Reviewerly/exHarmony huggingface dataset:
A Compact Framework for Voice Conversion Using Wavenet Conditioned on Phonetic Posteriorgrams

Voice conversion can benefit from WaveNet vocoder with improvement in converted speech's naturalness and quality. However, nowadays approaches segregate the training of conversion module and WaveNet vocoder towards different optimization objectives, which might lead to the difficulty in model tuning and coordination. In this paper, we propose a compact framework to unify the conversion and the vocoder parts. Multi-head self-attention structure and bidirectional long short-term memory (BLSTM) recurrent neural network (RNN) are employed to encode speaker independent phonetic posteriorgrams (PPGs) into an intermediate representation which is used as the condition input of WaveNet to generate target speaker's waveform. In this way, we unify the conversion and vocoder parts into a compact system in which all parameters can be tuned simultaneously for global optimization. We compared the proposed method with the baseline system that consists of separately trained conversion module and WaveNet vocoder. Subjective evaluations show that the proposed method can achieve better results in both naturalness and speaker similarity.
Empirical evaluation of feature projection algorithms for multi-view text classification

Abstract This study aims to propose (i) a multi-view text classification method and (ii) a ranking method that allows for selecting the best information fusion layer among many variations. Multi-view document classification is worth a detailed study as it makes it possible to combine different feature sets into yet another view that further improves text classification. For this purpose, we propose a multi-view framework for text classification that is composed of two levels of information fusion. At the first level, classifiers are constructed using different data views, i.e. different vector space models by various machine learning algorithms. At the second level, the information fusion layer uses input information using a features projection method and a meta-classifier modelled by a selected machine learning algorithm. A final decision based on classification results produced by the models positioned at the first layer is reached. Moreover, we propose a ranking method to assess various configurations of the fusion layer. We use heuristics that utilise statistical properties of F-score values calculated for classification results produced at the fusion layer. The information fusion layer of the classification framework and ranking method has been empirically evaluated. For this purpose, we introduce a use case checking whether companies’ domains identify their innovativeness. The results empirically demonstrate that the information fusion layer enhances classification quality. The Friedman’s aligned rank and Wilcoxon signed-rank statistical tests and the effect size support this hypothesis. In addition, the Spearman statistical test carried out for the obtained results demonstrated that the assessment made by the proposed ranking method converges to a well-established method named Hellinger - The Technique for Order Preference by Similarity to Ideal Solution (H-TOPSIS). Thus, the proposed approach may be used for the assessment of classifier performance.
Reimagining digital health education: Reflections on the possibilities of the storyboarding method

Objectives: To use the design method of storyboarding to challenge pre-service health education teachers to work together to think creatively and differently about digital health, to introduce pre-service teachers to the method as a pedagogical technique for use in their own classrooms, to experiment with our methods as a design sociology research project and to analyse the materials generated by the participants. Design: Storyboarding, a design research method for engagement and research that invites participants to generate a narrative using images and words, was used. Setting: We conducted a 3-hour workshop using storyboarding as part of an Australian university programme for pre-service health education teachers. Method: Following an introduction to the sociology of digital health and the possibilities of design methods, the pre-service teachers were formed into groups. Each group was provided with guidelines for imagining a new digital health device. They worked in their groups to generate a narrative in a storyboard format that described how this device would be used as part of everyday life. The groups then presented their storyboard to the class. The storyboards provided the research materials for analysing the sociotechnical imaginaries concerning digital health they presented. Results: We found that the storyboarding method worked well as an engaging and creative exercise for the participants and to generate insights for us as researchers on the ways in which they conceptualised and imagined the role of digital health technologies. However, despite attempts to educate the pre-service teachers in critical thinking in the lead-up to the workshop that emphasised the sociocultural and political contexts of health behaviours, their storyboards largely presented visions of digital health technologies that relied on individualistic behaviour change. Conclusion: After reflecting on the process, our conclusions provide some ideas for the way forward in using storyboarding as a transformative pedagogical and research tool.
Open Source Robotic Simulators Platforms for Teaching Deep Reinforcement Learning Algorithms

One of the primary goals of the artificial intelligence field is to produce fully autonomous agents that interact with theirenvironments to learn optimal behaviors, improving over time through trial and error. A mathematical principled framework for experience-driven autonomous learning is reinforcement learning, but they are inherently limited to low-dimensional problems,but the deep learning boom has provided new tools to overcome these problems. For deep reinforcement learning teaching, we do not have an appropriate platform for making optimal labs. In the article, after studying the theoretical foundations and the requirements of the main platforms, we selected two open source platforms, according to their characteristics: robotic simulators platforms for teaching and benchmarking deep reinforcement learning algorithms. The first platform was Gym and V-REP and the second one, KNIME Deeplearning4J Integration supports and Teaching-Box.
Manifold regularization based distributed semi-supervised learning algorithm using extreme learning machine over time-varying network

