pwc-archive/papers-with-abstracts huggingface dataset:
Probing the Robustness of Pre-trained Language Models for Entity Matching
Authors: Mehdi Akbarian Rastaghi, Ehsan Kamalloo, Davood Rafiei
Date: 2022-10-01 00:00:00
Proceeding: ACM International Conference on Information & Knowledge Management (CIKM) 2022 10
Tasks: Data Augmentation, Domain Generalization, Entity Resolution

The paradigm of fine-tuning Pre-trained Language Models (PLMs) has been successful in Entity Matching (EM). Despite their remarkable performance, PLMs exhibit tendency to learn spurious correlations from training data. In this work, we aim at investigating whether PLM-based entity matching models can be trusted in real-world applications where data distribution is different from that of training. To this end, we design an evaluation benchmark to assess the robustness of EM models to facilitate their deployment in the real-world settings. Our assessments reveal that data imbalance in the training data is a key problem for robustness. We also find that data augmentation alone is not sufficient to make a model robust. As a remedy, we prescribe simple modifications that can improve the robustness of PLM-based EM models. Our experiments show that while yielding superior results for in-domain generalization, our proposed model significantly improves the model robustness, compared to state-of-the-art EM models.
Local intraspecific aggregation in phytoplankton model communities: spatial scales of occurrence and implications for coexistence
Authors: Coralie Picoche, William R. Young, Frédéric Barraquand
Date: 2022-10-20 00:00:00
Tasks: Diversity

The coexistence of multiple phytoplankton species despite their reliance on similar resources is often explained with mean-field models assuming mixed populations. In reality, observations of phytoplankton indicate spatial aggregation at all scales, including at the scale of a few individuals. Local spatial aggregation can hinder competitive exclusion since individuals then interact mostly with other individuals of their own species, rather than competitors from different species. To evaluate how microscale spatial aggregation might explain phytoplankton diversity maintenance, an individual-based, multispecies representation of cells in a hydrodynamic environment is required. We formulate a three-dimensional and multispecies individual-based model of phytoplankton population dynamics at the Kolmogorov scale. The model is studied through both simulations and the derivation of spatial moment equations, in connection with point process theory. The spatial moment equations show a good match between theory and simulations. We parameterized the model based on phytoplankters' ecological and physical characteristics, for both large and small phytoplankton. Defining a zone of potential interactions as the overlap between nutrient depletion volumes, we show that local species composition -- within the range of possible interactions -- depends on the size class of phytoplankton. In small phytoplankton, individuals remain in mostly monospecific clusters. Spatial structure therefore favours intra- over inter-specific interactions for small phytoplankton, contributing to coexistence. Large phytoplankton cell neighbourhoods appear more mixed. Although some small-scale self-organizing spatial structure remains and could influence coexistence mechanisms, other factors may need to be explored to explain diversity maintenance in large phytoplankton.
Europarl: A Parallel Corpus for Statistical Machine Translation
Authors: Philipp Koehn
Date: 2005-09-13 00:00:00
Proceeding: MTSummit 2005 9
Tasks: Machine Translation, Translation

We collected a corpus of parallel text in 11 languages from the proceedings of the European Parliament, which are published on the web. This corpus has found widespread use in the NLP community. Here, we focus on its acquisition and its application as training data for statistical machine translation (SMT). We trained SMT systems for 110 language pairs, which reveal interesting clues into the challenges ahead.
Statistical Phrase-Based Translation
Authors: Philipp Koehn, Franz J. Och, Daniel Marcu
Date: 2003-05-27 00:00:00
Proceeding: HLT-NAACL 2003 5
Tasks: Machine Translation, Translation

