pwc-archive/papers-with-abstracts huggingface dataset:
Joint Power Optimization and AP Selection for Secure Cell-Free Massive MIMO
Authors: Yasseen Sadoon Atiya, Zahra Mobini, Hien Quoc Ngo, Michail Matthaiou
Date: 2024-01-12 00:00:00

In this paper, we investigate joint power control and access point (AP) selection scheme in a cell-free massive multiple-input multiple-output (CF-mMIMO) system under an active eavesdropping attack, where an eavesdropper tries to overhear the signal sent to one of the legitimate users by contaminating the uplink channel estimation. We formulate a joint optimization problem to minimize the eavesdropping spectral efficiency (SE) while guaranteeing a given SE requirement at legitimate users. The challenging formulated problem is converted into a more tractable form and an efficient low-complexity accelerated projected gradient (APG)-based approach is proposed to solve it. Our findings reveal that the proposed joint optimization approach significantly outperforms the heuristic approaches in terms of secrecy SE (SSE). For instance, the $50\%$ likely SSE performance of the proposed approach is $265\%$ higher than that of equal power allocation and random AP selection scheme.
A Dynamic Agent Based Model of the Real Economy with Monopolistic Competition, Perfect Product Differentiation, Heterogeneous Agents, Increasing Returns to Scale and Trade in Disequilibrium
Authors: Subhamon Supantha, Naresh Kumar Sharma
Date: 2024-01-13 00:00:00
Tasks: counterfactual

We have used agent-based modeling as our numerical method to artificially simulate a dynamic real economy where agents are rational maximizers of an objective function of Cobb-Douglas type. The economy is characterised by heterogeneous agents, acting out of local or imperfect information, monopolistic competition, perfect product differentiation, allowance for increasing returns to scale technology and trade in disequilibrium. An algorithm for economic activity in each period is devised and a general purpose open source agent-based model is developed which allows for counterfactual inquiries, testing out treatments, analysing causality of various economic processes, outcomes and studying emergent properties. 10,000 simulations, with 10 firms and 80 consumers are run with varying parameters and the results show that from only a few initial conditions the economy reaches equilibrium while in most of the other cases it remains in perpetual disequilibrium. It also shows that from a few initial conditions the economy reaches a disaster where all the consumer wealth falls to zero or only a single producer remains. Furthermore, from some initial conditions, an ideal economy with high wage rate, high consumer utility and no unemployment is also reached. It was also observed that starting from an equal endowment of wealth in consumers and in producers, inequality emerged in the economy. In majority of the cases most of the firms(6-7) shut down because they were not profitable enough and only a few firms remained. Our results highlight that all these varying outcomes are possible for a decentralized market economy with rational optimizing agents.
A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT
Authors: Francesco Pase, Marco Giordani, Sara Cavallero, Malte Schellmann, Josef Eichinger, Roberto Verdone, Michele Zorzi
Date: 2023-11-21 00:00:00
Tasks: Scheduling, Thompson Sampling

Industrial Internet of Things (IIoT) networks will provide Ultra-Reliable Low-Latency Communication (URLLC) to support critical processes underlying the production chains. However, standard protocols for allocating wireless resources may not optimize the latency-reliability trade-off, especially for uplink communication. For example, centralized grant-based scheduling can ensure almost zero collisions, but introduces delays in the way resources are requested by the User Equipments (UEs) and granted by the gNB. In turn, distributed scheduling (e.g., based on random access), in which UEs autonomously choose the resources for transmission, may lead to potentially many collisions especially when the traffic increases. In this work we propose DIStributed combinatorial NEural linear Thompson Sampling (DISNETS), a novel scheduling framework that combines the best of the two worlds. By leveraging a feedback signal from the gNB and reinforcement learning, the UEs are trained to autonomously optimize their uplink transmissions by selecting the available resources to minimize the number of collisions, without additional message exchange to/from the gNB. DISNETS is a distributed, multi-agent adaptation of the Neural Linear Thompson Sampling (NLTS) algorithm, which has been further extended to admit multiple parallel actions. We demonstrate the superior performance of DISNETS in addressing URLLC in IIoT scenarios compared to other baselines.
Efficient Parallel Data Optimization for Homogeneous Diffusion Inpainting of 4K Images
Authors: Niklas Kämper, Vassillen Chizhov, Joachim Weickert
Date: 2024-01-12 00:00:00
Tasks: 4k, GPU, Image Compression

