pwc-archive/papers-with-abstracts huggingface dataset:
An ADMM Based Framework for AutoML Pipeline Configuration
Authors: Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, Alexander Gray
Date: 2019-05-01 00:00:00
Tasks: AutoML, Binary Classification

We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints along-side the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits),and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML& OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical advantages of this framework.
Computer Science and Metaphysics: A Cross-Fertilization
Authors: Daniel Kirchner, Christoph Benzmüller, Edward N. Zalta
Date: 2019-05-01 00:00:00
Tasks: Philosophy

Computational philosophy is the use of mechanized computational techniques to unearth philosophical insights that are either difficult or impossible to find using traditional philosophical methods. Computational metaphysics is computational philosophy with a focus on metaphysics. In this paper, we (a) develop results in modal metaphysics whose discovery was computer assisted, and (b) conclude that these results work not only to the obvious benefit of philosophy but also, less obviously, to the benefit of computer science, since the new computational techniques that led to these results may be more broadly applicable within computer science. The paper includes a description of our background methodology and how it evolved, and a discussion of our new results.
Time-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States
Authors: Byungsoo Jeon, Eyal Shafran, Luke Breitfeller, Jason Levin, Carolyn P. Rose
Date: 2019-05-01 00:00:00
Tasks: Time Series, Time Series Analysis

This paper addresses a key challenge in Educational Data Mining, namely to
model student behavioral trajectories in order to provide a means for
identifying students most at-risk, with the goal of providing supportive
interventions. While many forms of data including clickstream data or data from
sensors have been used extensively in time series models for such purposes, in
this paper we explore the use of textual data, which is sometimes available in
the records of students at large, online universities. We propose a time series
model that constructs an evolving student state representation using both
clickstream data and a signal extracted from the textual notes recorded by
human mentors assigned to each student. We explore how the addition of this
textual data improves both the predictive power of student states for the
purpose of identifying students at risk for course failure as well as for
providing interpretable insights about student course engagement processes.
A Novel Trend Symbolic Aggregate Approximation for Time Series
Authors: Yu-Feng Yu, Yuelong Zhu, Dingsheng Wan, Qun Zhao, Huan Liu
Date: 2019-05-01 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

Symbolic Aggregate approximation (SAX) is a classical symbolic approach in
many time series data mining applications. However, SAX only reflects the
segment mean value feature and misses important information in a segment,
namely the trend of the value change in the segment. Such a miss may cause a
wrong classification in some cases, since the SAX representation cannot
distinguish different time series with similar average values but different
trends. In this paper, we present Trend Feature Symbolic Aggregate
approximation (TFSAX) to solve this problem. First, we utilize Piecewise
Aggregate Approximation (PAA) approach to reduce dimensionality and discretize
the mean value of each segment by SAX. Second, extract trend feature in each
segment by using trend distance factor and trend shape factor. Then, design
multi-resolution symbolic mapping rules to discretize trend information into
symbols. We also propose a modified distance measure by integrating the SAX
distance with a weighted trend distance. We show that our distance measure has
a tighter lower bound to the Euclidean distance than that of the original SAX.
The experimental results on diverse time series data sets demonstrate that our
proposed representation significantly outperforms the original SAX
representation and an improved SAX representation for classification.
The relationship between Biological and Artificial Intelligence
Authors: George Cevora
Date: 2019-05-01 00:00:00

Intelligence can be defined as a predominantly human ability to accomplish
tasks that are generally hard for computers and animals. Artificial
Intelligence [AI] is a field attempting to accomplish such tasks with
computers. AI is becoming increasingly widespread, as are claims of its
relationship with Biological Intelligence. Often these claims are made to imply
higher chances of a given technology succeeding, working on the assumption that
AI systems which mimic the mechanisms of Biological Intelligence should be more
successful.
  In this article I will discuss the similarities and differences between AI
and the extent of our knowledge about the mechanisms of intelligence in
biology, especially within humans. I will also explore the validity of the
assumption that biomimicry in AI systems aids their advancement, and I will
argue that existing similarity to biological systems in the way Artificial
Neural Networks [ANNs] tackle tasks is due to design decisions, rather than
inherent similarity of underlying mechanisms. This article is aimed at people
who understand the basics of AI (especially ANNs), and would like to be better
able to evaluate the often wild claims about the value of biomimicry in AI.
Restricted Connection Orthogonal Matching Pursuit For Sparse Subspace Clustering
Authors: Wenqi Zhu, Yuesheng Zhu, Li Zhong, Shuai Yang
Date: 2019-05-01 00:00:00
Tasks: Clustering

