pwc-archive/papers-with-abstracts huggingface dataset:
Creating and Operating Areas With Reduced Electromagnetic Field Exposure Thanks to Reconfigurable Intelligent Surfaces
Authors: Dinh-Thuy Phan-Huy, Yohann Bénédic, Sebastien Herraiz Gonzalez, Philippe Ratajczak
Date: 2022-06-14 00:00:00

Mobile network operators must provide a target quality of service within a target coverage area. Each generation of networks from the 2nd to the 5th has reached higher quality-of-service (QoS) targets and coverage area sizes. However, the deployment of new networks is sometimes challenged by Electromagnetic Field (EMF) exposure constraints. In this paper, to take into account these constraints, we assess the novel and recent concept of Reduced EMF Exposure Area. Such an area would be created and operated by a mobile network operator upon the request of its customers. In such an area, customers keep enjoying high data rate internet access while getting a reduced EMF exposure. To ensure this EMF exposure reduction, we propose to deploy Reconfigurable Intelligent Surfaces (RIS) connected to the mobile network and exploit a joint RIS-M-MIMO uplink beamforming scheme. We use our ray-tracing-based simulation tool to visualize and characterize the Reduced EMF Exposure Area in a challenging environment in terms of propagation. Our simulations show that an operator can create and operate such an area under the condition that it carefully places the RIS in the environment.
A Novel RIS-Aided EMF Exposure Aware Approach using an Angularly Equalized Virtual Propagation Channel
Authors: Nour Awarkeh, Dinh-Thuy Phan-Huy, Marco Di Renzo
Date: 2022-06-14 00:00:00

Massive Multiple-Input Multiple-Output systems with beamforming are key components of the 5th and the future 6th generation of networks. However, in some cases, where the BS serves the same user for a long period, and in some propagation conditions, such systems reduce their transmit power to avoid creating unwanted regions of electromagnetic field exposure exceeding the regulatory threshold, beyond the circle around the BS that limits the distance between people and the BS antenna. Such power reduction strongly degrades the received power at the target user. Recently, exposition aware beamforming schemes aided by self-tuning reconfigurable intelligent surfaces derived from maximum ratio transmission beamforming, have been proposed: truncated beamforming. However, such scheme is highly complex. In this paper, we propose a novel and low complexity reconfigurable intelligent surface aided beamforming scheme called Equalized beamforming, which applies maximum ratio transmission to an angularly equalized virtual propagation channel. Our simulations show that our proposed scheme outperforms the reduced beamforming scheme, whilst complying with the exposition regulation.
Design of optical voltage sensor based on electric field regulation and rotating isomerism electrode
Authors: Jun Li, Yifan Lin, Nan Xie
Date: 2022-06-14 00:00:00

Temperature drift, stress birefringence and low frequency vibration lead to the randomness and fluctuation of the output of optical voltage sensor(OVS). In order to solve the problem, this study adopts the lock-in amplifier technology with the aid of a high-speed rotating electrode to realize electric field modulation. This technology could shift the measured signal frequency band from near 50 Hz moved to several kilometer Hz, so as to make the output signal avoid the interference from low-frequency temperature drift, stress birefringence and vibration, leading to higher stability and reliability. The electro-optic coupling wave theory and static electric field finite element method are utilized to investigate the shape of modulation wave. The simulation results proves that lock-in technology is able to prevent the measured voltage signal from the large step signal interference and restore the perfect original signal. While the sample rate is decreased to the value of the modulation frequency.
RIS Assisted Device Activity Detection with Statistical Channel State Information
Authors: Friedemann Laue, Vahid Jamali, Robert Schober
Date: 2022-06-14 00:00:00
Tasks: Action Detection, Activity Detection

