pwc-archive/papers-with-abstracts huggingface dataset:
CCGL: Contrastive Cascade Graph Learning
Authors: Xovee Xu, Fan Zhou, Kunpeng Zhang, Siyuan Liu
Date: 2021-07-27 00:00:00
Tasks: Data Augmentation, Graph Learning, Graph Representation Learning, Information Cascade Popularity Prediction, Representation Learning, Self-Supervised Learning

Supervised learning, while prevalent for information cascade modeling, often requires abundant labeled data in training, and the trained model is not easy to generalize across tasks and datasets. It often learns task-specific representations, which can easily result in overfitting for downstream tasks. Recently, self-supervised learning is designed to alleviate these two fundamental issues in linguistic and visual tasks. However, its direct applicability for information cascade modeling, especially graph cascade related tasks, remains underexplored. In this work, we present Contrastive Cascade Graph Learning (CCGL), a novel framework for information cascade graph learning in a contrastive, self-supervised, and task-agnostic way. In particular, CCGL first designs an effective data augmentation strategy to capture variation and uncertainty by simulating the information diffusion in graphs. Second, it learns a generic model for graph cascade tasks via self-supervised contrastive pre-training using both unlabeled and labeled data. Third, CCGL learns a task-specific cascade model via fine-tuning using labeled data. Finally, to make the model transferable across datasets and cascade applications, CCGL further enhances the model via distillation using a teacher-student architecture. We demonstrate that CCGL significantly outperforms its supervised and semi-supervised counterparts for several downstream tasks.
Towards Industrial Private AI: A two-tier framework for data and model security
Authors: Sunder Ali Khowaja, Kapal Dev, Nawab Muhammad Faseeh Qureshi, Parus Khuwaja, Luca Foschini
Date: 2021-07-27 00:00:00
Tasks: Federated Learning

With the advances in 5G and IoT devices, the industries are vastly adopting artificial intelligence (AI) techniques for improving classification and prediction-based services. However, the use of AI also raises concerns regarding privacy and security that can be misused or leaked. Private AI was recently coined to address the data security issue by combining AI with encryption techniques, but existing studies have shown that model inversion attacks can be used to reverse engineer the images from model parameters. In this regard, we propose a Federated Learning and Encryption-based Private (FLEP) AI framework that provides two-tier security for data and model parameters in an IIoT environment. We proposed a three-layer encryption method for data security and provide a hypothetical method to secure the model parameters. Experimental results show that the proposed method achieves better encryption quality at the expense of slightly increased execution time. We also highlight several open issues and challenges regarding the FLEP AI framework's realization.
HPTMT: Operator-Based Architecture for Scalable High-Performance Data-Intensive Frameworks
Authors: Supun Kamburugamuve, Chathura Widanage, Niranda Perera, Vibhatha Abeykoon, Ahmet Uyar, Thejaka Amila Kanewala, Gregor von Laszewski, Geoffrey Fox
Date: 2021-07-27 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Data-intensive applications impact many domains, and their steadily increasing size and complexity demands high-performance, highly usable environments. We integrate a set of ideas developed in various data science and data engineering frameworks. They employ a set of operators on specific data abstractions that include vectors, matrices, tensors, graphs, and tables. Our key concepts are inspired from systems like MPI, HPF (High-Performance Fortran), NumPy, Pandas, Spark, Modin, PyTorch, TensorFlow, RAPIDS(NVIDIA), and OneAPI (Intel). Further, it is crucial to support different languages in everyday use in the Big Data arena, including Python, R, C++, and Java. We note the importance of Apache Arrow and Parquet for enabling language agnostic high performance and interoperability. In this paper, we propose High-Performance Tensors, Matrices and Tables (HPTMT), an operator-based architecture for data-intensive applications, and identify the fundamental principles needed for performance and usability success. We illustrate these principles by a discussion of examples using our software environments, Cylon and Twister2 that embody HPTMT.
PDF-Malware: An Overview on Threats, Detection and Evasion Attacks
Authors: Nicolas Fleury, Theo Dubrunquez, Ihsen Alouani
Date: 2021-07-27 00:00:00
Tasks: Malware Detection

