pwc-archive/papers-with-abstracts huggingface dataset:
Tensor decomposition of higher-order correlations by nonlinear Hebbian plasticity
Authors: Gabriel Koch Ocker, Michael A. Buice
Date: 2021-06-29 00:00:00
Tasks: Tensor Decomposition

Biological synaptic plasticity exhibits nonlinearities that are not accounted for by classic Hebbian learning rules. Here, we introduce a simple family of generalized nonlinear Hebbian learning rules. We study the computations implemented by their dynamics in the simple setting of a neuron receiving feedforward inputs. These nonlinear Hebbian rules allow a neuron to learn tensor decompositions of its higher-order input correlations. The particular input correlation decomposed and the form of the decomposition depend on the location of nonlinearities in the plasticity rule. For simple, biologically motivated parameters, the neuron learns eigenvectors of higher-order input correlation tensors. We prove that tensor eigenvectors are attractors and determine their basins of attraction. We calculate the volume of those basins, showing that the dominant eigenvector has the largest basin of attraction. We then study arbitrary learning rules and find that any learning rule that admits a finite Taylor expansion into the neural input and output also has stable equilibria at generalized eigenvectors of higher-order input correlation tensors. Nonlinearities in synaptic plasticity thus allow a neuron to encode higher-order input correlations in a simple fashion.
Machine Reading of Hypotheses for Organizational Research Reviews and Pre-trained Models via R Shiny App for Non-Programmers
Authors: Victor Zitian Chen, Felipe Montano-Campos, Wlodek Zadrozny, Evan Canfield
Date: 2021-06-30 00:00:00
Tasks: Reading Comprehension

The volume of scientific publications in organizational research becomes exceedingly overwhelming for human researchers who seek to timely extract and review knowledge. This paper introduces natural language processing (NLP) models to accelerate the discovery, extraction, and organization of theoretical developments (i.e., hypotheses) from social science publications. We illustrate and evaluate NLP models in the context of a systematic review of stakeholder value constructs and hypotheses. Specifically, we develop NLP models to automatically 1) detect sentences in scholarly documents as hypotheses or not (Hypothesis Detection), 2) deconstruct the hypotheses into nodes (constructs) and links (causal/associative relationships) (Relationship Deconstruction ), and 3) classify the features of links in terms causality (versus association) and direction (positive, negative, versus nonlinear) (Feature Classification). Our models have reported high performance metrics for all three tasks. While our models are built in Python, we have made the pre-trained models fully accessible for non-programmers. We have provided instructions on installing and using our pre-trained models via an R Shiny app graphic user interface (GUI). Finally, we suggest the next paths to extend our methodology for computer-assisted knowledge synthesis.
Context-Aware Attention-Based Data Augmentation for POI Recommendation
Authors: Yang Li, Yadan Luo, Zheng Zhang, Shazia W. Sadiq, Peng Cui
Date: 2021-06-30 00:00:00
Tasks: Data Augmentation, Decoder

With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendation, a natural extension of POI recommendation, has attracted much attention. It aims at suggesting the next POI to a user in spatial and temporal context, which is a practical yet challenging task in various applications. Existing approaches mainly model the spatial and temporal information, and memorize historical patterns through user's trajectories for recommendation. However, they suffer from the negative impact of missing and irregular check-in data, which significantly influences the model performance. In this paper, we propose an attention-based sequence-to-sequence generative model, namely POI-Augmentation Seq2Seq (PA-Seq2Seq), to address the sparsity of training set by making check-in records to be evenly-spaced. Specifically, the encoder summarises each check-in sequence and the decoder predicts the possible missing check-ins based on the encoded information. In order to learn time-aware correlation among user history, we employ local attention mechanism to help the decoder focus on a specific range of context information when predicting a certain missing check-in point. Extensive experiments have been conducted on two real-world check-in datasets, Gowalla and Brightkite, for performance and effectiveness evaluation.
Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation
Authors: Yang Li, Tong Chen, Yadan Luo, Hongzhi Yin, Zi Huang
Date: 2021-06-30 00:00:00
Tasks: Multi-Task Learning

Being an indispensable component in location-based social networks, next point-of-interest (POI) recommendation recommends users unexplored POIs based on their recent visiting histories. However, existing work mainly models check-in data as isolated POI sequences, neglecting the crucial collaborative signals from cross-sequence check-in information. Furthermore, the sparse POI-POI transitions restrict the ability of a model to learn effective sequential patterns for recommendation. In this paper, we propose Sequence-to-Graph (Seq2Graph) augmentation for each POI sequence, allowing collaborative signals to be propagated from correlated POIs belonging to other sequences. We then devise a novel Sequence-to-Graph POI Recommender (SGRec), which jointly learns POI embeddings and infers a user's temporal preferences from the graph-augmented POI sequence. To overcome the sparsity of POI-level interactions, we further infuse category-awareness into SGRec with a multi-task learning scheme that captures the denser category-wise transitions. As such, SGRec makes full use of the collaborative signals for learning expressive POI representations, and also comprehensively uncovers multi-level sequential patterns for user preference modelling. Extensive experiments on two real-world datasets demonstrate the superiority of SGRec against state-of-the-art methods in next POI recommendation.
Dual Adversarial Variational Embedding for Robust Recommendation
Authors: Qiaomin Yi, Ning Yang, Philip S. Yu
Date: 2021-06-30 00:00:00
Tasks: Variational Inference