This paper studies reconfigurable intelligent surface (RIS) assisted device activity detection for grant-free (GF) uplink transmission in wireless communication networks. In particular, we consider mobile devices located in an area where the direct link to an access point (AP) is blocked. Thus, the devices try to connect to the AP via a reflected link provided by an RIS. Therefore, a RIS phase-shift design is desired that covers the entire blocked area with a wide reflection beam because the exact locations and times of activity of the devices are unknown in GF transmission. In order to study the impact of the phase-shift design on the device activity detection, we derive a generalized likelihood ratio test (GLRT) based detector and present an analytical expression for the probability of detection. Assuming knowledge of statistical CSI, we formulate an optimization problem for the phase-shift design for maximization of the guaranteed probability of detection for all locations within a given coverage area. To tackle the non-convexity of the problem, we propose two different approximations of the objective function. The first approximation leads to a design that aims to reduce the variations of the end-to-end channel while taking system parameters such as transmit power, noise power, and probability of false alarm into account. The second approximation can be adopted for versatile RIS deployments because it only depends on the line-of-sight component of the end-to-end channel and is not affected by system parameters. For comparison, we also consider a phase-shift design maximizing the average channel gain and a baseline analytical phase-shift design for large blocked areas. Our performance evaluation shows that the proposed approximations result in phase-shift designs that guarantee high probability of detection across the coverage area and outperform the baseline designs.
SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction
Authors: Shuke Zhang, Yanzhao Jin, Tianmeng Liu, Qi Wang, Zhaohui Zhang, Shuliang Zhao, Bo Shan
Date: 2022-05-25 00:00:00
Tasks: Graph Neural Network, Prediction

Efficient and effective drug-target binding affinity (DTBA) prediction is a challenging task due to the limited computational resources in practical applications and is a crucial basis for drug screening. Inspired by the good representation ability of graph neural networks (GNNs), we propose a simple-structured GNN model named SS-GNN to accurately predict DTBA. By constructing a single undirected graph based on a distance threshold to represent protein-ligand interactions, the scale of the graph data is greatly reduced. Moreover, ignoring covalent bonds in the protein further reduces the computational cost of the model. The GNN-MLP module takes the latent feature extraction of atoms and edges in the graph as two mutually independent processes. We also develop an edge-based atom-pair feature aggregation method to represent complex interactions and a graph pooling-based method to predict the binding affinity of the complex. We achieve state-of-the-art prediction performance using a simple model (with only 0.6M parameters) without introducing complicated geometric feature descriptions. SS-GNN achieves Pearson's Rp=0.853 on the PDBbind v2016 core set, outperforming state-of-the-art GNN-based methods by 5.2%. Moreover, the simplified model structure and concise data processing procedure improve the prediction efficiency of the model. For a typical protein-ligand complex, affinity prediction takes only 0.2 ms. All codes are freely accessible at https://github.com/xianyuco/SS-GNN.
Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)
Authors: Alfonso Nieto-Castanon
Date: 2022-06-14 00:00:00
Tasks: Functional Connectivity, valid

Current functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations and from a limited number of subjects can be severely underpowered for any but the largest effect sizes. This manuscript discusses fc-MVPA (functional connectivity Multivariate Pattern Analysis), a novel application of multivariate pattern analysis techniques in the context of brain-wide connectome inferences. The theory behind fc-MVPA is presented, and several of its key concepts are illustrated through examples from a publicly available resting state dataset, including an example analysis evaluating gender differences across the entire functional connectome. Last, Monte Carlo simulations are used to demonstrated this method's validity and sensitivity. In addition to offering powerful whole-brain inferences, fc-MVPA also provides a meaningful characterization of the heterogeneity in functional connectivity across subjects.
Repenser le financement des entreprises vertueuses et les politiques prudentielles en int{é}grant la solvabilit{é} socio-environnementale
Authors: Laura Chémali, Camille Souffron
Date: 2022-06-14 00:00:00

