavy
computational costs on training models of deep neural networks, as the softmax
activations at the last layer involve computing probabilities over numerous
classes. In this work, we apply the idea of importance sampling which is often
used in Neural Machine Translation on large scale knowledge distillation. We
present a method called dynamic importance sampling, where ranked classes are
sampled from a dynamic distribution derived from the interaction between the
teacher and student in full distillation. We highlight the utility of our
proposal prior which helps the student capture the main information in the loss
function. Our approach manages to reduce the computational cost at training
time while maintaining the competitive performance on CIFAR-100 and Market-1501
person re-identification datasets
Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows
Distributed computing platforms provide a robust mechanism to perform
large-scale computations by splitting the task and data among multiple
locations, possibly located thousands of miles apart geographically. Although
such distribution of resources can lead to benefits, it also comes with its
associated problems such as rampant duplication of file transfers increasing
congestion, long job completion times, unexpected site crashing, suboptimal
data transfer rates, unpredictable reliability in a time range, and suboptimal
usage of storage elements. In addition, each sub-system becomes a potential
failure node that can trigger system wide disruptions. In this vision paper, we
outline our approach to leveraging Deep Learning algorithms to discover
solutions to unique problems that arise in a system with computational
infrastructure that is spread over a wide area. The presented vision, motivated
by a real scientific use case from Belle II experiments, is to develop
multilayer neural networks to tackle forecasting, anomaly detection and
optimization challenges in a complex and distributed data movement environment.
Through this vision based on Deep Learning principles, we aim to achieve
reduced congestion events, faster file transfer rates, and enhanced site
reliability
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Identification of drug-target interactions (DTIs) plays a key role in drug
discovery. The high cost and labor-intensive nature of in vitro and in vivo
experiments have highlighted the importance of in silico-based DTI prediction
approaches. In several computational models, conventional protein descriptors
are shown to be not informative enough to predict accurate DTIs. Thus, in this
study, we employ a convolutional neural network (CNN) on raw protein sequences
to capture local residue patterns participating in DTIs. With CNN on protein
sequences, our model performs better than previous protein descriptor-based
models. In addition, our model performs better than the previous deep learning
model for massive prediction of DTIs. By examining the pooled convolution
results, we found that our model can detect binding sites of proteins for DTIs.
In conclusion, our prediction model for detecting local residue patterns of
target proteins successfully enriches the protein features of a raw protein
sequence, yielding better prediction results than previous approaches.Comment: 26 pages, 7 figure
Echo State Networks for Proactive Caching in Cloud-Based Radio Access Networks with Mobile Users
In this paper, the problem of proactive caching is studied for cloud radio
access networks (CRANs). In the studied model, the baseband units (BBUs) can
predict the content request distribution and mobility pattern of each user,
determine which content to cache at remote radio heads and BBUs. This problem
is formulated as an optimization problem which jointly incorporates backhaul
and fronthaul loads and content caching. To solve this problem, an algorithm
that combines the machine learning framework of echo state networks with
sublinear algorithms is proposed. Using echo state networks (ESNs), the BBUs
can predict each user's content request distribution and mobility pattern while
having only limited information on the network's and user's state. In order to
predict each user's periodic mobility pattern with minimal complexity, the
memory capacity of the corresponding ESN is derived for a periodic input. This
memory capacity is shown to be able to record the maximum amount of user
information for the proposed ESN model. Then, a sublinear algorithm is proposed
to determine which content to cache while using limited content request
distribution samples. Simulation results using real data from Youku and the
Beijing University of Posts and Telecommunications show that the proposed
approach yields significant gains, in terms of sum effective capacity, that
reach up to 27.8% and 30.7%, respectively, compared to random caching with
clustering and random caching without clustering algorithm.Comment: Accepted in the IEEE Transactions on Wireless Communication
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