In the recent years, Portable Document Format, commonly known as PDF, has become a democratized standard for document exchange and dissemination. This trend has been due to its characteristics such as its flexibility and portability across platforms. The widespread use of PDF has installed a false impression of inherent safety among benign users. However, the characteristics of PDF motivated hackers to exploit various types of vulnerabilities, overcome security safeguards, thereby making the PDF format one of the most efficient malicious code attack vectors. Therefore, efficiently detecting malicious PDF files is crucial for information security. Several analysis techniques has been proposed in the literature, be it static or dynamic, to extract the main features that allow the discrimination of malware files from benign ones. Since classical analysis techniques may be limited in case of zero-days, machine-learning based techniques have emerged recently as an automatic PDF-malware detection method that is able to generalize from a set of training samples. These techniques are themselves facing the challenge of evasion attacks where a malicious PDF is transformed to look benign. In this work, we give an overview on the PDF-malware detection problem. We give a perspective on the new challenges and emerging solutions.
Evaluating Extractive Summarization Techniques on News Articles
Authors: Sreeya Reddy Kotrakona Harinatha, Beauty Tatenda Tasara, Nunung Nurul Qomariyah
Date: 2021-07-22 00:00:00
Proceeding: 2021 International Seminar on Intelligent Technology and Its Applications (ISITIA) 2021 7
Tasks: Articles, Extractive Summarization, Text Summarization

In recent years, due to the rise of deep learning and natural language processing, text summarization has become a huge topic among scholars. Text summarization derives a shorter coherent version of a longer document. There are two methods of summarization namely, abstractive and extractive. This paper focuses on extractive summarization using TextRank and BERT. These algorithms have been tested under various circumstances to determine the best and they all perform better on certain parameters. The goal of this paper is to determine which algorithm performs better as compared to human generated extractive summaries on news dataset. The same dataset was used for both these algorithms and the summaries were evaluated using ROUGE Score. The result showed that TextRank yielded a better ROUGE score as compared to BERT. TextRank showed higher F-measure and recall while BERT had higher precision.
Evaluating Extractive Summarization Techniques on News Articles
Authors: Sreeya Reddy Kotrakona Harinatha, Beauty Tatenda Tasara, Nunung Nurul Qomariyah
Date: 2021-07-22 00:00:00
Tasks: Articles, Extractive Summarization, Text Summarization

In recent years, due to the rise of deep learning and natural language processing, text summarization has become a huge topic among scholars. Text summarization derives a shorter coherent version of a longer document. There are two methods of summarization namely, abstractive and extractive. This paper focuses on extractive summarization using TextRank and BERT. These algorithms have been tested under various circumstances to determine the best and they all perform better on certain parameters. The goal of this paper is to determine which algorithm performs better as compared to human generated extractive summaries on news dataset. The same dataset was used for both these algorithms and the summaries were evaluated using ROUGE Score. The result showed that TextRank yielded a better ROUGE score as compared to BERT. TextRank showed higher F-measure and recall while BERT had higher precision.
Analytic Ray Splitting for Controlled Precision DVR
Authors: Sebastian Weiss, Rüdiger Westermann
Date: 2021-06-16 00:00:00
Proceeding: EuroVIS Short Papers 2021 6

For direct volume rendering of post-classified data, we propose an algorithm that analytically splits a ray through a cubical cell at the control points of a piecewise-polynomial transfer function. This splitting generates segments over which the variation of the optical properties is described by piecewise cubic functions. This allows using numerical quadrature rules with controlled precision to obtain an approximation with prescribed error bounds. The proposed splitting scheme can be used to find all piecewise linear or monotonic segments along a ray, and it can thus be used to improve the accuracy of direct volume rendering, scale-invariant volume rendering, and multi-isosurface rendering.
Color filter array demosaicking using densely connected residual network.
Authors: Bumjun Park, Jechang Jeong
Date: 2019-09-05 00:00:00
Proceeding: IEEE Access 2019 9
Tasks: Demosaicking

