pwc-archive/papers-with-abstracts huggingface dataset:
Person Identification using Seismic Signals generated from Footfalls
Authors: Bodhibrata Mukhopadhyay, Sahil Anchal, Subrat Kar
Date: 2018-09-24 00:00:00
Tasks: Data Compression, Person Identification

Footfall based biometric system is perhaps the only person identification
technique which does not hinder the natural movement of an individual. This is
a clear edge over all other biometric systems which require a formidable amount
of human intervention and encroach upon an individual's privacy to some extent
or the other. This paper presents a Fog computing architecture for implementing
footfall based biometric system using widespread geographically distributed
geophones (vibration sensor). Results were stored in an Internet of Things
(IoT) cloud. We have tested our biometric system on an indigenous database
(created by us) containing 46000 footfall events from 8 individuals and
achieved an accuracy of 73%, 90% and 95% in case of 1, 5 and 10 footsteps per
sample. We also proposed a basis pursuit based data compression technique DS8BP
for wireless transmission of footfall events to the Fog. DS8BP compresses the
original footfall events (sampled at 8 kHz) by a factor of 108 and also acts as
a smoothing filter. These experimental results depict the high viability of our
technique in the realm of person identification and access control systems.
Norm-Ranging LSH for Maximum Inner Product Search
Authors: Xiao Yan, Jinfeng Li, Xinyan Dai, Hongzhi Chen, James Cheng
Date: 2018-09-24 00:00:00
Proceeding: NeurIPS 2018 12

Neyshabur and Srebro proposed Simple-LSH, which is the state-of-the-art
hashing method for maximum inner product search (MIPS) with performance
guarantee. We found that the performance of Simple-LSH, in both theory and
practice, suffers from long tails in the 2-norm distribution of real datasets.
We propose Norm-ranging LSH, which addresses the excessive normalization
problem caused by long tails in Simple-LSH by partitioning a dataset into
multiple sub-datasets and building a hash index for each sub-dataset
independently. We prove that Norm-ranging LSH has lower query time complexity
than Simple-LSH. We also show that the idea of partitioning the dataset can
improve other hashing based methods for MIPS. To support efficient query
processing on the hash indexes of the sub-datasets, a novel similarity metric
is formulated. Experiments show that Norm-ranging LSH achieves an order of
magnitude speedup over Simple-LSH for the same recall, thus significantly
benefiting applications that involve MIPS.
Modeling longitudinal data using matrix completion
Authors: Łukasz Kidziński, Trevor Hastie
Date: 2018-09-24 00:00:00
Tasks: Gaussian Processes, Matrix Completion

In clinical practice and biomedical research, measurements are often collected sparsely and irregularly in time while the data acquisition is expensive and inconvenient. Examples include measurements of spine bone mineral density, cancer growth through mammography or biopsy, a progression of defective vision, or assessment of gait in patients with neurological disorders. Since the data collection is often costly and inconvenient, estimation of progression from sparse observations is of great interest for practitioners. From the statistical standpoint, such data is often analyzed in the context of a mixed-effect model where time is treated as both a fixed-effect (population progression curve) and a random-effect (individual variability). Alternatively, researchers analyze Gaussian processes or functional data where observations are assumed to be drawn from a certain distribution of processes. These models are flexible but rely on probabilistic assumptions, require very careful implementation, specific to the given problem, and tend to be slow in practice. In this study, we propose an alternative elementary framework for analyzing longitudinal data, relying on matrix completion. Our method yields estimates of progression curves by iterative application of the Singular Value Decomposition. Our framework covers multivariate longitudinal data, regression, and can be easily extended to other settings. As it relies on existing tools for matrix algebra it is efficient and easy to implement. We apply our methods to understand trends of progression of motor impairment in children with Cerebral Palsy. Our model approximates individual progression curves and explains 30% of the variability. Low-rank representation of progression trends enables identification of different progression trends in subtypes of Cerebral Palsy.
FCHD: Fast and accurate head detection in crowded scenes
Authors: Aditya Vora, Vinay Chilaka
Date: 2018-09-24 00:00:00
Tasks: Face Detection, General Classification, Head Detection