This paper aims to propose a distributed semi-supervised learning (SSL) algorithm using extreme learning machine (ELM) over time-varying communication network, whose topology changes over time rather than being fixed. In distributed SSL problems, training data including labeled and unlabeled samples is separately stored on each node over the communication network and cannot be centrally processed. In order to solve these problems, we propose an algorithm combining the semi-supervised ELM (SS-ELM) algorithm with the zero-gradient-sum (ZGS) distributed optimization strategy. The SS-ELM algorithm, based on the manifold regularization (MR) framework, is used to approximate the mapping of samples on each node over the communication network. Then, the ZGS strategy is used to train the globally optimal coefficients of the single layer feed-forward neural network (SLFNN) corresponding to the SS-ELM algorithm. Thus, we denote the proposed algorithm as the distributed SS-ELM (DSS-ELM) algorithm. During the training process, all nodes over the communication network exchange updated coefficients rather than raw data with their neighboring nodes. It means that the DSS-ELM algorithm is a fully distributed and privacy-preserving algorithm. The convergence of the proposed DSS-ELM is guaranteed by the Lyapunov method. At last, some simulations are presented to show the efficiency of the proposed algorithm.
Higher mathematics teaching resource scheduling system based on cloud computing

In order to improve the intelligence of higher mathematics teaching resource scheduling, a higher mathematics teaching resource scheduling system based on cloud computing is designed. A balanced modulation control algorithm for higher mathematics teaching resources is proposed on the overall design of this system and an association rule constraint model for higher mathematics teaching resource allocation is constructed based on the design results. The adaptive weighted control method is adopted for distributed control of higher mathematics teaching resources and a priority attribute list of higher teaching resource scheduling is established. The higher mathematics teaching resource allocation and adaptive information fusion are realized according to the priority list. On this basis, the higher mathematics teaching resource scheduling system based on cloud computing is designed optimally through the embedded control technology. The simulation results show that, in the allocation of teaching resources, the balance coefficient of higher mathematics teaching resource scheduling system based on cloud computing has increased by 12.54%, and the accuracy of sending and receiving teaching data provided by this system has improved by 13.6%, which indicates that this system has relatively good intelligence and strong human-computer interaction ability.
"Hello? Who Am I Talking to?" A Shallow CNN Approach for Human vs. Bot Speech Classification

Automatic speech generation algorithms, enhanced by deep learning techniques, enable an increasingly seamless and immediate machine-to-human interaction. As a result, the latest generation of phone-calling bots sounds more convincingly human than previous generations. The application of this technology has a strong social impact in terms of privacy issues (e.g., in customer-care services), fraudulent actions (e.g., social hacking) and erosion of trust (e.g., generation of fake conversation). For these reasons, it is crucial to identify the nature of a speaker, as either a human or a bot. In this paper, we propose a speech classification algorithm based on Convolutional Neural Networks (CNNs), which enables the automatic classification of human vs non-human speakers from the analysis of short audio excerpts. We evaluate the effectiveness of the proposed solution by exploiting a real human speech database populated with audio recordings from various sources, and automatically generated speeches using state-of-the-art text-to-speech generators based on deep learning (e.g., Google WaveNet).
DStore: A distributed system for outsourced data storage and retrieval

In this article, we propose a distributed outsourced data storage and retrieval system named DStore, which is constructed in a peer-to-peer networking environment. Peers (data owners) can rent the local idle disk space and large bandwidth of other peers (lessors) to store personal data and achieve fine-grained data retrieval in a distributed manner without the burden of local storage and maintenance. However, after data outsourcing, data owners no longer have physical possession of the data and face the risk of not being able to efficiently access the data. To solve the above issues, DStore employs smart contracts to audit the integrity of the outsourced data and develop a multikeyword retrieval module to support efficient access by data owners. In DStore, smart contracts can efficiently audit outsourced data without additional communication overhead for data owners; smart contracts and lessors do not compromise the privacy of data owners. Finally, we conduct an extensive security and performance analysis to ensure that DStore is secure and feasible.
Aggregation Graph Neural Networks

Graph neural networks (GNNs) regularize classical neural networks by exploiting the underlying irregular structure supporting graph data, extending its application to broader data domains. The aggregation GNN presented here is a novel GNN that exploits the fact that the data collected at a single node by means of successive local exchanges with neighbors exhibits a regular structure. Thus, regular convolution and regular pooling yield an appropriately regularized GNN. To address some scalability issues that arise when collecting all the information at a single node, we propose a multi-node aggregation GNN that constructs regional features that are later aggregated into more global features and so on. We show superior performance in a source localization problem on synthetic graphs and on the authorship attribution problem.
Learning Sparse Convolutional Neural Network via Quantization With Low Rank Regularization

