pwc-archive/papers-with-abstracts huggingface dataset:
Double Image Encryption System Using a Nonlinear Joint Transform Correlator in the Fourier Domain
Authors: Ronal A. Perez, Elisabet Pérez-Cabré, Juan M. Vilardy, María S. Millán, Cesar O. Torres
Date: 2023-02-02 00:00:00
Proceeding: Sensors 2023 2

Abstract: In this work, we present a new nonlinear joint transform correlator (JTC) architecture
in the Fourier domain (FD) for the encryption and decryption of two simultaneous images. The
main features of the proposed system are its increased level of security, the obtention of a single
real-valued encrypted signal that contains the ciphered information of the two primary images and,
additionally, a high image quality for the two final decrypted signals. The two images to be encrypted
can be either related to each other, or independent signals. The encryption system is based on the
double random phase encoding (DRPE), which is implemented by using a nonlinear JTC in the FD.
The input plane of the JTC has four non-overlapping data distributions placed side-by-side with
no blank spaces between them. The four data distributions are phase-only functions defined by
the two images to encrypt and four random phase masks (RPMs). The joint power spectrum (JPS)
is produced by the intensity of the Fourier transform (FT) of the input plane of the JTC. One of
the main novelties of the proposal consists of the determination of the appropriate two nonlinear
operations that modify the JPS distribution with a twofold purpose: to obtain a single real-valued
encrypted image with a high level of security and to improve the quality of the decrypted images.
The security keys of the encryption system are represented by the four RPMs, which are all necessary
for a satisfactory decryption. The decryption system is implemented using a 4f-processor where the
encrypted image and the security keys given by the four RPMs are introduced in the proper plane of
the processor. The double image encryption system based on a nonlinear JTC in the FD increases
the security of the system because there is a larger key space, and we can simultaneously validate
two independent information signals (original images to encrypt) in comparison to previous similar
proposals. The feasibility and performance of the proposed double image encryption and decryption
system based on a nonlinear JTC are validated through computational simulations. Finally, we
additionally comment on the proposed security system resistance against different attacks based on
brute force, plaintext and deep learning.
Power relations in Game Theory
Authors: Daniele De Luca
Date: 2023-07-26 00:00:00

The concept of power among players can be expressed as a combination of their utilities. A player who obeys another takes into account the utility of the dominant one. Technically it is a matter of superimposing some weighted sum or product function onto the individual utility function, where the weights can be represented through directed graphs that reflect a situation of power among the players. It is then possible to define some global indices of the system, such as the level of hierarchy, mutualism and freedom, and measure their effects on game equilibria.
The persistence of bipartite ecological communities with Lotka-Volterra dynamics
Authors: Matthew Dopson, Clive Emary
Date: 2023-07-26 00:00:00

The assembly and persistence of ecological communities can be understood as the result of the interaction and migration of species. Here we study a single community subject to migration from a species pool in which inter-specific interactions are organised according to a bipartite network. Considering the dynamics of species abundances to be governed by generalised Lotka-Volterra equations, we extend work on unipartite networks to we derive exact results for the phase diagram of this model. Focusing on antagonistic interactions, we describe factors that influence the persistence of the two guilds, locate transitions to multiple-attractor and unbounded phases, as well identify a region of parameter space in which consumers are essentially absent in the local community.
A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment Analysis
Authors: Zengzhi, Wang; Qiming, Xie; Rui, Xia
Date: 2023-07-18 00:00:00
Proceeding: SIGIR '23: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023 7
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Term Extraction and Sentiment Classification, Sentiment Analysis, Text Infilling, Transfer Learning