In this paper, we propose FCHD-Fully Convolutional Head Detector, an end-to-end trainable head detection model. Our proposed architecture is a single fully convolutional network which is responsible for both bounding box prediction and classification. This makes our model lightweight with low inference time and memory requirements. Along with run-time, our model has better overall average precision (AP) which is achieved by selection of anchor sizes based on the effective receptive field of the network. This can be concluded from our experiments on several head detection datasets with varying head counts. We achieve an AP of 0.70 on a challenging head detection dataset which is comparable to some standard benchmarks. Along with this our model runs at 5 FPS on Nvidia Quadro M1000M for VGA resolution images. Code is available at https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector.
Speaker Naming in Movies
Authors: Mahmoud Azab, Mingzhe Wang, Max Smith, Noriyuki Kojima, Jia Deng, Rada Mihalcea
Date: 2018-09-24 00:00:00
Proceeding: NAACL 2018 6

We propose a new model for speaker naming in movies that leverages visual,
textual, and acoustic modalities in an unified optimization framework. To
evaluate the performance of our model, we introduce a new dataset consisting of
six episodes of the Big Bang Theory TV show and eighteen full movies covering
different genres. Our experiments show that our multimodal model significantly
outperforms several competitive baselines on the average weighted F-score
metric. To demonstrate the effectiveness of our framework, we design an
end-to-end memory network model that leverages our speaker naming model and
achieves state-of-the-art results on the subtitles task of the MovieQA 2017
Challenge.
Low Frequency Adversarial Perturbation
Authors: Chuan Guo, Jared S. Frank, Kilian Q. Weinberger
Date: 2018-09-24 00:00:00
Tasks: Denoising, Speech Recognition

Adversarial images aim to change a target model's decision by minimally perturbing a target image. In the black-box setting, the absence of gradient information often renders this search problem costly in terms of query complexity. In this paper we propose to restrict the search for adversarial images to a low frequency domain. This approach is readily compatible with many existing black-box attack frameworks and consistently reduces their query cost by 2 to 4 times. Further, we can circumvent image transformation defenses even when both the model and the defense strategy are unknown. Finally, we demonstrate the efficacy of this technique by fooling the Google Cloud Vision platform with an unprecedented low number of model queries.
Learning to Detect Fake Face Images in the Wild
Authors: Chih-Chung Hsu, Chia-Yen Lee, Yi-Xiu Zhuang
Date: 2018-09-24 00:00:00
Tasks: Face Swapping, Fake Image Detection, GAN image forensics, Generative Adversarial Network, Image Generation

Although Generative Adversarial Network (GAN) can be used to generate the
realistic image, improper use of these technologies brings hidden concerns. For
example, GAN can be used to generate a tampered video for specific people and
inappropriate events, creating images that are detrimental to a particular
person, and may even affect that personal safety. In this paper, we will
develop a deep forgery discriminator (DeepFD) to efficiently and effectively
detect the computer-generated images. Directly learning a binary classifier is
relatively tricky since it is hard to find the common discriminative features
for judging the fake images generated from different GANs. To address this
shortcoming, we adopt contrastive loss in seeking the typical features of the
synthesized images generated by different GANs and follow by concatenating a
classifier to detect such computer-generated images. Experimental results
demonstrate that the proposed DeepFD successfully detected 94.7% fake images
generated by several state-of-the-art GANs.
Interactions as Social Practices: towards a formalization
Authors: Frank Dignum
Date: 2018-09-24 00:00:00

Multi-agent models are a suitable starting point to model complex social
interactions. However, as the complexity of the systems increase, we argue that
novel modeling approaches are needed that can deal with inter-dependencies at
different levels of society, where many heterogeneous parties (software agents,
robots, humans) are interacting and reacting to each other. In this paper, we
present a formalization of a social framework for agents based in the concept
of Social Practices as high level specifications of normal (expected) behavior
in a given social context. We argue that social practices facilitate the
practical reasoning of agents in standard social interactions.
A Survey of Conventional and Artificial Intelligence / Learning based Resource Allocation and Interference Mitigation Schemes in D2D Enabled Networks
Authors: Kamran Zia, Nauman Javed, Muhammad Nadeem Sial, Sohail Ahmed, Hifsa Iram, Asad Amir Pirzada
Date: 2018-09-24 00:00:00
Tasks: Autonomous Driving

