pwc-archive/papers-with-abstracts huggingface dataset:
Pre-training Meets Clustering: A Hybrid Extractive Multi-document Summarization Model
Authors: Akanksha Karotia, Seba Susan
Date: 2023-05-25 00:00:00
Proceeding: International Conference on Hybrid Intelligent Systems 2023 5
Tasks: Clustering, Document Summarization, document understanding, Extractive Text Summarization, Multi-Document Summarization, Retrieval, Text Summarization, Unsupervised Text Summarization

In this era where a large amount of information has flooded the Internet, manual extraction and consumption of relevant information is very difficult and time-consuming. Therefore, an automated document summarization tool is necessary to excerpt important information from a set of documents that have similar or related subjects. Multi-document summarization allows retrieval of important and relevant content from multiple documents while minimizing redundancy. A multi-document text summarization system is developed in this study using an unsupervised extractive-based approach. The proposed model is a fusion of two learning paradigms: the T5 pre-trained transformer model and the K-Means clustering algorithm. We perform the experiments on the benchmark news article corpus Document Understanding Conference (DUC2004). The ROUGE evaluation metrics were used to estimate the performance of the proposed approach on the DUC2004. Outcomes validate that our proposed model shows greatly enhanced performance as compared to the existent unsupervised state-of-the-art approaches.
CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19
Authors: Akanksha Karotia, Seba Susan
Date: 2023-04-26 00:00:00
Proceeding: The Journal of Supercomputing 2023 4
Tasks: Articles, Document Summarization, Extractive Text Summarization, Text Summarization, Unsupervised Text Summarization

The number of research articles published on COVID-19 has dramatically increased since the outbreak of the pandemic in November 2019. This absurd rate of productivity in research articles leads to information overload. It has increasingly become urgent for researchers and medical associations to stay up to date on the latest COVID-19 studies. To address information overload in COVID-19 scientific literature, the study presents a novel hybrid model named CovSumm, an unsupervised graph-based hybrid approach for single-document summarization, that is evaluated on the CORD-19 dataset. We have tested the proposed methodology on the scientific papers in the database dated from January 1, 2021 to December 31, 2021, consisting of 840 documents in total. The proposed text summarization is a hybrid of two distinctive extractive approaches (1) GenCompareSum (transformer-based approach) and (2) TextRank (graph-based approach). The sum of scores generated by both methods is used to rank the sentences for generating the summary. On the CORD-19, the recall-oriented understudy for gisting evaluation (ROUGE) score metric is used to compare the performance of the CovSumm model with various state-of-the-art techniques. The proposed method achieved the highest scores of ROUGE-1: 40.14%, ROUGE-2: 13.25%, and ROUGE-L: 36.32%. The proposed hybrid approach shows improved performance on the CORD-19 dataset when compared to existing unsupervised text summarization methods.
Feasible Conditional Belief Distributions
Authors: Itai Arieli, Yakov Babichenko, Fedor Sandomirskiy
Date: 2023-07-15 00:00:00

Agents receive private signals about an unknown state. The resulting joint belief distributions are complex and lack a simple characterization. Our key insight is that, when conditioned on the state, the structure of belief distributions simplifies: feasibility constrains only the marginal distributions of individual agents across states, with no joint constraints within a state. We apply this insight to multi-receiver persuasion, identifying new tractable cases and introducing optimal transportation and duality tools.
Datalism and Data Monopolies in the Era of A.I.: A Research Agenda
Authors: Catherine E. A. Mulligan, Phil Godsiff
Date: 2023-07-16 00:00:00

The increasing use of data in various parts of the economic and social systems is creating a new form of monopoly: data monopolies. We illustrate that the companies using these strategies, Datalists, are challenging the existing definitions used within Monopoly Capital Theory (MCT). Datalists are pursuing a different type of monopoly control than traditional multinational corporations. They are pursuing monopolistic control over data to feed their productive processes, increasingly controlled by algorithms and Artificial Intelligence (AI). These productive processes use information about humans and the creative outputs of humans as the inputs but do not classify those humans as employees, so they are not paid or credited for their labour. This paper provides an overview of this evolution and its impact on monopoly theory. It concludes with an outline for a research agenda for economics in this space.
HistoColAi: An Open-Source Web Platform for Collaborative Digital Histology Image Annotation with AI-Driven Predictive Integration
Authors: Cristian Camilo Pulgarín-Ospina, Rocío del Amor, Adrián Colomera, Julio Silva-Rodríguez, Valery Naranjo
Date: 2023-07-11 00:00:00
Tasks: Diagnostic, whole slide images

