Reviewerly/exHarmony huggingface dataset:
LM-VC: Zero-Shot Voice Conversion via Speech Generation Based on Language Models

Language model (LM) based audio generation frameworks, e.g., AudioLM, have recently achieved new state-of-the-art performance in zero-shot audio generation. In this paper, we explore the feasibility of LMs for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">zero-shot voice conversion</i> . An intuitive approach is to follow AudioLM – Tokenizing speech into semantic and acoustic tokens respectively by HuBERT and SoundStream, and converting source semantic tokens to target acoustic tokens conditioned on acoustic tokens of the target speaker. However, such an approach encounters several issues: 1) the linguistic content contained in semantic tokens may get dispersed during multi-layer modeling while the lengthy speech input in the voice conversion task makes contextual learning even harder; 2) the semantic tokens still contain speaker-related information, which may be leaked to the target speech, lowering the target speaker similarity; 3) the generation diversity in the sampling of the LM can lead to unexpected outcomes during inference, leading to unnatural pronunciation and speech quality degradation. To mitigate these problems, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LM-VC</i> , a two-stage language modeling approach that generates coarse acoustic tokens for recovering the source linguistic content and target speaker's timbre, and then reconstructs the fine for acoustic details as converted speech. Specifically, to enhance content preservation and facilitates better disentanglement, a masked prefix LM with a mask prediction strategy is used for coarse acoustic modeling. This model is encouraged to recover the masked content from the surrounding context and generate target speech based on the target speaker's utterance and corrupted semantic tokens. Besides, to further alleviate the sampling error in the generation, an external LM, which employs window attention to capture the local acoustic relations, is introduced to participate in the coarse acoustic modeling through shallow fusion. Finally, a prefix LM reconstructs fine acoustic tokens from the coarse and results in the converted speech. Experiments demonstrate that LM-VC outperforms competitive systems in speech naturalness and speaker similarity.
A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario

We have witnessed significant technological advancement over the past few years, including the Internet of Things (IoT). The IoT’s ability to connect consumer appliances to the internet has changed the way we live. As a result of the significant benefits that IoT has brought to household usage, it has become a topic of discussion for research departments, leading to its expansion into industrial sectors, commonly known as the Industrial Internet of Things (IIoT). IIoT enables automation and the use of intelligent machines to improve product manufacturing processes and enhance our lives as customers. However, as IIoT-enabling technology and applications continue to grow, security issues and privacy protection challenges become harder to manage, which frequently results in data breaches and sensitive information disclosures. This paper first explains what the reader needs to know about the IIoT system architecture in Industry 4.0 to make it easier for them to understand. Second, a hacking scenario is utilized as a methodology to conduct an in-depth analysis of various security issues, as well as their impacts and countermeasures, for each level of the IIoT architecture. Additionally, our hacking scenarios present a variety of targets from which malicious actors can launch their assaults. Third, we provide a thorough review of the various blockchain solutions currently being employed to protect IIoT systems. Finally, this paper draws to a close by outlining certain gaps and potential solutions that could be investigated in subsequent studies to strengthen security and enhance privacy for IIoT systems.
Adaptive Decentralized Sensor Fusion for Autonomous Vehicle: Estimating the Position of Surrounding Vehicles

The tracking accuracy of nearby vehicles determines the safety and feasibility of driver assistance systems or autonomous vehicles. Recent research has been active to employ additional sensors or to combine heterogeneous sensors for more accurate tracking performance. Especially, autonomous driving technologies require a sensor fusion technique that considers various driving environments. In this research, a novel method for high-level data fusion is proposed to improve the accuracy of tracking surrounding vehicles. In response to the changing driving environment, the locations of the vehicles are estimated in real-time using an adaptive track-to-track fusion technique and an interacting multiple model filter. Asynchronous measurements from multiple sensors such as radar, camera, and LiDAR, are utilized for the estimation. For each sensor, two motion models representing the vehicle’s movement are applied to increase the estimation accuracy. Utilizing a multimodal network-based track-to-track fusion approach, it combines the estimates of the target vehicle position from each sensor into a single estimate. The inputs of the network are intended to determine the reliability of each sensor, considering the driving conditions that may affect sensor accuracy. Also, multiple embeddings in the network are created so that the corresponding data maintains its relevance and enables the real-time computing. The proposed method is verified using real driving data collected from various environments.
An action analysis algorithm for teachers based on human pose estimation