The pre-training and fine-tuning paradigm has become the mainstream framework in the field of Aspect-Based Sentiment Analysis (ABSA). Although it has achieved sound performance in the domains containing enough fine-grained aspect-sentiment annotations, it is still challenging to conduct few-shot ABSA in domains where manual annotations are scarce. In this work, we argue that two kinds of gaps, i.e., domain gap and objective gap, hinder the transfer of knowledge from pre-training language models (PLMs) to ABSA tasks. To address this issue, we introduce a simple yet effective framework called FS-ABSA, which involves domain-adaptive pre-training and text-infilling fine-tuning. We approach the End-toEnd ABSA task as a text-infilling problem and perform domain-adaptive pre-training with the text-infilling objective, narrowing the
two gaps and consequently facilitating the knowledge transfer. Experiments show that the resulting model achieves more compelling performance than baselines under the few-shot setting while driving the state-of-the-art performance to a new level across datasets under the fully-supervised setting. Moreover, we apply our framework to two non-English low-resource languages to demonstrate its generality and effectiveness.
Improving existing segmentators performance with zero-shot segmentators
Authors: Nanni, L., Fusaro, D., Fantozzi, C., Pretto, A.
Date: 2023-07-26 00:00:00
Proceeding: journal 2023 7
Tasks: Camouflaged Object Segmentation, Segmentation, Zero-shot Generalization

This paper explores the potential of using the SAM segmentator to enhance the segmentation capability of known methods. SAM is a promptable segmentation system that offers zero-shot generalization to unfamiliar objects and images, eliminating the need for additional training. The open-source nature of SAM on GitHub allows for easy access and implementation. In our experiments, we aim to improve the segmentation performance by providing SAM with checkpoints extracted from the masks produced by DeepLabv3+, then merging the segmentation masks provided by these two networks. Additionally, we examine the \enquote{oracle} method (as upper bound baseline performance), where segmentation masks are inferred only by SAM with checkpoints extracted from ground truth. In addition, we tested in the CAMO datasets an ensemble of PVTv2 transformers; combining the ensemble and SAM yields state-of-the-art performance in that dataset. The results of our study provide valuable insights into the potential of incorporating the SAM segmentator into existing segmentation techniques. We release with this paper the open-source implementation of our method.
CAMP: A Context-Aware Cricket Players Performance Metric
Authors: Muhammad Sohaib Ayub, Naimat Ullah, Sarwan Ali, Imdad Ullah Khan, Mian Muhammad Awais, Muhammad Asad Khan, Safiullah Faizullah
Date: 2023-07-14 00:00:00
Tasks: Decision Making

Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, CAMP, to quantify individual players' contributions toward a cricket match outcome. CAMP employs data mining methods and enables effective data-driven decision-making for selection and drafting, coaching and training, team line-ups, and strategy development. CAMP incorporates the exact context of performance, such as opponents' strengths and specific circumstances of games, such as pressure situations. We empirically evaluate CAMP on data of limited-over cricket matches between 2001 and 2019. In every match, a committee of experts declares one player as the best player, called Man of the M}atch (MoM). The top two rated players by CAMP match with MoM in 83\% of the 961 games. Thus, the CAMP rating of the best player closely matches that of the domain experts. By this measure, CAMP significantly outperforms the current best-known players' contribution measure based on the Duckworth-Lewis-Stern (DLS) method.
$\text{EFO}_{k}$-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
Authors: Hang Yin, ZiHao Wang, Weizhi Fei, Yangqiu Song
Date: 2023-07-15 00:00:00
Tasks: Complex Query Answering, Knowledge Graphs, Logical Reasoning

To answer complex queries on knowledge graphs, logical reasoning over incomplete knowledge is required due to the open-world assumption. Learning-based methods are essential because they are capable of generalizing over unobserved knowledge. Therefore, an appropriate dataset is fundamental to both obtaining and evaluating such methods under this paradigm. In this paper, we propose a comprehensive framework for data generation, model training, and method evaluation that covers the combinatorial space of Existential First-order Queries with multiple variables ($\text{EFO}_{k}$). The combinatorial query space in our framework significantly extends those defined by set operations in the existing literature. Additionally, we construct a dataset, $\text{EFO}_{k}$-CQA, with 741 types of query for empirical evaluation, and our benchmark results provide new insights into how query hardness affects the results. Furthermore, we demonstrate that the existing dataset construction process is systematically biased that hinders the appropriate development of query-answering methods, highlighting the importance of our work. Our code and data are provided in~\url{https://github.com/HKUST-KnowComp/EFOK-CQA}.
EFL Students' Attitudes and Contradictions in a Machine-in-the-loop Activity System
Authors: David James Woo, Hengky Susanto, Kai Guo
Date: 2023-07-13 00:00:00