Homogeneous diffusion inpainting can reconstruct missing image areas with high quality from a sparse subset of known pixels, provided that their location as well as their gray or color values are well optimized. This property is exploited in inpainting-based image compression, which is a promising alternative to classical transform-based codecs such as JPEG and JPEG2000. However, optimizing the inpainting data is a challenging task. Current approaches are either fairly slow or do not produce high quality results. As a remedy we propose fast spatial and tonal optimization algorithms for homogeneous diffusion inpainting that efficiently utilize GPU parallelism, with a careful adaptation of some of the most successful numerical concepts. We propose a densification strategy using ideas from error-map dithering combined with a Delaunay triangulation for the spatial optimization. For the tonal optimization we design a domain decomposition solver that solves the corresponding normal equations in a matrix-free fashion and supplement it with a Voronoi-based initialization strategy. With our proposed methods we are able to generate high quality inpainting masks for homogeneous diffusion and optimized tonal values in a runtime that outperforms prior state-of-the-art by a wide margin.
Efficient Parallel Algorithms for Inpainting-Based Representations of 4K Images -- Part I: Homogeneous Diffusion Inpainting
Authors: Niklas Kämper, Vassillen Chizhov, Joachim Weickert
Date: 2024-01-12 00:00:00
Tasks: 4k, GPU, Image Reconstruction

In recent years inpainting-based compression methods have been shown to be a viable alternative to classical codecs such as JPEG and JPEG2000. Unlike transform-based codecs, which store coefficients in the transform domain, inpainting-based approaches store a small subset of the original image pixels and reconstruct the image from those by using a suitable inpainting operator. A good candidate for such an inpainting operator is homogeneous diffusion inpainting, as it is simple, theoretically well-motivated, and can achieve good reconstruction quality for optimized data. However, a major challenge has been to design fast solvers for homogeneous diffusion inpainting that scale to 4K image resolution ($3840 \times 2160$ pixels) and are real-time capable. We overcome this with a careful adaptation and fusion of two of the most efficient concept from numerical analysis: multigrid and domain decomposition. Our domain decomposition algorithm efficiently utilizes GPU parallelism by solving inpainting problems on small overlapping blocks. Unlike simple block decomposition strategies such as the ones in JPEG, our approach yields block artifact-free reconstructions. Furthermore, embedding domain decomposition in a full multigrid scheme provides global interactions and allows us to achieve optimal convergence by reducing both low- and high-frequency errors at the same rate. We are able to achieve 4K color image reconstruction at more than $60$ frames per second even from very sparse data - something which has been previously unfeasible.
Machine Learning-based xApp for Dynamic Resource Allocation in O-RAN Networks
Authors: Mohammed M. H. Qazzaz, Łukasz Kułacz, Adrian Kliks, Syed A. Zaidi, Marcin Dryjanski, Des McLernon
Date: 2024-01-15 00:00:00

The disaggregated, distributed and virtualised implementation of radio access networks allows for dynamic resource allocation. These attributes can be realised by virtue of the Open Radio Access Networks (O-RAN) architecture. In this article, we tackle the issue of dynamic resource allocation using a data-driven approach by employing Machine Learning (ML). We present an xApp-based implementation for the proposed ML algorithm. The core aim of this work is to optimise resource allocation and fulfil Service Level Specifications (SLS). This is accomplished by dynamically adjusting the allocation of Physical Resource Blocks (PRBs) based on traffic demand and Quality of Service (QoS) requirements. The proposed ML model effectively selects the best allocation policy for each base station and enhances the performance of scheduler functionality in O-RAN - Distributed Unit (O-DU). We show that an xApp implementing the Random Forest Classifier can yield high (85\%) performance accuracy for optimal policy selection. This can be attained using the O-RAN instance state input parameters over a short training duration.
AI-enabled Priority and Auction-Based Spectrum Management for 6G
Authors: Mina Khadem, Farshad Zeinali, Nader Mokari, Hamid Saeedi
Date: 2024-01-12 00:00:00
Tasks: Deep Reinforcement Learning, Management

