pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation
Authors: C. Chalmers, P. Fergus, C. Aday Curbelo Montanez, S. Sikdar, F. Ball, B. Kendall
Date: 2019-03-18 00:00:00

The emergence of an ageing population is a significant public health concern.
This has led to an increase in the number of people living with progressive
neurodegenerative disorders like dementia. Consequently, the strain this is
places on health and social care services means providing 24-hour monitoring is
not sustainable. Technological intervention is being considered, however no
solution exists to non-intrusively monitor the independent living needs of
patients with dementia. As a result many patients hit crisis point before
intervention and support is provided. In parallel, patient care relies on
feedback from informal carers about significant behavioural changes. Yet, not
all people have a social support network and early intervention in dementia
care is often missed. The smart meter rollout has the potential to change this.
Using machine learning and signal processing techniques, a home energy supply
can be disaggregated to detect which home appliances are turned on and off.
This will allow Activities of Daily Living (ADLs) to be assessed, such as
eating and drinking, and observed changes in routine to be detected for early
intervention. The primary aim is to help reduce deterioration and enable
patients to stay in their homes for longer. A Support Vector Machine (SVM) and
Random Decision Forest classifier are modelled using data from three test
homes. The trained models are then used to monitor two patients with dementia
during a six-month clinical trial undertaken in partnership with Mersey Care
NHS Foundation Trust. In the case of load disaggregation for appliance
detection, the SVM achieved (AUC=0.86074, Sen=0.756 and Spec=0.92838). While
the Decision Forest achieved (AUC=0.9429, Sen=0.9634 and Spec=0.9634). ADLs are
also analysed to identify the behavioural patterns of the occupant while
detecting alterations in routine.
Scope of Research on Particle Swarm Optimization Based Data Clustering
Authors: Vishakha A. Metre, Mr Pramod B Deshmukh
Date: 2018-12-06 00:00:00
Tasks: Clustering

Optimization is nothing but a mathematical technique which finds maxima or
minima of any function of concern in some realistic region. Different
optimization techniques are proposed which are competing for the best solution.
Particle Swarm Optimization (PSO) is a new, advanced, and most powerful
optimization methodology that performs empirically well on several optimization
problems. It is the extensively used Swarm Intelligence (SI) inspired
optimization algorithm used for finding the global optimal solution in a
multifaceted search region. Data clustering is one of the challenging real
world applications that invite the eminent research works in variety of fields.
Applicability of different PSO variants to data clustering is studied in the
literature, and the analyzed research work shows that, PSO variants give poor
results for multidimensional data. This paper describes the different
challenges associated with multidimensional data clustering and scope of
research on optimizing the clustering problems using PSO. We also propose a
strategy to use hybrid PSO variant for clustering multidimensional numerical,
text and image data.
A Model Counter's Guide to Probabilistic Systems
Authors: Marcell Vazquez-Chanlatte, Markus N. Rabe, Sanjit A. Seshia
Date: 2019-03-22 00:00:00
Tasks: model

In this paper, we systematize the modeling of probabilistic systems for the
purpose of analyzing them with model counting techniques. Starting from
unbiased coin flips, we show how to model biased coins, correlated coins, and
distributions over finite sets. From there, we continue with modeling
sequential systems, such as Markov chains, and revisit the relationship between
weighted and unweighted model counting. Thereby, this work provides a
conceptual framework for deriving #SAT encodings for probabilistic inference.
Image Quality Assessment and Color Difference
Authors: Dogancan Temel, Ghassan AlRegib
Date: 2018-11-22 00:00:00
Tasks: Image Quality Assessment, MS-SSIM, SSIM