Sparse Subspace Clustering (SSC) is one of the most popular methods for
clustering data points into their underlying subspaces. However, SSC may suffer
from heavy computational burden. Orthogonal Matching Pursuit applied on SSC
accelerates the computation but the trade-off is the loss of clustering
accuracy. In this paper, we propose a noise-robust algorithm, Restricted
Connection Orthogonal Matching Pursuit for Sparse Subspace Clustering
(RCOMP-SSC), to improve the clustering accuracy and maintain the low
computational time by restricting the number of connections of each data point
during the iteration of OMP. Also, we develop a framework of control matrix to
realize RCOMP-SCC. And the framework is scalable for other data point selection
strategies. Our analysis and experiments on synthetic data and two real-world
databases (EYaleB & Usps) demonstrate the superiority of our algorithm compared
with other clustering methods in terms of accuracy and computational time.
RadiX-Net: Structured Sparse Matrices for Deep Neural Networks
Authors: Ryan A. Robinett, Jeremy Kepner
Date: 2019-04-30 00:00:00

The sizes of deep neural networks (DNNs) are rapidly outgrowing the capacity
of hardware to store and train them. Research over the past few decades has
explored the prospect of sparsifying DNNs before, during, and after training by
pruning edges from the underlying topology. The resulting neural network is
known as a sparse neural network. More recent work has demonstrated the
remarkable result that certain sparse DNNs can train to the same precision as
dense DNNs at lower runtime and storage cost. An intriguing class of these
sparse DNNs is the X-Nets, which are initialized and trained upon a sparse
topology with neither reference to a parent dense DNN nor subsequent pruning.
We present an algorithm that deterministically generates RadiX-Nets: sparse DNN
topologies that, as a whole, are much more diverse than X-Net topologies, while
preserving X-Nets' desired characteristics. We further present a
functional-analytic conjecture based on the longstanding observation that
sparse neural network topologies can attain the same expressive power as dense
counterparts
The role of artificial intelligence in achieving the Sustainable Development Goals
Authors: Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, Simone Langhans, Max Tegmark, Francesco Fuso Nerini
Date: 2019-04-30 00:00:00

The emergence of artificial intelligence (AI) and its progressively wider
impact on many sectors across the society requires an assessment of its effect
on sustainable development. Here we analyze published evidence of positive or
negative impacts of AI on the achievement of each of the 17 goals and 169
targets of the 2030 Agenda for Sustainable Development. We find that AI can
support the achievement of 128 targets across all SDGs, but it may also inhibit
58 targets. Notably, AI enables new technologies that improve efficiency and
productivity, but it may also lead to increased inequalities among and within
countries, thus hindering the achievement of the 2030 Agenda. The fast
development of AI needs to be supported by appropriate policy and regulation.
Otherwise, it would lead to gaps in transparency, accountability, safety and
ethical standards of AI-based technology, which could be detrimental towards
the development and sustainable use of AI. Finally, there is a lack of research
assessing the medium- and long-term impacts of AI. It is therefore essential to
reinforce the global debate regarding the use of AI and to develop the
necessary regulatory insight and oversight for AI-based technologies.
Understanding Urban Dynamics via Context-aware Tensor Factorization with Neighboring Regularization
Authors: Jingyuan Wang, Junjie Wu, Ze Wang, Fei Gao, Zhang Xiong
Date: 2019-04-25 00:00:00
Tasks: Traffic Prediction

Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factorization model (NR-cNTF) to discover interpretable urban dynamics from urban heterogeneous data. Different from many existing studies concerned with prediction tasks via tensor completion, NR-cNTF focuses on gaining urban managerial insights from spatial, temporal, and spatio-temporal patterns. This is enabled by high-quality Tucker factorizations regularized by both POI-based urban contexts and geographically neighboring relations. NR-cNTF is also capable of unveiling long-term evolutions of urban dynamics via a pipeline initialization approach. We apply NR-cNTF to a real-life data set containing rich taxi GPS trajectories and POI records of Beijing. The results indicate: 1) NR-cNTF accurately captures four kinds of city rhythms and seventeen spatial communities; 2) the rapid development of Beijing, epitomized by the CBD area, indeed intensifies the job-housing imbalance; 3) the southern areas with recent government investments have shown more healthy development tendency. Finally, NR-cNTF is compared with some baselines on traffic prediction, which further justifies the importance of urban contexts awareness and neighboring regulations.
A Framework for Predicting Impactability of Healthcare Interventions Using Machine Learning Methods, Administrative Claims, Sociodemographic and App Generated Data
Authors: Heather Mattie, Patrick Reidy, Patrik Bachtiger, Emily Lindemer, Mohammad Jouni, Trishan Panch
Date: 2019-04-19 00:00:00
Tasks: Management