Teachers serve as transmitters of cultural knowledge and play a crucial role in ongoing development and continuity of human society. Analyzing their behavior can assist educators in identifying their strengths and weaknesses within the classroom, thereby enhancing their teaching skills and methods. With the development of artificial intelligence, it is possible to automatically analyze teachers' behavior in the classroom by offering timely feedback and suggestions to improve teaching strategies. In this paper, we propose an action classification method based on human skeletal posture to analyze teacher-behavior in the classroom. First, by using the HRNet pose estimation algorithm to extract teacher skeleton information as features, the algorithm could remove redundant information in images and accurately reflect the teacher's posture. Second, by considering the relative positions of skeletal points, we establish a set of quantifiable indicators to analyze the interrelationships among these points and then obtain the teacher's local posture. Finally, relying on the local posture obtained based on the indicators, we encode and classify common behaviors of teachers in the classroom. The approach's effectiveness is evidenced by experimental results, which enables dynamic management and assessment of teacher behavior in educational environments.
Proposal of a new Mining and Geology Impact Factor (MaGIF) index for the study of post-mining environment

The Babina mine closed almost 50 years ago, and reclamation works for reforestation began shortly after. Despite various technical and biological measures, the area is still experiencing negative consequences from mining, exacerbated by its complex glaciotectonic structure. These phenomena have an impact on the post-mining environment. The presented study aimed to identify areas within a mining area of the Babina mine, where the former mining activities and geological structure had the strongest influence on the changes in flora and soil conditions between 1989 and 2019. The research also revealed previously undocumented mining excavations. The study used the Mining and Geology Impact Factor, combining OLS models and spatial statistics based on spectral indices. Eighteen explanatory factors described the geological-mining and topographic conditions, while five dependent variables represented changes in vegetation and soil over 30 years. MaGIF values ranged between 0.13 and 0.97. The highest values indicated a significant impact on soil and vegetation in six regions. Extrapolation of the index values identified three areas where past mining activities may have occurred outside of archival records. This study confirms the substantial environmental influence of previous mining and the glaciotectonic structure. The use of remote sensing and spatial analysis aids in identifying vulnerable areas and preventing potential environmental damage.
Stories from the Field (of Networking): Lessons from Deploying Research Systems in the Real World.

As computer science has grown in importance throughout the world, there's been a similarly growing drive to apply foundational computer science to address core societal issues. For networking researchers, this effort has largely focused on bringing the benefits of Internet access to marginalized populations. In this work, we reflect on more than a decade of our own work deploying such research into the world in all of rural, urban, developed, and developing regions. We share key lessons and related stories that have influenced they way we do this type of work. We conclude with a call to other researchers to add their own stories to share back to the wider networking research community.
What's my Daily Value? Interpretation of network performance metrics in broadband consumer labels

Despite the importance of residential Internet, choosing Internet plans is often an opaque and frustrating process for consumers. To address this, United States regulatory authorities have proposed "broadband consumer labels", akin to nutrition labels, to improve transparency and empower consumers to make informed decisions, but to achieve their goal, such labels must both capture information relevant to consumers while being interpretable.
Multi-view enhanced zero-shot node classification

In recent years, Zero-shot Node Classification (ZNC), an emerging and more difficult task is starting to attract attention, where the classes of testing nodes are unobserved in the training stage. Existing studies for ZNC mainly utilize Graph Neural Networks (GNNs) to construct the feature subspace to align with the classes' semantic subspace, thus enabling knowledge transfer from seen classes to unseen classes. However, the modeling of the node feature is single-view and unilateral, e.g., the bag-of-words vector, which is not enough to fully describe the characteristics of the node itself. To address this dilemma, we propose to develop the Multi-View Enhanced zero-shot node classification paradigm (MVE) to promote the machine's generality to approach the human-like thinking mode. Specifically, multi-view features are obtained from different aspects such as pre-trained model embeddings, knowledge graphs, statistic methods, and then fused by a contrastive learning module into the compositional node representation. Meanwhile, a developed Graph Convolutional Network (GCN) is used to make the nodes fully absorb the information of neighbors while the over-smooth issue is alleviated by multi-view features and the proposed contrastive learning mechanism. Experimental results conducted on three public datasets show an average 25% improvement compared to baseline methods, proving the superiority of our multi-view learning framework. The code and data can be found at https://github.com/guaiqihen/MVE.
A strategy based on Wave Swarm for the formation task inspired by the Traveling Salesman Problem

