Reviewerly/exHarmony huggingface dataset:
Low-Resource Expressive Text-To-Speech Using Data Augmentation

While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style. In this work, we present a novel 3-step methodology to circumvent the costly operation of recording large amounts of target data in order to build expressive style voices with as little as 15 minutes of such recordings. First, we augment data via voice conversion by leveraging recordings in the desired speaking style from other speakers. Next, we use that synthetic data on top of the available recordings to train a TTS model. Finally, we fine-tune that model to further increase quality. Our evaluations show that the proposed changes bring significant improvements over non-augmented models across many perceived aspects of synthesised speech. We demonstrate the proposed approach on 2 styles (news-caster and conversational), on various speakers, and on both single and multi-speaker models, illustrating the robustness of our approach. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Bilinear Pairing-Based Hybrid Signcryption for Secure Heterogeneous Vehicular Communications

Vehicular ad-hoc networks (VANETs) enable vehicles to exchange safety information with the surrounding vehicles and infrastructure in order to ensure safety and efficiency in traffic management. Recently however, vehicles come from different manufacturers and therefore use different protocols for the transmission of messages. As a result, communication among vehicles become heterogeneous. Because of this, VANETs face problems related to security and performance. To address this, some heterogeneous vehicular communication-based schemes have been proposed. However, they do not perform well because of an increase in computational overhead. In order to resolve this, we propose a certificateless cryptosystem (CLC) and public key infrastructure (PKI)-based conditional privacy-preserving hybrid signcryption (CP-CPPHSC) scheme. This scheme utilizes bilinear pairings and provides security requirements in a single logical step. Through the CP-CPPHSC scheme, a message is transmitted by a vehicle using the CLC to a vehicle using the PKI. Additionally, the CP-CPPHSC scheme supports the batch unsigncryption method which helps a vehicle to unsigncrypt more than one messages simultaneously. In the random oracle model (ROM), our scheme ensures existential unforgeability against adaptive chosen message attack (EUF-CMA) with respect to a hardness assumption of q-strong Diffie-Hellman (q-SDH) and modified inverse computational Diffie-Hellman (mICDH) problems and indistinguishability against adaptive chosen ciphertext attack (IND-CCA2) with respect to a hardness assumption of q-bilinear Diffie-Hellman inversion (q-BDHI) problem. Our scheme effectively reduces computational cost when compared to state-of-the-art schemes without an increase in the communication cost.
Cross-frame keypoint-based and spatial motion information-guided networks for moving vehicle detection and tracking in satellite videos

Deep learning methods have achieved the state-of-the-art performance of object detection and tracking in natural images, such as keypoint-based detectors and appearance/motion-based trackers. However, for small and blurry moving vehicles in satellite videos, keypoint-based detectors cause the missed detection of keypoints and incorrect keypoint matching. In terms of multi-object tracking, it is difficult to track the crowded similar vehicles stably only by using the appearance or motion information. To address these problems, a novel deep learning framework is proposed for moving vehicle detection and tracking in the satellite videos. It is comprised of the cross-frame keypoint-based detection network (CKDNet) and spatial motion information-guided tracking network (SMTNet). In CKDNet, a customized cross-frame module is designed to assist the detection of keypoints by exploiting complementary information between frames. Furthermore, CKDNet improves keypoint matching by incorporating size prediction around the keypoints and defining the soft mismatch suppression for out-of-size keypoint pairs. Based on high-quality detection, SMTNet can track the densely-packed vehicles effectively by constructing two-branch long short-term memories. It extracts not only spatial information of the same frame by considering the relative spatial relationship of neighboring vehicles, but also motion information among consecutive frames by calculating the movement velocity. Especially, it regresses virtual positions for missed or occluded vehicles and keeps on tracking these vehicles while they reappear. Experimental results on Jilin-1 and SkySat satellite videos demonstrate the effectiveness of the proposed detection and tracking methods.
“We Just Use What They Give Us”: Understanding Passenger User Perspectives in Smart Homes

With a plethora of off-the-shelf smart home devices available commercially, people are increasingly taking a do-it-yourself approach to configuring their smart homes. While this allows for customization, users responsible for smart home configuration often end up with more control over the devices than other household members. This separates those who introduce new functionality to the smart home (pilot users) from those who do not (passenger users). To investigate the prevalence and impact of pilot-passenger user relationships, we conducted a Mechanical Turk survey and a series of one-hour interviews. Our results suggest that pilot-passenger relationships are common in multi-user households and shape how people form habits around devices. We find from interview data that smart homes reflect the values of their pilot users, making it harder for passenger users to incorporate their devices into daily life. We conclude the paper with design recommendations to improve passenger and pilot user experience.
XRgonomics: Facilitating the Creation of Ergonomic 3D Interfaces