In this paper, we present a quality of service (QoS)-aware priority-based spectrum management scheme to guarantee the minimum required bit rate of vertical sector players (VSPs) in the 5G and beyond generation, including the 6th generation (6G). VSPs are considered as spectrum leasers to optimize the overall spectrum efficiency of the network from the perspective of the mobile network operator (MNO) as the spectrum licensee and auctioneer. We exploit a modified Vickrey-Clarke-Groves (VCG) auction mechanism to allocate the spectrum to them where the QoS and the truthfulness of bidders are considered as two important parameters for prioritization of VSPs. The simulation is done with the help of deep deterministic policy gradient (DDPG) as a deep reinforcement learning (DRL)-based algorithm. Simulation results demonstrate that deploying the DDPG algorithm results in significant advantages. In particular, the efficiency of the proposed spectrum management scheme is about %85 compared to the %35 efficiency in traditional auction methods.
Transformer Masked Autoencoders for Next-Generation Wireless Communications: Architecture and Opportunities
Authors: Abdullah Zayat, Mahmoud A. Hasabelnaby, Mohanad Obeed, Anas Chaaban
Date: 2024-01-11 00:00:00
Tasks: Data Compression

Next-generation communication networks are expected to exploit recent advances in data science and cutting-edge communications technologies to improve the utilization of the available communications resources. In this article, we introduce an emerging deep learning (DL) architecture, the transformer-masked autoencoder (TMAE), and discuss its potential in next-generation wireless networks. We discuss the limitations of current DL techniques in meeting the requirements of 5G and beyond 5G networks, and how the TMAE differs from the classical DL techniques can potentially address several wireless communication problems. We highlight various areas in next-generation mobile networks which can be addressed using a TMAE, including source and channel coding, estimation, and security. Furthermore, we demonstrate a case study showing how a TMAE can improve data compression performance and complexity compared to existing schemes. Finally, we discuss key challenges and open future research directions for deploying the TMAE in intelligent next-generation mobile networks.
ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesis
Authors: Haobin Tang, xulong Zhang, Ning Cheng, Jing Xiao, Jianzong Wang
Date: 2024-01-16 00:00:00
Tasks: Denoising, Emotional Speech Synthesis, Emotion Recognition, Speech Emotion Recognition, Speech Synthesis

Existing emotional speech synthesis methods often utilize an utterance-level style embedding extracted from reference audio, neglecting the inherent multi-scale property of speech prosody. We introduce ED-TTS, a multi-scale emotional speech synthesis model that leverages Speech Emotion Diarization (SED) and Speech Emotion Recognition (SER) to model emotions at different levels. Specifically, our proposed approach integrates the utterance-level emotion embedding extracted by SER with fine-grained frame-level emotion embedding obtained from SED. These embeddings are used to condition the reverse process of the denoising diffusion probabilistic model (DDPM). Additionally, we employ cross-domain SED to accurately predict soft labels, addressing the challenge of a scarcity of fine-grained emotion-annotated datasets for supervising emotional TTS training.
Comparison of Frequency-Fusion Mechanisms for Binaural Direction-of-Arrival Estimation for Multiple Speakers
Authors: Daniel Fejgin, Elior Hadad, Sharon Gannot, Zbyněk Koldovský, Simon Doclo
Date: 2024-01-15 00:00:00
Tasks: Direction of Arrival Estimation

To estimate the direction of arrival (DOA) of multiple speakers with methods that use prototype transfer functions, frequency-dependent spatial spectra (SPS) are usually constructed. To make the DOA estimation robust, SPS from different frequencies can be combined. According to how the SPS are combined, frequency fusion mechanisms are categorized into narrowband, broadband, or speaker-grouped, where the latter mechanism requires a speaker-wise grouping of frequencies. For a binaural hearing aid setup, in this paper we propose an interaural time difference (ITD)-based speaker-grouped frequency fusion mechanism. By exploiting the DOA dependence of ITDs, frequencies can be grouped according to a common ITD and be used for DOA estimation of the respective speaker. We apply the proposed ITD-based speaker-grouped frequency fusion mechanism for different DOA estimation methods, namely the multiple signal classification, steered response power and a recently published method based on relative transfer function (RTF) vectors. In our experiments, we compare DOA estimation with different fusion mechanisms. For all considered DOA estimation methods, the proposed ITD-based speaker-grouped frequency fusion mechanism results in a higher DOA estimation accuracy compared with the narrowband and broadband fusion mechanisms.
Contrastive Learning With Audio Discrimination For Customizable Keyword Spotting In Continuous Speech
Authors: Yu Xi, Baochen Yang, Hao Li, Jiaqi Guo, Kai Yu
Date: 2024-01-12 00:00:00
Tasks: Contrastive Learning, Keyword Spotting, Text Matching

