pwc-archive/papers-with-abstracts huggingface dataset:
Topic Modeling on Podcast Short-Text Metadata
Authors: Francisco B. Valero, Marion Baranes, Elena V. Epure
Date: 2022-01-12 00:00:00
Tasks: Navigate

Podcasts have emerged as a massively consumed online content, notably due to wider accessibility of production means and scaled distribution through large streaming platforms. Categorization systems and information access technologies typically use topics as the primary way to organize or navigate podcast collections. However, annotating podcasts with topics is still quite problematic because the assigned editorial genres are broad, heterogeneous or misleading, or because of data challenges (e.g. short metadata text, noisy transcripts). Here, we assess the feasibility to discover relevant topics from podcast metadata, titles and descriptions, using topic modeling techniques for short text. We also propose a new strategy to leverage named entities (NEs), often present in podcast metadata, in a Non-negative Matrix Factorization (NMF) topic modeling framework. Our experiments on two existing datasets from Spotify and iTunes and Deezer, a new dataset from an online service providing a catalog of podcasts, show that our proposed document representation, NEiCE, leads to improved topic coherence over the baselines. We release the code for experimental reproducibility of the results.
Computational analyses of the topics, sentiments, literariness, creativity and beauty of texts in a large Corpus of English Literature
Authors: Arthur M. Jacobs, Annette Kinder
Date: 2022-01-12 00:00:00
Tasks: text-classification, Text Classification

The Gutenberg Literary English Corpus (GLEC, Jacobs, 2018a) provides a rich source of textual data for research in digital humanities, computational linguistics or neurocognitive poetics. In this study we address differences among the different literature categories in GLEC, as well as differences between authors. We report the results of three studies providing i) topic and sentiment analyses for six text categories of GLEC (i.e., children and youth, essays, novels, plays, poems, stories) and its >100 authors, ii) novel measures of semantic complexity as indices of the literariness, creativity and book beauty of the works in GLEC (e.g., Jane Austen's six novels), and iii) two experiments on text classification and authorship recognition using novel features of semantic complexity. The data on two novel measures estimating a text's literariness, intratextual variance and stepwise distance (van Cranenburgh et al., 2019) revealed that plays are the most literary texts in GLEC, followed by poems and novels. Computation of a novel index of text creativity (Gray et al., 2016) revealed poems and plays as the most creative categories with the most creative authors all being poets (Milton, Pope, Keats, Byron, or Wordsworth). We also computed a novel index of perceived beauty of verbal art (Kintsch, 2012) for the works in GLEC and predict that Emma is the theoretically most beautiful of Austen's novels. Finally, we demonstrate that these novel measures of semantic complexity are important features for text classification and authorship recognition with overall predictive accuracies in the range of .75 to .97. Our data pave the way for future computational and empirical studies of literature or experiments in reading psychology and offer multiple baselines and benchmarks for analysing and validating other book corpora.
PromptBERT: Improving BERT Sentence Embeddings with Prompts
Authors: Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, Qi Zhang
Date: 2022-01-12 00:00:00
Tasks: Contrastive Learning, Denoising, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, STS

We propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analyze the drawback of current sentence embedding from original BERT and find that it is mainly due to the static token embedding bias and ineffective BERT layers. Then we propose the first prompt-based sentence embeddings method and discuss two prompt representing methods and three prompt searching methods to make BERT achieve better sentence embeddings. Moreover, we propose a novel unsupervised training objective by the technology of template denoising, which substantially shortens the performance gap between the supervised and unsupervised settings. Extensive experiments show the effectiveness of our method. Compared to SimCSE, PromptBert achieves 2.29 and 2.58 points of improvement based on BERT and RoBERTa in the unsupervised setting.
PhysNLU: A Language Resource for Evaluating Natural Language Understanding and Explanation Coherence in Physics
Authors: Jordan Meadows, Zili Zhou, Andre Freitas
Date: 2022-01-12 00:00:00
Proceeding: LREC 2022 6
Tasks: Position, Sentence, Sentence Ordering