Arm discomfort is a common issue in Cross Reality applications involving prolonged mid-air interaction. Solving this problem is difficult because of the lack of tools and guidelines for 3D user interface design. Therefore, we propose a method to make existing ergonomic metrics available to creators during design by estimating the interaction cost at each reachable position in the user's environment. We present XRgonomics, a toolkit to visualize the interaction cost and make it available at runtime, allowing creators to identify UI positions that optimize users' comfort. Two scenarios show how the toolkit can support 3D UI design and dynamic adaptation of UIs based on spatial constraints. We present results from a walkthrough demonstration, which highlight the potential of XRgonomics to make ergonomics metrics accessible during the design and development of 3D UIs. Finally, we discuss how the toolkit may address design goals beyond ergonomics.
Improving sentence simplification model with ordered neurons network

Sentence simplification is an essential task in natural language processing and aims to simplify complex sentences while retaining their primary meanings. To date, the main research works on sentence simplification models have been based on sequence-to-sequence (Seq2Seq) models. However, these Seq2Seq models are incapable of analysing the hierarchical structure of sentences, which is of great significance for sentence simplification. The problem can be addressed with an ON-MULTI-STAGE model constructed based on the improved MULTI-STAGE encoder model. In this model, an ordered neurons network is introduced and can provide sentence-level structural information for the encoder and decoder. A weak attention connection method is then employed to make the decoder use the sentence-level structural details. Experimental results on two open data sets demonstrated that the constructed model outperforms the state-of-the-art baseline models in sentence simplification.
Adversarial unsupervised domain adaptation for 3D semantic segmentation with multi-modal learning

Semantic segmentation in 3D point-clouds plays an essential role in various applications, such as autonomous driving, robot control, and mapping. In general, a segmentation model trained on one source domain suffers a severe decline in performance when applied to a different target domain due to the cross-domain discrepancy. Various Unsupervised Domain Adaptation (UDA) approaches have been proposed to tackle this issue. However, most are only for uni-modal data and do not explore how to learn from the multi-modality data containing 2D images and 3D point clouds. We propose an Adversarial Unsupervised Domain Adaptation (AUDA) based 3D semantic segmentation framework for achieving this goal. The proposed AUDA can leverage the complementary information between 2D images and 3D point clouds by cross-modal learning and adversarial learning. On the other hand, there is a highly imbalanced data distribution in real scenarios. We further develop a simple and effective threshold-moving technique during the final inference stage to mitigate this issue. Finally, we conduct experiments on three unsupervised domain adaptation scenarios, ie., Country-to-Country (USA →Singapore), Day-to-Night, and Dataset-to-Dataset (A2D2 →SemanticKITTI). The experimental results demonstrate the effectiveness of proposed method that can significantly improve segmentation performance for rare classes. Code and trained models are available at https://github.com/weiliu-ai/auda.
Performance Comparison of Typical Binary-Integer Encodings in an Ising Machine

The differences in performance among binary-integer encodings in an Ising machine, which can solve combinatorial optimization problems, are investigated.Many combinatorial optimization problems can be mapped to find the lowest-energy (ground) state of an Ising model or its equivalent model, the Quadratic Unconstrained Binary Optimization (QUBO).Since the Ising model and QUBO consist of binary variables, they often express integers as binary when using Ising machines.A typical example is the combinatorial optimization problem under inequality constraints.Here, the quadratic knapsack problem is adopted as a prototypical problem with an inequality constraint.It is solved using typical binary-integer encodings: one-hot encoding, binary encoding, and unary encoding.Unary encoding shows the best performance for large-sized problems.
The ins and outs of speaker recognition: lessons from VoxSRC 2020

The VoxCeleb Speaker Recognition Challenge (VoxSRC) at Interspeech 2020 offers a challenging evaluation for speaker recognition systems, which includes celebrities playing different parts in movies. The goal of this work is robust speaker recognition of utterances recorded in these challenging environments. We utilise variants of the popular ResNet architecture for speaker recognition and perform extensive experiments using a range of loss functions and training parameters. To this end, we optimise an efficient training framework that allows powerful models to be trained with limited time and resources. Our trained models demonstrate improvements over most existing works with lighter models and a simple pipeline. The paper shares the lessons learned from our participation in the challenge.
A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages

