pwc-archive/papers-with-abstracts huggingface dataset:
Scientific Paper Recommendation: A Survey
Authors: Xiaomei Bai, Mengyang Wang, Ivan Lee, Zhuo Yang, Xiangjie Kong, Feng Xia
Date: 2020-08-10 00:00:00
Tasks: Articles, Collaborative Filtering, Recommendation Systems, Survey

Globally, recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields including economic, education, and scientific research. Different empirical studies have shown that recommender systems are more effective and reliable than keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this survey, we first introduce the importance and advantages of paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-Based methods, Collaborative Filtering methods, Graph-Based methods and Hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on scholarly paper recommendation.
Introductory review to quantum information retrieval
Authors: Alexander Lebedev, Andrei Khrennikov
Date: 2020-08-05 00:00:00
Tasks: Decision Making, Information Retrieval, Retrieval

Recently people started to understand that applications of the mathematical formalism of quantum theory are not reduced to physics. Nowadays, this formalism is widely used outside of quantum physics, in particular, in cognition, psychology, decision making, information processing, especially information retrieval. The latter is very promising. The aim of this brief introductory review is to stimulate research in this exciting area of information science. This paper is not aimed to present a complete review on the state of art in quantum information retrieval.
Variable Star Classification Using Multi-View Metric Learning
Authors: K. B. Johnston, S. M. Caballero-Nieves, V. Petit, A. M. Peter, R. Haber
Date: 2019-11-13 00:00:00
Tasks: Classification, General Classification, Metric Learning, MULTI-VIEW LEARNING

Our multi-view metric learning framework enables robust characterization of star categories by directly learning to discriminate in a multi-faceted feature space, thus, eliminating the need to combine feature representations prior to fitting the machine learning model. We also demonstrate how to extend standard multi-view learning, which employs multiple vectorized views, to the matrix-variate case which allows very novel variable star signature representations. The performance of our proposed methods is evaluated on the UCR Starlight and LINEAR datasets. Both the vector and matrix-variate versions of our multi-view learning framework perform favorably --- demonstrating the ability to discriminate variable star categories.
Hybrid beam selection scheme in millimeter-wave beamspaceMIMO-NOMA with lens antenna array
Authors: Md. Abdul Latif Sarkera, Md. Fazlul Kaderb, Dong Seog Han
Date: 2020-06-01 00:00:00
Proceeding: 2020 2020 6

Beam selection is a notable issue for recent wireless communications. A conventional single beamselection (SBS) concept is utilized to minimize the number of necessary radio frequency chains inmillimeter-wave  (mmWave)  multiple-input  multiple-output  non-orthogonal  multiple  access(MIMO-NOMA) systems. In general, the traditional SBS concept cannot implement a multiple beam groupselection opportunity for every supported user. In this circumstance, the user side spends too muchcomputational burden to measure the decoded message and suffer a beam optimization problem.Therefore, we propose a hybrid beam selection (HBS) scheme to utilize a multiple SBS scheme inbeamspace MIMO-NOMA systems. Consequently, each supported user can easily achieve a low beamoptimization problem by selecting a reliable SBS group from the proposed HBS scheme and significantlyincrease the spectral performance. Furthermore, we demonstrate an iterative beam power maximizationalgorithm utilizing the HBS scheme to solve the power allocation problem in this paper. The numericalresults are verified by computer simulations in terms of a downlink beamspace MIMO-NOMA channelwith a lens antenna array environme
Additive Gaussian Processes
Authors: David Duvenaud, Hannes Nickisch, Carl Edward Rasmussen
Date: 2011-12-19 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Additive models, Gaussian Processes, regression

We introduce a Gaussian process model of functions which are additive. An additive function is one which decomposes into a sum of low-dimensional functions, each depending on only a subset of the input variables. Additive GPs generalize both Generalized Additive Models, and the standard GP models which use squared-exponential kernels. Hyperparameter learning in this model can be seen as Bayesian Hierarchical Kernel Learning (HKL). We introduce an expressive but tractable parameterization of the kernel function, which allows efficient evaluation of all input interaction terms, whose number is exponential in the input dimension. The additional structure discoverable by this model results in increased interpretability, as well as state-of-the-art predictive power in regression tasks.
exploring the context of locations for personalized location recommendations
Authors: Xin Liu, Yong Liu and Xiaoli Li
Date: 2016-01-01 00:00:00
Proceeding: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) 2016 1

