Reviewerly/exHarmony huggingface dataset:
End-to-End Lyrics Recognition with Voice to Singing Style Transfer

Automatic transcription of monophonic/polyphonic music is a challenging task due to the lack of availability of large amounts of transcribed data. In this paper, we propose a data augmentation method that converts natural speech to singing voice based on vocoder based speech synthesizer. This approach, called voice to singing (V2S), performs the voice style conversion by modulating the F0 contour of the natural speech with that of a singing voice. The V2S model based style transfer can generate good quality singing voice thereby enabling the conversion of large corpora of natural speech to singing voice that is useful in building an E2E lyrics transcription system. In our experiments on monophonic singing voice data, the V2S style transfer provides a significant gain (relative improvements of 21 %) for the E2E lyrics transcription system. We also discuss additional components like transfer learning and lyrics based language modeling to improve the performance of the lyrics transcription system.
Viewer’s Role and Viewer Interaction in Cinematic Virtual Reality

Cinematic Virtual Reality (CVR) is a form of immersive storytelling widely used to create engaging and enjoyable experiences. However, issues related to the Narrative Paradox and Fear of Missing Out (FOMO) can negatively affect the user experience. In this paper, we review the literature about designing CVR content with the consideration of the viewer’s role in the story, the target scenario, and the level of viewer interaction, all aimed to resolve these issues. Based on our explorations, we propose a “Continuum of Interactivity” to explore appropriate spaces for creating CVR experiences to archive high levels of engagement and immersion. We also discuss two properties to consider when enabling interaction in CVR, the depth of impact and the visibility. We then propose the concept framework Adaptive Playback Control (APC), a machine-mediated narrative system with implicit user interaction and backstage authorial control. We focus on “swivel-chair” 360-degree video CVR with the aim of providing a framework of mediated CVR storytelling with interactivity. We target content creators who develop engaging CVR experiences for education, entertainment, and other applications without requiring professional knowledge in VR and immersive systems design.
High-Intelligibility Speech Synthesis for Dysarthric Speakers with LPCNet-Based TTS and CycleVAE-Based VC

This paper presents a high-intelligibility speech synthesis method for persons with dysarthria caused by athetoid cerebral palsy. The muscular control of such speakers is unstable because of their athetoid symptoms, and their pronunciation is unclear, which makes it difficult for them to communicate. In this paper, we present a method for generating highly intelligible speech that preserves the individuality of dysarthric speakers by combining Transformer-TTS, CycleVAE-VC, and a LPCNet vocoder. Rather than repairing prosody from the dysarthric speech, this method transfers the dysarthric speaker’s individuality to the speech of a healthy person generated by TTS synthesis. This task is both important and challenging. From the results of our evaluation experiments, we confirmed that the proposed method can partially transfer the individuality of the target dysarthric speaker while maintaining the intelligibility of the source speech.
Adversarial Defense for Deep Speaker Recognition Using Hybrid Adversarial Training

Deep neural network based speaker recognition systems can easily be deceived by an adversary using minuscule imperceptible perturbations to the input speech samples. These adversarial attacks pose serious security threats to the speaker recognition systems that use speech biometric. To address this concern, in this work, we propose a new defense mechanism based on a hybrid adversarial training (HAT) setup. In contrast to existing works on countermeasures against adversarial attacks in deep speaker recognition that only use class-boundary information by supervised cross-entropy (CE) loss, we propose to exploit additional information from supervised and unsupervised cues to craft diverse and stronger perturbations for adversarial training. Specifically, we employ multi-task objectives using CE, feature-scattering (FS), and margin losses to create adversarial perturbations and include them for adversarial training to enhance the robustness of the model. We conduct speaker recognition experiments on the Librispeech dataset, and compare the performance with state-of-the-art projected gradient descent (PGD)-based adversarial training which employs only CE objective. The proposed HAT improves adversarial accuracy by absolute 3.29% and 3.18% for PGD and Carlini-Wagner (CW) attacks respectively, while retaining high accuracy on benign examples.
An improved evolutionary wrapper-filter feature selection approach with a new initialisation scheme

Treated as one of the popular measures in information theory, fuzzy mutual information quantifies the amount of information that one random variable has about another one. Different from standard mutual information, fuzzy mutual information can deal with not only discrete-valued but also real-valued variables. Therefore, fuzzy mutual information has been recently used in evolutionary filter feature selection approaches to measure the correlation between the classes and the features, and the dependencies within a feature set. Typically, this way can be considered as computationally efficient but sometimes it may not contribute to the performance of a classification algorithm. To address this issue, an improved evolutionary wrapper-filter approach which integrates an initialisation scheme and a local search module based on fuzzy mutual information in differential evolution is proposed. According to a number of experiments conducted on several real-world benchmark datasets, the proposed approach does not only significantly improve the computational efficiency of an evolutionary computation technique but also the performance of a classification algorithm.
Sensor fault–tolerant control of a quadrotor unmanned aerial vehicle

