pwc-archive/papers-with-abstracts huggingface dataset:
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data
Authors: Ali Oskooei, Sophie Mai Chau, Jonas Weiss, Arvind Sridhar, María Rodríguez Martínez, Bruno Michel
Date: 2019-11-18 00:00:00
Tasks: Clustering, Heart Rate Variability, Time Series, Time Series Analysis

In this work we perform a study of various unsupervised methods to identify mental stress in firefighter trainees based on unlabeled heart rate variability data. We collect RR interval time series data from nearly 100 firefighter trainees that participated in a drill. We explore and compare three methods in order to perform unsupervised stress detection: 1) traditional K-Means clustering with engineered time and frequency domain features 2) convolutional autoencoders and 3) long short-term memory (LSTM) autoencoders, both trained on the raw RRI measurements combined with DBSCAN clustering and K-Nearest-Neighbors classification. We demonstrate that K-Means combined with engineered features is unable to capture meaningful structure within the data. On the other hand, convolutional and LSTM autoencoders tend to extract varying structure from the data pointing to different clusters with different sizes of clusters. We attempt at identifying the true stressed and normal clusters using the HRV markers of mental stress reported in the literature. We demonstrate that the clusters produced by the convolutional autoencoders consistently and successfully stratify stressed versus normal samples, as validated by several established physiological stress markers such as RMSSD, Max-HR, Mean-HR and LF-HF ratio.
Algorithmically generating new algebraic features of polynomial systems for machine learning
Authors: Dorian Florescu, Matthew England
Date: 2019-06-03 00:00:00
Tasks: BIG-bench Machine Learning

There are a variety of choices to be made in both computer algebra systems (CASs) and satisfiability modulo theory (SMT) solvers which can impact performance without affecting mathematical correctness. Such choices are candidates for machine learning (ML) approaches, however, there are difficulties in applying standard ML techniques, such as the efficient identification of ML features from input data which is typically a polynomial system. Our focus is selecting the variable ordering for cylindrical algebraic decomposition (CAD), an important algorithm implemented in several CASs, and now also SMT-solvers. We created a framework to describe all the previously identified ML features for the problem and then enumerated all options in this framework to automatically generation many more features. We validate the usefulness of these with an experiment which shows that an ML choice for CAD variable ordering is superior to those made by human created heuristics, and further improved with these additional features. We expect that this technique of feature generation could be useful for other choices related to CAD, or even choices for other algorithms with polynomial systems for input.
Photonic human identification based on deep learning of back scattered laser speckle patterns
Authors: Zeev Kalyzhner, Or Levitas, Felix Kalichman, Ron Jacobson, and Zeev Zalevsky
Date: 2019-11-25 00:00:00
Proceeding: Optics Express 2019 11

The analysis of the dynamics of speckle patterns that are generated when laser light is back scattered from a tissue has been recently shown as very applicable for remote sensing of various bio-medical parameters. In this work, we present how the analysis of a static single speckle pattern scattered from the forehead of a subject, together with advanced machine learning techniques based on multilayered neural networks, can offer novel approach to accurate identification within a small predefined number of classes (e.g., a ‘smart home’ setting which restricts its operations for family members only). Processing the static scattering speckle pattern by neural networks enables extraction of unique features with no previous expert knowledge being required. Using the right model allows for a very accurate differentiation between desirable categories, and that model can form a basis for using speckles patterns as a form of identity measure of ‘forehead-print’.
DC2Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning
Authors: Cheng-Bin Jin, Hakil Kim, Mingjie Liu, In Ho Han, Jae Il Lee, Jung Hwan Lee, Seongsu Joo, Eunsik Park, Young Saem Ahn, Xuenan Cui
Date: 2019-06-18 00:00:00
Proceeding: journal 2019 6
Tasks: Computed Tomography (CT)

Magnetic resonance imaging (MRI) plays a significant role in the diagnosis of lumbar disc disease. However, the use of MRI is limited because of its high cost and significant operating and processing time. More importantly, MRI is contraindicated for some patients with claustrophobia or cardiac pacemakers due to the possibility of injury. In contrast, computed tomography (CT) scans are much less expensive, are faster, and do not face the same limitations. In this paper, we propose a method for estimating lumbar spine MR images based on CT images using a novel objective function and a dual cycle-consistent adversarial network (DC2Anet) with semi-supervised learning. The objective function includes six independent loss terms to balance quantitative and qualitative losses, enabling the generation of a realistic and accurate synthetic MR image. DC2Anet is also capable of semi-supervised learning, and the network is general enough for supervised or unsupervised setups. Experimental results prove that the method is accurate, being able to construct MR images that closely approximate reference MR images, while also outperforming four other state-of-the-art methods.
development of multi-user TDMA based DSSS system
Authors: ameena sajid, saqid ali
Date: 2019-06-04 00:00:00
Proceeding: Conference 2019 6

calculate the error probability of spreading factor length of 7,11 and 13
Interest aware influential information disseminators in social networks
Authors: Santhoshkumar Srinivasan, L. D. Dhinesh Babu
Date: 2019-10-22 00:00:00
Proceeding: Springer Nature Applied Sciences 2019 10

