Reviewerly/exHarmony huggingface dataset:
The Role of Audio Features in Accent Recognition: A Comparative Analysis

This study focuses on enhancing Automatic Speech Recognition (ASR) systems, crucial in Science and Technology, by addressing challenges tied to diverse speaker accents. The research delves into accent recognition, comparing Mel Frequency Cepstral Coefficients (MFCC) and Chroma as feature sets. Surprisingly, Chroma surpasses MFCC in accent classification. This study widens our understanding of accent recognition techniques, aiding ASR accuracy. By investigating MFCC and Chroma, finding an optimal feature for superior accent recognition, thus enhancing ASR system performance with varied accents.
Convolution, Virtual Acoustics, and Interactions with Impossible Worlds

This paper presents a discussion on a series of artworks created by the authors that explore the potential of using sound produced in virtual spaces as artistic materials. Specifically, the works under discussion generate unique spatial and auralization effects that are achieved through the use of novel convolution techniques and immersive virtual acoustic technologies. We demonstrate our novel convolution techniques for deriving impulse responses from non-spatial sources, such as audio signals, and explain how these impulse responses have been employed in creating immersive installations for both web-based experiences and in-person interactive installations and performances. The impulse responses used in this work are obtained from sources including recordings of medieval hand-bells, the singing of Gesualdo madrigals, spoken texts, ambient street sounds, and more. The online works discussed in this paper are diffused through loudspeakers and headphones, while the interactive in-person installations and performances are diffused over headphones or through the loudspeakers of mobile convolution-based virtual acoustic systems. Several audio-visual examples to illustrate the techniques and outcomes of the work are included.
Efficient Hamiltonian Reduction for Quantum Annealing on SatCom Beam Placement Problem

Beam Placement (BP) is a well-known problem in Low-Earth Orbit (LEO) satellite communication (SatCom) systems, which can be modelled as an NP-hard clique cover problem. Recently, quantum computing has emerged as a novel technology which revolutionizes how to solve challenging optimization problems by formulating Quadratic Unconstrained Binary Optimization (QUBO), then preparing Hamiltonians as inputs for quantum computers. In this paper, we study how to use quantum computing to solve BP problems. However, due to limited hardware resources, existing quantum computers are unable to tackle large optimization spaces. Therefore, we propose an efficient Hamiltonian Reduction method that allows quantum processors to solve large BP instances encountered in LEO systems. We conduct our simulations on real quantum computers (D-Wave Advantage) using a real dataset of vessel locations in the US. Numerical results show that our algorithm outperforms commercialized solutions of D-Wave by allowing existing quantum annealers to solve 17.5 times larger BP instances while maintaining high solution quality. Although quantum computing cannot theoretically overcome the hardness of BP problems, this work contributes early efforts to applying quantum computing in satellite optimization problems, especially applications formulated as clique cover/graph coloring problems.
A General Solution for Straggler Effect and Unreliable Communication in Federated Learning

The straggler effect is the main bottleneck for Federated Learning (FL), where the performance of training is degraded by the slowest member. Another significant problem is unreliable communication, which somehow has been neglected in previous studies. That is, the transmission of local models is not successful every time. In this paper, we find that the problems of straggler effect and unreliable communication are implicitly caused by time divergence of User Equipments (UEs) in each training round. Based on this, we propose our solutions for these two problems and show that our solutions can be merged into a general one: the problem of the straggler effect and unreliable communication can be solved with a simple UE selection method. This method consists of two steps: First, we cluster UEs into several groups based on UEs' physical parameters or performance metrics; Second, in each training round, only UEs from the same group are chosen for FL operation. Full explanations are given why the time divergence is statistically reduced, and therefore it can mitigate the aforementioned two problems. Our solutions are further illustrated with some examples and validated by simulations.
Reconfigurable Quantum Internet Service Provider