5th generation networks are envisioned to provide seamless and ubiquitous
connection to 1000-fold more devices and is believed to provide ultra-low
latency and higher data rates up to tens of Gbps. Different technologies
enabling these requirements are being developed including mmWave
communications, Massive MIMO and beamforming, Device to Device (D2D)
communications and Heterogeneous Networks. D2D communication is a promising
technology to enable applications requiring high bandwidth such as online
streaming and online gaming etc. It can also provide ultra- low latencies
required for applications like vehicle to vehicle communication for autonomous
driving. D2D communication can provide higher data rates with high energy
efficiency and spectral efficiency compared to conventional communication. The
performance benefits of D2D communication can be best achieved when D2D users
reuses the spectrum being utilized by the conventional cellular users. This
spectrum sharing in a multi-tier heterogeneous network will introduce complex
interference among D2D users and cellular users which needs to be resolved.
Motivated by limited number of surveys for interference mitigation and resource
allocation in D2D enabled heterogeneous networks, we have surveyed different
conventional and artificial intelligence based interference mitigation and
resource allocation schemes developed in recent years. Our contribution lies in
the analysis of conventional interference mitigation techniques and their
shortcomings. Finally, the strengths of AI based techniques are determined and
open research challenges deduced from the recent research are presented.
Matrix Linear Discriminant Analysis
Authors: Wei Hu, Weining Shen, Hua Zhou, Dehan Kong
Date: 2018-09-24 00:00:00
Tasks: General Classification, regression

We propose a novel linear discriminant analysis approach for the classification of high-dimensional matrix-valued data that commonly arises from imaging studies. Motivated by the equivalence of the conventional linear discriminant analysis and the ordinary least squares, we consider an efficient nuclear norm penalized regression that encourages a low-rank structure. Theoretical properties including a non-asymptotic risk bound and a rank consistency result are established. Simulation studies and an application to electroencephalography data show the superior performance of the proposed method over the existing approaches.
Scalable inference of topic evolution via models for latent geometric structures
Authors: Mikhail Yurochkin, Zhiwei Fan, Aritra Guha, Paraschos Koutris, XuanLong Nguyen
Date: 2018-09-24 00:00:00
Proceeding: NeurIPS 2019 12

We develop new models and algorithms for learning the temporal dynamics of the topic polytopes and related geometric objects that arise in topic model based inference. Our model is nonparametric Bayesian and the corresponding inference algorithm is able to discover new topics as the time progresses. By exploiting the connection between the modeling of topic polytope evolution, Beta-Bernoulli process and the Hungarian matching algorithm, our method is shown to be several orders of magnitude faster than existing topic modeling approaches, as demonstrated by experiments working with several million documents in under two dozens of minutes.
Modern Convex Optimization to Medical Image Analysis
Authors: Jing Yuan, Aaron Fenster
Date: 2018-09-24 00:00:00
Tasks: Computed Tomography (CT), Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Semantic Segmentation