We propose a new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previously proposed phrase-based translation models. Within our framework, we carry out a large number of experiments to understand better and explain why phrase-based models outperform word-based models. Our empirical results, which hold for all examined language pairs, suggest that the highest levels of performance can be obtained through relatively simple means: heuristic learning of phrase translations from word-based alignments and lexical weighting of phrase translations. Surprisingly, learning phrases longer than three words and learning phrases from high-accuracy word-level alignment models does not have a strong
impact on performance. Learning only syntactically motivated phrases degrades the performance of our systems.
Application of Explainable Machine Learning in Detecting and Classifying Ransomware Families Based on API Call Analysis
Authors: Rawshan Ara Mowri, Madhuri Siddula, Kaushik Roy
Date: 2022-10-16 00:00:00
Tasks: Feature Engineering, Interpretable Machine Learning, regression

Ransomware has appeared as one of the major global threats in recent days. The alarming increasing rate of ransomware attacks and new ransomware variants intrigue the researchers to constantly examine the distinguishing traits of ransomware and refine their detection strategies. Application Programming Interface (API) is a way for one program to collaborate with another; API calls are the medium by which they communicate. Ransomware uses this strategy to interact with the OS and makes a significantly higher number of calls in different sequences to ask for taking action. This research work utilizes the frequencies of different API calls to detect and classify ransomware families. First, a Web-Crawler is developed to automate collecting the Windows Portable Executable (PE) files of 15 different ransomware families. By extracting different frequencies of 68 API calls, we develop our dataset in the first phase of the two-phase feature engineering process. After selecting the most significant features in the second phase of the feature engineering process, we deploy six Supervised Machine Learning models: Na"ive Bayes, Logistic Regression, Random Forest, Stochastic Gradient Descent, K-Nearest Neighbor, and Support Vector Machine. Then, the performances of all the classifiers are compared to select the best model. The results reveal that Logistic Regression can efficiently classify ransomware into their corresponding families securing 99.15% overall accuracy. Finally, instead of relying on the 'Black box' characteristic of the Machine Learning models, we present the post-hoc analysis of our best-performing model using 'SHapley Additive exPlanations' or SHAP values to ascertain the transparency and trustworthiness of the model's prediction.
The State-of-the-Art in AI-Based Malware Detection Techniques: A Review
Authors: Adam Wolsey
Date: 2022-10-12 00:00:00
Tasks: Malware Detection

Artificial Intelligence techniques have evolved rapidly in recent years, revolutionising the approaches used to fight against cybercriminals. But as the cyber security field has progressed, so has malware development, making it an economic imperative to strengthen businesses' defensive capability against malware attacks. This review aims to outline the state-of-the-art AI techniques used in malware detection and prevention, providing an in-depth analysis of the latest studies in this field. The algorithms investigated consist of Shallow Learning, Deep Learning and Bio-Inspired Computing, applied to a variety of platforms, such as PC, cloud, Android and IoT. This survey also touches on the rapid adoption of AI by cybercriminals as a means to create ever more advanced malware and exploit the AI algorithms designed to defend against them.
Detecting Backdoors in Deep Text Classifiers
Authors: You Guo, Jun Wang, Trevor Cohn
Date: 2022-10-11 00:00:00
Tasks: Data Poisoning, text-classification, Text Classification

Deep neural networks are vulnerable to adversarial attacks, such as backdoor attacks in which a malicious adversary compromises a model during training such that specific behaviour can be triggered at test time by attaching a specific word or phrase to an input. This paper considers the problem of diagnosing whether a model has been compromised and if so, identifying the backdoor trigger. We present the first robust defence mechanism that generalizes to several backdoor attacks against text classification models, without prior knowledge of the attack type, nor does our method require access to any (potentially compromised) training resources. Our experiments show that our technique is highly accurate at defending against state-of-the-art backdoor attacks, including data poisoning and weight poisoning, across a range of text classification tasks and model architectures. Our code will be made publicly available upon acceptance.
Minimizing low-rank models of high-order tensors: Hardness, span, tight relaxation, and applications
Authors: Nicholas D. Sidiropoulos, Paris Karakasis, Aritra Konar
Date: 2022-10-16 00:00:00
Tasks: Graph Mining, Recommendation Systems, Retrieval