Conventional location recommendation models
rely on users’ visit history, geographical influence,
temporal influence, etc., to infer users’ preferences
for locations. However, systematically modeling
a location’s context (i.e., the set of locations visited before or after this location) is relatively unexplored. In this paper, by leveraging the Skipgram model, we learn the latent representation for
a location to capture the influence of its context.
A pair-wise ranking loss that considers the confidences of observed user preferences for locations
is then proposed to learn users’ latent representations for personalized top-N location recommendations. Moreover, we also extend our model by
taking into account temporal influence. Stochastic
gradient descent based optimization algorithms are
developed to fit the models. We conduct comprehensive experiments over four real datasets. Experimental results demonstrate that our approach significantly outperforms the state-of-the-art location
recommendation methods.
Robot-based facade spatial assembly optimization
Authors: Ahmed KhairadeenAli, One JaeLee, HayubSong
Date: 2020-08-01 00:00:00
Proceeding: Journal of Building Engineering 2020 8
Tasks: Decision Making, Position

Robotic involvement in construction is still in its initial stages compared to other industries. Conventionally, the facade panel picking position is done manually by trial and error. The designer chooses a place to pick up a facade piece within reach of the robot arm, and then simulates the entire pick and place process in a digital model before applying to the assembly on the construction job site. After that the designer might detect errors, collisions, or singularities, which require the designer to modify the position of picking by changing the location or orientation of the module, thus repeating the simulation cycle until they reach a satisfactory result with no errors or collisions. This work is usually considered monotonous, inefficient, and time consuming. Therefore, this research proposes an optimization process implemented in design stage of construction project via static performance criteria in order to automatically search for the best picking location within the reach of the robot arm. The goal is to automate the process of robot location finding and solve the limitations of modular robot assembly simulation processes in order to allow for effective execution during the robotic construction implementation. The proposed approach, called iFobot, consists of three modules: Facade Generative Modeling (iFobot-D), Robot Position Optimization (iFobot-B), and Culminating Feedback to BIM (iFobot-L). Specifically, the scope of the paper is limited to the robot arm and facade picking and placing location finding processes. This research allows initial assessment of the possible assembly process as regards the dimensions of modules and hence the overall dimensions of the system, which subsequently influences assembly implementation in the construction job site. A set of generative algorithms were developed using commercially developed visual programming language that automatically populate facade modules on the building envelope, find the robot and facade assembly locations with their quantity take-off, and integrate the module with the BIM environment. A case study has been developed to validate and test the proposed system. The results prove that the system generates optimized locations for the robot arm workstations with the lowest possible collision and reachability rate while addressing robot operation time reduction, thus reducing risks encountered during facade assembly and increasing productivity. Moreover, the iFobot is predicted to influence decision making during the facade assembly process on a physical construction job site.
Multi-Agent-Based Urban Vegetation Design
Authors: Ahmed Khairadeen, Hayub Song, One Jae Lee, Eun Seok Kim, Haneen Hashim Mohammed Ali
Date: 2020-04-28 00:00:00
Proceeding: International Journal of Environmental Research and Public Health 2020 4
Tasks: Decision Making

Urban vegetation is an essential element of the urban city pedestrian walkway. Despite city forest regulations and urban planning best practices, vegetation planning lacks clear comprehension and compatibility with other urban elements surrounding it. Urban planners and academic researchers currently devote vital attention to include most of the urban elements and their impact on the occupants and the environment in the planning stage of urban development. With the advancement in computational design, they have developed various algorithms to generate design alternatives and measure their impact on the environment that meets occupants’ needs and perceptions of their city. In particular, multi-agent-based simulations show great promise in developing rule compliance with urban vegetation design tools. This paper proposed an automatic urban vegetation city rule compliance approach for pedestrian pathway vegetation, leveraging multi-agent system and algorithmic modeling tools. This approach comprises three modules: rule compliance (T-Rule), street vegetation design tool (T-Design), and multi-agent alternative generation (T-Agent). Notably, the scope of the paper is limited to trees, shrubbery, and seating area configurations in the urban pathway context. To validate the developed design tool, a case study was tested, and the vegetation design tool generated the expected results successfully. A questionnaire was conducted to give feedback on the use of the developed tool for enhancing positive experience of the developed tool. It is anticipated that the proposed tool has the potential to aid urban planners in decision-making and develop more practical vegetation planting plans compared with the conventional Two-Dimensional (2D) plans, and give the city occupants the chance to take part in shaping their city by merely selecting from predefined parameters in a user interface to generate their neighborhood pathway vegetation plans. Moreover, this approach can be extended to be embedded in an interactive map where city occupants can shape their neighborhood greenery and give feedback to urban planners for decision-making.
Generic design aided robotically facade pick and place in construction site dataset
Authors: Ahmed KhairadeenAli, One JaeLee, HayubSong
Date: 2020-08-01 00:00:00
Proceeding: Data in Brief 2020 8