This article handles the issue of fault-tolerant control of a quadrotor unmanned aerial vehicle (UAV) in the existence of sensor faults. A general non-linear model of the quadrotor is presented. Several non-linear Kalman filters namely, the extended Kalman filter, the unscented Kalman filter and the cubature Kalman filter (CKF) are utilized to estimate the states of the quadrotor and to compare the estimation performances. Some flight scenarios are simulated, and the simulation results show that the CKF has the smallest estimation error as expected in theory. Control of the quadrotor heavily depends on the measured values received from sensors. Therefore, the control system requires fault-free sensors. However, small quadrotors and UAVs are mostly equipped with low-cost and low-quality sensors, and hence, they may fail to indicate correct measurement values. If the sensors are faulty, then the control system itself should be actively tolerant to sensor faults. Measurements of these kinds of sensors suffer from bias and external noise due to temperature variations, vibration and other external conditions. Since the bias is one of the very common faults in these sensors, a sensor bias is taken into consideration as a fault and occurs abruptly at a certain time and continues throughout the considered scenarios. By using the residual signals generated by the non-linear filters, sensor faults are detected and isolated. Then, two different methods are proposed for removing the effects of faults and achieving active fault–tolerant control. The effectiveness of the presented two techniques is shown in the simulations.
Anomaly Detection With Kernel Preserving Embedding

Similarity representation plays a central role in increasingly popular anomaly detection techniques, which have been successfully applied in various realistic scenes. Until now, many low-rank representation techniques have been introduced to measure the similarity relations of data; yet, they only concern to minimize reconstruction errors, without involving the structural information of data. Besides, the traditional low-rank representation methods often take nuclear norm as their low-rank constraints, easily yielding a suboptimal solution. To address the problems above, in this article, we propose a novel anomaly detection method, which exploits kernel preserving embedding, as well as the double nuclear norm, to explore the similarity relations of data. Based on the similarity relations, a kind of probability transition matrix is derived, and a tailored random walk is further adopted to reveal anomalies. The proposed method can not only preserve the manifold structural properties of the data, but also alleviate the suboptimal problem. To validate the superiority of our method, extensive experiments with eight popular anomaly detection algorithms were conducted on 12 widely used datasets. The experimental results show that our detection method outperformed the state-of-the-art anomaly detection algorithms in most cases.
A PV generation data reconstruction method based on improved super-resolution generative adversarial network

With the growing penetration of solar photovoltaic (PV) generation, advanced data analysis methods have been applied to the smart grid operation. However, the low-temporal-resolution PV generation data limits the utilization of the data analysis methods, because the low-temporal-resolution PV generation data contains too little information. On the other hand, the existing data reconstruction methods are less than satisfactory in reconstructing high-temporal-resolution PV generation data from low-temporal-resolution data, since most of them cannot fully capture the characteristics of PV generation data. To address this issue, a PV generation data reconstruction method based on improved super-resolution generative adversarial network is proposed in this paper. First, a data-image construction method is proposed to encode the PV generation data into the so-called data-images. Furthermore, we develop a data-image super-resolution generative adversarial network (DISRGAN) model, and the data-images are used to train the DISRGAN model. Finally, based on the trained DISRGAN model, a general framework is developed to reconstruct high-temporal-resolution PV generation data from low-temporal-resolution data. Numerical experiments have been carried out based on PV generation data from the State Grid Corporation of China, to reconstruct the high-temporal-resolution data from low-temporal-resolution data. The results demonstrate the superior performance of the proposed framework compared with a series of state-of-the-art methods.
Arguments for the unsuitability of convolutional neural networks for non-local tasks

Convolutional neural networks have established themselves over the past years as the state of the art method for image classification, and for many datasets, they even surpass humans in categorizing images. Unfortunately, the same architectures perform much worse when they have to compare parts of an image to each other to correctly classify this image. Until now, no well-formed theoretical argument has been presented to explain this deficiency. In this paper, we will argue that convolutional layers are of little use for such problems, since comparison tasks are global by nature, but convolutional layers are local by design. We will use this insight to reformulate a comparison task into a sorting task and use findings on sorting networks to propose a lower bound for the number of parameters a neural network needs to solve comparison tasks in a generalizable way. We will use this lower bound to argue that attention, as well as iterative/recurrent processing, is needed to prevent a combinatorial explosion.
A Hybrid Framework for Securing Data Transmission in Internet of Things (IoTs) Environment using Blockchain Approach