Despite the amount of savings available and the money supply managed by financial institutions, significant market failures and the failure of carbon pricing strategies prevent sufficient financing of the transition, notably through bank credit. Aware of the links between natural, monetary and productive aggregates, we propose the development of ''eco-systemic'' prudential policies by exposing the interdependence between macro, micro and environmental prudential measures. These would be based on a reorientation of corporate accounting standards towards the concept of socio-environmental solvency, notably the CARE-TDL model (integration of human and natural capital alongside financial capital on the liabilities side of the balance sheet). In an ecosystemic framework, this solvency of virtuous companies would compensate in accounting terms for the lack of financial solvency. The State would then be the guarantor in order to facilitate their access to financing, also reduced by Basel III and Solvency II. This policy develops a system of reallocation of financing capacities from non-virtuous companies to the most virtuous ones with public guarantees, aiming to reduce the debt ratio while increasing green investments, with monetary policies of rates but also of volumes and ratios differentiated according to the types of assets and the greening of bank balance sheets, and finally forms of public-private partnership. Facilitating the financing of green companies would green capital but increase it, partly neutralising the positive environmental impact. It is therefore necessary to limit the credit expansion of ''brown'' companies. This would reduce risky operations and favour less leveraged investments more connected to the real economy, reducing systemic financial risk. -- The Agenda 2030 Policy Briefs series (PoCFiN Kedge Business School - SDSN France - Institut Rousseau) mobilises economists and practitioners to identify an agenda of economic and financial reforms to achieve the 2030 Agenda, at territorial, national and supranational levels. These are published after peer review.
Natural Language Sentence Generation from API Specifications
Authors: Siyu Huo, Kushal Mukherjee, Jayachandu Bandlamudi, Vatche Isahagian, Vinod Muthusamy, Yara Rizk
Date: 2022-06-01 00:00:00
Tasks: Chatbot, Intent Recognition, Sentence

APIs are everywhere; they provide access to automation solutions that could help businesses automate some of their tasks. Unfortunately, they may not be accessible to the business users who need them but are not equipped with the necessary technical skills to leverage them. Wrapping these APIs with chatbot capabilities is one solution to make these automation solutions interactive. In this work, we propose a system to generate sentences to train intent recognition models, a crucial component within chatbots to understand natural language utterances from users. Evaluation of our approach based on deep learning models showed promising and inspiring results, and the human-in-the-loop interaction will provide further improvement on the system.
Does a Technique for Building Multimodal Representation Matter? -- Comparative Analysis
Authors: Maciej Pawłowski, Anna Wróblewska, Sylwia Sysko-Romańczuk
Date: 2022-06-09 00:00:00

Creating a meaningful representation by fusing single modalities (e.g., text, images, or audio) is the core concept of multimodal learning. Although several techniques for building multimodal representations have been proven successful, they have not been compared yet. Therefore it has been ambiguous which technique can be expected to yield the best results in a given scenario and what factors should be considered while choosing such a technique. This paper explores the most common techniques for building multimodal data representations -- the late fusion, the early fusion, and the sketch, and compares them in classification tasks. Experiments are conducted on three datasets: Amazon Reviews, MovieLens25M, and MovieLens1M datasets. In general, our results confirm that multimodal representations are able to boost the performance of unimodal models from 0.919 to 0.969 of accuracy on Amazon Reviews and 0.907 to 0.918 of AUC on MovieLens25M. However, experiments on both MovieLens datasets indicate the importance of the meaningful input data to the given task. In this article, we show that the choice of the technique for building multimodal representation is crucial to obtain the highest possible model's performance, that comes with the proper modalities combination. Such choice relies on: the influence that each modality has on the analyzed machine learning (ML) problem; the type of the ML task; the memory constraints while training and predicting phase.
Benchmarking Bayesian neural networks and evaluation metrics for regression tasks
Authors: Brian Staber, Sébastien da Veiga
Date: 2022-06-08 00:00:00
Tasks: Benchmarking, Open-Ended Question Answering, regression, Uncertainty Quantification