This Dataset provides a method of optimizing robot arm, facade pick and place locations in the construction site during facade assembly activity using generative design. A set of generative algorithms are provided in the form of graphical algorithm editors. The dataset is divided into three sets, each set controlling an essential subtask of facade assembly in the construction site. the dataset is called (iFOBOT) and consist of the following sub datasets: generative tool for facade population on building envelop (iFOBOT-D), Generative algorithm aided robot spatial location optimizer (iFOBOT-B), and Quantity take-off generative (iFOBOT-L). A sample project associated with its script and outcome results are included in this dataset to guide readers how to use this tool. This dataset only focuses on robot arm and facade module placement in construction sites. This dataset can generate optimized location of robot arm workstation in jobsite while also reducing robot collision with its body and surrounding objects, 2) reducing reachability rate, 3) reducing robot time travel during operation which in result minimize risk in facade assembly and increase productivity. This dataset is in parametric format which makes it reusable with all its history data using the reproducing guide provided here. More details of how to reuse this dataset and developed tool in construction site is covered in Robot-based Facade Spatial Assembly Optimization paper
Artificial Intelligence Forecasting of Covid-19 in China
Authors: Zixin Hu, Qiyang Ge, Shudi Li, Li Jin, Momiao Xiong
Date: 2020-02-17 00:00:00

BACKGROUND An alternative to epidemiological models for transmission dynamics of Covid-19 in China, we propose the artificial intelligence (AI)-inspired methods for real-time forecasting of Covid-19 to estimate the size, lengths and ending time of Covid-19 across China. METHODS We developed a modified stacked auto-encoder for modeling the transmission dynamics of the epidemics. We applied this model to real-time forecasting the confirmed cases of Covid-19 across China. The data were collected from January 11 to February 27, 2020 by WHO. We used the latent variables in the auto-encoder and clustering algorithms to group the provinces/cities for investigating the transmission structure. RESULTS We forecasted curves of cumulative confirmed cases of Covid-19 across China from Jan 20, 2020 to April 20, 2020. Using the multiple-step forecasting, the estimated average errors of 6-step, 7-step, 8-step, 9-step and 10-step forecasting were 1.64%, 2.27%, 2.14%, 2.08%, 0.73%, respectively. We predicted that the time points of the provinces/cities entering the plateau of the forecasted transmission dynamic curves varied, ranging from Jan 21 to April 19, 2020. The 34 provinces/cities were grouped into 9 clusters. CONCLUSIONS The accuracy of the AI-based methods for forecasting the trajectory of Covid-19 was high. We predicted that the epidemics of Covid-19 will be over by the middle of April. If the data are reliable and there are no second transmissions, we can accurately forecast the transmission dynamics of the Covid-19 across the provinces/cities in China. The AI-inspired methods are a powerful tool for helping public health planning and policymaking.
Trend and forecasting of the COVID-19 outbreak in China
Authors: Qiang Li, Wei Feng
Date: 2020-02-14 00:00:00

By using the public data from Jan. 20 to Feb. 11, 2020, we perform data-driven analysis and forecasting on the COVID-19 epidemic in mainland China, especially Hubei province. Our results show that the turning points of the daily infections are predicted to be Feb. 6 and Feb. 1, 2020, for Hubei and China other than Hubei, respectively. The epidemic in China is predicted to end up after Mar. 10, 2020, and the number of the total infections are predicted to be 51600. The data trends reveal that quick and active strategies taken by China to reduce human exposure have already had a good impact on the control of the epidemic.
RESEARCH ARTICLE A Reinforcement Learning Model of Joy, Distress, Hope and Fear
Authors: Joost Broekens∗, Elmer Jacobs, Catholijn M. Jonkera
Date: 2014-09-08 00:00:00
Proceeding: 无 2014 9
Tasks: reinforcement-learning, Reinforcement Learning (RL), Relation

In this paper we computationally study the relation between adaptive behavior and emotion.
Using the Reinforcement Learning framework, we propose that learned state utility, V(s),
models fear (negative) and hope (positive) based on the fact that both signals are about
anticipation of loss or gain. Further, we propose that joy/distress is a signal similar to the error
signal. We present agent-based simulation experiments that show that this model replicates
psychological and behavioral dynamics of emotion. This work distinguishes itself by assessing
the dynamics of emotion in an adaptive agent framework - coupling it to the literature on
habituation, development, extinction, and hope theory. Our results support the idea that
the function of emotion is to provide a complex feedback signal for an organism to adapt its
behavior. Our work is relevant for understanding the relation between emotion and adaptation
in animals, as well as for human-robot interaction, in particular how emotional signals can be
used to communicate between adaptive agents and humans.
Reward Function and Initial Values: Better Choices for Accelerated Goal-Directed Reinforcement Learning
Authors: Laetitia Matignon, guillaume.laurent, nadine.piat
Date: 2016-09-01 00:00:00
Proceeding: 无 2016 9
Tasks: reinforcement-learning, Reinforcement Learning (RL)