In order for language models to aid physics research, they must first encode representations of mathematical and natural language discourse which lead to coherent explanations, with correct ordering and relevance of statements. We present a collection of datasets developed to evaluate the performance of language models in this regard, which measure capabilities with respect to sentence ordering, position, section prediction, and discourse coherence. Analysis of the data reveals equations and sub-disciplines which are most common in physics discourse, as well as the sentence-level frequency of equations and expressions. We present baselines that demonstrate how contemporary language models are challenged by coherence related tasks in physics, even when trained on mathematical natural language objectives.
Evolutionary Optimization for Proactive and Dynamic Computing Resource Allocation in Open Radio Access Network
Authors: Gan Ruan, Leandro L. Minku, Zhao Xu, Xin Yao
Date: 2022-01-12 00:00:00

Intelligent techniques are urged to achieve automatic allocation of the computing resource in Open Radio Access Network (O-RAN), to save computing resource, increase utilization rate of them and decrease the delay. However, the existing problem formulation to solve this resource allocation problem is unsuitable as it defines the capacity utility of resource in an inappropriate way and tends to cause much delay. Moreover, the existing problem has only been attempted to be solved based on greedy search, which is not ideal as it could get stuck into local optima. Considering those, a new formulation that better describes the problem is proposed. In addition, as a well-known global search meta heuristic approach, an evolutionary algorithm (EA) is designed tailored for solving the new problem formulation, to find a resource allocation scheme to proactively and dynamically deploy the computing resource for processing upcoming traffic data. Experimental studies carried out on several real-world datasets and newly generated artificial datasets with more properties beyond the real-world datasets have demonstrated the significant superiority over a baseline greedy algorithm under different parameter settings. Moreover, experimental studies are taken to compare the proposed EA and two variants, to indicate the impact of different algorithm choices.
HTSS: A Novel Hybrid Text Summarisation and Simplification Architecture
Authors: Farooq Zaman, Matthew Shardlow, Saeed-Ul Hassan, Naif Radi Aljohani, Raheel Nawaz
Date: 2020-07-13 00:00:00
Proceeding: Information Processing and Management 2020 7
Tasks: Text Generation, Text Simplification

Text simplification and text summarisation are related, but different sub-tasks in Natural Language Generation. Whereas summarisation attempts to reduce the length of
a document, whilst keeping the original meaning, simplification attempts to reduce
the complexity of a document. In this work, we combine both tasks of summarisation and simplification using a novel hybrid architecture of abstractive and extractive
summarisation called HTSS. We extend the well-known pointer generator model for
the combined task of summarisation and simplification. We have collected our parallel corpus from the simplified summaries written by domain experts published on the
science news website EurekaAlert (www.eurekalert.org). Our results show that
our proposed HTSS model outperforms neural text simplification (NTS) on SARI score
and abstractive text summarisation (ATS) on the ROUGE score. We further introduce
a new metric (CSS1) which combines SARI and Rouge and demonstrates that our proposed HTSS model outperforms NTS and ATS on the joint task of simplification and
summarisation by 38.94% and 53.40%, respectively.
We provide all code, models and corpora to the scientific community for future
research at the following URL: https://github.com/slab-itu/HTSS/.
Viewpoint and Scale Consistency Reinforcement for UAV Vehicle Re-Identification
Authors: Shangzhi Teng, Shiliang Zhang, Qingming Huang, Nicu Sebe
Date: 2021-03-01 00:00:00
Proceeding: IJCV 2021 3
Tasks: Vehicle Re-Identification

