pwc-archive/papers-with-abstracts huggingface dataset:
Optimization of side lobe level of linear antenna array using nature optimized ants bridging solutions(NOABS)
Authors: Sunit Shantanu Digamber Fulari
Date: 2022-10-16 00:00:00

Nature inspired algorithms has brought solutions to complex problems in optimization where the optimization and solution of complex problems is highly complex and nonlinear. There is a need to use proper design of the cost function or the fitness function in terms of the parameters to be optimized, this can be used in solving any type of such problems. In this paper the nature inspired algorithms has played important role in the optimal design of antenna array with improved radiation characteristics. In this paper, 20 elements linearly spaced array is used as an example of nature inspired optimization in antenna array system. This bridge inspired army ant algorithm(NOABS) is used to reduce the side lobes and to improve the other radiation characteristics to show the effect of the optimization on design characteristics by implementation of NOABS nature inspired algorithm. The entire simulation is carried out on 20 elements linear antenna array.
An Analysis of Fusion Functions for Hybrid Retrieval
Authors: Sebastian Bruch, Siyu Gai, Amir Ingber
Date: 2022-10-21 00:00:00
Tasks: Retrieval, Text Retrieval

We study hybrid search in text retrieval where lexical and semantic search are fused together with the intuition that the two are complementary in how they model relevance. In particular, we examine fusion by a convex combination (CC) of lexical and semantic scores, as well as the Reciprocal Rank Fusion (RRF) method, and identify their advantages and potential pitfalls. Contrary to existing studies, we find RRF to be sensitive to its parameters; that the learning of a CC fusion is generally agnostic to the choice of score normalization; that CC outperforms RRF in in-domain and out-of-domain settings; and finally, that CC is sample efficient, requiring only a small set of training examples to tune its only parameter to a target domain.
Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing
Authors: Peter Müllner, Stefan Schmerda, Dieter Theiler, Stefanie Lindstaedt, Dominik Kowald
Date: 2022-10-21 00:00:00
Tasks: Recommendation Systems

Data and algorithm sharing is an imperative part of data and AI-driven economies. The efficient sharing of data and algorithms relies on the active interplay between users, data providers, and algorithm providers. Although recommender systems are known to effectively interconnect users and items in e-commerce settings, there is a lack of research on the applicability of recommender systems for data and algorithm sharing. To fill this gap, we identify six recommendation scenarios for supporting data and algorithm sharing, where four of these scenarios substantially differ from the traditional recommendation scenarios in e-commerce applications. We evaluate these recommendation scenarios using a novel dataset based on interaction data of the OpenML data and algorithm sharing platform, which we also provide for the scientific community. Specifically, we investigate three types of recommendation approaches, namely popularity-, collaboration-, and content-based recommendations. We find that collaboration-based recommendations provide the most accurate recommendations in all scenarios. Plus, the recommendation accuracy strongly depends on the specific scenario, e.g., algorithm recommendations for users are a more difficult problem than algorithm recommendations for datasets. Finally, the content-based approach generates the least popularity-biased recommendations that cover the most datasets and algorithms.
Multi-view Semantic Matching of Question retrieval using Fine-grained Semantic Representations
Authors: Li Chong, Denghao Ma, Yueguo Chen
Date: 2022-10-21 00:00:00
Tasks: Keyword Extraction, Question Answering, Retrieval, Semantic Similarity, Semantic Textual Similarity

As a key task of question answering, question retrieval has attracted much attention from the communities of academia and industry. Previous solutions mainly focus on the translation model, topic model, and deep learning techniques. Distinct from the previous solutions, we propose to construct fine-grained semantic representations of a question by a learned importance score assigned to each keyword, so that we can achieve a fine-grained question matching solution with these semantic representations of different lengths. Accordingly, we propose a multi-view semantic matching model by reusing the important keywords in multiple semantic representations. As a key of constructing fine-grained semantic representations, we are the first to use a cross-task weakly supervised extraction model that applies question-question labelled signals to supervise the keyword extraction process (i.e. to learn the keyword importance). The extraction model integrates the deep semantic representation and lexical matching information with statistical features to estimate the importance of keywords. We conduct extensive experiments on three public datasets and the experimental results show that our proposed model significantly outperforms the state-of-the-art solutions.
Design Representation as Semantic Networks
Authors: Serhad Sarica, Ji Han, Jianxi Luo
Date: 2022-10-20 00:00:00