An average healthy person does not perceive the world in just black and
white. Moreover, the perceived world is not composed of pixels and through
vision humans perceive structures. However, the acquisition and display systems
discretize the world. Therefore, we need to consider pixels, structures and
colors to model the quality of experience. Quality assessment methods use the
pixel-wise and structural metrics whereas color science approaches use the
patch-based color differences. In this work, we combine these approaches by
extending CIEDE2000 formula with perceptual color difference to assess image
quality. We examine how perceptual color difference-based metric (PCDM)
performs compared to PSNR, CIEDE2000, SSIM, MS-SSIM and CW-SSIM on the LIVE
database. In terms of linear correlation, PCDM obtains compatible results under
white noise (97.9%), Jpeg (95.9%) and Jp2k (95.6%) with an overall correlation
of 92.7%. We also show that PCDM captures color-based artifacts that can not be
captured by structure-based metrics.
Bayesian Learning via Stochastic Gradient Langevin Dynamics
Authors: Max Welling, Yee Whye Teh
Date: 2011-07-28 00:00:00
Proceeding: ICML 2011 2011 7
Tasks: regression

In this paper we propose a new framework for learning from large scale datasets based on iterative learning from small mini-batches. By adding the right amount of noise to a standard stochastic gradient optimization al- gorithm we show that the iterates will con- verge to samples from the true posterior dis- tribution as we anneal the stepsize. This seamless transition between optimization and Bayesian posterior sampling provides an in- built protection against overfitting. We also propose a practical method for Monte Carlo estimates of posterior statistics which moni- tors a “sampling threshold” and collects sam- ples after it has been surpassed. We apply the method to three models: a mixture of Gaussians, logistic regression and ICA with natural gradients.
FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud
Authors: Sagar Karandikar, Howard Mao, Donggyu Kim, David Biancolin, Alon Amid, Dayeol Lee, Nathan Pemberton, Emmanuel Amaro, Colin Schmidt, Aditya Chopra, Qijing Huang, Kyle Kovacs, Borivoje Nikolic, Randy Katz, Jonathan Bachrach, Krste Asanovic
Date: 2018-06-02 00:00:00
Proceeding: 45th ACM/IEEE International Symposium on Computer Architecture (ISCA 2018) 2018 6

We present FireSim, an open-source simulation platform that enables cycle-exact microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated simulation of silicon-proven RTL designs with a scalable, distributed network simulation. Unlike prior FPGA-accelerated simulation tools, FireSim runs on Amazon EC2 F1, a public cloud FPGA platform, which greatly improves usability, provides elasticity, and lowers the cost of large-scale FPGAbased experiments. We describe the design and implementation of FireSim and show how it can provide sufficient performance to run modern applications at scale, to enable true hardware-software co-design. As an example, we demonstrate automatically generating and deploying a target cluster of 1,024 3.2 GHz quad-core server nodes, each with 16 GB of DRAM, interconnected by a 200 Gbit/s network with 2 microsecond latency, which simulates at a 3.4 MHz processor clock rate (less than 1,000x slowdown over real-time). In aggregate, this FireSim instantiation simulates 4,096 cores and 16 TB of memory, runs ˜14 billion instructions per second, and harnesses 12.8 million dollars worth of FPGAs—at a total cost of only ˜$100 per simulation hour to the user. We present several examples to show how FireSim can be used to explore various research directions in warehouse-scale machine design, including modeling networks with high-bandwidth and low-latency, integrating arbitrary RTL designs for a variety of commodity and specialized datacenter nodes, and modeling a variety of datacenter organizations, as well as reusing the scale-out FireSim infrastructure to enable fast, massively parallel cycle-exact single-node microarchitectural experimentation.
HeartBEAT: Heart Beat Estimation through Adaptive Tracking
Authors: Huijie Pan, Dogancan Temel, Ghassan AlRegib
Date: 2018-10-19 00:00:00
Tasks: Photoplethysmography (PPG)

In this paper, we propose an algorithm denoted as HeartBEAT that tracks heart
rate from wrist-type photoplethysmography (PPG) signals and simultaneously
recorded three-axis acceleration data. HeartBEAT contains three major parts:
spectrum estimation of PPG signals and acceleration data, elimination of motion
artifacts in PPG signals using recursive least Square (RLS) adaptive filters,
and auxiliary heuristics. We tested HeartBEAT on the 22 datasets provided in
the 2015 IEEE Signal Processing Cup. The first ten datasets were recorded from
subjects performing forearm and upper-arm exercises, jumping, or pushing-up.
The last twelve datasets were recorded from subjects running on tread mills.
The experimental results were compared to the ground truth heart rate, which
comes from simultaneously recorded electrocardiogram (ECG) signals. Compared to
state-of-the-art algorithms, HeartBEAT not only produces comparable Pearson's
correlation and mean absolute error, but also higher Spearman's rho and
Kendall's tau.
Powered Outer Probabilistic Clustering
Authors: Peter Taraba
Date: 2017-10-25 00:00:00
Proceeding: World Congress on Engineering and Computer Science 2017 2017 10
Tasks: BIG-bench Machine Learning, Clustering