Robust recommendation aims at capturing true preference of users from noisy data, for which there are two lines of methods have been proposed. One is based on noise injection, and the other is to adopt the generative model Variational Auto-encoder (VAE). However, the existing works still face two challenges. First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns. Second, the VAE based models are not expressive enough to capture the true preference since VAE often yields an embedding space of a single modal, while in real world, user-item interactions usually exhibit multi-modality on user preference distribution. In this paper, we propose a novel model called Dual Adversarial Variational Embedding (DAVE) for robust recommendation, which can provide personalized noise reduction for different users and items, and capture the multi-modality of the embedding space, by combining the advantages of VAE and adversarial training between the introduced auxiliary discriminators and the variational inference networks. The extensive experiments conducted on real datasets verify the effectiveness of DAVE on robust recommendation.
Recent Advances in Fibrosis and Scar Segmentation from Cardiac MRI: A State-of-the-Art Review and Future Perspectives
Authors: Yinzhe Wu, Zeyu Tang, Binghuan Li, David Firmin, Guang Yang
Date: 2021-06-28 00:00:00
Tasks: Segmentation

Segmentation of cardiac fibrosis and scar are essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful for its efficacy in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities, balanced steady-state free precession (bSSFP) and cine magnetic resonance imaging (MRI) for example, can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilising different modalities for accurate cardiac fibrosis and scar segmentation.
Emotions in Macroeconomic News and their Impact on the European Bond Market
Authors: Sergio Consoli, Luca Tiozzo Pezzoli, Elisa Tosetti
Date: 2021-06-15 00:00:00

We show how emotions extracted from macroeconomic news can be used to explain and forecast future behaviour of sovereign bond yield spreads in Italy and Spain. We use a big, open-source, database known as Global Database of Events, Language and Tone to construct emotion indicators of bond market affective states. We find that negative emotions extracted from news improve the forecasting power of government yield spread models during distressed periods even after controlling for the number of negative words present in the text. In addition, stronger negative emotions, such as panic, reveal useful information for predicting changes in spread at the short-term horizon, while milder emotions, such as distress, are useful at longer time horizons. Emotions generated by the Italian political turmoil propagate to the Spanish news affecting this neighbourhood market.
Reservoir Based Edge Training on RF Data To Deliver Intelligent and Efficient IoT Spectrum Sensors
Authors: Silvija Kokalj-Filipovic, Paul Toliver, William Johnson, Rob Miller
Date: 2021-04-01 00:00:00
Tasks: regression

Current radio frequency (RF) sensors at the Edge lack the computational resources to support practical, in-situ training for intelligent spectrum monitoring, and sensor data classification in general. We propose a solution via Deep Delay Loop Reservoir Computing (DLR), a processing architecture that supports general machine learning algorithms on compact mobile devices by leveraging delay-loop reservoir computing in combination with innovative electrooptical hardware. With both digital and photonic realizations of our design of the loops, DLR delivers reductions in form factor, hardware complexity and latency, compared to the State-of-the-Art (SoA). The main impact of the reservoir is to project the input data into a higher dimensional space of reservoir state vectors in order to linearly separate the input classes. Once the classes are well separated, traditionally complex, power-hungry classification models are no longer needed for the learning process. Yet, even with simple classifiers based on Ridge regression (RR), the complexity grows at least quadratically with the input size. Hence, the hardware reduction required for training on compact devices is in contradiction with the large dimension of state vectors. DLR employs a RR-based classifier to exceed the SoA accuracy, while further reducing power consumption by leveraging the architecture of parallel (split) loops. We present DLR architectures composed of multiple smaller loops whose state vectors are linearly combined to create a lower dimensional input into Ridge regression. We demonstrate the advantages of using DLR for two distinct applications: RF Specific Emitter Identification (SEI) for IoT authentication, and wireless protocol recognition for IoT situational awareness.
Application of deep reinforcement learning for Indian stock trading automation
Authors: Supriya Bajpai
Date: 2021-05-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In stock trading, feature extraction and trading strategy design are the two important tasks to achieve long-term benefits using machine learning techniques. Several methods have been proposed to design trading strategy by acquiring trading signals to maximize the rewards. In the present paper the theory of deep reinforcement learning is applied for stock trading strategy and investment decisions to Indian markets. The experiments are performed systematically with three classical Deep Reinforcement Learning models Deep Q-Network, Double Deep Q-Network and Dueling Double Deep Q-Network on ten Indian stock datasets. The performance of the models are evaluated and comparison is made.
Query Focused Multi-document Summarisation of Biomedical Texts: Macquarie Universiy and the Australian National University at BioASQ8b
Authors: Diego Molla, Christopher Jones, Vincent Nguyen
Date: 2020-08-27 00:00:00
Proceeding: CLEF 2020 8
Tasks: regression, reinforcement-learning, Reinforcement Learning (RL), Sentence, Sentence Embeddings, Word Embeddings