Design representation is a common task in the design process to facilitate learning, analysis, redesign, communication, and other design activities. Traditional representation techniques rely on human expertise and manual construction and are difficult to repeat and scale. Here, we propose a methodology that utilizes a pre-trained large-scale cross-domain design knowledge base to automatically generate design representation as a semantic network, i.e., a network of the entities and relations, based on design descriptions in texts or natural languages. Our methodology requires no ad hoc statistics. Based on a participatory study, we reveal the effectiveness and differences of the semantic network representations that are automatically generated with alternative knowledge bases. The findings illuminate future research directions to enhance design representation as semantic networks.
A new Deep Learning Based Intrusion Detection System for Cloud Security
Authors: Selman Hizal, Unal Cavusoglu, Devrim Akgun
Date: 2021-06-13 00:00:00
Proceeding: 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) 2021 6
Tasks: Cloud Computing, Deep Learning, Intrusion Detection

Cloud computing is used in many different research areas thanks to its high computing power and network capacity. Data security, cost-effectiveness, and flexibility of working options for remote workers have made this technology even more attractive today. Today, servers in cloud computing should protect themselves from threats more intelligently and provide security by preventing a new threat. A new deep learning model based on convolutional neural networks and recurrent neural networks for intrusion detection has been developed for cloud security in this study. The proposed model was trained and tested using NSL-KDD train dataset. With our deep learning model, any detected and not approved traffic is prevented from reaching the server in the cloud. The proposed system has 99.86% accuracy for five-class classification, which is the best result comparative to studies in the literature.
Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks
Authors: Joash Lee, Yanyu Cheng, Dusit Niyato, Yong Liang Guan, David González G.
Date: 2022-10-17 00:00:00
Proceeding: IEEE Transactions on Vehicular Technology 2022 10
Tasks: Autonomous Driving, Autonomous Vehicles, Deep Reinforcement Learning, Distributional Reinforcement Learning, General Reinforcement Learning, Graph Neural Network, Joint Radar-Communication, Sensor Fusion

Autonomous vehicles produce high data rates of sensory information from sensing systems. To achieve the advantages of sensor fusion among different vehicles in a cooperative driving scenario, high data-rate communication becomes essential. Current strategies for joint radar-communication (JRC) often rely on specialized hardware, prior knowledge of the system model, and entail diminished capability in either radar or communication functions. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge of the system model and a tunable performance balance, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help an intelligent vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a generalized form of the Markov Decision Process (MDP). We identify deep reinforcement learning algorithms (DRL) and algorithmic extensions suitable for solving our JRC problem. For multi-agent scenarios, we introduce a Graph Neural Network (GNN) framework via a control channel. This framework enables modular and fair comparisons of various algorithmic extensions. Our experiments show that DRL results in superior performance compared to non-learning algorithms. Learning of inter-agent coordination in the GNN framework, based only on the Markov task reward, further improves performance.
K-means for unsupervised instance segmentation using a self-supervised transformer
Authors: Lim SeongTaek, Park JaeEon, Lee MinYoung, Lee HongChul
Date: 2022-10-04 00:00:00
Proceeding: Pattern Recognition 2022 10
Tasks: Instance Segmentation, Object Detection, Object Discovery, Segmentation, Semantic Segmentation, Single-object discovery, Unsupervised Instance Segmentation