In recent days, finding influential disseminators in social networks has become a crucial issue due to its importance in the spread control of information, product advertisement, and rumor control. Most current researches on influencer identification are focused on topological factors such as coreness, centrality, and degree distribution. But these methods do not consider the interest of receiver though it plays a vital role in carrying information forward. To consider the receiver’s interest in finding influential spreaders, this paper proposes a robust and reliable two-step influencer finder model which considers the individual’s interest on spreader as well as the spreading information. This approach combines the individual’s location and the interest on the neighbor/topic. In step 1, a novel method to find a trust vertex of spreaders is proposed. In step 2, a weighted neighborhood centrality method is proposed to identify one or more influential spreaders using the trust vertex. The experiments conducted on six different datasets to prove the effectiveness of the proposed approach. The results show that the proposed approach is better than other recent and well-known state-of-the-art algorithms
Deep Convolutional Neural Networks for Thermal Infrared Object Tracking
Authors: QiaoLiu, Xiaohuan Lu, Zhenyu He, Chunkai Zhang, WenSheng Chen
Date: 2017-10-15 00:00:00
Proceeding: Knowledge-Based Systems 2017 10
Tasks: Object, Object Tracking, Thermal Infrared Object Tracking, Visual Object Tracking, Visual Tracking

Unlike the visual object tracking, thermal infrared object tracking can track a target object in total darkness. Therefore, it has broad applications, such as in rescue and video surveillance at night. However, there are few studies in this field mainly because thermal infrared images have several unwanted attributes, which make it difficult to obtain the discriminative features of the target. Considering the powerful representational ability of convolutional neural networks and their successful application in visual tracking, we transfer the pre-trained convolutional neural networks based on visible images to thermal infrared tracking. We observe that the features from the fully-connected layer are not suitable for thermal infrared tracking due to the lack of spatial information of the target, while the features from the convolution layers are. Besides, the features from a single convolution layer are not robust to various challenges. Based on this observation, we propose a correlation filter based ensemble tracker with multi-layer convolutional features for thermal infrared tracking (MCFTS). Firstly, we use pre-trained convolutional neural networks to extract the features of the multiple convolution layers of the thermal infrared target. Then, a correlation filter is used to construct multiple weak trackers with the corresponding convolution layer features. These weak trackers give the response maps of the target's location. Finally, we propose an ensemble method that coalesces these response maps to get a stronger one. Furthermore, a simple but effective scale estimation strategy is exploited to boost the tracking accuracy. To evaluate the performance of the proposed tracker, we carry out experiments on two thermal infrared tracking benchmarks: VOT-TIR 2015 and VOT-TIR 2016. The experimental results demonstrate that our tracker is effective and achieves promising performance.
Quantum Computation with Machine-Learning-Controlled Quantum Stuff
Authors: Lucien Hardy, Adam G. M. Lewis
Date: 2019-11-29 00:00:00
Tasks: BIG-bench Machine Learning

We describe how one may go about performing quantum computation with arbitrary "quantum stuff", as long as it has some basic physical properties. Imagine a long strip of stuff, equipped with regularly spaced wires to provide input settings and to read off outcomes. After showing how the corresponding map from settings to outcomes can be construed as a quantum circuit, we provide a machine learning algorithm to tomographically "learn" which settings implement the members of a universal gate set. At optimum, arbitrary quantum gates, and thus arbitrary quantum programs, can be implemented using the stuff.
Latent Semantic Search and Information Extraction Architecture
Authors: Anton Kolonin
Date: 2019-11-30 00:00:00
Tasks: Entity Extraction using GAN

The motivation, concept, design and implementation of latent semantic search for search engines have limited semantic search, entity extraction and property attribution features, have insufficient accuracy and response time of latent search, may impose privacy concerns and the search results are unavailable in offline mode for robotic search operations. The alternative suggestion involves autonomous search engine with adaptive storage consumption, configurable search scope and latent search response time with built-in options for entity extraction and property attribution available as open source platform for mobile, desktop and server solutions. The suggested architecture attempts to implement artificial general intelligence (AGI) principles as long as autonomous behaviour constrained by limited resources is concerned, and it is applied for specific task of enabling Web search for artificial agents implementing the AGI.
Semantic Enrichment of Streaming Healthcare Data
Authors: Daniel Cotter, V. K. Cody Bumgardner
Date: 2019-12-01 00:00:00