Customizable keyword spotting (KWS) in continuous speech has attracted increasing attention due to its real-world application potential. While contrastive learning (CL) has been widely used to extract keyword representations, previous CL approaches all operate on pre-segmented isolated words and employ only audio-text representations matching strategy. However, for KWS in continuous speech, co-articulation and streaming word segmentation can easily yield similar audio patterns for different texts, which may consequently trigger false alarms. To address this issue, we propose a novel CL with Audio Discrimination (CLAD) approach to learning keyword representation with both audio-text matching and audio-audio discrimination ability. Here, an InfoNCE loss considering both audio-audio and audio-text CL data pairs is employed for each sliding window during training. Evaluations on the open-source LibriPhrase dataset show that the use of sliding-window level InfoNCE loss yields comparable performance compared to previous CL approaches. Furthermore, experiments on the continuous speech dataset LibriSpeech demonstrate that, by incorporating audio discrimination, CLAD achieves significant performance gain over CL without audio discrimination. Meanwhile, compared to two-stage KWS approaches, the end-to-end KWS with CLAD achieves not only better performance, but also significant speed-up.
Towards High-Quality and Efficient Speech Bandwidth Extension with Parallel Amplitude and Phase Prediction
Authors: Ye-Xin Lu, Yang Ai, Hui-Peng Du, Zhen-Hua Ling
Date: 2024-01-12 00:00:00
Tasks: Bandwidth Extension, CPU, Generative Adversarial Network, GPU

Speech bandwidth extension (BWE) refers to widening the frequency bandwidth range of speech signals, enhancing the speech quality towards brighter and fuller. This paper proposes a generative adversarial network (GAN) based BWE model with parallel prediction of Amplitude and Phase spectra, named AP-BWE, which achieves both high-quality and efficient wideband speech waveform generation. The proposed AP-BWE generator is entirely based on convolutional neural networks (CNNs). It features a dual-stream architecture with mutual interaction, where the amplitude stream and the phase stream communicate with each other and respectively extend the high-frequency components from the input narrowband amplitude and phase spectra. To improve the naturalness of the extended speech signals, we employ a multi-period discriminator at the waveform level and design a pair of multi-resolution amplitude and phase discriminators at the spectral level, respectively. Experimental results demonstrate that our proposed AP-BWE achieves state-of-the-art performance in terms of speech quality for BWE tasks targeting sampling rates of both 16 kHz and 48 kHz. In terms of generation efficiency, due to the all-convolutional architecture and all-frame-level operations, the proposed AP-BWE can generate 48 kHz waveform samples 292.3 times faster than real-time on a single RTX 4090 GPU and 18.1 times faster than real-time on a single CPU. Notably, to our knowledge, AP-BWE is the first to achieve the direct extension of the high-frequency phase spectrum, which is beneficial for improving the effectiveness of existing BWE methods.
The NPU-ASLP-LiAuto System Description for Visual Speech Recognition in CNVSRC 2023
Authors: He Wang, Pengcheng Guo, Wei Chen, Pan Zhou, Lei Xie
Date: 2024-01-07 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition, Visual Speech Recognition