Quantum network is a research topic involving fundamental quantum information science, engineering designs, and experimental implementations. Existing research works primarily pertain to theoretical architecture designs and proof-of-concept experimental demonstrations. With the recent developments in engineering quantum systems, the realization of scalable local-area quantum networks has become viable. However, the design and implementation of a quantum network is a holistic task that is way beyond the scope of an abstract design problem. As such, a testbed on which multiple disciplines can verify the design and implementation across a full networking stack has become a necessary infrastructure for the future development of quantum networks. In this work, we demonstrate the concept of quantum internet service provider (QISP), in analogy to the conventional ISP that allows for the sharing of classical information between the network nodes. The QISP is significant for the next-generation quantum networks as it coordinates the production, management, control, and sharing of quantum information across the end-users of a quantum network. We construct a reconfigurable QISP comprising both the quantum hardware and classical control software. Building on the fiber-based quantum-network testbed of the Center for Quantum Networks (CQN) at the University of Arizona (UA), we develop an integrated QISP prototype based on a Platform-as-a-Service (PaaS) architecture, whose classical control software is abstracted and modularized as an open-source QISP framework. To verify and characterize the QISP's performance, we demonstrate multi-channel entanglement distribution and routing among multiple quantum-network nodes with a time-energy entangled-photon source. We further perform field tests of concurrent services for multiple users across the quantum-network testbed. Our experiment demonstrates the robust capabilities of the QISP, laying the foundation for the design and verification of architectures and protocols for future quantum networks.
Endogenous Security-Aware Device Scheduling for Federated Learning-Assisted Low-Carbon Smart Park

Device scheduling plays a key role in federated learning model training for energy management in low-carbon smart park. It is intuitive to achieve high-accuracy and low-latency model training by scheduling devices with smaller local training loss function and better channel condition. However, the adverse impact of model poisoning attack on model training performance and device scheduling adjustment cannot be neglected. The error model parameters uploaded by malicious attackers-controlled devices significantly reduce model training accuracy and convergence speed. To address this challenge, we propose an Endogenous Security-Aware Deep Q Network (ESA-DQN) based device scheduling algorithm. ESA-DQN integrates model poisoning attach detection with DQN networks to actively adjust device scheduling in accordance with estimated attack probability, thereby achieving endogenous security awareness. Numerical results show that ESA-DQN has excellent performances in terms of model training accuracy and delay.
Application of Convolutional Neural Network (CNN) and Word Embeddings on Plot Summaries for Movie Prediction

In this paper, we propose an approach that applies deep learning and natural language processing techniques including neural networks and word embeddings to identify movie titles through short descriptions of its plot. Specifically, we propose the usage of convolutional neural networks (CNNs) combined with the Word2Vec word embedding library. We compare our proposed method with a more conventional machine learning approach, including the use of Term Frequency-Inverse Document Frequency (TF-IDF) and logistic regression. The performances of both models will be mainly evaluated through a comparison of their accuracies when given test samples as well as shorter, generic sentences as input. Ultimately, the deep learning method is shown to be more accurate than the machine learning method in terms of training accuracy, but less accurate in terms of validation accuracy. However, the proposed method is observed to generalize better on simpler sentences, which is more in line with what is expected from users. Our findings also conclude that the proposed method is able to better relate the semantics of the prompt provided to the contextual information obtained from the corpus, such as setting, cast and theme. These results suggest that the proposed method better suits the needs of users for real-world applications.
Multi-Lingual Sentence Alignment with GPT Models

This paper investigates sentence alignment technology, an essential element in Natural Language Processing tasks like machine translation, that pairs corresponding sentences within bilingual documents. Despite the existence of standard bilingual alignment corpora, they often fall short in covering low-resource languages and specific domains, underscoring the need for advanced sentence alignment algorithms. We explore the potential of the GPT model for sentence alignment, focusing on aspects like prompt construction, experiment design, and results evaluation. Utilizing the OPUS corpus, our experimental design covers eight commonly used languages in 28 directions. The study reveals that the GPT-aligner outperforms traditional algorithms by 5-30% in most scenarios, with the performance contingent on language, prompting, the GPT system model, and few-shot demonstration strategies. This work illuminates the significant potential of sentence alignment algorithms in the future development of language models.
EEOKD: Energy-Efficient Online Knowledge Distillation for Mobile Video Inference

Wearable devices can assist users in cognitive decline through context-aware scene interpretation. They should function in real-time with sufficient functions, performance, and usability. However, the high-accuracy and low-delay scene interpretation rely on the Deep Neural Network (DNN) inference of continuous video streams, which poses enormous challenges to wearable devices because of the tight energy budget and unpredictable delay impact. In this paper, we propose a novel framework dubbed EEOKD: Energy-Efficient Online Knowledge Distillation. The framework specializes in a high-accuracy and low-cost object detection model to automatically adapt to the target video while occupying a small amount of bandwidth and tolerating changes in network delay. First, we propose efficient asynchronous distributed algorithms based on the loss gradient to alleviate the impact of delay changes. Then, we propose a novel online knowledge distillation scheme with freshness-based importance sampling and batch training to improve the generalization ability of the student model using fewer samples at lower weight updating frequencies. The new method saves energy by accelerating the model convergence and keeps good detection performance while network delay changes considerably. Finally, we implement a system prototype and evaluate its performance and energy consumption. The experimental results show that our EEOKD saves almost 15% of the energy and about 60% of the network bandwidth, and it also improves the detection accuracy by 3% on average compared with the existing approach.
Vowels' Articulatory Location Classification based on Formant Frequency

