Reviewerly/exHarmony huggingface dataset:
VQVAE Unsupervised Unit Discovery and Multi-Scale Code2Spec Inverter for Zerospeech Challenge 2019

We describe our submitted system for the ZeroSpeech Challenge 2019. The current challenge theme addresses the difficulty of constructing a speech synthesizer without any text or phonetic labels and requires a system that can (1) discover subword units in an unsupervised way, and (2) synthesize the speech with a target speaker's voice. Moreover, the system should also balance the discrimination score ABX, the bit-rate compression rate, and the naturalness and the intelligibility of the constructed voice. To tackle these problems and achieve the best trade-off, we utilize a vector quantized variational autoencoder (VQ-VAE) and a multi-scale codebook-to-spectrogram (Code2Spec) inverter trained by mean square error and adversarial loss. The VQ-VAE extracts the speech to a latent space, forces itself to map it into the nearest codebook and produces compressed representation. Next, the inverter generates a magnitude spectrogram to the target voice, given the codebook vectors from VQ-VAE. In our experiments, we also investigated several other clustering algorithms, including K-Means and GMM, and compared them with the VQ-VAE result on ABX scores and bit rates. Our proposed approach significantly improved the intelligibility (in CER), the MOS, and discrimination ABX scores compared to the official ZeroSpeech 2019 baseline or even the topline.
SLA-Aware Best Fit Decreasing Techniques for Workload Consolidation in Clouds

Cloud computing emerged as one of the leading computational paradigms due to elastic resource provisioning and pay-as-you-go model. Large data centers are used by the service providers to host the various services. These data centers consume enormous energy, which leads to increase in operating costs and carbon footprints. Therefore, green cloud computing is a necessity, which not only reduces energy consumption, but also affects the environment positively. In order to reduce the energy consumption, workload consolidation approach is used that consolidates the tasks in minimum possible servers. However, workload consolidation may lead to service level agreement (SLA) violations due to non-availability of resources on the server. Therefore, workload consolidation techniques should consider the aforementioned problem. In this paper, we present two consolidation based energy-efficient techniques that reduce energy consumption along with resultant SLA violations. In addition to that, we also enhanced the existing Enhanced-Conscious Task Consolidation (ECTC) and Maximum Utilization (MaxUtil) techniques that attempt to reduce energy consumption and SLA violations. Experimental results show that the proposed techniques perform better than the selected heuristic based techniques in terms of energy, SLA, and migrations.
Heuristic-based strategy for Phishing prediction: A survey of URL-based approach

In the fight against phishing attacks, phishing prediction heuristics are important in developing solutions. However, phishing attacks continue to grow today, reflecting on the need for higher precision solutions. This article focuses on phishing prediction based on a set of features. The purpose of this proposal is to evaluate the static features used and observe their occurrence in the current phishing. Static aspects refer to elements such as keywords and patterns over the phishing URL. The study methodology makes use of a survey with a set of 12 features, raised both in this study and from third-party studies, submitted to three distinct samples of phishing and legitimate sites during the year 2018. Although research on phishing prediction has developed considerably, it is possible to note that certain features are of low relevant and others have not accompanied the changes in the scenario and may need to be discarded. Some features are found more regularly in phishing and could be more efficiently exploited, indicating that further investigations need to be carried out. In addition to the quantitative data, the study also performed a qualitative analysis of behaviors, managing to identify aspects such as relevance, relationships, and similarities among the features. It is expected that these results obtained can help in developing new heuristic approaches or improve the robustness of the existing ones.
Global-Local Mutual Attention Model for Text Classification