With the refinement of tasks in artificial intelligence, bringing in exponential level increments in computation cost and storage.Therefore, the augment of computation resource for complicated neural networks severely hinders their applications on limited-power devices in recent years.As a result, there is an impending necessity to compress and accelerate the deep networks by special ways.Considering the different peculiarities of weight quantization and sparse regularization, in this paper, we propose a low rank sparse quantization (LRSQ) method to quantize network weights and regularize the corresponding structures at the same time.Our LRSQ can: 1) obtain low-bit quantized networks to reduce memory and computation cost and 2) learn a compact structure from complex convolutional networks for subsequent channel pruning which has significant reduction on FLOPs.In experimental sections, we evaluate the proposed method on several popular models such as VGG-7/16/19 and ResNet-18/34/50, and results show that this method can dramatically reduce parameters and channels of the network with slight inference accuracy loss.Furthermore, we also visualize and analyze the four-dimensional weight tensors, which shows the low rank and groupsparsity structure of it.Finally, we try pruning unimportant channels which are zero-channels in our quantized model, and finding even a little better precision than the standard full-precision network.
Smoothed Variable Sample-Size Accelerated Proximal Methods for Nonsmooth Stochastic Convex Programs

We consider minimizing $f(x) = \mathbb{E}[f(x,\omega)]$ when $f(x,\omega)$ is possibly nonsmooth and either strongly convex or convex in $x$. (I) Strongly convex. When $f(x,\omega)$ is $\mu-$strongly convex in $x$, we propose a variable sample-size accelerated proximal scheme (VS-APM) and apply it on $f_{\eta}(x)$, the ($\eta$-)Moreau smoothed variant of $\mathbb{E}[f(x,\omega)]$; we term such a scheme as (m-VS-APM). We consider three settings. (a) Bounded domains. In this setting, VS-APM displays linear convergence in inexact gradient steps, each of which requires utilizing an inner (SSG) scheme. Specifically, mVS-APM achieves an optimal oracle complexity in SSG steps; (b) Unbounded domains. In this regime, under a weaker assumption of suitable state-dependent bounds on subgradients, an unaccelerated variant mVS-PM is linearly convergent; (c) Smooth ill-conditioned $f$. When $f$ is $L$-smooth and $\kappa = L/\mu \ggg 1$, we employ mVS-APM where increasingly accurate gradients $\nabla_x f_{\eta}(x)$ are obtained by VS-APM. Notably, mVS-APM displays linear convergence and near-optimal complexity in inner proximal evaluations (upto a log factor) compared to VS-APM. But, unlike a direct application of VS-APM, this scheme is characterized by larger steplengths and better empirical behavior; (II) Convex. When $f(x,\omega)$ is merely convex but smoothable, by suitable choices of the smoothing, steplength, and batch-size sequences, smoothed VS-APM (or sVS-APM) produces sequences for which expected sub-optimality diminishes at the rate of $\mathcal{O}(1/k)$ with an optimal oracle complexity of $\mathcal{O}(1/\epsilon^2)$. Finally, sVS-APM and VS-APM produce sequences that converge almost surely to a solution of the original problem.
A task scheduling algorithm based on priority list and task duplication in cloud computing environment

In cloud computing environment, a larger number of tasks are executed simultaneously, and therefore task scheduling strategy is a key factor to determine the performance of the system. For the problem of static scheduling regarding related tasks in cloud environment, this paper minimizes the schedu ling length and keeps load balance as the main goal. By combining the list scheduling and task duplication algorithms, a new task scheduling algorithm is proposed. This algorithm is composed of three steps of operations. Firstly, a task scheduling queue is constructed by computing the priority value for each task; secondly, in order to reduce communication latency between tasks, the parent tasks of the current task are duplicated selectively by taking advantage of the timeslots of the current virtual machine rationally; lastly, each task is assigned to the virtual machine that made the task have the earliest execution time and keep load balanced of system. The experimental results display the influence of the number of tasks on the performance of the algorithm when CCR is different and the new task scheduling algorithm balances the loads among virtual machines in the cloud computing system, and improves the resource utilization effectively.
Bayesian population correlation: A probabilistic approach to inferring and comparing population distributions for detrital zircon ages