Multi-robots can perform complex tasks such as exploration, foraging, and formation. Efficient communication between robots can contribute to the accomplishment of collective tasks through efficient message exchange. This paper proposes a strategy based on the message’s propagation technique, Wave Swarm, for the formation task inspired by the Traveling Salesman Problem. The Wave Swarm communication approach uses the concept of wave propagation for message exchange between neighbors, establishing a Father and Son relationship between robots. However, different pairs of Father and Son robots can impact the simulation time, the average distance traveled by each robot, and the number of messages exchanged during the formation task. Thus, given a random distribution of robots into a swarm, we model the problem of assigning one position and route for each robot to achieve its place in the formation as a Traveling Salesman Problem. The routes resulting from the TSP solution establish a new parental relationship between the robots in the swarm. We performed preliminary experiments to define the technique used in the TSP resolution. We tested reinforcement learning and the genetic algorithm using the TSPLIB95 library. Thus, we develop a strategy for formation tasks based on Wave Swarm and TSP solved with reinforcement learning. We adopted the leader–follower approach in an unknown environment to validate the proposal. The results show the behavior of different sizes of robot groups for various desired shapes. Experiments with the robot simulator CoppeliaSim (V-REP) validate the proposed strategy and highlight its efficiency and robustness while running the formation task.
Exploring Language Variation in Internet Buzzwords: A Case Study of the Phrase “Officially Diagnosed as XX”

Internet buzzwords, characterized by their interactivity and permeability, serve as indicators of the shifting emotional landscapes and emotional appeals of netizens within distinct temporal contexts. However, these buzzwords remain far from stagnant, continually undergoing linguistic variations influenced by the psychological dispositions of users and the ever-shifting environmental backdrop. In this case study, a comprehensive exploration of linguistic variation within internet buzzwords is undertaken, focusing intently on the dynamic phrase "officially diagnosed as XX" and its profound reflection of prevailing social mentalities and linguistic motivations. Guided by the conceptual framework of eco-linguistics, which offers a lens for comprehending the economic underpinnings of language and rationalizing the phenomena of internet buzzwords, this study delves into an intricate analysis. By meticulously dissecting online datasets and user commentary, the study unveils the multifaceted employment of "officially diagnosed as XX" as an instrument to articulate an array of emotions and cognitive states, ranging from humor to frustration and even self-deprecation. Through the prism of language evolution in the digital era, the study engages in a scholarly discourse on the intricate phenomenon of linguistic variation within the realm of internet discourse. Ultimately, this research not only illuminates the intricate dynamics between language, society, and technology in the contemporary age but also advances our comprehension of the profound interplay between language, emotion, and human communication within the digital realm.
On a Meta Learning-Based Scheduler for Deep Learning Clusters

Deep learning (DL) has become a dominating type of workloads on AI computing platforms. The performance of such platforms highly depends on how distributed DL jobs are scheduled. Reinforcement learning (RL)-based schedulers have been extensively studied and are capable of modeling interferences between concurrent jobs competing for resources. However, existing RL-based schedulers must learn from large number of samples and adapt to workload changes in real systems, which is a huge cost for production clusters. This paper proposes an intelligent, autonomous scheduler that employs sample-efficient RL for real-world resource scheduling on complex DL clusters. Specifically, we design a closed-loop meta-RL-based worker placement algorithm for DL training jobs. Instead of random exploration, we encourage the scheduler to explore combinatorial subspaces, where the performance model might be inaccurate, to improve the sampling efficiency of the scheduler agent. Extensive experimental results demonstrate that our algorithm outperforms other baselines in terms of average job completion time with 12.29% to 16.24% improvements. Further experiments with workload variations yield 15.76% to 22.13% improvements.
Speaker Adaptive Text-to-Speech With Timbre-Normalized Vector-Quantized Feature

Achieving high fidelity and speaker similarity in text-to-speech speaker adaptation with limited amount of data is a challenging task. Most existing methods only consider adapting to the timbre of the target speakers but fail to capture their speaking styles from little data. In this work, we propose a novel TTS system, TN-VQTTS, which leverages timbre-normalized vector-quantized (TN-VQ) acoustic feature for speaker adaptation with little data. With the TN-VQ feature, speaking style and timbre can be effectively decomposed and controlled by the acoustic model and the vocoder separately of VQTTS. Such decomposition enables us to finely mimic both the two characteristics of the target speaker in adaptation with little data. Specifically, we first reduce the dimensionality of self-supervised VQ acoustic feature via PCA and normalize its timbre with a normalizing flow model. The feature is then quantized with k-means and used as the TN-VQ feature for a multi-speaker VQ-TTS system. Furthermore, we optimize timbre-independent style embeddings of the training speakers jointly with the acoustic model and store them in a lookup table. The embedding table later serves as a selectable codebook or a group of basis for representing the style of unseen speakers. Our experiments on LibriTTS dataset first show that the proposed model architecture for VQ feature achieves better performance in multi-speaker text-to-speech synthesis than several existing methods. We also find that the reconstruction performance and the naturalness are almost unchanged after applying timbre normalization and k-means quantization. Finally, we show that TN-VQTTS achieves better performance on speaker similarity in adaptation than both speaker embedding based adaptation method and fine-tuning based baseline AdaSpeech.
Temperature-Prediction Based Rate-Adjusted Time and Space Mapping Algorithm for 3D CNN Accelerator Systems