Recently, diagnosis, therapy and monitoring of human diseases involve a
variety of imaging modalities, such as magnetic resonance imaging(MRI),
computed tomography(CT), Ultrasound(US) and Positron-emission tomography(PET)
as well as a variety of modern optical techniques. Over the past two decade, it
has been recognized that advanced image processing techniques provide valuable
information to physicians for diagnosis, image guided therapy and surgery, and
monitoring of the treated organ to the therapy. Many researchers and companies
have invested significant efforts in the developments of advanced medical image
analysis methods; especially in the two core studies of medical image
segmentation and registration, segmentations of organs and lesions are used to
quantify volumes and shapes used in diagnosis and monitoring treatment;
registration of multimodality images of organs improves detection, diagnosis
and staging of diseases as well as image-guided surgery and therapy,
registration of images obtained from the same modality are used to monitor
progression of therapy. These challenging clinical-motivated applications
introduce novel and sophisticated mathematical problems which stimulate
developments of advanced optimization and computing methods, especially convex
optimization attaining optimum in a global sense, hence, bring an enormous
spread of research topics for recent computational medical image analysis.
Particularly, distinct from the usual image processing, most medical images
have a big volume of acquired data, often in 3D or 4D (3D + t) along with great
noises or incomplete image information, and form the challenging large-scale
optimization problems; how to process such poor 'big data' of medical images
efficiently and solve the corresponding optimization problems robustly are the
key factors of modern medical image analysis.
Neural Transductive Learning and Beyond: Morphological Generation in the Minimal-Resource Setting
Authors: Katharina Kann, Hinrich Schütze
Date: 2018-09-24 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Transductive Learning

Neural state-of-the-art sequence-to-sequence (seq2seq) models often do not perform well for small training sets. We address paradigm completion, the morphological task of, given a partial paradigm, generating all missing forms. We propose two new methods for the minimal-resource setting: (i) Paradigm transduction: Since we assume only few paradigms available for training, neural seq2seq models are able to capture relationships between paradigm cells, but are tied to the idiosyncracies of the training set. Paradigm transduction mitigates this problem by exploiting the input subset of inflected forms at test time. (ii) Source selection with high precision (SHIP): Multi-source models which learn to automatically select one or multiple sources to predict a target inflection do not perform well in the minimal-resource setting. SHIP is an alternative to identify a reliable source if training data is limited. On a 52-language benchmark dataset, we outperform the previous state of the art by up to 9.71% absolute accuracy.
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
Authors: Katharina Kann, Sascha Rothe, Katja Filippova
Date: 2018-09-24 00:00:00
Proceeding: CONLL 2018 10
Tasks: Language Modeling, Language Modelling, Sentence, Text Generation

Motivated by recent findings on the probabilistic modeling of acceptability
judgments, we propose syntactic log-odds ratio (SLOR), a normalized language
model score, as a metric for referenceless fluency evaluation of natural
language generation output at the sentence level. We further introduce WPSLOR,
a novel WordPiece-based version, which harnesses a more compact language model.
Even though word-overlap metrics like ROUGE are computed with the help of
hand-written references, our referenceless methods obtain a significantly
higher correlation with human fluency scores on a benchmark dataset of
compressed sentences. Finally, we present ROUGE-LM, a reference-based metric
which is a natural extension of WPSLOR to the case of available references. We
show that ROUGE-LM yields a significantly higher correlation with human
judgments than all baseline metrics, including WPSLOR on its own.
Deformable Stacked Structure for Named Entity Recognition
Authors: Shuyang Cao, Xipeng Qiu, Xuanjing Huang
Date: 2018-09-24 00:00:00
Tasks: Decoder, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Neural architecture for named entity recognition has achieved great success
in the field of natural language processing. Currently, the dominating
architecture consists of a bi-directional recurrent neural network (RNN) as the
encoder and a conditional random field (CRF) as the decoder. In this paper, we
propose a deformable stacked structure for named entity recognition, in which
the connections between two adjacent layers are dynamically established. We
evaluate the deformable stacked structure by adapting it to different layers.
Our model achieves the state-of-the-art performances on the OntoNotes dataset.
Context-Aware Attention for Understanding Twitter Abuse
Authors: Tuhin Chakrabarty, Kilol Gupta
Date: 2018-09-24 00:00:00
Tasks: Abuse Detection

The original goal of any social media platform is to facilitate users to indulge in healthy and meaningful conversations. But more often than not, it has been found that it becomes an avenue for wanton attacks. We want to alleviate this issue and hence we try to provide a detailed analysis of how abusive behavior can be monitored in Twitter. The complexity of the natural language constructs makes this task challenging. We show how applying contextual attention to Long Short Term Memory networks help us give near state of art results on multiple benchmarks abuse detection data sets from Twitter.
Unified recurrent neural network for many feature types
Authors: Alexander Stec, Diego Klabjan, Jean Utke
Date: 2018-09-24 00:00:00
Tasks: Time Series, Time Series Analysis