We consider the problem of finding the smallest or largest entry of a tensor of order N that is specified via its rank decomposition. Stated in a different way, we are given N sets of R-dimensional vectors and we wish to select one vector from each set such that the sum of the Hadamard product of the selected vectors is minimized or maximized. We show that this fundamental tensor problem is NP-hard for any tensor rank higher than one, and polynomial-time solvable in the rank-one case. We also propose a continuous relaxation and prove that it is tight for any rank. For low-enough ranks, the proposed continuous reformulation is amenable to low-complexity gradient-based optimization, and we propose a suite of gradient-based optimization algorithms drawing from projected gradient descent, Frank-Wolfe, or explicit parametrization of the relaxed constraints. We also show that our core results remain valid no matter what kind of polyadic tensor model is used to represent the tensor of interest, including Tucker, HOSVD/MLSVD, tensor train, or tensor ring. Next, we consider the class of problems that can be posed as special instances of the problem of interest. We show that this class includes the partition problem (and thus all NP-complete problems via polynomial-time transformation), integer least squares, integer linear programming, integer quadratic programming, sign retrieval (a special kind of mixed integer programming / restricted version of phase retrieval), and maximum likelihood decoding of parity check codes. We demonstrate promising experimental results on a number of hard problems, including state-of-art performance in decoding low density parity check codes and general parity check codes.
Mean field teams and games with correlated types
Authors: Deepanshu Vasal
Date: 2022-10-20 00:00:00

Mean field games have traditionally been defined~[1,2] as a model of large scale interaction of players where each player has a private type that is independent across the players. In this paper, we introduce a new model of mean field teams and games with \emph{correlated types} where there are a large population of homogeneous players sequentially making strategic decisions and each player is affected by other players through an aggregate population state. Each player has a private type that only she observes and types of any $N$ players are correlated through a kernel $Q$. All players commonly observe a correlated mean-field population state which represents the empirical distribution of any $N$ players' correlated joint types. We define the Mean-Field Team optimal Strategies (MFTO) as strategies of the players that maximize total expected joint reward of the players. We also define Mean-Field Equilibrium (MFE) in such games as solution of coupled Bellman dynamic programming backward equation and Fokker Planck forward equation of the correlated mean field state, where a player's strategy in an MFE depends on both, her private type and current correlated mean field population state. We present sufficient conditions for the existence of such an equilibria. We also present a backward recursive methodology equivalent of master's equation to compute all MFTO and MFEs of the team and game respectively. Each step in this methodology consists of solving an optimization problem for the team problem and a fixed-point equation for the game. We provide sufficient conditions that guarantee existence of this fixed-point equation for the game for each time $t$.
Multi-Head Cross-Attentional PPG and Motion Signal Fusion for Heart Rate Estimation
Authors: Panagiotis Kasnesis, Lazaros Toumanidis, Alessio Burrello, Christos Chatzigeorgiou, Charalampos Z. Patrikakis
Date: 2022-10-14 00:00:00
Tasks: Heart rate estimation, Photoplethysmography (PPG)

Nowadays, Hearth Rate (HR) monitoring is a key feature of almost all wrist-worn devices exploiting photoplethysmography (PPG) sensors. However, arm movements affect the performance of PPG-based HR tracking. This issue is usually addressed by fusing the PPG signal with data produced by inertial measurement units. Thus, deep learning algorithms have been proposed, but they are considered too complex to deploy on wearable devices and lack the explainability of results. In this work, we present a new deep learning model, PULSE, which exploits temporal convolutions and multi-head cross-attention to improve sensor fusion's effectiveness and achieve a step towards explainability. We evaluate the performance of PULSE on three publicly available datasets, reducing the mean absolute error by 7.56% on the most extensive available dataset, PPG-DaLiA. Finally, we demonstrate the explainability of PULSE and the benefits of applying attention modules to PPG and motion data.
Model-matching Principle Applied to the Design of an Array-based All-neural Binaural Rendering System for Audio Telepresence
Authors: Yicheng Hsu, Chenghumg Ma, Mingsian R. Bai
Date: 2022-10-20 00:00:00
Tasks: All