An important issue in Reinforcement Learning (RL) is to
accelerate or improve the learning process. In this paper, we study the
influence of some RL parameters over the learning speed. Indeed, al-
though RL convergence properties have been widely studied, no precise
rules exist to correctly choose the reward function and initial Q-values.
Our method helps the choice of these RL parameters within the context
of reaching a goal in a minimal time. We develop a theoretical study and
also provide experimental justifications for choosing on the one hand the
reward function, and on the other hand particular initial Q-values based
on a goal bias function.
Lunar Crater Identification in Digital Images
Authors: John A. Christian, Harm Derksen, Ryan Watkins
Date: 2020-09-02 00:00:00

It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera's location. This so-called "lost-in-space" crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.
Seeing wake words: Audio-visual Keyword Spotting
Authors: Liliane Momeni, Triantafyllos Afouras, Themos Stafylakis, Samuel Albanie, Andrew Zisserman
Date: 2020-09-02 00:00:00
Tasks: Keyword Spotting, Lip Reading, Visual Keyword Spotting

The goal of this work is to automatically determine whether and when a word of interest is spoken by a talking face, with or without the audio. We propose a zero-shot method suitable for in the wild videos. Our key contributions are: (1) a novel convolutional architecture, KWS-Net, that uses a similarity map intermediate representation to separate the task into (i) sequence matching, and (ii) pattern detection, to decide whether the word is there and when; (2) we demonstrate that if audio is available, visual keyword spotting improves the performance both for a clean and noisy audio signal. Finally, (3) we show that our method generalises to other languages, specifically French and German, and achieves a comparable performance to English with less language specific data, by fine-tuning the network pre-trained on English. The method exceeds the performance of the previous state-of-the-art visual keyword spotting architecture when trained and tested on the same benchmark, and also that of a state-of-the-art lip reading method.
Differentially private $k$-means clustering via exponential mechanism and max cover
Authors: Anamay Chaturvedi, Huy Nguyen, Eric Xu
Date: 2020-09-02 00:00:00
Tasks: Clustering, Privacy Preserving

We introduce a new $(\epsilon_p, \delta_p)$-differentially private algorithm for the $k$-means clustering problem. Given a dataset in Euclidean space, the $k$-means clustering problem requires one to find $k$ points in that space such that the sum of squares of Euclidean distances between each data point and its closest respective point among the $k$ returned is minimised. Although there exist privacy-preserving methods with good theoretical guarantees to solve this problem [Balcan et al., 2017; Kaplan and Stemmer, 2018], in practice it is seen that it is the additive error which dictates the practical performance of these methods. By reducing the problem to a sequence of instances of maximum coverage on a grid, we are able to derive a new method that achieves lower additive error then previous works. For input datasets with cardinality $n$ and diameter $\Delta$, our algorithm has an $O(\Delta^2 (k \log^2 n \log(1/\delta_p)/\epsilon_p + k\sqrt{d \log(1/\delta_p)}/\epsilon_p))$ additive error whilst maintaining constant multiplicative error. We conclude with some experiments and find an improvement over previously implemented work for this problem.
WaveGrad: Estimating Gradients for Waveform Generation
Authors: Nanxin Chen, Yu Zhang, Heiga Zen, Ron J. Weiss, Mohammad Norouzi, William Chan
Date: 2020-09-02 00:00:00
Proceeding: ICLR 2021 1
Tasks: Speech Synthesis, Text-To-Speech Synthesis

This paper introduces WaveGrad, a conditional model for waveform generation which estimates gradients of the data density. The model is built on prior work on score matching and diffusion probabilistic models. It starts from a Gaussian white noise signal and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad offers a natural way to trade inference speed for sample quality by adjusting the number of refinement steps, and bridges the gap between non-autoregressive and autoregressive models in terms of audio quality. We find that it can generate high fidelity audio samples using as few as six iterations. Experiments reveal WaveGrad to generate high fidelity audio, outperforming adversarial non-autoregressive baselines and matching a strong likelihood-based autoregressive baseline using fewer sequential operations. Audio samples are available at https://wavegrad.github.io/.