For the state-of-the-art convolutional neural networks (CNNs), the accuracy of the inference is increased by raising the number of the layer. However, it can cause the higher computation complexities of the CNNs. The 3D CNN accelerator is applied to solve the computation complexities for the CNNs. However, the 3D CNN accelerator will encounter the serious thermal problem. In this article, we propose the predicted rate-controlled and task-allocated dynamic thermal management (prctaDTM) for the 3D CNN accelerator systems. The proposed prctaDTM contains the rate-controlled temperature prediction (RCTP), workload movement (WM), and workload adjustment (WA) to control the workloads of the 3D CNN accelerator and adjust the parallelism for the 3D CNN accelerator. The goal is to solve the thermal problem of the 3D CNN accelerator. In our experiments, the latency for the 3D CNN accelerator can be reduced by 18.1%–40.1% and the peak temperature for the 3D accelerator can be controlled close to the predefined thermal limitation. Moreover, the prediction errors with RCTP can be constrained below 0.66˚C for different CNN applications.
Determining the Appropriate Accounting Treatment of Cryptocurrencies Based on Accounting Theory

The International Financial Reporting Standards (IFRS) do not make explicit provisions, in terms of a specifically dedicated standard, for the accounting treatment of cryptocurrencies. This creates uncertainty, and guidance is therefore required in terms of accounting for such investments. Accounting theory has the potential to provide the foundation for this guidance. This study aimed to determine the most appropriate accounting treatment for cryptocurrencies based on the International Accounting Standards Board’s (IASB) Conceptual Framework for Financial Reporting (as a form of accounting theory) that results in decision-useful information. The research further investigated the proposed accounting treatment in terms of IFRS and sought to determine whether this treatment was aligned with the IASB’s conceptual framework. This qualitative study conducted a non-empirical interpretative analysis of the literature (focusing specifically on accounting theory) to address the research aim. The conceptual framework indicated that the most appropriate way to account for cryptocurrencies was to recognise an asset at fair value. This accounting treatment aligns with accounting for assets under International Accounting Standard (IAS) 2 commodities held by broker-traders and the IAS 38 revaluation model. Addressing the problem of accounting for cryptocurrencies with reference to accounting theory makes this study novel. The guidance provided could reduce uncertainty among entities holding investments in cryptocurrencies and could increase the decision-usefulness of financial information.
Variational Quantum Circuit Topology Grid Search for Hypocalcemia Following Thyroid Surgery

Quantum computing’s potential to revolutionise medical applications has spurred interest in leveraging quantum algorithms for healthcare challenges. In this research, the authors explored the application of variational quantum circuits to predicting hypocalcemia risk following thyroid surgery. Hypocalcemia, resulting from hypoparathyroidism, is a common post-surgical complication. This novel approach includes a topology grid search of the variational quantum circuits. To execute the grid search, our research employed a classical optimiser that guided the adjustment of different circuit topologies, assessing their impact on predictive performance. Our research used, as relevant features, an intra-operative PTH (parathyroid hormone) at 10 min post-removal and percentage decrease of pre-operative and intra-operative PTH levels. The findings revealed insights into the interplay between variational quantum circuit topologies and predictive accuracy for hypocalcemia risk assessment.
Reinforcement Procedure for Randomized Machine Learning

This paper is devoted to problem-oriented reinforcement methods for the numerical implementation of Randomized Machine Learning. We have developed a scheme of the reinforcement procedure based on the agent approach and Bellman’s optimality principle. This procedure ensures strictly monotonic properties of a sequence of local records in the iterative computational procedure of the learning process. The dependences of the dimensions of the neighborhood of the global minimum and the probability of its achievement on the parameters of the algorithm are determined. The convergence of the algorithm with the indicated probability to the neighborhood of the global minimum is proved.