During the pandemic, when people needed to physically distance, social media platforms have been one of the outlets where people expressed their opinions, thoughts, sentiments, and emotions regarding the pandemic situation. The core object of this research study is the sentiment analysis of peoples’ opinions expressed on Facebook regarding the current pandemic situation in low-resource languages. To do this, we have created a large-scale dataset comprising of 10,742 manually classified comments in the Albanian language. Furthermore, in this paper we report our efforts on the design and development of a sentiment analyser that relies on deep learning. As a result, we report the experimental findings obtained from our proposed sentiment analyser using various classifier models with static and contextualized word embeddings, that is, fastText and BERT, trained and validated on our collected and curated dataset. Specifically, the findings reveal that combining the BiLSTM with an attention mechanism achieved the highest performance on our sentiment analysis task, with an F1 score of 72.09%.
An Efficient and Flexible Accelerator Design for Sparse Convolutional Neural Networks

Designing hardware accelerators for convolutional neural networks (CNNs) has recently attracted tremendous attention. Plenty of existing accelerators are built for dense CNNs or structured sparse CNNs. By contrast, unstructured sparse CNNs can achieve higher compression ratio with equivalent accuracy. However, their corresponding hardware implementations generally suffer from load imbalance and conflict access to on-chip buffers, which results in under utilization of processing elements (PEs). To tackle these issues, we propose a hardware/power-efficient and highly flexible architecture to support both unstructured and structured sparse CNNs with various configurations. Firstly, we propose an efficient weight reordering algorithm to preprocess compressed weights and balance the workload of PEs. Secondly, an adaptive on-chip dataflow, namely hybrid parallel (HP) dataflow, is introduced to promote weight reuse. Thirdly, the partial fusion scheme, which was first introduced in one of our prior works, is incorporated as the off-chip dataflow. Benefited from dataflow optimizations, the repetitive data exchanges between on-chip buffers and external memories are significantly reduced. We implement the design on the Intel Arria10 SX660 platform and evaluate with MobileNet-v2, ResNet-50, and ResNet-18 on ImageNet dataset. Compared to existing sparse accelerators on FPGAs, the proposed accelerator can achieve 1.35 ~ 1.81× improvement in power efficiency with the same sparsity. Compared to prior dense accelerators, this accelerator can achieve an improvement of 1.92 ~ 5.84× in DSP efficiency.
How to tune the RBF SVM hyperparameters? An empirical evaluation of 18 search algorithms

SVM with an RBF kernel is usually one of the best classification algorithms for most data sets, but it is important to tune the two hyperparameters C and $$\gamma $$ to the data itself. In general, the selection of the hyperparameters is a non-convex optimization problem and thus many algorithms have been proposed to solve it, among them: grid search, random search, Bayesian optimization, simulated annealing, particle swarm optimization, Nelder Mead, and others. There have also been proposals to decouple the selection of $$\gamma $$ and C. We empirically compare 18 of these proposed search algorithms (with different parameterizations for a total of 47 combinations) on 115 real-life binary data sets. We find (among other things) that trees of Parzen estimators and particle swarm optimization select better hyperparameters with only a slight increase in computation time with respect to a grid search with the same number of evaluations. We also find that spending too much computational effort searching the hyperparameters will not likely result in better performance for future data and that there are no significant differences among the different procedures to select the best set of hyperparameters when more than one is found by the search algorithms.
Applications of Secured Blockchain Technology in the Manufacturing Industry

The manufacturing industry inclines to worldwide business operations due to the economic advantage of product design and development. In this way, globalized manufacturing supply chains make their management and control more difficult. As a distributed ledger technology that ensures transparency, trust, traceability, and cybersecurity, blockchain technology promises to ease some global manufacturing operation problems. This chapter presents blockchain technology basics and analyses the issues (e.g., traceability, cybersecurity, flexibility, and smart contracts) related to the blockchain-based manufacturing information system. Next, the chapter presents related research work in the manufacturing industry in recent years. It also includes a classification mechanism for manufacturing information systems based on specific properties. It is followed by discussing the critical issues that need to consider in designing industry-specific reference information system architecture. Finally, the chapter discusses the scope of future research.
A blockchain-based trust system for decentralised applications: When trustless needs trust

Blockchain technology has been envisaged to commence an era of decentralised applications and services (DApps) without the need for a trusted intermediary. Such DApps open a marketplace in which services are delivered to end-users by contributors which are then incentivised by cryptocurrencies in an automated, peer-to-peer, and trustless fashion. However, blockchain, consolidated by smart contracts, only ensures on-chain data security, autonomy and integrity of the business logic execution defined in smart contracts. It cannot guarantee the quality of service of DApps, which entirely depends on the services’ performance. Thus, there is a critical need for a trust system to reduce the risk of dealing with fraudulent counterparts in a blockchain network. These reasons motivate us to develop a fully decentralised trust framework deployed on top of a blockchain platform, operating along with DApps in the marketplace to demoralise deceptive entities while encouraging trustworthy ones. The trust system works as an underlying decentralised service providing a feedback mechanism for end-users and maintaining trust relationships among them in the ecosystem accordingly. We believe this research fortifies the DApps ecosystem by introducing an universal trust middleware for DApps as well as shedding light on the implementation of a decentralised trust system.
Digital Strategies: A Systematic Literature Review