This paper delineates the visual speech recognition (VSR) system introduced by the NPU-ASLP-LiAuto (Team 237) in the first Chinese Continuous Visual Speech Recognition Challenge (CNVSRC) 2023, engaging in the fixed and open tracks of Single-Speaker VSR Task, and the open track of Multi-Speaker VSR Task. In terms of data processing, we leverage the lip motion extractor from the baseline1 to produce multi-scale video data. Besides, various augmentation techniques are applied during training, encompassing speed perturbation, random rotation, horizontal flipping, and color transformation. The VSR model adopts an end-to-end architecture with joint CTC/attention loss, comprising a ResNet3D visual frontend, an E-Branchformer encoder, and a Transformer decoder. Experiments show that our system achieves 34.76% CER for the Single-Speaker Task and 41.06% CER for the Multi-Speaker Task after multi-system fusion, ranking first place in all three tracks we participate.
Deep Learning Based Cyberbullying Detection in Bangla Language
Authors: Sristy Shidul Nath, Razuan Karim, Mahdi H. Miraz
Date: 2024-01-07 00:00:00
Tasks: Deep Learning

The Internet is currently the largest platform for global communication including expressions of opinions, reviews, contents, images, videos and so forth. Moreover, social media has now become a very broad and highly engaging platform due to its immense popularity and swift adoption trend. Increased social networking, however, also has detrimental impacts on the society leading to a range of unwanted phenomena, such as online assault, intimidation, digital bullying, criminality and trolling. Hence, cyberbullying has become a pervasive and worrying problem that poses considerable psychological and emotional harm to the people, particularly amongst the teens and the young adults. In order to lessen its negative effects and provide victims with prompt support, a great deal of research to identify cyberbullying instances at various online platforms is emerging. In comparison to other languages, Bangla (also known as Bengali) has fewer research studies in this domain. This study demonstrates a deep learning strategy for identifying cyberbullying in Bengali, using a dataset of 12282 versatile comments from multiple social media sites. In this study, a two-layer bidirectional long short-term memory (Bi-LSTM) model has been built to identify cyberbullying, using a variety of optimisers as well as 5-fold cross validation. To evaluate the functionality and efficacy of the proposed system, rigorous assessment and validation procedures have been employed throughout the project. The results of this study reveals that the proposed model's accuracy, using momentum-based stochastic gradient descent (SGD) optimiser, is 94.46%. It also reflects a higher accuracy of 95.08% and a F1 score of 95.23% using Adam optimiser as well as a better accuracy of 94.31% in 5-fold cross validation.
MultiSiam: A Multiple Input Siamese Network For Social Media Text Classification And Duplicate Text Detection
Authors: Sudhanshu Bhoi, Swapnil Markhedkar, Shruti Phadke, Prashant Agrawal
Date: 2024-01-06 00:00:00
Tasks: text-classification, Text Classification, Text Detection

Social media accounts post increasingly similar content, creating a chaotic experience across platforms, which makes accessing desired information difficult. These posts can be organized by categorizing and grouping duplicates across social handles and accounts. There can be more than one duplicate of a post, however, a conventional Siamese neural network only considers a pair of inputs for duplicate text detection. In this paper, we first propose a multiple-input Siamese network, MultiSiam. This condensed network is then used to propose another model, SMCD (Social Media Classification and Duplication Model) to perform both duplicate text grouping and categorization. The MultiSiam network, just like the Siamese, can be used in multiple applications by changing the sub-network appropriately.
A Dynamic Capacitance Matching (DCM)-based Current Response Algorithm for Signal Line RC Network
Authors: Zhoujie Wu, Cai Luo, Zhong Guan
Date: 2024-01-16 00:00:00

This paper proposes a dynamic capacitance matching (DCM)-based RC current response algorithm for calculating the current waveform of a signal line without performing SPICE simulation. Specifically, unlike previous method such as CCS model, driver linear representation, waveform functional fitting or equivalent load capacitance, our algorithm does not rely on fixed reduced model of both standard cell driver and RC load. Instead, our algorithm approaches the current waveform dynamically by computing current responses of the target driver for various load scenarios. Besides, we creatively use symbolic expression to combine the y-parameter of RC network with the pre-characterized driver library in order to perform capacitance matching by considering over/under-shoot effect. Our algorithm is experimentally verified on 40nm CMOS technology and has been partially adopted by latest commercial tool for other nodes. Experimental results show that our algorithm has excellent resolution and promising efficiency compared with traditional methods and SPICE golden result, especially for application in computing delay, power and signal line electromigration.