This paper presents the participation of Macquarie University and the Australian National University for Task B Phase B of the 2020 BioASQ Challenge (BioASQ8b). Our overall framework implements Query  focused  multi-document  extractive  summarisation  by  applying either  a  classification  or  a  regression  layer  to  the  candidate  sentence embeddings and to the comparison between the question and sentence embeddings. We experiment with variants using BERT and BioBERT, Siamese architectures, and reinforcement learning. We observe the best results when BERT is used to obtain the word embeddings, followed by an LSTM layer to obtain sentence embeddings. Variants using Siamese architectures or BioBERT did not improve the results.
Education-to-Skill Mapping Using Hierarchical Classification and Transformer Neural Network
Authors: Vilija Kuodytė, Linas Petkevičius
Date: 2021-06-24 00:00:00
Proceeding: Applied Sciences 2021 6
Tasks: Occupation prediction

Skills gained from vocational or higher education form an essential component of country’s economy, determining the structure of the national labor force. Therefore, knowledge on how people’s education converts to jobs enables data-driven choices concerning human resources within an ever-changing job market. Moreover, the relationship between education and occupation is also relevant in times of global crises, such as the COVID-19 pandemic. Healthcare system overload and skill shortage on one hand, and job losses related to lock-downs on the other, have exposed a necessity to identify target groups with relevant education backgrounds in order to facilitate their occupational transitions. However, the relationship between education and employment is complex and difficult to model. This study aims to propose the methodology that would allow us to model education-to-skill mapping. Multiple challenges arising from administrative datasets, namely imbalanced data, complex labeling, hierarchical structure and textual data, were addressed using six neural network-based algorithms of incremental complexity. The final proposed mathematical model incorporates the textual data from descriptions of education programs that are transformed into embeddings, utilizing transformer neural networks. The output of the final model is constructed as the hierarchical classification task. The effectiveness of the proposed model is demonstrated using experiments on national level data, which covers whole population of Lithuania. Finally, we provide the recommendations for the usage of proposed model. This model can be used for practical applications and scenario forecasting. Some possible applications for such model usage are demonstrated and described in this article. The code for this research has been made available on GitHub.
Explaining Caste-based Digital Divide in India
Authors: R Vaidehi, A Bheemeshwar Reddy, Sudatta Banerjee
Date: 2021-06-30 00:00:00

With the increasing importance of information and communication technologies in access to basic services like education and health, the question of the digital divide based on caste assumes importance in India where large socioeconomic disparities persist between different caste groups. Studies on caste-based digital inequality are still scanty in India. Using nationally representative survey data, this paper analyzes the first-level digital divide (ownership of computer and access to the internet) and the second-level digital divide (individual's skill to use computer and the internet) between the disadvantaged caste group and the others. Further, this paper identifies the caste group-based differences in socioeconomic factors that contribute to the digital divide between these groups using a non-linear decomposition method. The results show that there exists a large first-level and second-level digital divide between the disadvantaged caste groups and others in India. The non-linear decomposition results indicate that the caste-based digital divide in India is rooted in historical socioeconomic deprivation of disadvantaged caste groups. More than half of the caste-based digital gap is attributable to differences in educational attainment and income between the disadvantaged caste groups and others. The findings of this study highlight the urgent need for addressing educational and income inequality between the different caste groups in India in order to bridge the digital divide.
Probabilistic Control of Heterogeneous Swarms Subject to Graph Temporal Logic Specifications: A Decentralized and Scalable Approach
Authors: Franck Djeumou, Zhe Xu, Murat Cubuktepe, Ufuk Topcu
Date: 2021-06-29 00:00:00