It is not clear how to target patients who are most likely to benefit from digital care management programs ex-ante, a shortcoming of current risk score based approaches. This study focuses on defining impactability by identifying those patients most likely to benefit from technology enabled care management, delivered through a digital health platform, including a mobile app and clinician web dashboard. Anonymized insurance claims data were used from a commercially insured population across several U.S. states and combined with inferred sociodemographic data and data derived from the patient-held mobile application itself. Our approach involves the creation of two models and the comparative analysis of the methodologies and performances therein. We first train a cost prediction model to calculate the differences in predicted (without intervention) versus actual (with onboarding onto digital health platform) healthcare expenditure for patients (N = 1,242). This enables the classification of impactability if differences in predicted versus actual costs meet a predetermined threshold. A random forest machine learning model was then trained to accurately categorize new patients as impactable versus not impactable, reaching an overall accuracy of 71.9%. We then modify these parameters through grid search to define the parameters that deliver optimal performance. A roadmap is proposed to iteratively improve the performance of the model. As the number of newly onboarded patients and length of use continues to increase, the accuracy of predicting impactability will improve commensurately as more advanced machine learning techniques such as deep learning become relevant. This approach is generalizable to analyzing the impactability of any intervention and is a key component of realising closed loop feedback systems for continuous improvement in healthcare.
Hybrid Mortality Prediction using Multiple Source Systems
Authors: Isaac Mativo, Yelena Yesha, Michael Grasso, Tim Oates, Qian Zhu
Date: 2019-04-18 00:00:00
Tasks: Mortality Prediction, Prediction

The use of artificial intelligence in clinical care to improve decision
support systems is increasing. This is not surprising since, by its very
nature, the practice of medicine consists of making decisions based on
observations from different systems both inside and outside the human body. In
this paper, we combine three general systems (ICU, diabetes, and comorbidities)
and use them to make patient clinical predictions. We use an artificial
intelligence approach to show that we can improve mortality prediction of
hospitalized diabetic patients. We do this by utilizing a machine learning
approach to select clinical input features that are more likely to predict
mortality. We then use these features to create a hybrid mortality prediction
model and compare our results to non-artificial intelligence models. For
simplicity, we limit our input features to patient comorbidities and features
derived from a well-known mortality measure, the Sequential Organ Failure
Assessment (SOFA).
Galaxy Learning -- A Position Paper
Authors: Chao Wu, Jun Xiao, Gang Huang, Fei Wu
Date: 2019-04-22 00:00:00
Tasks: BIG-bench Machine Learning, Position

The recent rapid development of artificial intelligence (AI, mainly driven by machine learning research, especially deep learning) has achieved phenomenal success in various applications. However, to further apply AI technologies in real-world context, several significant issues regarding the AI ecosystem should be addressed. We identify the main issues as data privacy, ownership, and exchange, which are difficult to be solved with the current centralized paradigm of machine learning training methodology. As a result, we propose a novel model training paradigm based on blockchain, named Galaxy Learning, which aims to train a model with distributed data and to reserve the data ownership for their owners. In this new paradigm, encrypted models are moved around instead, and are federated once trained. Model training, as well as the communication, is achieved with blockchain and its smart contracts. Pricing of training data is determined by its contribution, and therefore it is not about the exchange of data ownership. In this position paper, we describe the motivation, paradigm, design, and challenges as well as opportunities of Galaxy Learning.
Internet of Intelligence: The Collective Advantage for Advancing Communications and Intelligence
Authors: Rongpeng Li, Zhifeng Zhao, Xing Xu, Fei Ni, Honggang Zhang
Date: 2019-04-26 00:00:00

The fifth-generation cellular networks (5G) has boosted the unprecedented convergence between the information world and physical world. On the other hand, empowered with the enormous amount of data and information, artificial intelligence (AI) has been universally applied and pervasive AI is believed to be an integral part of the six-generation cellular networks (6G). Consequently, benefiting from the advancement in communication technology and AI, we boldly argue that the conditions for collective intelligence (CI) will be mature in the 6G era and CI will emerge among the widely connected beings and things. Afterwards, we highlight the potential huge impact of CI on both communications and intelligence. In particular, we introduce a regular language (i.e., the information economy metalanguage) supporting the future collective communications to augment human intelligence and explain its potential applications in naming Internet information and pushing information centric networks forward. Meanwhile, we propose a stigmergy-based federated collective intelligence and demonstrate its achievement in a simulated scenario where the agents collectively work together to form a pattern through simple indirect communications. In a word, CI could advance both communications and intelligence.
Graph based Entropy for Detecting Explanatory Signs of Changes in Market
Authors: Yukio Ohsawa
Date: 2018-11-06 00:00:00
Tasks: Change Detection, Diversity