Deep convolutional neural networks have been used extensively in recent image processing research, exhibiting drastically improved performance. In this study, we apply convolutional neural networks to color filter array demosaicking, which plays an essential role in single-sensor digital cameras. Contrary to conventional convolutional neural network-based demosaicking models, the proposed model does not require any initial interpolation step for mosaicked input images, which increases the computational complexity. Using a mosaicked image as input, the proposed model is trained in an end-to-end manner to generate demosaicked images outputs. Many deep neural networks experience vanishing-gradient problem, which makes models hard to be trained. To solve this problem, we apply residual learning and densely connected convolutional neural network. Moreover, we apply block-wise convolutional neural networks to consider local features. Finally, we apply a sub-pixel interpolation layer to generate demosaicked output images more efficiently and accurately. Experimental results show that our proposed model outperforms conventional solutions and state-of-the-art models.
Reradiation and Scattering from a Reconfigurable Intelligent Surface: A General Macroscopic Model
Authors: V. Degli-Esposti, E. M. Vitucci, M. Di Renzo, S. Tretyakov
Date: 2021-07-27 00:00:00
Tasks: Benchmarking

Reconfigurable Intelligent Surfaces (RISs) have attracted major attention in the last few years, thanks to their useful characteristics. An RIS is a nearly passive thin surface that can dynamically change the reradiated field, and can therefore realize anomalous reflection, refraction, focalization, or other wave transformations for engineering the radio propagation environment or realizing novel surface-type antennas. Evaluating the performance and optimizing the deployment of RISs in wireless networks need physically consistent frameworks that account for the electromagnetic characteristics of dynamic metasurfaces. In this paper, we introduce a general macroscopic model for evaluating the scattering from an RIS. The proposed method decomposes the wave reradiated from an RIS into multiple scattering contributions and is aimed at being embedded into ray-based models. Since state-of-the-art ray-based models can already efficiently simulate specular wave reflection, diffraction, and diffuse scattering, but not anomalous reradiation, we enhance them with an approach based on Huygens' principle and propose two possible implementations for it. Multiple reradiation modes can be modeled through the proposed approach, using the power conservation principle. We validate the accuracy of the proposed model by benchmarking it against several case studies available in the literature, which are based on analytical models, full-wave simulations, and measurements.
End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection
Authors: Hemlata Tak, Jee-weon Jung, Jose Patino, Madhu Kamble, Massimiliano Todisco, Nicholas Evans
Date: 2021-07-27 00:00:00
Tasks: Audio Deepfake Detection, DeepFake Detection, Face Swapping, Graph Attention, Speaker Verification

Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments. Various approaches can be used to capture and model such artefacts, however, none works well across a spectrum of diverse spoofing attacks. Reliable detection then often depends upon the fusion of multiple detection systems, each tuned to detect different forms of attack. In this paper we show that better performance can be achieved when the fusion is performed within the model itself and when the representation is learned automatically from raw waveform inputs. The principal contribution is a spectro-temporal graph attention network (GAT) which learns the relationship between cues spanning different sub-bands and temporal intervals. Using a model-level graph fusion of spectral (S) and temporal (T) sub-graphs and a graph pooling strategy to improve discrimination, the proposed RawGAT-ST model achieves an equal error rate of 1.06 % for the ASVspoof 2019 logical access database. This is one of the best results reported to date and is reproducible using an open source implementation.
An Optimal Piezoelectric Beam for Acoustic Energy Harvesting
Authors: Amir Panahi, Alireza Hassanzadeh, Ali Moulavi, Ata Golparvar
Date: 2021-07-27 00:00:00