Internet of Things (IoT) is a relatively new concept in computer science that connects devices with constrained resources to insecure internet through different technologies. IoT's fundamental components, including the wireless sensor networks and the internet, have an unsecured foundation that leads to DoS attacks, namely, sinkhole, blackhole, and grey hole attacks. To maintain the integrity and security of the IoT networks, many researchers implemented distributed ledgers in IoT environments. In this paper, we designed a hybrid framework between blockchain and IoT devices; the aim is to secure data transmission in IoT networks using blockchain technology to defend against DoS attacks. Our framework is called HFSDT-IoT. The method proposed in this paper consists of two phases to maintain security in the IoT. In the first phase, both a list of attackers and a safe list based on the Ethereum Proof-of-Stake (PoS) protocol is used, which complemented by utilizing the proposed IDSs Intrusion Detection System to discover malicious things. In the second phase, to decrease the obstacles in facilitating communication between blockchains, the inter-blockchain communication model creates a network of multiple blockchains with secp256k1 encryption used for heterogeneous blockchains. The experimental results of simulated scenarios show the HFSDT-IoT strategy can achieve better results when DoS attacks were launched compared to other blockchain-based methods, namely Bubble of Trust and Credibility Verification Method.
Mixed Reality in Asymmetric Collaborative Environments: A Research Prototype for Virtual City Tours

Augmented and virtual reality have the potential to improve various aspects of everyday life such as tourism. An elementary part of tourism is the organization and execution of guided city tours. In case of tours for virtual cities, such as in the case of no longer existing ancient settlements, a combination of augmented and virtual reality may render high benefit for collaboration between guide and visitors. Immersive technology has been shown suitable for collaborative systems by previous research, as they can provide a better experience than other technologies. However, recent literature analyses showed that there is a lack of research regarding the role of augmented reality supporting the active/leading user role in such scenarios. Usually, augmented reality is used for the users who are passively experiencing the virtual environment and not for the one controlling it. However, we think that augmented reality has a large potential to improve the workflow of such a primary user. To investigate this research question, we present a collaborative system in this work, which implements a virtual city tour. In this system, the guide is using augmented reality and the visitor is immersed in the virtual city using virtual reality glasses. To implement a valid research prototype, the system has been developed in close feedback with experts in the field of guided city tours.
Topological Volterra Filters

To deal with high-dimensional data, graph filters have shown their power in both graph signal processing and data science. However, graph filters process signals exploiting only pairwise interactions between the nodes, and they are not able to exploit more complicated topological structures. Graph Volterra models, on the other hand, are also able to exploit relations between triplets, quadruplets and so on. However, they have only been exploited for topology identification and are only based on one-hop relations. In this paper, we first review graph filters and graph Volterra models and then merge the two concepts resulting in so-called topological Volterra filters (TVFs). TVFs process signals over multiple hops of higher-level topological structures. First-level TVFs are basically similar to traditional graph filters, yet higher-level TVFs provide a more general processing framework. We apply TVFs to inverse filtering and recommender systems.
Smart Home Sensor Anomaly Detection Using Convolutional Autoencoder Neural Network

We propose an autoencoder based approach to anomaly detection in smart grid systems. Data collecting sensors within smart home systems are susceptible to many data corruption issues, such as malicious attacks or physical malfunctions. By applying machine learning to a smart home or grid, sensor anomalies can be detected automatically for secure data collection and sensor-based system functionality. In addition, we tested the effectiveness of this approach on real smart home sensor data collected for multiple years. An early detection of such data corruption issues is essential to the security and functionality of the various sensors and devices within a smart home.
Content-based Clothing Recommender System using Deep Neural Network

A recommender system primary purpose is to provide a series of item suggestions on a topic to its user. Deep learning is used in many fields and solved difficult and complex problems with large volumes of data. Deep learning can also be used in referral systems. Today, online shopping systems are looking for a method that can recommend items according to the user preference and interest in order to increase their sales. Clothing sales systems offer a set of recommendation based on the needs and interests of the users. Today, due to the current situation caused by the Coronavirus, the majority of tasks are done online. In this paper, we propose a content-based clothing recommender system using deep neural network. In content-based systems, product features are required for prediction of unobserved items ratings. In our proposed system by using a deep neural network, the cloth category is obtained and the need to manually extract the product features is eliminated by producing the required features with a large and useful volume. The advantage of this system is that it uses the same network to specify gender as a feature in making suggestions then shows the results to the user. Different machine learning algorithms are tested and analyzed with and without considering demographic information such as gender. The experimental results show that the loss of our proposed system is lower than the other related systems and solves the cold start problem for new items. Our proposed system also recommends novel, relevant and unexpected items.
Zero-Shot Voice Conversion with Adjusted Speaker Embeddings and Simple Acoustic Features