Graph based entropy, an index of the diversity of events in their
distribution to parts of a co-occurrence graph, is proposed for detecting signs
of structural changes in the data that are informative in explaining latent
dynamics of consumers behavior. For obtaining graph-based entropy, connected
subgraphs are first obtained from the graph of co-occurrences of items in the
data. Then, the distribution of items occurring in events in the data to these
sub-graphs is reflected on the value of graph-based entropy. For the data on
the position of sale, a change in this value is regarded as a sign of the
appearance, the separation, the disappearance, or the uniting of consumers
interests. These phenomena are regarded as the signs of dynamic changes in
consumers behavior that may be the effects of external events and information.
Experiments show that graph-based entropy outperforms baseline methods that can
be used for change detection, in explaining substantial changes and their signs
in consumers preference of items in supermarket stores.
Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA Architectures
Authors: Artem Lutov, Mourad Khayati, Philippe Cudré-Mauroux
Date: 2018-11-17 00:00:00
Proceeding: 2018 IEEE International Conference on Data Mining Workshops (ICDMW) 2018 11
Tasks: Benchmarking, Clustering, Clustering Algorithms Evaluation, Community Detection, CPU, Diversity

There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems. To the best of our knowledge, however, there does not exist yet any uniform benchmarking framework, which is publicly available and suitable for the parallel benchmarking of diverse clustering algorithms on a wide range of synthetic and real-world datasets. In this paper, we introduce Clubmark, a new extensible framework that aims to fill this gap by providing a parallel isolation benchmarking platform for clustering algorithms and their evaluation on NUMA servers. Clubmark allows for fine-grained control over various execution variables (timeouts, memory consumption, CPU affinity and cache policy) and supports the evaluation of a wide range of clustering algorithms including multi-level, hierarchical and overlapping clustering techniques on both weighted and unweighted input networks with built-in evaluation of several extrinsic and intrinsic measures. Our framework is open-source and provides a consistent and systematic way to execute, evaluate and profile clustering techniques considering a number of aspects that are often missing in state-of-the-art frameworks and benchmarking systems.
Non-Uniform Subset Selection for Active Learning in Structured Data
Authors: Sujoy Paul, Jawadul H. Bappy, Amit Roy-Chowdhury
Date: 2017-06-01 00:00:00
Proceeding: Computer Vision and Pattern Recognition (CVPR) 2017 6
Tasks: Active Learning, Activity Recognition, Object Recognition

Several works have shown that relationships between data points (i.e., context) in structured data can be exploited
to obtain better recognition performance. In this paper, we explore a different, but related, problem: how can these interrelationships be used to efficiently learn and continuously update a recognition model, with minimal human labeling
effort. Towards this goal, we propose an active learning framework to select an optimal subset of data points for manual labeling by exploiting the relationships between them. We construct a graph from the unlabeled data to represent
the underlying structure, such that each node represents a data point, and edges represent the inter-relationships between them. Thereafter, considering the flow of beliefs in this graph, we choose those samples for labeling which minimize the joint entropy of the nodes of the graph. This results in significant reduction in manual labeling effort without compromising recognition performance. Our method chooses non-uniform number of samples from each batch of streaming data depending on its information content. Also, the
submodular property of our objective function makes it computationally efficient to optimize. The proposed framework
is demonstrated in various applications, including document
analysis, scene-object recognition, and activity recognition.
Stock price prediction using support vector regression on daily and up to the minute prices
Authors: Bruno Miranda, HenriqueVinicius, AmorimSobreiro, HerbertKimura
Date: 2018-09-01 00:00:00
Proceeding: journal 2018 9
Tasks: BIG-bench Machine Learning, Management, regression, Stock Price Prediction

The purpose of predictive stock price systems is to provide abnormal returns for financial market operators and serve as a basis for risk management tools. Although the Efficient Market Hypothesis (EMH) states that it is not possible to anticipate market movements consistently, the use of computationally intensive systems that employ machine learning algorithms is increasingly common in the development of stock trading mechanisms. Several studies, using daily stock prices, have presented predictive system applications trained on fixed periods without considering new model updates. In this context, this study uses a machine learning technique called Support Vector Regression (SVR) to predict stock prices for large and small capitalisations and in three different markets, employing prices with both daily and up-to-the-minute frequencies. Prediction errors are measured, and the model is compared to the random walk model proposed by the EMH. The results suggest that the SVR has predictive power, especially when using a strategy of updating the model periodically. There are also indicative results of increased predictions precision during lower volatility periods.