Telepresence aims to create an immersive but virtual experience of the audio and visual scene at the far end for users at the near end. In this contribution, we propose an array-based binaural rendering system that converts the array microphone signals into the head-related transfer function (HRTF) filtered output signals for headphone-rendering. The proposed approach is formulated in light of a model-matching principle (MMP) and is capable of delivering more immersive experience than the conventional localization-beamforming-HRTF filtering (LBH) approach. The MMP-based rendering system can be realized via multichannel inverse filtering (MIF) and multichannel deep filtering (MDF). In this study, we adopted the MDF approach and used the LBH as well as MIF as the baselines. The all-neural system jointly captures the spatial information (spatial rendering), preserves ambient sound (enhancement), and reduces noise (enhancement) before generating binaural outputs. Objective and subjective tests are employed to compare the proposed telepresence system with two baselines.
Speech Dereverberation with a Reverberation Time Shortening Target
Authors: Rui Zhou, Wenye Zhu, Xiaofei Li
Date: 2022-10-20 00:00:00
Tasks: Denoising, Speech Denoising, Speech Dereverberation

This work proposes a new learning target based on reverberation time shortening (RTS) for speech dereverberation. The learning target for dereverberation is usually set as the direct-path speech or optionally with some early reflections. This type of target suddenly truncates the reverberation, and thus it may not be suitable for network training. The proposed RTS target suppresses reverberation and meanwhile maintains the exponential decaying property of reverberation, which will ease the network training, and thus reduce signal distortion caused by the prediction error. Moreover, this work experimentally study to adapt our previously proposed FullSubNet speech denoising network to speech dereverberation. Experiments show that RTS is a more suitable learning target than direct-path speech and early reflections, in terms of better suppressing reverberation and signal distortion. FullSubNet is able to achieve outstanding dereverberation performance.
Optimizing Surveillance Satellites for the Synthetic Theater Operations Research Model
Authors: Steven M. Warner, Johannes O. Royset
Date: 2022-10-20 00:00:00

The Synthetic Theater Operations Research Model (STORM) simulates theater-level conflict and requires inputs about utilization of surveillance satellites to search large geographical areas. We develop a mixed-integer linear optimization model that prescribes plans for how satellites and their sensors should be directed to best search an area of operations. It also specifies the resolution levels employed by the sensors to ensure a suitable fidelity of the resulting images. We solve large-scale instances of the model involving up to 22 million variables and 11 million constraints in scenarios derived from STORM. On average, the model yields 55% improvement in search coverage relative to an existing heuristic algorithm in STORM.
Robust prescribed-time coordination control of cooperative-antagonistic networks with disturbances
Authors: Zhen-Hua Zhu, Huaiyu Wu, Zhi-Hong Guan, Zhi-Wei Liu, Yang Chen, Xiujuan Zheng
Date: 2022-10-20 00:00:00

This article targets at addressing the robust prescribed-time coordination control (PTCC) problems for single-integrator cooperative-antagonistic networks (CANs) with external disturbances under arbitrary fixed signed digraphs without any structural constraints. Toward this end, the PTCC problems for nominal single-integrator CANs without disturbances are first investigated and a fully distributed control protocol with a time-varying gain, which grows to infinity as the time approaches the settling time, is proposed utilizing the relative states of neighboring agents. Then, based on the proposed control protocol for the nominal single-integrator CANs, a new second-order prescribed-time sliding mode control protocol is constructed to achieve accurate PTCC for single-integrator CANs in the presence of external disturbances. Using Lyapunov based analysis, sufficient conditions to guarantee the prescribed-time stability, bipartite consensus, interval bipartite consensus, and bipartite containment of single-integrator CANs without or with disturbances are, respectively, derived. In the end, numerical simulations are given to confirm the derived results.
Competitive Equilibrium for Dynamic Multi-Agent Systems: Social Shaping and Price Trajectories
Authors: Zeinab Salehi, Yijun Chen, Elizabeth L. Ratnam, Ian R. Petersen, Guodong Shi
Date: 2022-10-20 00:00:00