Instance segmentation is a fundamental task in computer vision that assigns every pixel to an
appropriate class and localizes objects into bounding boxes. However, collecting pixel-level segmentation labels is more resource- and time-consuming than collecting classification and detection
labels. Herein, we present a novel approach, iterative mask refinement using a self-supervised
transformer (IMST), which performs class agnostic unsupervised instance segmentation using simple K-means clustering and a self-supervised vision transformer. IMST generates pseudo-ground-truth labels that can be used to train an off-the-shelf instance segmentation model. The pseudo labels
demonstrate improved performance on multiple datasets. The instance segmentation model trained
on the pseudo labels outperforms state-of-the-art unsupervised instance segmentation methods on
COCO20k (+4.0 average precision (AP)) and COCO val2017(+2.6 AP) without modifications to
the training loss or architecture. We demonstrate that our method can be extended to tasks such as
single/multiple object discovery and supervised fine-tuning instance segmentation while outperforming previous methods.
Improved Normalizing Flow-Based Speech Enhancement using an All-pole Gammatone Filterbank for Conditional Input Representation
Authors: Martin Strauss, Matteo Torcoli, Bernd Edler
Date: 2022-10-21 00:00:00
Tasks: All, Speech Enhancement

Deep generative models for Speech Enhancement (SE) received increasing attention in recent years. The most prominent example are Generative Adversarial Networks (GANs), while normalizing flows (NF) received less attention despite their potential. Building on previous work, architectural modifications are proposed, along with an investigation of different conditional input representations. Despite being a common choice in related works, Mel-spectrograms demonstrate to be inadequate for the given scenario. Alternatively, a novel All-Pole Gammatone filterbank (APG) with high temporal resolution is proposed. Although computational evaluation metric results would suggest that state-of-the-art GAN-based methods perform best, a perceptual evaluation via a listening test indicates that the presented NF approach (based on time domain and APG) performs best, especially at lower SNRs. On average, APG outputs are rated as having good quality, which is unmatched by the other methods, including GAN.
Anchored Speech Recognition with Neural Transducers
Authors: Desh Raj, Junteng Jia, Jay Mahadeokar, Chunyang Wu, Niko Moritz, Xiaohui Zhang, Ozlem Kalinli
Date: 2022-10-20 00:00:00
Tasks: speech-recognition, Speech Recognition

Neural transducers have achieved human level performance on standard speech recognition benchmarks. However, their performance significantly degrades in the presence of cross-talk, especially when the primary speaker has a low signal-to-noise ratio. Anchored speech recognition refers to a class of methods that use information from an anchor segment (e.g., wake-words) to recognize device-directed speech while ignoring interfering background speech. In this paper, we investigate anchored speech recognition to make neural transducers robust to background speech. We extract context information from the anchor segment with a tiny auxiliary network, and use encoder biasing and joiner gating to guide the transducer towards the target speech. Moreover, to improve the robustness of context embedding extraction, we propose auxiliary training objectives to disentangle lexical content from speaking style. We evaluate our methods on synthetic LibriSpeech-based mixtures comprising several SNR and overlap conditions; they improve relative word error rates by 19.6% over a strong baseline, when averaged over all conditions.
Discriminatory and orthogonal feature learning for noise robust keyword spotting
Authors: Donghyeon Kim, Kyungdeuk Ko, David K. Han, Hanseok Ko
Date: 2022-10-20 00:00:00
Tasks: Keyword Spotting, Triplet

Keyword Spotting (KWS) is an essential component in a smart device for alerting the system when a user prompts it with a command. As these devices are typically constrained by computational and energy resources, the KWS model should be designed with a small footprint. In our previous work, we developed lightweight dynamic filters which extract a robust feature map within a noisy environment. The learning variables of the dynamic filter are jointly optimized with KWS weights by using Cross-Entropy (CE) loss. CE loss alone, however, is not sufficient for high performance when the SNR is low. In order to train the network for more robust performance in noisy environments, we introduce the LOw Variant Orthogonal (LOVO) loss. The LOVO loss is composed of a triplet loss applied on the output of the dynamic filter, a spectral norm-based orthogonal loss, and an inner class distance loss applied in the KWS model. These losses are particularly useful in encouraging the network to extract discriminatory features in unseen noise environments.
Exponentially Stable Adaptive Control Under Semi-PE Condition
Authors: Anton Glushchenko, Konstantin Lastochkin
Date: 2022-10-21 00:00:00