Text classification is a central field of inquiry in natural language processing (NLP). Although some models learn local semantic features and global long-term dependencies simultaneously, they simply combine them through concatenation either in a cascade way or in parallel while mutual effects between them are ignored. In this paper, we propose the Global-Local Mutual Attention (GLMA) model for text classification problems, which introduces a mutual attention mechanism for mutual learning between local semantic features and global long-term dependencies. The mutual attention mechanism consists of a Local-Guided Global-Attention (LGGA) and a Global-Guided Local-Attention (GGLA). The LGGA allows to assign weights and combine global long-term dependencies of word positions that are semantic related. It captures combined semantics and alleviates the gradient vanishing problem. The GGLA automatically assigns more weights to relevant local semantic features, which captures key local semantic information and filters both noises and irrelevant words/phrases. Furthermore, a weighted-over-time pooling operation is developed to aggregate the most informative and discriminative features for classification. Extensive experiments demonstrate that our model obtains the state-of-the-art performance on seven benchmark datasets and sixteen Amazon product reviews datasets. Both the result analysis and the mutual attention weights visualization further demonstrate the effectiveness of the proposed model.
Probabilistic solutions to ordinary differential equations as nonlinear Bayesian filtering: a new perspective

Abstract We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with nonlinear measurement functions. This is achieved by defining the measurement sequence to consist of the observations of the difference between the derivative of the GP and the vector field evaluated at the GP—which are all identically zero at the solution of the ODE. When the GP has a state-space representation, the problem can be reduced to a nonlinear Bayesian filtering problem and all widely used approximations to the Bayesian filtering and smoothing problems become applicable. Furthermore, all previous GP-based ODE solvers that are formulated in terms of generating synthetic measurements of the gradient field come out as specific approximations. Based on the nonlinear Bayesian filtering problem posed in this paper, we develop novel Gaussian solvers for which we establish favourable stability properties. Additionally, non-Gaussian approximations to the filtering problem are derived by the particle filter approach. The resulting solvers are compared with other probabilistic solvers in illustrative experiments.
Virtual Reality in Art Therapy: A Pilot Qualitative Study of the Novel Medium and Implications for Practice

Virtual reality (VR) therapies have been used in health and mental health care settings. The applicability of VR expressive tools has not yet been examined adequately in the creative arts therapies. This pilot qualitative study reports on 17 participants' experiences with VR art-making tools to determine its relevance to art therapy practice and research. Findings indicate that VR-based self-expression is an embodied visual expression, generates novel artistic and imaginal responses, and requires a developmental trajectory in expression and mastery. The unique characteristics of art therapy in VR were identified as positive emotions, play and exploration, learning and mastery, access, and storage. VR has the potential to enhance psychological health and well-being through creativity, enhanced imagination, interactivity, and problem solving.
An improved image encryption algorithm for satellite applications

Encryption on-board Earth observation satellites aims to secure the amount of data transmitted to the ground station. During the last two decades, chaos-based encryption has reached an important achievement in securing data transfers. In this paper, an efficient image encryption method based on chaotic maps and the Advanced Encryption Standard (AES) is proposed for application on-board Earth observation satellites. The proposed method has many technical and attractive features, including a high security level and tolerance to Single Event Upsets (SEU). Performance results for the proposed image encryption system are evaluated and compared to recent works. Moreover, the proposed algorithm was implemented on a modern reconfigurable FPGA device (XILINX Artix-7 XC7A100T) to evaluate the resulting performance and resource utilization. The results show that a fast (1.75 Gbit/s) and a highly secure data encryption is achieved with low power consumption (1.37 W) and that the algorithm is robust, which makes it applicable on board Earth Observation Satellites (EOS). Beyond satellite images, the proposed algorithm is also better suited for the secure encryption of normal digital images.
CCFR2: A more efficient cooperative co-evolutionary framework for large-scale global optimization