In this paper, we consider dynamic multi-agent systems (MAS) for decentralized resource allocation. The MAS operates at a competitive equilibrium to ensure supply and demand are balanced. First, we investigate the MAS over a finite horizon. The utility functions of agents are parameterized to incorporate individual preferences. We shape individual preferences through a set of utility functions to guarantee the resource price at a competitive equilibrium remains socially acceptable, i.e., the price is upper-bounded by an affordability threshold. We show this problem is solvable at the conceptual level. Next, we consider quadratic MAS and formulate the associated social shaping problem as a multi-agent linear quadratic regulator (LQR) problem which enables us to propose explicit utility sets using quadratic programming and dynamic programming. Then, a numerical algorithm is presented for calculating a tight range of the preference function parameters which guarantees a socially accepted price. We investigate the properties of a competitive equilibrium over an infinite horizon. Considering general utility functions, we show that under feasibility assumptions, any competitive equilibrium maximizes the social welfare. Then, we prove that for sufficiently small initial conditions, the social welfare maximization solution constitutes a competitive equilibrium with zero price. We also prove for general feasible initial conditions, there exists a time instant after which the optimal price, corresponding to a competitive equilibrium, becomes zero. Finally, we specifically focus on quadratic MAS and propose explicit results.
Reference Governor for Input-Constrained MPC to Enforce State Constraints at Lower Computational Cost
Authors: Miguel Castroviejo Fernandez, Jordan Leung, Ilya Kolmanovsky
Date: 2022-10-20 00:00:00

In this paper, a control scheme is developed based on an input constrained Model Predictive Controller (MPC) and the idea of modifying the reference command to enforce constraints, usual of Reference Governors (RG). The proposed scheme, referred to as the RGMPC, requires optimization for MPC with input constraints for which fast algorithms exist, and can handle (possibly nonlinear) state and input constraints. Conditions are given that ensure recursive feasibility of the RGMPC scheme and finite-time convergence of the modified command to the the desired reference command. Simulation results for a spacecraft rendezvous maneuver with linear and nonlinear constraints demonstrate that the RGMPC scheme has lower average computational time as compared to state and input constrained MPC with similar performance.
Control Design under Actuator Saturation and Multi-Rate Sampling
Authors: Francesco Ferrante, Ricardo G. Sanfelice, Sophie Tarbouriech
Date: 2022-10-19 00:00:00

The problem of designing a stabilizing feedback controller in the presence of saturating actuators and multi-rate (asynchronous) aperiodic state measurements is studied. Specifically, we consider a scenario in which measurements of the plant states are collected at the controller end in a sporadic and asynchronous fashion. A hybrid controller is used to perform a fusion of measurements sampled at different times. In between sampling events, the controller behaves as a copy of the plant and provides a feedback control signal based on the reconstruction of the plant state. The presence of saturation at the plant input limits the value of the components of this signal to a bounded range. When a new measurement is available, the controller state undergoes an instantaneous jump. The resulting system is augmented with a set of timers triggering the arrival of new measurements and analyzed in a hybrid systems framework. Relying on Lyapunov tools for hybrid systems and techniques for control design under saturation, we propose sufficient conditions in the form of matrix inequalities to ensure regional exponential stability of a closed-set containing the origin of the plant, i.e., exponential stability with a guaranteed region of attraction. Specifically, explicit estimates of the basin of attraction are provided in the form of ellipsoidal sets. Leveraging those conditions, a design procedure based on semidefinite programming is proposed to design a stabilizing controller with maximized size of the basin attraction. The effectiveness of the proposed methodology is shown in an example.