This study aims to demonstrate a comprehensive view of digital strategies as a key enabling of organization digital transformation success. A systematic literature review of 40 related studies, including five dimensions of digital strategies, analyzed. Findings show that dimensions considered in both studies models a few studies link business model digital capabilities and strategies. This review demonstrates that research about digital transformation governance and digitization performance management is limited and requires more attention by research in the future.
A Closer Look at Fourier Spectrum Discrepancies for CNN-generated Images Detection

CNN-based generative modelling has evolved to produce synthetic images indistinguishable from real images in the RGB pixel space. Recent works have observed that CNN-generated images share a systematic shortcoming in replicating high frequency Fourier spectrum decay attributes. Furthermore, these works have successfully exploited this systematic shortcoming to detect CNN-generated images reporting up to 99% accuracy across multiple state-of-the-art GAN models.In this work, we investigate the validity of assertions claiming that CNN-generated images are unable to achieve high frequency spectral decay consistency. We meticulously construct a counterexample space of high frequency spectral decay consistent CNN-generated images emerging from our handcrafted experiments using DCGAN, LSGAN, WGAN-GP and StarGAN, where we empirically show that this frequency discrepancy can be avoided by a minor architecture change in the last upsampling operation. We subsequently use images from this counterexample space to successfully bypass the recently proposed forensics detector which leverages on high frequency Fourier spectrum decay attributes for CNN-generated image detection.Through this study, we show that high frequency Fourier spectrum decay discrepancies are not inherent characteristics for existing CNN-based generative models—contrary to the belief of some existing work—, and such features are not robust to perform synthetic image detection. Our results prompt re-thinking of using high frequency Fourier spectrum decay attributes for CNN-generated image detection. Code and models are available at https://keshik6.github.io/Fourier-Discrepancies-CNN-Detection/
Synthesis of covert actuator and sensor attackers

In this work, we shall investigate the problem of covert attacker synthesis in the framework of supervisory control of discrete-event systems. Intuitively, the covertness property says that the attacker cannot reach a situation where its existence has been detected by the supervisor while no damage can be caused. We consider covert attackers that can exercise both actuator attacks (including enablement attacks and disablement attacks) and sensor attacks (restricted to sensor replacement attacks), where the (partial-observation) attackers may or may not eavesdrop the control commands issued by the supervisor. We shall develop an exponential time reduction from the covert attacker synthesis problem to the well studied Ramadge–Wonham supervisor synthesis problem, which generalizes our previous work on a reduction based approach for covert actuator attacker synthesis, for both the damage-reachable goal and the damage-nonblocking goal. We also provide discussions on conditions under which the exponential blowup in state sizes, due to the reduction construction, can be avoided.
Privacy-preserving Decentralized Learning Framework for Healthcare System

Clinical trials and drug discovery would not be effective without the collaboration of institutions. Earlier, it has been at the cost of individual’s privacy. Several pacts and compliances have been enforced to avoid data breaches. The existing schemes collect the participant’s data to a central repository for learning predictions as the collaboration is indispensable for research advances. The current COVID pandemic has put a question mark on our existing setup where the existing data repository has proved to be obsolete. There is a need for contemporary data collection, processing, and learning. The smartphones and devices held by the last person of the society have also made them a potential contributor. It demands to design a distributed and decentralized Collaborative Learning system that would make the knowledge inference from every data point. Federated Learning [21], proposed by Google, brings the concept of in-place model training by keeping the data intact to the device. Though it is privacy-preserving in nature, however, it is susceptible to inference, poisoning, and Sybil attacks. Blockchain is a decentralized programming paradigm that provides a broader control of the system, making it attack resistant. It poses challenges of high computing power, storage, and latency. These emerging technologies can contribute to the desired learning system and motivate them to address their security and efficiency issues. This article systematizes the security issues in Federated Learning, its corresponding mitigation strategies, and Blockchain’s challenges. Further, a Blockchain-based Federated Learning architecture with two layers of participation is presented, which improves the global model accuracy and guarantees participant’s privacy. It leverages the channel mechanism of Blockchain for parallel model training and distribution. It facilitates establishing decentralized trust between the participants and the gateways using the Blockchain, which helps to have only honest participants.