Clustering is one of the most important concepts for unsupervised learning in machine learning. While there are numerous clustering algorithms already, many, including the popular one — k-means algorithm, require the number of clusters to be specified in advance, a huge drawback. Some studies use the silhouette coefficient to determine the optimal number of clusters. In this study, we introduce a novel algorithm called Powered Outer Probabilistic Clustering, show how it works through back-propagation (starting with many clusters and ending with an optimal number of clusters), and show that the algorithm converges to the expected (optimal) number of clusters on theoretical examples.
Clustering for Binary Featured Datasets
Authors: Peter Taraba
Date: 2018-10-25 00:00:00
Proceeding: Transactions on Engineering Technologies 2018 10
Tasks: BIG-bench Machine Learning, Clustering

Clustering is one of the most important concepts for unsupervised learning in machine learning. While there are numerous clustering algorithms already, many, including the popular one—k-means algorithm, require the number of clusters to be specified in advance, a huge drawback. Some studies use the silhouette coefficient to determine the optimal number of clusters. In this study, we introduce a novel algorithm called Powered Outer Probabilistic Clustering, show how it works through back-propagation (starting with many clusters and ending with an optimal number of clusters) , and show that the algorithm converges to the expected (optimal) number of clusters on theoretical examples.
Optimal Control Mesh
Authors: Peter Taraba
Date: 2012-10-24 00:00:00
Proceeding: World Congress on Engineering and Computer Science 2012 2012 10

We present a novel algorithm for optimal control of nonlinear systems, which creates control function over a mesh on a region of interest. The algorithm presented in this paper is an alternative to a set-oriented approach and subdivision algorithm for optimal control. The main contribution of this paper is error estimation for a found solution. We show on two dimensional and three dimensional problems, that this new algorithm is faster than a subdivision algorithm. In comparison with a set-oriented approach, the new algorithm keeps the same advantages as the subdivision algorithm which include a smaller memory foot-print of the final solution, no need for discretization and knowledge when to stop increasing the mesh size.
From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews
Authors: Julian McAuley, Jure Leskovec
Date: 2013-03-18 00:00:00

Recommending products to consumers means not only understanding their tastes,
but also understanding their level of experience. For example, it would be a
mistake to recommend the iconic film Seven Samurai simply because a user enjoys
other action movies; rather, we might conclude that they will eventually enjoy
it -- once they are ready. The same is true for beers, wines, gourmet foods --
or any products where users have acquired tastes: the `best' products may not
be the most `accessible'. Thus our goal in this paper is to recommend products
that a user will enjoy now, while acknowledging that their tastes may have
changed over time, and may change again in the future. We model how tastes
change due to the very act of consuming more products -- in other words, as
users become more experienced. We develop a latent factor recommendation system
that explicitly accounts for each user's level of experience. We find that such
a model not only leads to better recommendations, but also allows us to study
the role of user experience and expertise on a novel dataset of fifteen million
beer, wine, food, and movie reviews.
Training neural networks to encode symbols enables combinatorial generalization
Authors: Ivan Vankov, Jeffrey Bowers
Date: 2019-03-29 00:00:00