Modelling vowels is a crucial step in many applications such as speech understanding, human age analysis, accents differentiation, security and E-learning. This paper proposes building a statistical model for the US English vowels based on formant frequencies, which are extracted from real speech samples. Vowels are classified according to their articulatory-acoustic characteristics into front and back vowels. Three different approaches have been tested in this research, the K-means clustering, Fisher's linear discriminant analysis (LDA) and non-linear Gaussian mixture model (GMM). The proposed GMM model is used to classify vowels with the help of the Expectation Maximization (EM) algorithm. The results show that K-means analysis did not produce a convenient classification of vowel formants. Meanwhile, the automatic perception is highly achievable when formant data are projected on different coordinates using Fisher's LDA (FLDA). The classification error in the FLDA is close to that of the quadratic discriminant function of the EM algorithm. Although EM algorithm that was based on GMM gave better classification when it was tested, Fisher's LDA gave nearly the same results with significantly less computations.
Communication and Storage Efficient Federated Split Learning

Federated learning (FL) is a popular distributed machine learning (ML) paradigm, but is often limited by significant communication costs and edge device computation capabilities. Federated Split Learning (FSL) preserves the parallel model training principle of FL, with a reduced device computation requirement thanks to splitting the ML model between the server and clients. However, FSL still incurs very high communication overhead due to transmitting the smashed data and gradients between the clients and the server in each global round. Furthermore, the server has to maintain separate models for every client, resulting in a significant computation and storage requirement that grows linearly with the number of clients. This paper aims at solving these two issues by proposing a communication and storage efficient federated split learning (CSE-FSL) strategy, which utilizes an auxiliary network to locally update the client models while keeping only a single model at the server, hence avoiding the communication of gradients from the server and greatly reducing the server resource requirement. Communication cost is further reduced by only sending the smashed data in selected epochs from the clients. We provide a rigorous theoretical analysis of CSE-FSL that guarantees its convergence for non-convex loss functions. Extensive experimental results demonstrate that CSE-FSL has a significant communication reduction over existing FSL techniques, while achieving state-of-the-art convergence and model accuracy, using several real-world FL tasks.
Enabling the Wireless Metaverse via Semantic Multiverse Communication

Metaverse over wireless networks is an emerging use case of the sixth generation (6G) wireless systems, posing unprecedented challenges in terms of its multi-modal data transmissions with stringent latency and reliability requirements.Towards enabling this wireless metaverse, in this article we propose a novel semantic communication (SC) framework by decomposing the metaverse into human/machine agent-specific semantic multiverses (SMs).An SM stored at each agent comprises a semantic encoder and a generator, leveraging recent advances in generative artificial intelligence (AI).To improve communication efficiency, the encoder learns the semantic representations (SRs) of multimodal data, while the generator learns how to manipulate them for locally rendering scenes and interactions in the metaverse.Since these learned SMs are biased towards local environments, their success hinges on synchronizing heterogeneous SMs in the background while communicating SRs in the foreground, turning the wireless metaverse problem into the problem of semantic multiverse communication (SMC).Based on this SMC architecture, we propose several promising algorithmic and analytic tools for modeling and designing SMC, ranging from distributed learning and multi-agent reinforcement learning (MARL) to signaling games and symbolic AI.
Layer Ensembles

Deep Ensembles, as a type of Bayesian Neural Networks, can be used to estimate uncertainty on the prediction of multiple neural networks by collecting votes from each network and computing the difference in those predictions. In this paper, we introduce a method for uncertainty estimation which considers a set of independent categorical distributions for each layer of the network, giving many more possible samples with overlapped layers than in the regular Deep Ensembles. We further introduce an optimized inference procedure that reuses common layer outputs, achieving up to 19x speed up and reducing memory usage quadratically. We also show that the method can be further improved by ranking samples, resulting in models that require less memory and time to run while achieving higher uncertainty quality than Deep Ensembles.
Recommendation System Based Collaborative Filtering for Deciding Travelling Place