Populations of detrital zircons are shaped by geologic factors such as sediment transport, erosion mechanisms, and the zircon fertility of source areas. Zircon U-Pb age datasets are influenced both by these geologic factors and by the statistical effects of sampling. Such statistical effects introduce significant uncertainty into the inference of parent population age distributions from detrital zircon samples. This uncertainty must be accounted for in order to understand which features of sample age distributions are attributable to earth processes and which are sampling effects. Sampling effects are likely to be significant at a range of common detrital zircon sample sizes (particularly when n < 300). In order to more accurately account for the uncertainty in estimating parent population age distributions, we introduce a new method to infer probability model ensembles (PMEs) from detrital zircon samples. Each PME represents a set of the potential parent populations that are likely to have produced a given zircon age sample. PMEs form the basis of a new metric of correspondence between two detrital zircon samples, Bayesian Population Correlation (BPC), which is shown in a suite of numerical experiments to be unbiased with respect to sample size. BPC uncertainties can be directly estimated for a specific sample comparison, and BPC results conform to analytical predictions when comparing populations with known proportions of shared ages. We implement all of these features in a set of MATLAB® scripts made freely available as open-source code and as a standalone application. The robust uncertainties, lack of sample size bias, and predictability of BPC are desirable features that differentiate it from existing detrital zircon correspondence metrics. Additionally, analysis of other sample limited datasets with complex probability distributions may also benefit from our approach.
Micro-Expression Spotting Using Facial Landmarks

Micro-expressions (MEs) are brief involuntary facial expressions and it usually happens when people try to mask their emotions in high-stake situations. As a result, it is hard to detect the occurrence of a spontaneous MEs due to the limitation of human vision in spotting the brief and subtle change of facial expression. A MEs recognition system can be categorized into two major tasks which are the spotting of the fleeting change of facial expression and the classification of the emotion behind the spotted MEs. In fact, most of the spotting of MEs proposed in the earlier work by other researchers have low accuracy and complex system model. As a result, this paper proposed a MEs spotting system that requires no training and it spots the spontaneous MEs from the videos by detecting the changes in the ratio of the Euclidean distances of facial landmark in three facial regions (left eyebrow, right eyebrow and mouth) .The proposed method was evaluated on CASME II dataset with an average accuracy of 64.77 % and the highest accuracy was 82.30 %.
Underneath the Skin

During the last decade, people have started to experiment with insertable technology like RFID or NFC chips and use them for e.g. identification. However, little is known about how people in fact interact with and adapt insertables. We conducted a video analysis of 122 YouTube videos to gain insight into the interaction with the insertables. Second, we implemented an online survey to complement our data from the video analysis. Our findings show that there are many opportunities for interaction with insertables both for task-oriented and creative purposes. However, there are also multiple challenges and obstacles as well as side effects and health concerns. Our findings conclude that the current infrastructure is not ready to support the use of insertables yet, and we discuss implications of this.
An empirical study of mnemonic password creation tips

Usually, strong passwords tend to be hard to memorize, and easy-to-remember passwords tend to be predictable. User-created passwords are memorable but may be easily cracked. Is there any strategy that can help users choose a strong but still memorable password? Some users have already used mnemonic tips to create passwords, are the passwords created by these tips strong passwords? In this paper, we studied the security and usability of four common mnemonic password creation tips. We recruited 209 participants in an online study, and compared the strength of passwords created by the 4 tips with two commonly used datasets 178 website and phpBB website against guessing attacks. We utilized PCFG and Markov Model to analyze the guessability of passwords created by the 4 tips and the two control groups. We also used the approach of the statistical quantities to measure the distributions of the passwords for each group, for example, we chose to use minimum entropy, β-success-rate and α-guesswork. In addition, we also analyzed the security of these four password creation tips under known attacks. To evaluate the usability of these tips, we conducted user study in the lab. We found some expected and unexpected results, these include: Under an unknown attack, the strength of passwords created by the 4 tips is stronger than those in the two control groups. Passwords created by mnemonic tips are not necessarily strong passwords, some mnemonic tips are more convenient to create strong passwords, while others are the opposite. Under known attacks, some mnemonic tips are not resistant to offline guessing attacks. Usability results shown that these mnemonic tips exhibit inconsistent memorability.
"Can you believe [1:21]?!"

As videos become increasingly ubiquitous, so is video-based commenting. To contextualize comments, people often reference specific audio/visual content within video. However, the literature falls short of explaining the types of video content people refer to, how they establish references and identify referents, how video characteristics (e.g., genre) impact referencing behaviors, and how references impact social engagement. We present a taxonomy for classifying video references by referent type and temporal specificity. Using our taxonomy, we analyzed 2.5K references with quotations and timestamps collected from public YouTube comments. We found: 1) people reference intervals of video more frequently than time-points, 2) visual entities are referenced more often than sounds, and 3) comments with quotes are more likely to receive replies but not more "likes". We discuss the need for in-situ dereferencing user interfaces, illustrate design concepts for typed referencing features, and provide a dataset for future studies.