A novel method of exponentially stable adaptive control to compensate for matched parametric uncertainty under a mild condition of semi-persistent excitation (s-PE) of a regressor with piecewise-constant rank and nullspace is proposed. It is based on the generalized dynamic regressor extension and mixing procedure developed earlier by the authors, does not require high adaptive gain or data stacks and ensures: 1) exponential convergence of the tracking error to zero and the parameter one to a bounded set when the regressor is s-PE, 2) adjustable parameters transients of first-order type (each scalar parameter is adjusted using a separate first-order scalar differential equation), 3) alertness to change of the uncertainty parameters values, and 4) boundedness of all signals when the regressor is not s-PE. The main salient feature of the proposed approach is that the exponential stability is guaranteed when the controller parameters estimates converge to the values that are indistinguishable from the true ones. The results of numerical experiments fully support the theoretical analysis and demonstrate the advantages of the proposed method.
Sum Capacity Maximization in Multi-Hop Mobile Networks with Flying Base Stations
Authors: Mohammadsaleh Nikooroo, Omid Esrafilian, Zdenek Becvar, David Gesbert
Date: 2022-10-21 00:00:00
Tasks: Management

Deployment of multi-hop network of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) presents a remarkable potential to effectively enhance the performance of wireless networks. Such potential enhancement, however, relies on an efficient positioning of the FlyBSs as well as a management of resources. In this paper, we study the problem of sum capacity maximization in an extended model for mobile networks where multiple FlyBSs are deployed between the ground base station and the users. Due to an inclusion of multiple hops, the existing solutions for two-hop networks cannot be applied due to the incurred backhaul constraints for each hop. To this end, we propose an analytical approach based on an alternating optimization of the FlyBSs' 3D positions as well as the association of the users to the FlyBSs over time. The proposed optimization is provided under practical constraints on the FlyBS's flying speed and altitude as well as the constraints on the achievable capacity at the backhaul link. The proposed solution is of a low complexity and extends the sum capacity by 23%-38% comparing to state-of-the-art solutions.
QoS-Aware Sum Capacity Maximization for Mobile Internet of Things Devices Served by UAVs
Authors: Mohammadsaleh Nikooroo, Zdenek Becvar, Omid Esrafilian, David Gesbert
Date: 2022-10-21 00:00:00

The use of unmanned aerial vehicles (UAVs) acting as flying base stations (FlyBSs) is considered as an effective tool to improve performance of the mobile networks. Nevertheless, such potential improvement requires an efficient positioning of the FlyBS. In this paper, we maximize the sum downlink capacity of the mobile Internet of Things devices (IoTD) served by the FlyBSs while a minimum required capacity to every device is guaranteed. To this end, we propose a geometrical approach allowing to derive the 3D positions of the FlyBS over time as the IoTDs move and we determine the transmission power allocation for the IoTDs. The problem is formulated and solved under practical constraints on the FlyBS's transmission and propulsion power consumption as well as on flying speed. The proposed solution is of a low complexity and increases the sum capacity by 15%-46% comparing to state-of-the-art works.
Change Point Detection Approach for Online Control of Unknown Time Varying Dynamical Systems
Authors: Deepan Muthirayan, Ruijie Du, Yanning Shen, Pramod P. Khargonekar
Date: 2022-10-21 00:00:00
Tasks: Change Point Detection