There are time series that are amenable to recurrent neural network (RNN)
solutions when treated as sequences, but some series, e.g. asynchronous time
series, provide a richer variation of feature types than current RNN cells take
into account. In order to address such situations, we introduce a unified RNN
that handles five different feature types, each in a different manner. Our RNN
framework separates sequential features into two groups dependent on their
frequency, which we call sparse and dense features, and which affect cell
updates differently. Further, we also incorporate time features at the
sequential level that relate to the time between specified events in the
sequence and are used to modify the cell's memory state. We also include two
types of static (whole sequence level) features, one related to time and one
not, which are combined with the encoder output. The experiments show that the
modeling framework proposed does increase performance compared to standard
cells.
Give me a hint! Navigating Image Databases using Human-in-the-loop Feedback
Authors: Bryan A. Plummer, M. Hadi Kiapour, Shuai Zheng, Robinson Piramuthu
Date: 2018-09-24 00:00:00
Tasks: Attribute, Image Retrieval

In this paper, we introduce an attribute-based interactive image search which
can leverage human-in-the-loop feedback to iteratively refine image search
results. We study active image search where human feedback is solicited
exclusively in visual form, without using relative attribute annotations used
by prior work which are not typically found in many datasets. In order to
optimize the image selection strategy, a deep reinforcement model is trained to
learn what images are informative rather than rely on hand-crafted measures
typically leveraged in prior work. Additionally, we extend the recently
introduced Conditional Similarity Network to incorporate global similarity in
training visual embeddings, which results in more natural transitions as the
user explores the learned similarity embeddings. Our experiments demonstrate
the effectiveness of our approach, producing compelling results on both active
image search and image attribute representation tasks.
Deep Knowledge Tracing and Dynamic Student Classification for Knowledge Tracing
Authors: Sein Minn, Yi Yu, Michel C. Desmarais, Feida Zhu, Jill Jenn Vie
Date: 2018-09-24 00:00:00
Tasks: Classification, General Classification, Knowledge Tracing

In Intelligent Tutoring System (ITS), tracing the student's knowledge state during learning has been studied for several decades in order to provide more supportive learning instructions. In this paper, we propose a novel model for knowledge tracing that i) captures students' learning ability and dynamically assigns students into distinct groups with similar ability at regular time intervals, and ii) combines this information with a Recurrent Neural Network architecture known as Deep Knowledge Tracing. Experimental results confirm that the proposed model is significantly better at predicting student performance than well known state-of-the-art techniques for student modelling.
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues
Authors: Yaohua Sun, Mugen Peng, Yangcheng Zhou, Yuzhe Huang, Shiwen Mao
Date: 2018-09-24 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Management, Recommendation Systems

As a key technique for enabling artificial intelligence, machine learning
(ML) is capable of solving complex problems without explicit programming.
Motivated by its successful applications to many practical tasks like image
recognition, both industry and the research community have advocated the
applications of ML in wireless communication. This paper comprehensively
surveys the recent advances of the applications of ML in wireless
communication, which are classified as: resource management in the MAC layer,
networking and mobility management in the network layer, and localization in
the application layer. The applications in resource management further include
power control, spectrum management, backhaul management, cache management,
beamformer design and computation resource management, while ML based
networking focuses on the applications in clustering, base station switching
control, user association and routing. Moreover, literatures in each aspect is
organized according to the adopted ML techniques. In addition, several
conditions for applying ML to wireless communication are identified to help
readers decide whether to use ML and which kind of ML techniques to use, and
traditional approaches are also summarized together with their performance
comparison with ML based approaches, based on which the motivations of surveyed
literatures to adopt ML are clarified. Given the extensiveness of the research
area, challenges and unresolved issues are presented to facilitate future
studies, where ML based network slicing, infrastructure update to support ML
based paradigms, open data sets and platforms for researchers, theoretical
guidance for ML implementation and so on are discussed.