Digital pathology has become a standard in the pathology workflow due to its many benefits. These include the level of detail of the whole slide images generated and the potential immediate sharing of cases between hospitals. Recent advances in deep learning-based methods for image analysis make them of potential aid in digital pathology. However, a major limitation in developing computer-aided diagnostic systems for pathology is the lack of an intuitive and open web application for data annotation. This paper proposes a web service that efficiently provides a tool to visualize and annotate digitized histological images. In addition, to show and validate the tool, in this paper we include a use case centered on the diagnosis of spindle cell skin neoplasm for multiple annotators. A usability study of the tool is also presented, showing the feasibility of the developed tool.
Voting-based Multimodal Automatic Deception Detection
Authors: Lana Touma, Mohammad Al Horani, Manar Tailouni, Anas Dahabiah, Khloud Al Jallad
Date: 2023-06-30 00:00:00
Tasks: Deception Detection

Automatic Deception Detection has been a hot research topic for a long time, using machine learning and deep learning to automatically detect deception, brings new light to this old field. In this paper, we proposed a voting-based method for automatic deception detection from videos using audio, visual and lexical features. Experiments were done on two datasets, the Real-life trial dataset by Michigan University and the Miami University deception detection dataset. Video samples were split into frames of images, audio, and manuscripts. Our Voting-based Multimodal proposed solution consists of three models. The first model is CNN for detecting deception from images, the second model is Support Vector Machine (SVM) on Mel spectrograms for detecting deception from audio and the third model is Word2Vec on Support Vector Machine (SVM) for detecting deception from manuscripts. Our proposed solution outperforms state of the art. Best results achieved on images, audio and text were 97%, 96%, 92% respectively on Real-Life Trial Dataset, and 97%, 82%, 73% on video, audio and text respectively on Miami University Deception Detection.
Power-Efficient Video Streaming on Mobile Devices Using Optimal Spatial Scaling
Authors: Christian Herglotz, André Kaup, Stéphane Coulombe, Ahmad Vakili
Date: 2023-07-17 00:00:00

This paper derives optimal spatial scaling and rate control parameters for power-efficient wireless video streaming on portable devices. A video streaming application is studied, which receives a high-resolution and high-quality video stream from a remote server and displays the content to the end-user.We show that the resolution of the input video can be adjusted such that the quality-power trade-off is optimized. Making use of a power model from the literature and subjective quality evaluation using a perceptual metric, we derive optimal combinations of the scaling factor and the rate-control parameter for encoding. For HD sequences, up to 10% of power can be saved at negligible quality losses and up to 15% of power can be saved at tolerable distortions. To show general validity, the method was tested for Wi-Fi and a mobile network as well as for two different smartphones.
Data-Driven Optimal Control of Tethered Space Robot Deployment with Learning Based Koopman Operator
Authors: Ao Jin, Fan Zhang, Panfeng Huang
Date: 2023-07-15 00:00:00
Tasks: Data Driven Optimal Control

To avoid complex constraints of the traditional nonlinear method for tethered space robot (TSR) deployment, this paper proposes a data-driven optimal control framework with an improved deep learning based Koopman operator that could be applied to complex environments. In consideration of TSR's nonlinearity, its finite dimensional lifted representation is derived with the state-dependent only embedding functions in the Koopman framework. A deep learning approach is adopted to approximate the global linear representation of TSR. Deep neural networks (DNN) are developed to parameterize Koopman operator and its embedding functions. An auxiliary neural network is developed to encode the nonlinear control term of finite dimensional lifted system. In addition, the state matrix A and control matrix B of lifted linear system in the embedding space are also estimated during training DNN. Then three loss functions that related to reconstruction and prediction ability of network and controllability of lifted linear system are designed for training the entire network. With the global linear system produced from DNN, Linear Quadratic Regulator (LQR) is applied to derive the optimal control policy for the TSR deployment. Finally, simulation results verify the effectiveness of proposed framework and show that it could deploy tethered space robot more quickly with less swing of in-plane angle.
Exploring Binary Classification Loss For Speaker Verification
Authors: Bing Han, Zhengyang Chen, Yanmin Qian
Date: 2023-07-17 00:00:00
Tasks: Binary Classification, Classification, Metric Learning, Speaker Verification

The mismatch between close-set training and open-set testing usually leads to significant performance degradation for speaker verification task. For existing loss functions, metric learning-based objectives depend strongly on searching effective pairs which might hinder further improvements. And popular multi-classification methods are usually observed with degradation when evaluated on unseen speakers. In this work, we introduce SphereFace2 framework which uses several binary classifiers to train the speaker model in a pair-wise manner instead of performing multi-classification. Benefiting from this learning paradigm, it can efficiently alleviate the gap between training and evaluation. Experiments conducted on Voxceleb show that the SphereFace2 outperforms other existing loss functions, especially on hard trials. Besides, large margin fine-tuning strategy is proven to be compatible with it for further improvements. Finally, SphereFace2 also shows its strong robustness to class-wise noisy labels which has the potential to be applied in the semi-supervised training scenario with inaccurate estimated pseudo labels. Codes are available in https://github.com/Hunterhuan/sphereface2_speaker_verification
Parametric and State Estimation of Stationary MEMS-IMUs: A Tutorial
Authors: Daniel Engelsman, Yair Stolero, Itzik Klein
Date: 2023-07-17 00:00:00
Tasks: State Estimation