This paper studies vehicle ReID in aerial videos taken by Unmanned Aerial Vehicles (UAVs). Compared with existing vehicle ReID tasks performed with fixed surveillance cameras, UAV vehicle ReID is still under-explored and could be more challenging, e.g., aerial videos have dynamic and complex backgrounds, different vehicles show similar appearance, and the same vehicle commonly show distinct viewpoints and scales. To facilitate the research on UAV vehicle ReID, this paper contributes a novel dataset called UAV-VeID. UAV-VeID contains 41,917 images of 4601 vehicles captured by UAVs, where each vehicle has multiple images taken from different viewpoints. UAV-VeID also includes a large-scale distractor set to encourage the research on efficient ReID schemes. Compared with existing vehicle ReID datasets, UAV-VeID exhibits substantial variances in viewpoints and scales of vehicles, thus requires more robust features. To alleviate the negative effects of those variances, this paper also proposes a viewpoint adversarial training strategy and a multi-scale consensus loss to promote the robustness and discriminative power of learned deep features. Extensive experiments on UAV-VeID show our approach outperforms recent vehicle ReID algorithms. Moreover, our method also achieves competitive performance compared with recent works on existing vehicle ReID datasets including VehicleID, VeRi-776 and VERI-Wild.
A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter
Authors: JinChen
Date: 2004-01-30 00:00:00
Proceeding: Remote Sensing of Environment 2004 1
Tasks: Time Series, Time Series Analysis

Although the Normalized Difference Vegetation Index (NDVI) time-series data, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, has been successfully used in research regarding global environmental change, residual noise in the NDVI time-series data, even after applying strict pre-processing, impedes further analysis and risks generating erroneous results. Based on the assumptions that NDVI time-series follow annual cycles of growth and decline of vegetation, and that clouds or poor atmospheric conditions usually depress NDVI values, we have developed in the present study a simple but robust method based on the Savitzky–Golay filter to smooth out noise in NDVI time-series, specifically that caused primarily by cloud contamination and atmospheric variability. Our method was developed to make data approach the upper NDVI envelope and to reflect the changes in NDVI patterns via an iteration process. From the results obtained by applying the newly developed method to a 10-day MVC SPOT VGT-S product, we provide optimized parameters for the new method and compare this technique with the BISE algorithm and Fourier-based fitting method. Our results indicate that the new method is more effective in obtaining high-quality NDVI time-series.
Remote photonic detection of human senses using secondary speckle patterns
Authors: Zeev Kalyuzhner, Sergey Agdarov, Aviya Bennett, Yafim Beiderman, Zeev Zalevsky
Date: 2022-01-11 00:00:00
Proceeding: Scientific Reports 2022 1

Neural activity research has recently gained significant attention due to its association with sensory information and behavior control. However, the current methods of brain activity sensing require expensive equipment and physical contact with the tested subject. We propose a novel photonic-based method for remote detection of human senses. Physiological processes associated with hemodynamic activity due to activation of the cerebral cortex affected by different senses have been detected by remote monitoring of nano‐vibrations generated by the transient blood flow to the specific regions of the human brain. We have found that a combination of defocused, self‐interference random speckle patterns with a spatiotemporal analysis, using Deep Neural Network, allows associating between the activated sense and the seemingly random speckle patterns.
Implementation of FGPA based Channel Sounder for Large scale antenna systems using RFNoC on USRP Platform
Authors: Bhargav Gokalgandhi, Prasanthi Maddala, Ivan Seskar
Date: 2022-01-12 00:00:00

This paper concentrates on building a multi-antenna FPGA based Channel Sounder with single transmitter and multiple receivers to realize wireless propagation characteristics of an indoor environment. A DSSS signal (spread with a real maximum length PN sequence) is transmitted, which is correlated with the same PN sequence at each receiver to obtain the power delay profile . Multiple power delay profiles are averaged and the result is then sent to host. To utilize high bandwidth, the computationally expensive tasks related to generation and parallel correlation of PN sequences are moved to the FPGA present in each USRP (Universal Software Radio Peripheral). Channel sounder blocks were built using Vivado HLS and integrated with RFNoC (RF Network on Chip) framework, which were then used on USRP X310 devices.
Towards Integrated Sensing and Communications for 6G
Authors: Qi Wang, Anastasios Kakkavas, Xitao Gong, Richard A. Stirling-Gallacher
Date: 2022-01-12 00:00:00