We develop a probabilistic control algorithm, $\texttt{GTLProCo}$, for swarms of agents with heterogeneous dynamics and objectives, subject to high-level task specifications. The resulting algorithm not only achieves decentralized control of the swarm but also significantly improves scalability over state-of-the-art existing algorithms. Specifically, we study a setting in which the agents move along the nodes of a graph, and the high-level task specifications for the swarm are expressed in a recently-proposed language called graph temporal logic (GTL). By constraining the distribution of the swarm over the nodes of the graph, GTL can specify a wide range of properties, including safety, progress, and response. $\texttt{GTLProCo}$, agnostic to the number of agents comprising the swarm, controls the density distribution of the swarm in a decentralized and probabilistic manner. To this end, it synthesizes a time-varying Markov chain modeling the time evolution of the density distribution under the GTL constraints. We first identify a subset of GTL, namely reach-avoid specifications, for which we can reduce the synthesis of such a Markov chain to either linear or semi-definite programs. Then, in the general case, we formulate the synthesis of the Markov chain as a mixed-integer nonlinear program (MINLP). We exploit the structure of the problem to provide an efficient sequential mixed-integer linear programming scheme with trust regions to solve the MINLP. We empirically demonstrate that our sequential scheme is at least three orders of magnitude faster than off-the-shelf MINLP solvers and illustrate the effectiveness of $\texttt{GTLProCo}$ in several swarm scenarios.
Fast processing explains the effect of sound reflection on binaural unmasking
Authors: Norbert Kolotzek, Pierre G. Aublin, Bernhard U. Seeber
Date: 2021-06-30 00:00:00

Sound reflections and late reverberation alter energetic and binaural cues of a target source, thereby affecting it's detection in noise. Two experiments investigated detection of harmonic complex tones, centered around 500 Hz, in noise in a virtual room with different modifications of simulated room impulse responses (RIR). Stimuli were auralized using the SOFE's loudspeakers in anechoic space. The target was presented from the front or at 0$^\circ$ azimuth, while an anechoic noise masker was simultaneously presented at 0$^\circ$. In the first experiment, early reflections were progressively added to the RIR and detection thresholds of the reverberant target were measured. For a frontal sound source, detection thresholds decreased while adding the first 45 ms of early reflections, whereas for a lateral sound source thresholds remained constant. In the second experiment, early reflections were cut out while late reflections were kept along with the direct sound. Results for a target at 0$^\circ$ show that even reflections as late as 150 ms reduce detection thresholds compared to only the direct sound. A binaural model with a sluggishness component following the computation of binaural unmasking in short windows predicts measured and literature results better than when large windows are used.
An Integrated Framework for Two-pass Personalized Voice Trigger
Authors: Dexin Liao, Jing Li, Yiming Zhi, Song Li, Qingyang Hong, Lin Li
Date: 2021-06-30 00:00:00
Tasks: Keyword Spotting, Multi-Task Learning, Speaker Verification, Vocal Bursts Valence Prediction

In this paper, we present the XMUSPEECH system for Task 1 of 2020 Personalized Voice Trigger Challenge (PVTC2020). Task 1 is a joint wake-up word detection with speaker verification on close talking data. The whole system consists of a keyword spotting (KWS) sub-system and a speaker verification (SV) sub-system. For the KWS system, we applied a Temporal Depthwise Separable Convolution Residual Network (TDSC-ResNet) to improve the system's performance. For the SV system, we proposed a multi-task learning network, where phonetic branch is trained with the character label of the utterance, and speaker branch is trained with the label of the speaker. Phonetic branch is optimized with connectionist temporal classification (CTC) loss, which is treated as an auxiliary module for speaker branch. Experiments show that our system gets significant improvements compared with baseline system.
Effect of acoustic scene complexity and visual scene representation on auditory perception in virtual audio-visual environments
Authors: Stefan Fichna, Thomas Biberger, Bernhard U. Seeber, Stephan D. Ewert
Date: 2021-06-30 00:00:00

In daily life, social interaction and acoustic communication often take place in complex acoustic environments (CAE) with a variety of interfering sounds and reverberation. For hearing research and the evaluation of hearing systems, simulated CAEs using virtual reality techniques have gained interest in the context of ecological validity. In the current study, the effect of scene complexity and visual representation of the scene on psychoacoustic measures like sound source location, distance perception, loudness, speech intelligibility, and listening effort in a virtual audio-visual environment was investigated. A 3-dimensional, 86-channel loudspeaker array was used to render the sound field in combination with or without a head-mounted display (HMD) to create an immersive stereoscopic visual representation of the scene. The scene consisted of a ring of eight (virtual) loudspeakers which played a target speech stimulus and nonsense speech interferers in several spatial conditions. Either an anechoic (snowy outdoor scenery) or echoic environment (loft apartment) with a reverberation time (T60) of about 1.5 s was simulated. In addition to varying the number of interferers, scene complexity was varied by assessing the psychoacoustic measures in isolated consecutive measurements orcsimultaneously. Results showed no significant effect of wearing the HMD on the data. Loudness and distance perception showed significantly different results when they were measured simultaneously instead of consecutively in isolation. The advantage of the suggested setup is that it can be directly transferred to a corresponding real room, enabling a 1:1 comparison and verification of the perception experiments in the real and virtual environment.