Cooperative co-evolution (CC) is an explicit means of divide-and-conquer strategy in evolutionary algorithms for solving large-scale optimization problems. The subcomponents generated by CC may have different characteristics. When optimizing the subcomponents, the settings of the subpopulations should take the characteristics into account. CCFR is a previously published CC framework which allocates computational resources among subpopulations according to the contributions of subpopulations to the improvement of the best overall objective value. In this paper, we propose an improved version of CCFR named CCFR2, which can specify unequal-sized subpopulations for optimizing different subcomponents of variables. CCFR2 computes the average improvement of the best overall objective value per fitness evaluation as the contribution of a subpopulation, which considers the subpopulation size in the contribution computation. A control parameter is adopted by CCFR2 to balance the effects of the historical and real-time improvements of the best overall objective value on the contribution computation. Compared with CCFR, CCFR2 is able to save computational resources from obtaining the best overall solution before the evolution starts and evaluating individuals in co-evolutionary cycles. Our experimental results and analysis suggest that CCFR2 improves the performance of CCFR and is a more efficient CC framework for solving large-scale optimization problems.
Teaching International Business Using Virtual Reality

New generations of International Business (IB) students include the Centennials, also known as Generation Z. These students have experienced their whole life in a virtual and physical reality. They learn differently than previous generations and have an even shorter span of attention than Millennials. We – IB educators – need to acknowledge these changes in students, and propose new educational solutions that keep on preparing them to succeed in the international arena. In this paper, we suggest Virtual Reality (VR) as an innovative, cheaper and effective alternative solution to make IB students practice competencies. Based on Experiential Learning Theory, Social Cognitive Theory and Generational Theory, we offer ideas of several VR applications in the field. As an example, we describe an ongoing VR project using avatars to make students practice negotiation and intercultural communication. We report its preliminary findings. Paper ends with a discussion on VR teaching implications. Today, when multinational organizations are increasing the use of state-of-the-art technologies, initiatives like this can contribute to make IB students to exercise digital competencies too.
3D sign language recognition with joint distance and angular coded color topographical descriptor on a 2 – stream CNN

Currently, one of the challenging and most interesting human action recognition (HAR) problems is the 3D sign language recognition problem. The sign in the 3D video can be characterized in the form of 3D joint location information in 3D space over time. Therefore, the objective of this study is to construct a color coded topographical descriptor from joint distances and angles computed from joint locations. We call these two color coded images the joint distance topographic descriptor (JDTD) and joint angle topographical descriptor (JATD) respectively. For the classification we propose a two stream convolutional neural network (2CNN) architecture, which takes as input the color-coded images JDTD and JATD. The two independent streams were merged and concatenated together with features from both streams in the dense layer. For a given query 3D sign (or action), a list of class scores was obtained as a text label corresponding to the sign. The results showed improvement in classifier performance over the predecessors due to the mixing of distance and angular features for predicting closely related spatio temporal discriminative features. To benchmark the performance of our proposed model, we compared our results with the state-of-the-art baseline action recognition frameworks by using our own 3D sign language dataset and two publicly available 3D mocap action datasets, namely, HDM05 and CMU.
Correlation-Based Ensemble Feature Selection Using Bioinspired Algorithms and Classification Using Backpropagation Neural Network

A framework for clinical diagnosis which uses bioinspired algorithms for feature selection and gradient descendant backpropagation neural network for classification has been designed and implemented. The clinical data are subjected to data preprocessing, feature selection, and classification. Hot deck imputation has been used for handling missing values and min-max normalization is used for data transformation. Wrapper approach that employs bioinspired algorithms, namely, Differential Evolution, Lion Optimization, and Glowworm Swarm Optimization with accuracy of AdaBoostSVM classifier as fitness function has been used for feature selection. Each bioinspired algorithm selects a subset of features yielding three feature subsets. Correlation-based ensemble feature selection is performed to select the optimal features from the three feature subsets. The optimal features selected through correlation-based ensemble feature selection are used to train a gradient descendant backpropagation neural network. Ten-fold cross-validation technique has been used to train and test the performance of the classifier. Hepatitis dataset and Wisconsin Diagnostic Breast Cancer (WDBC) dataset from University of California Irvine (UCI) Machine Learning repository have been used to evaluate the classification accuracy. An accuracy of 98.47% is obtained for Wisconsin Diagnostic Breast Cancer dataset, and 95.51% is obtained for Hepatitis dataset. The proposed framework can be tailored to develop clinical decision-making systems for any health disorders to assist physicians in clinical diagnosis.
Computational Sleep Behavior Analysis: A Survey