This study applies Activity Theory and investigates the attitudes and contradictions of 67 English as a foreign language (EFL) students from four Hong Kong secondary schools towards machine-in-the-loop writing, where artificial intelligence (AI) suggests ideas during composition. Students answered an open-ended question about their feelings on writing with AI. Results revealed mostly positive attitudes, with some negative or mixed feelings. From a thematic analysis, contradictions or points of tension between students and AI stemmed from AI inadequacies, students' balancing enthusiasm with preference, and their striving for language autonomy. The research highlights the benefits and challenges of implementing machine-in-the-loop writing in EFL classrooms, suggesting educators align activity goals with students' values, language abilities, and AI capabilities to enhance students' activity systems.
Perceptual Quality Enhancement of Sound Field Synthesis Based on Combination of Pressure and Amplitude Matching
Authors: Keisuke Kimura, Shoichi Koyama, Hiroshi Saruwatari
Date: 2023-07-26 00:00:00

A sound field synthesis method enhancing perceptual quality is proposed. Sound field synthesis using multiple loudspeakers enables spatial audio reproduction with a broad listening area; however, synthesis errors at high frequencies called spatial aliasing artifacts are unavoidable. To minimize these artifacts, we propose a method based on the combination of pressure and amplitude matching. On the basis of the human's auditory properties, synthesizing the amplitude distribution will be sufficient for horizontal sound localization. Furthermore, a flat amplitude response should be synthesized as much as possible to avoid coloration. Therefore, we apply amplitude matching, which is a method to synthesize the desired amplitude distribution with arbitrary phase distribution, for high frequencies and conventional pressure matching for low frequencies. Experimental results of numerical simulations and listening tests using a practical system indicated that the perceptual quality of the sound field synthesized by the proposed method was improved from that synthesized by pressure matching.
Exploring the Interactions between Target Positive and Negative Information for Acoustic Echo Cancellation
Authors: Chang Han, Xinmeng Xu, Weiping tu, Yuhong Yang, Yajie Liu
Date: 2023-07-26 00:00:00
Tasks: Acoustic echo cancellation, Decoder

Acoustic echo cancellation (AEC) aims to remove interference signals while leaving near-end speech least distorted. As the indistinguishable patterns between near-end speech and interference signals, near-end speech can't be separated completely, causing speech distortion and interference signals residual. We observe that besides target positive information, e.g., ground-truth speech and features, the target negative information, such as interference signals and features, helps make pattern of target speech and interference signals more discriminative. Therefore, we present a novel AEC model encoder-decoder architecture with the guidance of negative information termed as CMNet. A collaboration module (CM) is designed to establish the correlation between the target positive and negative information in a learnable manner via three blocks: target positive, target negative, and interactive block. Experimental results demonstrate our CMNet achieves superior performance than recent methods.
Sliding Mode Control of Active Magnetic Bearings -- A Cascaded Architecture
Authors: Dimitrios Papageorgiou, Ilmar Santos
Date: 2023-07-26 00:00:00
Tasks: Position