Inertial navigation systems (INS) are widely used in almost any operational environment, including aviation, marine, and land vehicles. Inertial measurements from accelerometers and gyroscopes allow the INS to estimate position, velocity, and orientation of its host vehicle. However, as inherent sensor measurement errors propagate into the state estimates, accuracy degrades over time. To mitigate the resulting drift in state estimates, different approaches of parametric and state estimation are proposed to compensate for undesirable errors, using frequency-domain filtering or external information fusion. Another approach uses multiple inertial sensors, a field with rapid growth potential and applications. The increased sampling of the observed phenomenon results in the improvement of several key factors such as signal accuracy, frequency resolution, noise rejection, and higher redundancy. This study offers an analysis tutorial of basic multiple inertial operation, with a new perspective on the error relationship to time, and number of sensors. To that end, a stationary and levelled sensors array is taken, and its robustness against the instrumental errors is analyzed. Subsequently, the hypothesized analytical model is compared with the experimental results, and the level of agreement between them is thoroughly discussed. Ultimately, our results showcase the vast potential of employing multiple sensors, as we observe improvements spanning from the signal level to the navigation states. This tutorial is suitable for both newcomers and people experienced with multiple inertial sensors.
Image-Based Abnormal Data Detection and Cleaning Algorithm via Wind Power Curve
Authors: Huan Long, Linwei Sang, Zaijun Wu, Wei Gu
Date: 2023-07-17 00:00:00

This paper proposes an image-based algorithm for detecting and cleaning the wind turbine abnormal data based on wind power curve (WPC) images. The abnormal data are categorized into three types, negative points, scattered points, and stacked points. The proposed algorithm includes three steps, data pre-cleaning, normal data extraction, and data marking. The negative abnormal points, whose wind speed is greater than cut-in speed and power is below zero, are first filtered in the data pre-cleaning step. The scatter figure of the rest wind power data forms the WPC image and corresponding binary image. In the normal data extraction step, the principle part of the WPC binary image, representing the normal data, is extracted by the mathematical morphology operation (MMO). The optimal parameter setting of MMO is determined by minimizing the dissimilarity between the extracted principle part and the reference WPC image based on Hu moments. In the data mark step, the pixel points of scattered and stacked abnormal data are successively identified. The mapping relationship between the wind power points and image pixel points is built to mark the wind turbine normal and abnormal data. The proposed image-based algorithm is compared with k-means, local outlier factor, combined algorithm based on change point grouping algorithm and quartile algorithm (CA). Numerous experiments based on 33 wind turbines from two wind farms are conducted to validate the effectiveness, efficiency, and universality of the proposed method.
Steering Control of an Autonomous Unicycle
Authors: Máté Benjámin Vizi, Gábor Orosz, Dénes Takács, Gábor Stépán
Date: 2023-07-17 00:00:00
Tasks: Steering Control

The steering control of an autonomous unicycle is considered. The underlying dynamical model of a single rolling wheel is discussed regarding the steady state motions and their stability. The unicycle model is introduced as the simplest possible extension of the rolling wheel where the location of the center of gravity is controlled. With the help of the Appellian approach, a state space representation of the controlled nonholonomic system is built in a way that the most compact nonlinear equations of motions are constructed. Based on controllability analysis, feedback controllers are designed which successfully carry out lane changing and turning maneuvers. The behavior of the closed-loop system is demonstrated by numerical simulations.
A Two-Layers Predictive Algorithm for Workplace EV Charging
Authors: Saif Ahmad, Jochem Baltussen, Pauline Kergus, Zohra Kader, Stéphane Caux
Date: 2023-07-17 00:00:00
Tasks: Scheduling

In this paper, the problem of electric vehicle (EV) charging at the workplace is addressed via a two-layer predictive algorithm. We consider a time of use (TOU) pricing model for energy drawn from the grid and try to minimize the charging cost incurred by the EV charging station (EVCS) operator via an economic layer based on dynamic programming (DP) approach. An adaptive prediction algorithm based on a non-parametric stochastic model computes the projected EV load demand over the day which helps in the selection of optimal loading policy for the EVs in the economic layer. The second layer is a scheduling algorithm designed to share the allocated power limit (obtained from economic layer) among the charging EVs during each charge cycle. The modeling and validation is performed using ACN data-set from Caltech. Comparison of the proposed scheme with a conventional DP algorithm illustrates its effectiveness in terms of supplying the requested energy despite lacking user input for departure time.
Enhancing Next-Generation Urban Connectivity: Is the Integrated HAPS-Terrestrial Network a Solution?
Authors: Afsoon Alidadi Shamsabadi, Animesh Yadav, Halim Yanikomeroglu
Date: 2023-07-17 00:00:00
Tasks: Fairness