For the next generation of mobile communications systems, the integration of sensing and communications promises benefits in terms of spectrum utilization, cost, latency, area and weight. In this paper, we categorize and summarize the key features and technical considerations for different integration approaches and discuss related waveform design issues for a future 6G system. We provide results on new candidate waveforms for monostatic sensing and finally highlight important open issues and directions that deserve future in-depth research.
Joint Transmit and Reflective Beamformer Design for Secure Estimation in IRS-Aided WSNs
Authors: Mohammad Faisal Ahmed, Kunwar Pritiraj Rajput, Naveen K. D. Venkategowda, Kumar Vijay Mishra, Aditya K. Jagannatham
Date: 2022-01-12 00:00:00
Tasks: parameter estimation

Wireless sensor networks (WSNs) are vulnerable to eavesdropping as the sensor nodes (SNs) communicate over an open radio channel. Intelligent reflecting surface (IRS) technology can be leveraged for physical layer security in WSNs. In this paper, we propose a joint transmit and reflective beamformer (JTRB) design for secure parameter estimation at the fusion center (FC) in the presence of an eavesdropper (ED) in a WSN. We develop a semidefinite relaxation (SDR)-based iterative algorithm, which alternately yields the transmit beamformer at each SN and the corresponding reflection phases at the IRS, to achieve the minimum mean-squared error (MSE) parameter estimate at the FC, subject to transmit power and ED signal-to-noise ratio constraints. Our simulation results demonstrate robust MSE and security performance of the proposed IRS-based JTRB technique.
The Geometry of Navigation Problems
Authors: Axel Barrau, Silvere Bonnabel
Date: 2022-01-12 00:00:00
Tasks: State Estimation

While many works exploiting an existing Lie group structure have been proposed for state estimation, in particular the Invariant Extended Kalman Filter (IEKF), few papers address the construction of a group structure that allows casting a given system into the framework of invariant filtering. In this paper we introduce a large class of systems encompassing most problems involving a navigating vehicle encountered in practice. For those systems we introduce a novel methodology that systematically provides a group structure for the state space, including vectors of the body frame such as biases. We use it to derive observers having properties akin to those of linear observers or filters. The proposed unifying and versatile framework encompasses all systems where IEKF has proved successful, improves state-of-the art "imperfect" IEKF for inertial navigation with sensor biases, and allows addressing novel examples, like GNSS antenna lever arm estimation.
Onboard Safety Guarantees for Racing Drones: High-speed Geofencing with Control Barrier Functions
Authors: Andrew Singletary, Aiden Swann, Yuxiao Chen, Aaron D. Ames
Date: 2022-01-12 00:00:00

This paper details the theory and implementation behind practically ensuring safety of remotely piloted racing drones. We demonstrate robust and practical safety guarantees on a 7" racing drone at speeds exceeding 100 km/h, utilizing only online computations on a 10 gram micro-controller. To achieve this goal, we utilize the framework of control barrier functions (CBFs) which give guaranteed safety encoded as forward set invariance. To make this methodology practically applicable, we present an implicitly defined CBF which leverages backup controllers to enable gradient-free evaluations that ensure safety. The method applied to hardware results in smooth, minimally conservative alterations of the pilots' desired inputs, enabling them to push the limits of their drone without fear of crashing. Moreover, the method works in conjunction with the preexisting flight controller, resulting in unaltered flight when there are no nearby safety risks. Additional benefits include safety and stability of the drone when losing line-of-sight or in the event of radio failure.
Safe-by-Design Planner-Tracker Synthesis
Authors: Katherine S. Schweidel, He Yin, Stanley W. Smith, Murat Arcak
Date: 2022-01-12 00:00:00
Tasks: Model Predictive Control, Trajectory Planning

We present a safe-by-design trajectory planning and tracking framework for nonlinear dynamical systems using a hierarchy of system models. The planning layer uses a low-fidelity model to plan a feasible trajectory satisfying the planning constraints, and the tracking layer utilizes the high-fidelity model to design a controller that restricts the error states between the low- and high-fidelity models to a bounded set. The low-fidelity model enables the planning to be performed online (e.g. using Model Predictive Control) and the tracking controller and error bound are derived offline (e.g. using sum-of-squares programming). To provide freedom in the choice of the low-fidelity model, we allow the tracking error to depend on both the states and inputs of the planner. The goal of this article is to provide a tutorial review of this hierarchical framework and to illustrate it with examples, including a design for vehicle obstacle avoidance.
Feedback linearisation of mechanical systems using data-driven models
Authors: Merijn Floren, Koen Classens, Tom Oomen, Jean-Philippe Noël
Date: 2022-01-12 00:00:00