This Indonesia has a vast number of tourist destinations, and it can be overwhelming for people to decide where to go, either because they have many options to choose from or because they are bored with places they have visited before. To address this issue, this research uses collaborative filtering-based recommendation systems to provide users with personalized recommendations based on their previous ratings. The research will compare five recommendation system algorithms: the Hybrid Algorithm with ContentKNN (Content K Nearest Neighbors) and RBM (Restricted Boltzmann Machine), SVD++ (Singular Value Decomposition++), Efficient Deep Learning, Scalable Deep Learning, and Naïve Bayes. These algorithms will be tested based on their evaluation metrics, namely RMSE (Root Mean Squared Error) and MAE (Mean Absolute Error). The model that has the best evaluation metrics will be selected to provide travelling place recommendations. To develop algorithm models for the recommendation system, the research will use the CRISP-DM (Cross-Industry Standard Process for Data Mining) model. Based on the workflow, Efficient Deep Learning has the best RMSE and MAE value from all of the models, with these following parameters: 250 neurons and SELU (Scaled Exponential Linear Unit) activation type. Future works for this research includes additional rating dataset, adding different combinations of fields, distance metrics, and other evaluation metrics.
User Cooperation with Power Control for Federated Learning in CFmMIMO Networks

In synchronous federated learning (SFL), only if all users successfully deliver their local parameters to the central server, the central server can efficiently aggregate a global model. In this case, the user with the worst quality link becomes the bottleneck limiting the training performance of SFL. To cope with this issue for SFL in cell-free massive multiple-input multiple-output (CFmMIMO) network, this paper proposes a joint user cooperation and power control (JUCPC) method. Particularly, the users with good quality links are employed to assist the ones with worse quality links to transfer their parameters to the central server. Then, the power control is employed to coordinate the interference among users to further improve the transmission performance for SFL. To find the joint optimal user cooperation pairing and the power control coefficients, we formulate an optimization problem to minimize the maximum transmission time among users. As the problem is non-convex, we decouple it into two sub-problems. For the first sub-problem, we propose a fairness-based algorithm to select the helping users and also pair them with the ones who required to be assisted. For the second sub-problem, a deep deterministic policy gradient (DDPG)-based algorithm is proposed to find the optimized power control coefficients. Experiments show that the proposed JUCPC method can efficiently reduce the transmission time of the worst user for SFL. Moreover, compared to the traditional optimization method based on convex optimization solver (CVX), our proposed DDPG-based one requires much shorter execution time while achieves approximating result to the CVX-based algorithm.
QoS-aware Resource Optimization for Hierarchical Cross-Edge Video Analytics

As the killer application of edge computing, video analytics typically involves multiple vision components in the pipeline, which together determine the quality of service (QoS) for users. By exploiting diverse resource demands of different components, a fine-grained cross-layer orchestration with QoS-aware configuration adaptation can further boost the system efficiency of heterogeneous resources. Thus, we study a video analytics pipeline system with vertical and horizontal resource collaboration across device-edge-cloud hierarchy to achieve QoS-aware cost optimization. To judiciously match the component diversity and the resource heterogeneity, we explore smooth configuration adaptation to model a mixed-integer nonlinear problem, which jointly optimizes long-term resource cost and QoS (including accuracy and latency). However, it is nontrivial to efficiently solve such a NP-hard problem in an online manner without the future information as a prior knowledge due to the time-coupling deployment cost caused by fluctuating input traffic. To address the above challenges, we decouple the intractable problem according to the traffic routing constraints. By leveraging the lazy-switching method, we derive the component orchestration decisions for the decoupled subproblems in each slot and further design a dependent rounding scheme to obtain an efficient feasible solution while guaranteeing the knapsack resource constraints. We rigorously analyze the performance guarantee of our online algorithms by a parameterized competitive ratio, and further verify the empirical performance of our approach through extensive trace-driven experiments.
FlexSplit: A Configurable, Privacy-Preserving Federated-Split Learning Framework

In this paper, we address the challenges of preserving client data and model sharing privacy in a distributed machine learning system. Different from conventional federated learning where clients share their entire model, we propose a distributed learning framework named FlexSplit where each client can select the number of layers trained between it and the server to further control their individual privacy levels. FlexSplit improves scalability by performing part of the training process at multiple edge servers in parallel before the aggregation at the cloud server. Preliminary experimental results show that FlexSplit can achieve higher validation accuracy than a conventional federated learning model. We also highlight the privacy-utility trade-off where clients can increase their privacy level by sharing fewer layers to minimise privacy attacks at the cost of lower validation accuracy of their local model.