This study presents a novel piezoelectric beam structure for acoustic energy harvesting. The beams have been designed to maximize output energy in areas where the noise level is loud such as highway traffic. The beam consists of two layers of copper and polyvinylidene fluoride that convert the ambient noise's vibration energy to electrical energy. The piezoelectric material's optimum placement has been studied, and its best position is obtained on the substrate for the maximum yield. Unlike previous studies, in which the entire beam substrate used to be covered by a material, this study presents a modest material usage and contributes to lowering the harvester's final production cost. Additionally, in this study, an electrical model was developed for the sensor and a read-out circuitry was proposed for the converter. Moreover, the sensor was validated at different noise levels at various lengths and locations. The simulations were performed in COMSOL Multiphysics and MATLAB and report a maximum sound pressure of 140 dB from 100 dB point sources in an enclosed air-filled cubic meter chamber.
Microphone Array Generalization for Multichannel Narrowband Deep Speech Enhancement
Authors: Siyuan Zhang, Xiaofei Li
Date: 2021-07-27 00:00:00
Tasks: Speech Enhancement

This paper addresses the problem of microphone array generalization for deep-learning-based end-to-end multichannel speech enhancement. We aim to train a unique deep neural network (DNN) potentially performing well on unseen microphone arrays. The microphone array geometry shapes the network's parameters when training on a fixed microphone array, and thus restricts the generalization of the trained network to another microphone array. To resolve this problem, a single network is trained using data recorded by various microphone arrays of different geometries. We design three variants of our recently proposed narrowband network to cope with the agnostic number of microphones. Overall, the goal is to make the network learn the universal information for speech enhancement that is available for any array geometry, rather than learn the one-array-dedicated characteristics. The experiments on both simulated and real room impulse responses (RIR) demonstrate the excellent across-array generalization capability of the proposed networks, in the sense that their performance measures are very close to, or even exceed the network trained with test arrays. Moreover, they notably outperform various beamforming methods and other advanced deep-learning-based methods.
A Greedy Data Collection Scheme For Linear Dynamical Systems
Authors: Karim Cherifi, Pawan Goyal, Peter Benner
Date: 2021-07-27 00:00:00

Mathematical models are essential to analyze and understand the dynamics of complex systems. Recently, data-driven methodologies have got a lot of attention which is leveraged by advancements in sensor technology. However, the quality of obtained data plays a vital role in learning a good and reliable model. Therefore, in this paper, we propose an efficient heuristic methodology to collect data both in the frequency domain and time-domain, aiming at the best possible information gain from limited experimental data. The efficiency of the proposed methodology is illustrated by means of several examples, and also, its robustness in the presence of noisy data is shown.
Optimal Frequency Regulation using Packetized Energy Management
Authors: Sarnaduti Brahma, Adil Khurram, Hamid Ossareh, Mads Almassalkhi
Date: 2021-07-27 00:00:00
Tasks: energy management, Management, Model Predictive Control

Packetized energy management (PEM) is a demand dispatch scheme that can be used to provide ancillary services such as frequency regulation. In PEM, distributed energy resources (DERs) are granted uninterruptible access to the grid for a pre-specified time interval called the packet length. This results in a down ramp-limited response in PEM for DERs that can only consume power from the grid. In this work, a linearized virtual battery model of PEM is provided that is capable of predicting the down-ramp limited output of PEM and is used in a model predictive control (MPC) framework to improve the performance of PEM in tracking an automatic generation control (AGC) signal. By performing statistical analysis on the AGC regulation signal, PJM Reg-D, an ARMA model is derived as a predictor for the MPC-based precompensator. Finally, as an alternative to MPC, it is shown that by varying the packet length as a function of time, for example through packet randomization, frequency regulation can be improved under PEM.
Finite-Time Gradient Descent-Based Adaptive Neural Network Finite-Time Control Design for Attitude Tracking of a 3-DOF Helicopter
Authors: Xidong Wang
Date: 2021-07-27 00:00:00