Linearising the dynamics of nonlinear mechanical systems is an important and open research area. A common approach is feedback linearisation, which is a nonlinear control method that transforms the input-output response of a nonlinear system into an equivalent linear one. The main problem with feedback linearisation is that it requires an accurate first-principles model of the system, which are typically hard to obtain. In this paper, we design an alternative control approach that exploits data-driven models to linearise the input-output response of nonlinear mechanical systems. Specifically, a model-based reference tracking architecture is developed for nonlinear feedback systems with output nonlinearities. The overall methodology shows a high degree of performance combined with significant robustness against imperfect modelling and extrapolation. These findings are demonstrated using large set of synthetic experiments conducted on a asymmetric Duffing oscillator and using an experimental prototype of a high-precision motion system.
Power-to-Gas in a gas and electricity distribution network: a sensitivity analysis of modeling approaches
Authors: Gabriele Fambri, Cesar Diaz-Londono, Andrea Mazza, Marco Badami, Robert Weiss
Date: 2022-01-12 00:00:00
Tasks: Sensitivity

Power-to-Gas (P2G) has been one of the most frequently discussed technologies in the last few years. This technology allows producing CO2 free fuels. Thanks to its high flexibility, it may offer services to the power system, fostering Variable Renewable Energy Sources (VRES) and the electricity demand match, mitigating the issues related to VRES overproduction. The role of P2G plants connected to the transmission system as flexibility asset has been extensively analyzed in the literature. Conversely, the analysis of these systems used at distribution level has only been dealt with in a few studies: however, in this case critical operation conditions can easily arise, both on electrical and gas infrastructure. This article presents a methodological analysis on the impact of different simulation approaches when P2G is installed at distribution system level. The choice of the most appropriate modeling approaches for electricity and distribution grids is required in order to avoid overestimating or underestimating the potential flexibility that P2G plants connected to distribution networks can offer. The aim of this paper is to understand the impact of different modeling approaches in order to determine whether, and under which conditions, this is acceptable. An illustrative case study has been developed to perform this kind of analysis. The results demonstrated that it is important to take into account the electric distribution network topology, as the performance of P2G plants could be affected by their placement in the network. Neglecting the dynamics of a gas network or the interactions between P2G plant components under a low gas demand condition can lead to an underestimation of the flexibility of the entire system. If the demand for gas is high enough, the use of simplified assumptions that do not consider the dynamics of the gas network and P2G systems may be acceptable.
Numerical and Experimental Characterization of LoRa-Based Helmet-to-Unmanned Aerial Vehicle Links on Flat Lands: A Numerical-Statistical Approach to Link Modeling
Authors: Giulio Maria Bianco, Abraham Mejia-Aguilar, Gaetano Marrocco
Date: 2022-01-12 00:00:00
Tasks: Position

The use of the LoRa communication protocol in a new generation of transceivers is attractive for search and rescue(SaR) procedures because they can operate in harsh environmentscovering vast areas while maintaining a low power consumption.The possibility of wearing helmets equipped with LoRa-radiosand installing LoRa transceivers in unmanned aerial vehicles (UAVs) will accelerate the localization of the targets, probably unconscious. In this paper, the achievable communication ranges of such links are theoretically and experimentally evaluated by considering the possible positions of the helmet wearer (standing or lying) on a flat field, representing a simple SaR scenario.Simulations and experimental tests demonstrated that, for the standing position, the ground-bounce multi-path produces strong fluctuations of the received power versus the Tx-Rx distances. Such fluctuations can be kept confined within 100 m from the target by lowering the UAV altitude. Instead, for a more critical lying position, the received power profile is monotonic and nearly insensitive to the posture. For all the considered cases, the signal emitted by the body-worn transceiver can be exploited to localize the helmet wearer based on its strength, and it is theoretically detectable by the UAV radio up to 5 km on flat terrain.