Located in the stratospheric layer of Earth's atmosphere, high altitude platform station (HAPS) is a promising network infrastructure, which can bring significant advantages to sixth-generation (6G) and beyond wireless communications systems by forming vertical heterogeneous networks (vHetNets). However, if not dealt with properly, integrated networks suffer from several performance challenges compared to standalone networks. In harmonized spectrum integrated networks, where different tiers share the same frequency spectrum, interference is an important challenge to be addressed. This work focuses on an integrated HAPS-terrestrial network, serving users in an overlapped urban geographic area, and formulates a fairness optimization problem, aiming to maximize the minimum spectral efficiency (SE) of the network. Due to the highly nonconvex nature of the formulated problem, we develop a rapid converging iterative algorithm that designs the multiple-input multiple-output (MIMO) beamforming weights and the user association scheme such that the propagated inter- and intra-tier interference is managed. Simulation results demonstrate the proposed algorithm's superiority over standalone terrestrial networks and scenario where only the beamforming weights are optimized.
What influences occupants' behavior in residential buildings: An experimental study on window operation in the KTH Live-In Lab
Authors: Mahsa Farjadnia, Angela Fontan, Alessio Russo, Karl Henrik Johansson, Marco Molinari
Date: 2023-07-16 00:00:00

Window-opening and window-closing behaviors play an important role in indoor environmental conditions and therefore have an impact on building energy efficiency. On the other hand, the same environmental conditions drive occupants to interact with windows. Understanding this mutual relationship of interaction between occupants and the residential building is thus crucial to improve energy efficiency without disregarding occupants' comfort. This paper investigates the influence of physical environmental variables (i.e., indoor and outside climate parameters) and categorical variables (i.e., time of the day) on occupants' behavior patterns related to window operation, utilizing a multivariate logistic regression analysis. The data considered in this study are collected during winter months, when the effect on the energy consumption of the window operation is the highest, at a Swedish residential building, the KTH Live-In Lab, accommodating four occupants in separate studio apartments. Although all the occupants seem to share a sensitivity to some common factors, such as air quality and time of the day, we can also observe individual variability with respect to the most significant drivers influencing window operation behaviors.
A Multi-model and Multi-scenario Assessment of the Impact of Climate Change on the Heating and Cooling Load Components of an Archetypical Residential Room in Major Indian Cities
Authors: Raj S. Srivastava, A. Aravinda De Chinnu, Aakash C. Rai
Date: 2023-07-16 00:00:00

Residential heating and cooling currently account for approximately 7% of electricity consumption of India. A warming climate will increase residential cooling requirements, while heating needs will decrease which is an alarming consequence for India, which has predominantly cooling requirements. Thus, to reduce the energy and carbon footprint of Indian building sector, it is essential to assess the impact of climate change on future heating and cooling needs and develop energy-efficiency solutions. This research evaluated the effect of climate change on the heating and cooling energy needs of an archetypical residential room in India, covering all climate zones. We developed a novel approach to quantify the heating and cooling load components (walls, windows, etc.) for identifying building elements to be targeted for improving energy efficiency. Our median climate model predicted that the cooling energy demand of the room archetype would increase by 23-155% by the 2090s compared to the 1990s, depending on the city and emission scenario. Walls and windows account for over 60% of the cooling needs and should be the prime targets for energy-efficiency measures. We also predict between 45-100% reductions in the heating requirements of the archetype room by the 2090s. Walls contribute over 67% to heating needs and should be targeted for heating energy reductions.
Modeling Physical Activity Impact on Glucose Dynamics in People with Type 1 Diabetes for a Fully Automated Artificial Pancreas
Authors: Mehrad Jaloli, Marzia Cescon
Date: 2023-07-16 00:00:00

In this paper, models of the blood glucose (BG) dynamics in people with Type 1 diabetes (T1D) in response to moderate intensity aerobic activity are derived from physiology-based first principles and system identification experiments. We show that by enhancing insulin-dependent glucose utilization by the tissues in two phases, a rapid short-term increase in insulin-independent glucose clearance and augmented glucose uptake, and a long-term sustained increase sensitivity to insulin action, a metabolic model able to reproduce the effects of activity on glucose disposal is obtained. Second, a control-oriented transfer function model is proposed to predict the BG response to an exercise bout modeled as a step change in heart rate (HR). Results comparing model predictions with actual patients data collected in a series of experimental sessions including physical activity (PA) are presented. The findings will contribute to the design of a fully automated closed-loop for improved glucose control in conditions of daily life for people with T1D.