This paper investigates a novel finite-time gradient descent-based adaptive neural network finite-time control strategy for the attitude tracking of a 3-DOF lab helicopter platform subject to composite disturbances. First, the radial basis function neural network (RBFNN) is applied to estimate lumped disturbances, where the weights, centers and widths of the RBFNN are trained online via finite-time gradient descent algorithm. Then, a finite-time backstepping control scheme is constructed to fulfill the tracking control of the elevation and pitch angles. In addition, a hybrid finite-time differentiator (HFTD) is introduced for approximating the intermediate control signal and its derivative to avoid the problem of "explosion of complexity" in the traditional backstepping design protocol. Moreover, the errors caused by the HFTD can be attenuated by the combination of compensation signals. With the aid of the stability theorem, it is proved that the closed-loop system is semi-globally uniformly ultimately boundedness in finite time. Finally, a comparison result is provided to illustrate the effectiveness and advantages of the designed control strategy.
Higher-order sliding mode observer design for linear time-invariant multivariable systems based on a new observer normal form
Authors: Helmut Niederwieser, Markus Tranninger, Richard Seeber, Markus Reichhartinger
Date: 2021-07-27 00:00:00
Tasks: Form

In various applications in the field of control engineering the estimation of the state variables of dynamic systems in the presence of unknown inputs plays an important role. Existing methods require the so-called observer matching condition to be satisfied, rely on the boundedness of the state variables or exhibit an increased observer order of twice the plant order. In this article, a novel observer normal form for strongly observable linear time-invariant multivariable systems is proposed. In contrast to classical normal forms, the proposed approach also takes the unknown inputs into account. The proposed observer normal form allows for the straightforward construction of a higher-order sliding mode observer, which ensures global convergence of the estimation error within finite time even in the presence of unknown bounded inputs. Its application is not restricted to systems which satisfy the aforementioned limitations of already existing unknown input observers. The proposed approach can be exploited for the reconstruction of unknown inputs with bounded derivative and robust state-feedback control, which is shown by means of a tutorial example. Numerical simulations confirm the effectiveness of the presented work.
Conflict-Free Four-Dimensional Path Planning for Urban Air Mobility Considering Airspace Occupations
Authors: Wei Dai, Bizhao Pang, Kin Huat Low
Date: 2021-07-27 00:00:00
Tasks: Management

Urban air mobility (UAM) has attracted the attention of aircraft manufacturers, air navigation service providers and governments in recent years. Preventing the conflict among urban aircraft is crucial to UAM traffic safety, which is a key in enabling large scale UAM operation. Pre-flight conflict-free path planning can provide a strategic layer in the maintenance of safety performance, thus becomes an important element in UAM. This paper aims at tackling conflict-free path planning problem for UAM operation with a consideration of four-dimensional airspace management. In the first place, we introduced and extended a four-dimensional airspace management concept, AirMatrix. On the basis of AirMatrix, we formulated the shortest flight time path planning problem considering resolution of conflicts with both static and dynamic obstacles. A Conflict-Free A-Star algorithm was developed for planning four-dimensional paths based on first-come-first-served scheme. The algorithm contains a novel design of heuristic function as well as a conflict detection and resolution strategy. Numerical experiment was carried out in Jurong East area in Singapore, and the results show that the algorithm can generate paths resolving a significant number of potential conflicts in airspace utilization, with acceptable computational time and flight delay. The contributions of this study provide references for stakeholders to support the development of UAM.
Toward simple "in silico" experiments for drugs administration in some cancer treatments
Authors: Michel Fliess, Cédric Join, Kaouther Moussa, Seddik M. Djouadi, Mohamed W. Alsager
Date: 2021-07-27 00:00:00

We present some "in silico" experiments to design combined chemo- and immunotherapy treatment schedules. We introduce a new framework by combining flatness-based control, which is a model-based setting, along with model-free control. The flatness property of the used mathematical model yields straightforward reference trajectories. They provide us with the nominal open-loop control inputs. Closing the loop via model-free control allows to deal with the uncertainties on the injected drug doses. Several numerical simulations illustrating different case studies are displayed. We show in particular that the considered health indicators are driven to the safe region, even for critical initial conditions. Furthermore, in some specific cases there is no need to inject chemotherapeutic agents.