Due to the growing adoption of deep neural networks in many fields of science and engineering, modeling and estimating their uncertainties has become of primary importance. Despite the growing literature about uncertainty quantification in deep learning, the quality of the uncertainty estimates remains an open question. In this work, we assess for the first time the performance of several approximation methods for Bayesian neural networks on regression tasks by evaluating the quality of the confidence regions with several coverage metrics. The selected algorithms are also compared in terms of predictivity, kernelized Stein discrepancy and maximum mean discrepancy with respect to a reference posterior in both weight and function space. Our findings show that (i) some algorithms have excellent predictive performance but tend to largely over or underestimate uncertainties (ii) it is possible to achieve good accuracy and a given target coverage with finely tuned hyperparameters and (iii) the promising kernel Stein discrepancy cannot be exclusively relied on to assess the posterior approximation. As a by-product of this benchmark, we also compute and visualize the similarity of all algorithms and corresponding hyperparameters: interestingly we identify a few clusters of algorithms with similar behavior in weight space, giving new insights on how they explore the posterior distribution.
Anno-MI: A Dataset of Expert-Annotated Counselling Dialogues
Authors: Zixiu Wu, Simone Balloccu, Vivek Kumar, Rim Helaoui, Ehud Reiter, Diego Reforgiato Recupero, Daniele Riboni
Date: 2022-04-27 00:00:00
Proceeding: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2022 4
Tasks: Dialogue Generation, Natural Language Understanding

Research on natural language processing for counselling dialogue analysis has seen substantial development in recent years, but access to this area remains extremely limited due to the lack of publicly available expert-annotated therapy conversations. In this work, we introduce AnnoMI, the first publicly and freely accessible dataset of professionally transcribed and expert-annotated therapy dialogues. It consists of 133 conversations that demonstrate high- and low-quality motivational interviewing (MI), an effective counselling technique, and the annotations by domain experts cover key MI attributes. We detail the data collection process including dialogue selection, transcription and annotation. We also present analyses of AnnoMI and discuss its potential applications.
PASSL
Authors: Duanboqiang
Date: 2022-02-24 00:00:00
Proceeding: ICML 2020 2022 2
Tasks: Self-Supervised Learning

PASSL aims to accelerate research cycle in self-supervised learning: from designing a new self-supervised task to evaluating the learned representations.
An Interacting Multiple Model for Trajectory Prediction of Intelligent Vehicles in Typical Road Traffic Scenario
Authors: Hongbo Gao; Yechen Qin; Chuan Hu; Yuchao Liu; Keqiang Li
Date: 2021-12-31 00:00:00
Proceeding: IEEE Transactions on Neural Networks and Learning Systems ( Early Access ) 2021 12
Tasks: Prediction, Trajectory Prediction

This article presents an interacting multiple model (IMM) for short-term prediction and long-term trajectory prediction of an intelligent vehicle. This model is based on vehicle's physics model and maneuver recognition model. The long-term trajectory prediction is challenging due to the dynamical nature of the system and large uncertainties. The vehicle physics model is composed of kinematics and dynamics models, which could guarantee the accuracy of short-term prediction. The maneuver recognition model is realized by means of hidden Markov model, which could guarantee the accuracy of long-term prediction, and an IMM is adopted to guarantee the accuracy of both short-term prediction and long-term prediction. The experiment results of a real vehicle are presented to show the effectiveness of the prediction method.
Sequential Alignment Methods for Ensemble Part-of-Speech Tagging
Authors: Jeesu Jung, SangKeun Jung, Yoon-Hyung Roh
Date: 2022-03-23 00:00:00
Proceeding: 17-20 January 2022 3
Tasks: Part-Of-Speech Tagging

Part-of-speech tagging is a fundamental task that provides the elemental structure and content information for additional natural language processing. Although Part-of-speech tagging problems have traditionally been formulated as sequential labeling tasks, none of the proposed ensemble approaches have focused on sequence alignment during post-processing. Herein, we present a weighted ensemble technique using a sequence alignment approach for a Part-of-speech tagger. Through this technique, we introduce a simple but powerful post-processor, which is a sub-sequence selector using a similarity score calculated through sequence alignment methods. Such methods are based on an existing DNA alignment approach applied toward natural language. Experiments were conducted using an ensemble of sequence alignment methods with three different sub-sequence units, i.e., the sequence, word, and character span. Experiments on English and Korean datasets show that our sequence alignment ensemble technique outperforms a basic hard voting method. Most of the results of the ensemble sequence alignment approach with various sub-sequence units showed an increase in F1-score over hard voting. F1-score increased up to 0.36 for the general hard voting method on the test dataset.
AMT 1.x: A toolbox for reproducible research in auditory modeling
Authors: Piotr Majdak, Clara Hollomey, Robert Baumgartner
Date: 2022-05-09 00:00:00
Proceeding: Acta Acustica 2022 5