Combinatorial generalization - the ability to understand and produce novel combinations of already familiar elements - is considered to be a core capacity of the human mind and a major challenge to neural network models. A significant body of research suggests that conventional neural networks can't solve this problem unless they are endowed with mechanisms specifically engineered for the purpose of representing symbols. In this paper we introduce a novel way of representing symbolic structures in connectionist terms - the vectors approach to representing symbols (VARS), which allows training standard neural architectures to encode symbolic knowledge explicitly at their output layers. In two simulations, we show that neural networks not only can learn to produce VARS representations, but in doing so they achieve combinatorial generalization in their symbolic and non-symbolic output. This adds to other recent work that has shown improved combinatorial generalization under specific training conditions, and raises the question of whether specific mechanisms or training routines are needed to support symbolic processing.
MCTS-based Automated Negotiation Agent (Extended Abstract)
Authors: Cédric Buron, Zahia Guessoum, Sylvain Ductor
Date: 2019-03-29 00:00:00

This paper introduces a new Negotiating Agent for automated negotiation on
continuous domains and without considering a specified deadline. The agent
bidding strategy relies on Monte Carlo Tree Search, which is a trendy method
since it has been used with success on games with high branching factor such as
Go. It uses two opponent modeling techniques for its bidding strategy and its
utility: Gaussian process regression and Bayesian learning. Evaluation is done
by confronting the existing agents that are able to negotiate in such context:
Random Walker, Tit-for-tat and Nice Tit-for-Tat. None of those agents succeeds
in beating our agent; moreover the modular and adaptive nature of our approach
is a huge advantage when it comes to optimize it in specific applicative
contexts.
A Local Approach to Forward Model Learning: Results on the Game of Life Game
Authors: Simon M. Lucas, Alexander Dockhorn, Vanessa Volz, Chris Bamford, Raluca D. Gaina, Ivan Bravi, Diego Perez-Liebana, Sanaz Mostaghim, Rudolf Kruse
Date: 2019-03-29 00:00:00

This paper investigates the effect of learning a forward model on the
performance of a statistical forward planning agent. We transform Conway's Game
of Life simulation into a single-player game where the objective can be either
to preserve as much life as possible or to extinguish all life as quickly as
possible.
  In order to learn the forward model of the game, we formulate the problem in
a novel way that learns the local cell transition function by creating a set of
supervised training data and predicting the next state of each cell in the grid
based on its current state and immediate neighbours. Using this method we are
able to harvest sufficient data to learn perfect forward models by observing
only a few complete state transitions, using either a look-up table, a decision
tree or a neural network.
  In contrast, learning the complete state transition function is a much harder
task and our initial efforts to do this using deep convolutional auto-encoders
were less successful.
  We also investigate the effects of imperfect learned models on prediction
errors and game-playing performance, and show that even models with significant
errors can provide good performance.
Degrees of Laziness in Grounding: Effects of Lazy-Grounding Strategies on ASP Solving
Authors: Richard Taupe, Antonius Weinzierl, Gerhard Friedrich
Date: 2019-03-29 00:00:00

The traditional ground-and-solve approach to Answer Set Programming (ASP) suffers from the grounding bottleneck, which makes large-scale problem instances unsolvable. Lazy grounding is an alternative approach that interleaves grounding with solving and thus uses space more efficiently. The limited view on the search space in lazy grounding poses unique challenges, however, and can have adverse effects on solving performance. In this paper we present a novel characterization of degrees of laziness in grounding for ASP, i.e. of compromises between lazily grounding as little as possible and the traditional full grounding upfront. We investigate how these degrees of laziness compare to each other formally as well as, by means of an experimental analysis using a number of benchmarks, in terms of their effects on solving performance. Our contributions are the introduction of a range of novel lazy grounding strategies, a formal account on their relationships and their correctness, and an investigation of their effects on solving performance. Experiments show that our approach performs significantly better than state-of-the-art lazy grounding in many cases.
Towards Brain-inspired System: Deep Recurrent Reinforcement Learning for Simulated Self-driving Agent
Authors: Jieneng Chen, Jingye Chen, Ruiming Zhang, Xiaobin Hu
Date: 2019-03-29 00:00:00
Tasks: Decision Making, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