Accurate and robust positioning of rotor axle is essential for efficient and safe operation of high-speed rotational machines with active magnetic bearings. This study presents a cascaded nonlinear control strategy for vertical axial positioning of an active magnetic bearing system. The proposed scheme employs two sliding mode controllers for regulating rotor vertical position and current and an adaptive estimator to invert the nonlinear input mapping. Uniform asymptotic stability is proven for the closed-loop system and the efficacy and performance of the proposed design is evaluated in simulation.
Evaluating the Impact of Numerology and Retransmission on 5G NR V2X Communication at A System-Level Simulation
Authors: Donglin Wang, Pranav Balasaheb Mohite, Qiuheng Zhou, Anjie Qiu, Hans D. Schotten
Date: 2023-07-26 00:00:00

In recent years, Vehicle-to-Everything (V2X) communication opens an ample amount of opportunities to increase the safety of drivers and passengers and improve traffic efficiency which enables direct communication between vehicles. The Third Generation Partnership Project (3GPP) has specified a 5G New Radio (NR) Sidelink (SL) PC5 interface for supporting Cellular V2X (C-V2X) communication in Release 16 in 2017. 5G NR V2X communication is expected to provide high reliability, extra-low latency, and a high data rate for vehicular networks. In this paper, the newly introduced features of 5G NR standards like flexible numerology, variable Subcarrier Spacing (SCS), and allocated Physical Resource Blocks (PRBs) have been inspected in 5G NR V2X communications. Moreover, the 5G NR V2X data packet will be distributed to all nearby User Equipment (UE) by the Transmitter (Tx). However, there may be instances where certain UEs fail to receive the data packets in a single transmission. Unfortunately, the SL Tx lacks a feedback channel to verify if the Receivers (Rxs) have received the information. To meet the stringent reliability and latency requirements of C-V2X communication, we suggest and assess a retransmission scheme along with a scheme that incorporates varying resource allocations for retransmission in NR V2X communication. The effect of retransmission schemes on NR V2X communication systems has been detected.
Distributed robust secondary frequency control of inverter-based microgrids under time-varying communication delays
Authors: Milad Gholami, Gianni Bianchini, Antonio Vicino
Date: 2023-07-26 00:00:00

This paper presents a robust secondary control strategy for frequency synchronization and active power sharing for inverter-based microgrids. The problem is addressed in a multi-agent fashion where the local controllers of the distributed generators play the role of agents, and communication is affected by time-varying delays. {The approach is fully distributed and based on a synergic combination of linear consensus and integral sliding-mode control. Lyapunovanalysis is presented to assess the stability properties of the closed loop.} Delay-dependent stability conditions are expressed as a set of linear matrix inequalities whose solution yields appropriate control gains such that frequency restoration is achieved despite delays and active power sharing constraints. Simulations confirm the effectiveness of the proposed control strategy.
On Kernel Design for Regularized Non-Causal System Identification
Authors: Xiaozhu Fang, Tianshi Chen
Date: 2023-07-26 00:00:00

Through one decade's development, the kernel-based regularization method (KRM) has become a complement to the classical maximum likelihood/prediction error method and an emerging new system identification paradigm. One recent example is its application in the non-causal system identification, and the key issue lies in the design and analysis of kernels for non-causal systems. In this paper, we develop systematic ways to deal with this issue. In particular, we first introduce the guidelines for kernel design and then extend the system theoretic framework to design the so-called non-causal simulation-induced (NCSI) kernel, and we also study its structural properties, including stability and semiseparability. Finally, we consider some special cases of the NCSI kernel and show their advantage over the existing kernels through numerical simulations.
Data-Driven Reduced-Order Aeroelastic Modeling of Highly Flexible Aircraft by Parametric Dynamic Mode Decomposition
Authors: Tianyi He, Weihua Su
Date: 2023-07-26 00:00:00
Tasks: Scheduling