In the past decade, the healthcare industry has made significant advances in the digitization of patient information. However, a lack of interoperability among healthcare systems still imposes a high cost to patients, hospitals, and insurers. Currently, most systems pass messages using idiosyncratic messaging standards that require specialized knowledge to interpret. This increases the cost of systems integration and often puts more advanced uses of data out of reach. In this project, we demonstrate how two open standards, FHIR and RDF, can be combined both to integrate data from disparate sources in real-time and make that data queryable and susceptible to automated inference. To validate the effectiveness of the semantic engine, we perform simulations of real-time data feeds and demonstrate how they can be combined and used by client-side applications with no knowledge of the underlying sources.
A Fast Matrix-Completion-Based Approach for Recommendation Systems
Authors: Meng Qiao, Zheng Shan, Fudong Liu, Wenjie Sun
Date: 2019-12-02 00:00:00
Tasks: Matrix Completion, Recommendation Systems

Matrix completion is widely used in machine learning, engineering control, image processing, and recommendation systems. Currently, a popular algorithm for matrix completion is Singular Value Threshold (SVT). In this algorithm, the singular value threshold should be set first. However, in a recommendation system, the dimension of the preference matrix keeps changing. Therefore, it is difficult to directly apply SVT. In addition, what the users of a recommendation system need is a sequence of personalized recommended results rather than the estimation of their scores. According to the above ideas, this paper proposes a novel approach named probability completion model~(PCM). By reducing the data dimension, the transitivity of the similar matrix, and singular value decomposition, this approach quickly obtains a completion matrix with the same probability distribution as the original matrix. The approach greatly reduces the computation time based on the accuracy of the sacrifice part, and can quickly obtain a low-rank similarity matrix with data trend approximation properties. The experimental results show that PCM can quickly generate a complementary matrix with similar data trends as the original matrix. The LCS score and efficiency of PCM are both higher than SVT.
An Investigation of Biases in Web Search Engine Query Suggestions
Authors: Malte Bonart, Anastasiia Samokhina, Gernot Heisenberg, Philipp Schaer
Date: 2019-12-02 00:00:00

Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. In this study, we analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. We test our approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017. This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test our framework, we collected data from the Google, Bing, and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party, or age and with regards to the stability of the suggestions over time.
A Human-AI Loop Approach for Joint Keyword Discovery and Expectation Estimation in Micropost Event Detection
Authors: Akansha Bhardwaj, Jie Yang, Philippe Cudré-Mauroux
Date: 2019-12-02 00:00:00
Tasks: Event Detection, Informativeness

Microblogging platforms such as Twitter are increasingly being used in event detection. Existing approaches mainly use machine learning models and rely on event-related keywords to collect the data for model training. These approaches make strong assumptions on the distribution of the relevant micro-posts containing the keyword -- referred to as the expectation of the distribution -- and use it as a posterior regularization parameter during model training. Such approaches are, however, limited as they fail to reliably estimate the informativeness of a keyword and its expectation for model training. This paper introduces a Human-AI loop approach to jointly discover informative keywords for model training while estimating their expectation. Our approach iteratively leverages the crowd to estimate both keyword specific expectation and the disagreement between the crowd and the model in order to discover new keywords that are most beneficial for model training. These keywords and their expectation not only improve the resulting performance but also make the model training process more transparent. We empirically demonstrate the merits of our approach, both in terms of accuracy and interpretability, on multiple real-world datasets and show that our approach improves the state of the art by 24.3%.
Corpus-Level End-to-End Exploration for Interactive Systems
Authors: Zhiwen Tang, Grace Hui Yang
Date: 2019-11-23 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Retrieval, Text Retrieval

A core interest in building Artificial Intelligence (AI) agents is to let them interact with and assist humans. One example is Dynamic Search (DS), which models the process that a human works with a search engine agent to accomplish a complex and goal-oriented task. Early DS agents using Reinforcement Learning (RL) have only achieved limited success for (1) their lack of direct control over which documents to return and (2) the difficulty to recover from wrong search trajectories. In this paper, we present a novel corpus-level end-to-end exploration (CE3) method to address these issues. In our method, an entire text corpus is compressed into a global low-dimensional representation, which enables the agent to gain access to the full state and action spaces, including the under-explored areas. We also propose a new form of retrieval function, whose linear approximation allows end-to-end manipulation of documents. Experiments on the Text REtrieval Conference (TREC) Dynamic Domain (DD) Track show that CE3 outperforms the state-of-the-art DS systems.
Learning a faceted customer segmentation for discovering new business opportunities at Intel
Authors: Itay Lieder, Meirav Segal, Eran Avidan, Asaf Cohen, Tom Hope
Date: 2019-11-27 00:00:00
Tasks: Marketing