An effective way to achieve intelligence is to simulate various intelligent
behaviors in the human brain. In recent years, bio-inspired learning methods
have emerged, and they are different from the classical mathematical
programming principle. In the perspective of brain inspiration, reinforcement
learning has gained additional interest in solving decision-making tasks as
increasing neuroscientific research demonstrates that significant links exist
between reinforcement learning and specific neural substrates. Because of the
tremendous research that focuses on human brains and reinforcement learning,
scientists have investigated how robots can autonomously tackle complex tasks
in the form of a self-driving agent control in a human-like way. In this study,
we propose an end-to-end architecture using novel deep-Q-network architecture
in conjunction with a recurrence to resolve the problem in the field of
simulated self-driving. The main contribution of this study is that we trained
the driving agent using a brain-inspired trial-and-error technique, which was
in line with the real world situation. Besides, there are three innovations in
the proposed learning network: raw screen outputs are the only information
which the driving agent can rely on, a weighted layer that enhances the
differences of the lengthy episode, and a modified replay mechanism that
overcomes the problem of sparsity and accelerates learning. The proposed
network was trained and tested under a third-partied OpenAI Gym environment.
After training for several episodes, the resulting driving agent performed
advanced behaviors in the given scene. We hope that in the future, the proposed
brain-inspired learning system would inspire practicable self-driving control
solutions.
The Algorithmic Automation Problem: Prediction, Triage, and Human Effort
Authors: Maithra Raghu, Katy Blumer, Greg Corrado, Jon Kleinberg, Ziad Obermeyer, Sendhil Mullainathan
Date: 2019-03-28 00:00:00
Tasks: Prediction

In a wide array of areas, algorithms are matching and surpassing the
performance of human experts, leading to consideration of the roles of human
judgment and algorithmic prediction in these domains. The discussion around
these developments, however, has implicitly equated the specific task of
prediction with the general task of automation. We argue here that automation
is broader than just a comparison of human versus algorithmic performance on a
task; it also involves the decision of which instances of the task to give to
the algorithm in the first place. We develop a general framework that poses
this latter decision as an optimization problem, and we show how basic
heuristics for this optimization problem can lead to performance gains even on
heavily-studied applications of AI in medicine. Our framework also serves to
highlight how effective automation depends crucially on estimating both
algorithmic and human error on an instance-by-instance basis, and our results
show how improvements in these error estimation problems can yield significant
gains for automation as well.
Amortized Object and Scene Perception for Long-term Robot Manipulation
Authors: Ferenc Balint-Benczedi, Michael Beetz
Date: 2019-03-28 00:00:00
Tasks: Robot Manipulation

Mobile robots, performing long-term manipulation activities in human
environments, have to perceive a wide variety of objects possessing very
different visual characteristics and need to reliably keep track of these
throughout the execution of a task. In order to be efficient, robot perception
capabilities need to go beyond what is currently perceivable and should be able
to answer queries about both current and past scenes. In this paper we
investigate a perception system for long-term robot manipulation that keeps
track of the changing environment and builds a representation of the perceived
world. Specifically we introduce an amortized component that spreads perception
tasks throughout the execution cycle. The resulting query driven perception
system asynchronously integrates results from logged images into a symbolic and
numeric (what we call sub-symbolic) representation that forms the perceptual
belief state of the robot.
Relation-Aware Graph Attention Network for Visual Question Answering
Authors: Linjie Li, Zhe Gan, Yu Cheng, Jingjing Liu
Date: 2019-03-29 00:00:00
Proceeding: ICCV 2019 10
Tasks: Graph Attention, Implicit Relations, Question Answering, Relation, Visual Question Answering, Visual Question Answering (VQA)

In order to answer semantically-complicated questions about an image, a Visual Question Answering (VQA) model needs to fully understand the visual scene in the image, especially the interactive dynamics between different objects. We propose a Relation-aware Graph Attention Network (ReGAT), which encodes each image into a graph and models multi-type inter-object relations via a graph attention mechanism, to learn question-adaptive relation representations. Two types of visual object relations are explored: (i) Explicit Relations that represent geometric positions and semantic interactions between objects; and (ii) Implicit Relations that capture the hidden dynamics between image regions. Experiments demonstrate that ReGAT outperforms prior state-of-the-art approaches on both VQA 2.0 and VQA-CP v2 datasets. We further show that ReGAT is compatible to existing VQA architectures, and can be used as a generic relation encoder to boost the model performance for VQA.