We propose a novel change point detection approach for online learning control with full information feedback (state, disturbance, and cost feedback) for unknown time-varying dynamical systems. We show that our algorithm can achieve a sub-linear regret with respect to the class of Disturbance Action Control (DAC) policies, which are a widely studied class of policies for online control of dynamical systems, for any sub-linear number of changes and very general class of systems: (i) matched disturbance system with general convex cost functions, (ii) general system with linear cost functions. Specifically, a (dynamic) regret of $\Gamma_T^{1/5}T^{4/5}$ can be achieved for these class of systems, where $\Gamma_T$ is the number of changes of the underlying system and $T$ is the duration of the control episode. That is, the change point detection approach achieves a sub-linear regret for any sub-linear number of changes, which other previous algorithms such as in \cite{minasyan2021online} cannot. Numerically, we demonstrate that the change point detection approach is superior to a standard restart approach \cite{minasyan2021online} and to standard online learning approaches for time-invariant dynamical systems. Our work presents the first regret guarantee for unknown time-varying dynamical systems in terms of a stronger notion of variability like the number of changes in the underlying system. The extension of our work to state and output feedback controllers is a subject of future work.
Nonlinear Attitude Estimation Using Intermittent and Multi-Rate Vector Measurements
Authors: Miaomiao Wang, Abdelhamid Tayebi
Date: 2022-10-20 00:00:00

This paper considers the problem of nonlinear attitude estimation for a rigid body system using intermittent and multi-rate inertial vector measurements as well as continuous (high-rate) angular velocity measurements. Two types of hybrid attitude observers on Lie group $SO(3)$ are proposed. First, we propose a hybrid attitude observer where almost global asymptotic stability is guaranteed using the notion of almost global input-to-state stability on manifolds. Thereafter, this hybrid attitude observer is extended by introducing a switching mechanism to achieve global asymptotic stability. Both simulation and experimental results are presented to illustrate the performance of the proposed hybrid observers.
Influence of density-dependent diffusion on pattern formation in a refuge
Authors: G. G. Piva, C. Anteneodo
Date: 2022-10-20 00:00:00

We investigate a nonlocal generalization of the Fisher-KPP equation, which incorporates logistic growth and diffusion, for a single species population in a viable patch (refuge). In this framework, diffusion plays an homogenizing role, while nonlocal interactions can destabilize the spatially uniform state, leading to the emergence of spontaneous patterns. Notably, even when the uniform state is stable, spatial perturbations, such as the presence of a refuge, can still induce patterns. These phenomena are well known for environments with constant diffusivity. Our goal is to investigate how the formation of winkles in the population distribution is affected when the diffusivity is density-dependent. Then, we explore scenarios in which diffusivity is sensitive to either rarefaction or overcrowding. We find that state-dependent diffusivity affects the shape and stability of the patterns, potentially leading to either explosive growth or fragmentation of the population distribution, depending on how diffusion reacts to changes in density.
Multi-scale data reconstruction of turbulent rotating flows with Gappy POD, Extended POD and Generative Adversarial Networks
Authors: Tianyi Li, Michele Buzzicotti, Luca Biferale, Fabio Bonaccorso, Shiyi Chen, Minping Wan
Date: 2022-10-21 00:00:00
Tasks: Benchmarking, Generative Adversarial Network

Data reconstruction of rotating turbulent snapshots is investigated utilizing data-driven tools. This problem is crucial for numerous geophysical applications and fundamental aspects, given the concurrent effects of direct and inverse energy cascades, which lead to non-Gaussian statistics at both large and small scales. Data assimilation also serves as a tool to rank physical features within turbulence, by evaluating the performance of reconstruction in terms of the quality and quantity of the information used. Additionally, benchmarking various reconstruction techniques is essential to assess the trade-off between quantitative supremacy, implementation complexity, and explicability. In this study, we use linear and non-linear tools based on the Proper Orthogonal Decomposition (POD) and Generative Adversarial Network (GAN) for reconstructing rotating turbulence snapshots with spatial damages (inpainting). We focus on accurately reproducing both statistical properties and instantaneous velocity fields. Different gap sizes and gap geometries are investigated in order to assess the importance of coherency and multi-scale properties of the missing information. Surprisingly enough, concerning point-wise reconstruction, the non-linear GAN does not outperform one of the linear POD techniques. On the other hand, supremacy of the GAN approach is shown when the statistical multi-scale properties are compared. Similarly, extreme events in the gap region are better predicted when using GAN. The balance between point-wise error and statistical properties is controlled by the adversarial ratio, which determines the relative importance of the generator and the discriminator in the GAN training. Robustness against the measurement noise is also discussed.