Zero-shot voice conversion (VC) where both source and target speakers are unseen in the training dataset has become a new research direction. Using speaker embeddings instead of one-hot vectors to represent speaker identity is a key point, which makes VC models work on unseen speakers. In our work, a newly designed neural network was used to adjust the speaker embeddings of unseen speakers. This enables speaker embeddings to perform better on zero-shot VC. In addition, disentangled representation of features is the mainstream method to achieve zero-shot VC. In terms of input features of VC model, we use Mel-cepstral and F0 as simple acoustic features (SAF) rather than Mel-spectrograms. This avoids F0 conflicts in decoder that existed in the previous methods. The evaluations demonstrate that our proposed methods improve the quality of converted speech in terms of naturalness and similarity.
DepOwl: Detecting Dependency Bugs to Prevent Compatibility Failures

Applications depend on libraries to avoid reinventing the wheel. Libraries may have incompatible changes during evolving. As a result, applications will suffer from compatibility failures. There has been much research on addressing detecting incompatible changes in libraries, or helping applications co-evolve with the libraries. The existing solution helps the latest application version work well against the latest library version as an afterthought. However, end users have already been suffering from the failures and have to wait for new versions. In this paper, we propose DepOwl, a practical tool helping users prevent compatibility failures. The key idea is to avoid using incompatible versions from the very beginning. We evaluated DepOwl on 38 known compatibility failures from StackOverflow, and DepOwl can prevent 35 of them. We also evaluated DepOwl using the software repository shipped with Ubuntu-19.10. DepOwl detected 77 unknown dependency bugs, which may lead to compatibility failures.
Application of Blockchain Technology in Supply Chain Finance

This article discusses the basic structure, application characteristics, and basic classification of blockchain technology. The author studies the development trend of blockchain technology in supply chain finance through the combination of the specific application of blockchain technology in the process of information integration, accounts receivable processing, credit transmission process, information supervision and convenience, chattel pledge management, service platform operation, financial financing process and other links. The author's purpose is to improve the stability of the supply chain financial development process and promote the healthy development of the financial industry economy.
Bi-APC: Bidirectional Autoregressive Predictive Coding for Unsupervised Pre-Training and its Application to Children’s ASR

We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children’s automatic speech recognition (ASR). An obstacle to improving child ASR is the scarcity of child speech databases. A common approach to alleviate this problem is model pre-training using data from adult speech. Pre-training can be done using supervised (SPT) or unsupervised methods, depending on the availability of annotations. Typically, SPT performs better. In this paper, we focus on UPT to address the situations when pre-training data are unlabeled. Autoregressive predictive coding (APC), a UPT method, predicts frames from only one direction, limiting its use to uni-directional pre-training. Conventional bidirectional UPT methods, however, predict only a small portion of frames. To extend the benefits of APC to bi-directional pre-training, Bi-APC is proposed. We then use adaptation techniques to transfer knowledge learned from adult speech (using the Librispeech corpus) to child speech (OGI Kids corpus). LSTM-based hybrid systems are investigated. For the uni-LSTM structure, APC obtains similar WER improvements to SPT over the baseline. When applied to BLSTM, however, APC is not as competitive as SPT, but our proposed Bi-APC has comparable improvements to SPT.
Composite matrices from group rings, composite G-codes and constructions of self-dual codes

Abstract In this work, we define composite matrices which are derived from group rings. We extend the idea of G -codes to composite G -codes. We show that these codes are ideals in a group ring, where the ring is a finite commutative Frobenius ring and G is an arbitrary finite group. We prove that the dual of a composite G -code is also a composite G -code. We also define quasi-composite G -codes. Additionally, we study generator matrices, which consist of the identity matrices and the composite matrices. Together with the generator matrices, the well known extension method, the neighbour method and its generalization, we find extremal binary self-dual codes of length 68 with new weight enumerators for the rare parameters $$\gamma =7,8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>γ</mml:mi> <mml:mo>=</mml:mo> <mml:mn>7</mml:mn> <mml:mo>,</mml:mo> <mml:mn>8</mml:mn> </mml:mrow> </mml:math> and 9. In particular, we find 49 new such codes. Moreover, we show that the codes we find are inaccessible from other construction