This paper presents a method of data-driven parametric Dynamic Mode Decomposition (p-DMD) to derive a linear parameter-varying reduced-order model (LPV-ROM) for the nonlinear aeroelasticity of highly flexible aircraft. It directly uses the data snapshots obtained at varying flight conditions, and encodes the physical understanding of the nonlinear model's polynomial dependency on flight conditions to produce a polynomial-dependent LPV-ROM. Therefore, this method can handle not only the equilibrium flight conditions but also the cases of continuously-varying flight conditions. In the numerical studies, a highly flexible cantilever wing and a slender vehicle built based on it are first studied with fixed angles of attack as the scheduling parameter. The comparisons between traditional linearization-based parametric modeling and the data-driven p-DMD modeling are performed to verify the modeling accuracy. The results demonstrate that the current p-DMD modeling method can capture the aeroelastic and flight dynamic responses of highly flexible aircraft in both time and frequency domains. In addition, the proposed p-DMD method is applied to the highly flexible aircraft in a perturbed longitudinal flight with varying angles of attack as the scheduling parameter. The nonlinear aeroelastic and flight dynamic data are compared with the simulation results of the data-driven p-DMD model. The comparison results demonstrate that it can accurately capture the non-equilibrium (or transient) aeroelastic and flight dynamic behaviors of such slender vehicles.
The Robust Orlicz Risk with an Application to the Green Photovoltaic Power Generation
Authors: H. Yoshioka, M. Tsujimura
Date: 2023-07-26 00:00:00

We propose a novel recursive utility for controlling stochastic processes under risk and uncertainty. Our formulation uses a robustified Orlicz risk that can evaluate risk and uncertainty simultaneously. We focus on a control problem of a photovoltaic power generation system that supplies excess electricity to a secondary purpose for generating green hydrogen. The corresponding Hamilton-Jacobi-Bellman equation having a novel nonlinear term is then derived. Computational examples with the available data are finally presented, demonstrating that our methodology can be used for the photovoltaic power generation under different meteorological and operational conditions.
Investigating structural and functional aspects of the brain's criticality in stroke
Authors: Jakub Janarek, Zbigniew Drogosz, Jacek Grela, Jeremi K. Ochab, Paweł Oświęcimka
Date: 2023-07-26 00:00:00

This paper addresses the question of the brain's critical dynamics after an injury such as a stroke. It is hypothesized that the healthy brain operates near a phase transition (critical point), which provides optimal conditions for information transmission and responses to inputs. If structural damage could cause the critical point to disappear and thus make self-organized criticality unachievable, it would offer the theoretical explanation for the post-stroke impairment of brain function. In our contribution, however, we demonstrate using network models of the brain, that the dynamics remain critical even after a stroke. In cases where the average size of the second-largest cluster of active nodes, which is one of the commonly used indicators of criticality, shows an anomalous behavior, it results from the loss of integrity of the network, quantifiable within graph theory, and not from genuine non-critical dynamics. We propose a new simple model of an artificial stroke that explains this anomaly. The proposed interpretation of the results is confirmed by an analysis of real connectomes acquired from post-stroke patients and a control group. The results presented refer to neurobiological data; however, the conclusions reached apply to a broad class of complex systems that admit a critical state.
Causal Effects in Matching Mechanisms with Strategically Reported Preferences
Authors: Marinho Bertanha, Margaux Luflade, Ismael Mourifié
Date: 2023-07-26 00:00:00

A growing number of central authorities use assignment mechanisms to allocate students to schools in a way that reflects student preferences and school priorities. However, most real-world mechanisms incentivize students to strategically misreport their preferences. In this paper, we provide an approach for identifying the causal effects of school assignment on future outcomes that accounts for strategic misreporting. Misreporting may invalidate existing point-identification approaches, and we derive sharp bounds for causal effects that are robust to strategic behavior. Our approach applies to any mechanism as long as there exist placement scores and cutoffs that characterize that mechanism's allocation rule. We use data from a deferred acceptance mechanism that assigns students to more than 1,000 university-major combinations in Chile. Matching theory predicts that students' behavior in Chile should be strategic because they can list only up to eight options, and we find empirical evidence consistent with such behavior. Our bounds are informative enough to reveal significant heterogeneity in graduation success with respect to preferences and school assignment.