The Auditory Modeling Toolbox (AMT) is a MATLAB/Octave toolbox for the development and application of computational auditory models with a particular focus on binaural hearing. The AMT aims for a consistent implementation of auditory models, well-structured in-code documentation, and inclusion of auditory data required to run the models. The motivation is to provide a toolbox able to reproduce the model predictions and allowing students and researchers to work with and to advance existing models. In the AMT, model implementations can be evaluated in two stages: by running so-called demonstrations, which are quick presentations of a model, and by starting so-called experiments aimed at reproducing results from the corresponding publications. Here, we describe the tools and mechanisms available within the framework of all AMT 1.x versions. The recently released AMT 1.1 includes over 60 models and is freely available as an open-source package from https://www.amtoolbox.org.
RNNIDS: Enhancing Network Intrusion Detection Systems through Deep Learning
Authors: Soroush M. Sohia, Jean-Pierre Seifertac, Fatemeh Ganji
Date: 2020-12-21 00:00:00
Proceeding: ScienceRedirect 2020 12
Tasks: Deep Learning, Intrusion Detection, Network Intrusion Detection

Security of information passing through the Internet is threatened by today’s most advanced malware
ranging from orchestrated botnets to simpler polymorphic worms. These threats, as examples of zero-day
attacks, are able to change their behavior several times in the early phases of their existence to bypass the
network intrusion detection systems (NIDS). In fact, even well-designed, and frequently-updated signaturebased NIDS cannot detect the zero-day treats due to the lack of an adequate signature database, adaptive
to intelligent attacks on the Internet. More importantly, having an NIDS, it should be tested on malicious
traffic dataset that not only represents known attacks, but also can to some extent reflect the characteristics of
unknown, zero-day attacks. Generating such traffic is identified in the literature as one of the main obstacles
for evaluating the effectiveness of NIDS. To address these issues, we introduce RNNIDS that applies Recurrent
Neural Networks (RNNs) to find complex patterns in attacks and generate similar ones. In this regard, for
the first time, we demonstrate that RNNs are helpful to generate new, unseen mutants of attacks as well as
synthetic signatures from the most advanced malware to improve the intrusion detection rate. Besides, to
further enhance the design of an NIDS, RNNs can be employed to generate malicious datasets containing, e.g.,
unseen mutants of a malware. To evaluate the feasibility of our approaches, we conduct extensive experiments
by incorporating publicly available datasets, where we show a considerable improvement in the detection rate
of an off-the-shelf NIDS (up to 16.67%).
KE-QI: A Knowledge Enhanced Article Quality Identification Dataset
Authors: Chunhui Ai, Derui Wang, Xu Yan, Yang Xu, Wenrui Xie, Ziqiang Cao
Date: 2022-06-15 00:00:00
Tasks: Articles, Text Classification

With so many articles of varying qualities being produced every moment, it is a very urgent task to screen outstanding articles and commit them to social media. To our best knowledge, there is a lack of datasets and mature research works in identifying high-quality articles. Consequently, we conduct some surveys and finalize 7 objective indicators to annotate the quality of 10k articles. During annotation, we find that many characteristics of high-quality articles (e.g., background) rely more on extensive external knowledge than inner semantic information of articles. In response, we link extracted article entities to Baidu Encyclopedia, then propose Knowledge Enhanced article Quality Identification (KE-QI) dataset. To make better use of external knowledge, we propose a compound model which fuses the text and external knowledge information via a gate unit to classify the quality of an article. Our experimental results on KE-QI show that with initialization of our pre-trained Node2Vec model, our model achieves about 78\% $F_1$, outperforming other baselines.