For sales and marketing organizations within large enterprises, identifying and understanding new markets, customers and partners is a key challenge. Intel's Sales and Marketing Group (SMG) faces similar challenges while growing in new markets and domains and evolving its existing business. In today's complex technological and commercial landscape, there is need for intelligent automation supporting a fine-grained understanding of businesses in order to help SMG sift through millions of companies across many geographies and languages and identify relevant directions. We present a system developed in our company that mines millions of public business web pages, and extracts a faceted customer representation. We focus on two key customer aspects that are essential for finding relevant opportunities: industry segments (ranging from broad verticals such as healthcare, to more specific fields such as 'video analytics') and functional roles (e.g., 'manufacturer' or 'retail'). To address the challenge of labeled data collection, we enrich our data with external information gleaned from Wikipedia, and develop a semi-supervised multi-label, multi-lingual deep learning model that parses customer website texts and classifies them into their respective facets. Our system scans and indexes companies as part of a large-scale knowledge graph that currently holds tens of millions of connected entities with thousands being fetched, enriched and connected to the graph by the hour in real time, and also supports knowledge and insight discovery. In experiments conducted in our company, we are able to significantly boost the performance of sales personnel in the task of discovering new customers and commercial partnership opportunities.
Towards more effective consumer steering via network analysis
Authors: Jacopo Arpetti, Antonio Iovanella
Date: 2019-03-27 00:00:00
Tasks: Relation

Increased data gathering capacity, together with the spread of data analytics techniques, has prompted an unprecedented concentration of information related to the individuals' preferences in the hands of a few gatekeepers. In the present paper, we show how platforms' performances still appear astonishing in relation to some unexplored data and networks properties, capable to enhance the platforms' capacity to implement steering practices by means of an increased ability to estimate individuals' preferences. To this end, we rely on network science whose analytical tools allow data representations capable of highlighting relationships between subjects and/or items, extracting a great amount of information. We therefore propose a measure called Network Information Patrimony, considering the amount of information available within the system and we look into how platforms could exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages. Our measure takes into account the quality of the connections among nodes as the one of a hypothetical user in relation to its neighbourhood, detecting how users with a good neighbourhood -- hence of a superior connections set -- obtain better information. We tested our measures on Amazons' instances, obtaining evidence which confirm the relevance of information extracted from nodes' neighbourhood in order to steer targeted users.
A Hybrid Approach Towards Two Stage Bengali Question Classification Utilizing Smart Data Balancing Technique
Authors: Md. Hasibur Rahman, Chowdhury Rafeed Rahman, Ruhul Amin, Md. Habibur Rahman Sifat, Afra Anika
Date: 2019-11-30 00:00:00
Tasks: Classification, General Classification, Question Answering

Question classification (QC) is the primary step of the Question Answering (QA) system. Question Classification (QC) system classifies the questions in particular classes so that Question Answering (QA) System can provide correct answers for the questions. Our system categorizes the factoid type questions asked in natural language after extracting features of the questions. We present a two stage QC system for Bengali. It utilizes one dimensional convolutional neural network for classifying questions into coarse classes in the first stage. Word2vec representation of existing words of the question corpus have been constructed and used for assisting 1D CNN. A smart data balancing technique has been employed for giving data hungry convolutional neural network the advantage of a greater number of effective samples to learn from. For each coarse class, a separate Stochastic Gradient Descent (SGD) based classifier has been used in order to differentiate among the finer classes within that coarse class. TF-IDF representation of each word has been used as feature for the SGD classifiers implemented as part of second stage classification. Experiments show the effectiveness of our proposed method for Bengali question classification.
XGBOD: Improving Supervised Outlier Detection with Unsupervised Representation Learning
Authors: Yue Zhao, Maciej K. Hryniewicki
Date: 2019-12-01 00:00:00
Tasks: Anomaly Detection, Outlier Detection, Representation Learning

A new semi-supervised ensemble algorithm called XGBOD (Extreme Gradient Boosting Outlier Detection) is proposed, described and demonstrated for the enhanced detection of outliers from normal observations in various practical datasets. The proposed framework combines the strengths of both supervised and unsupervised machine learning methods by creating a hybrid approach that exploits each of their individual performance capabilities in outlier detection. XGBOD uses multiple unsupervised outlier mining algorithms to extract useful representations from the underlying data that augment the predictive capabilities of an embedded supervised classifier on an improved feature space. The novel approach is shown to provide superior performance in comparison to competing individual detectors, the full ensemble and two existing representation learning based algorithms across seven outlier datasets.