Sleep is a key marker of health, as it can either be a cause or a consequence. It is traditionally studied in clinical environments using dedicated medical devices. Recent technological developments, e.g., in sensing and data analysis, have led to new approaches for sleep monitoring and assessment, which are attracting increasing attention in the emerging domain of personalized smart healthcare. Nevertheless, a high-level overview of technology-enabled research on sleep that can inform related communities of the latest developments is lacking. In this paper, we present a comprehensive review to examine the current status of various aspects of technology-based sleep research. We first characterize sleep behavior and key areas of sleep assessment, and we introduce a general review of the methodologies used in this domain. We review the major technological methods and trends associated with sleep monitoring, data collection and sleep behavior analysis, from which strengths and weaknesses are highlighted. Finally, we also discuss challenges and promising directions for future research.
Unsupervised Monocular Depth Estimation From Light Field Image

Learning based depth estimation from light field has made significant progresses in recent years. However, most existing approaches are under the supervised framework, which requires vast quantities of ground-truth depth data for training. Furthermore, accurate depth maps of light field are hardly available except for a few synthetic datasets. In this paper, we exploit the multi-orientation epipolar geometry of light field and propose an unsupervised monocular depth estimation network. It predicts depth from the central view of light field without any ground-truth information. Inspired by the inherent depth cues and geometry constraints of light field, we then introduce three novel unsupervised loss functions: photometric loss, defocus loss and symmetry loss. We have evaluated our method on a public 4D light field synthetic dataset. As the first unsupervised method published in the 4D Light Field Benchmark website, our method can achieve satisfactory performance in most error metrics. Comparison experiments with two state-of-the-art unsupervised methods demonstrate the superiority of our method. We also prove the effectiveness and generality of our method on real-world light-field images.
Memory- and Communication-Aware Model Compression for Distributed Deep Learning Inference on IoT

Model compression has emerged as an important area of research for deploying deep learning models on Internet-of-Things (IoT). However, for extremely memory-constrained scenarios, even the compressed models cannot fit within the memory of a single device and, as a result, must be distributed across multiple devices. This leads to a distributed inference paradigm in which memory and communication costs represent a major bottleneck. Yet, existing model compression techniques are not communication-aware. Therefore, we propose Network of Neural Networks (NoNN), a new distributed IoT learning paradigm that compresses a large pretrained ‘teacher’ deep network into several disjoint and highly-compressed ‘student’ modules, without loss of accuracy. Moreover, we propose a network science-based knowledge partitioning algorithm for the teacher model, and then train individual students on the resulting disjoint partitions. Extensive experimentation on five image classification datasets, for user-defined memory/performance budgets, show that NoNN achieves higher accuracy than several baselines and similar accuracy as the teacher model, while using minimal communication among students. Finally, as a case study, we deploy the proposed model for CIFAR-10 dataset on edge devices and demonstrate significant improvements in memory footprint (up to 24×), performance (up to 12×), and energy per node (up to 14×) compared to the large teacher model. We further show that for distributed inference on multiple edge devices, our proposed NoNN model results in up to 33× reduction in total latency w.r.t. a state-of-the-art model compression baseline.
An Efficient Outsourced Privacy Preserving Machine Learning Scheme With Public Verifiability

Cloud computing has been widely applied in numerous applications for storage and data analytics tasks. However, cloud servers engaged through a third party cannot be fully trusted by multiple data users. Thus, security and privacy concerns become the main obstructions to use machine learning services, especially with multiple data providers. Additionally, some recent outsourcing machine learning schemes have been proposed in order to preserve the privacy of data providers. Yet, these schemes cannot satisfy the property of public verifiability. In this paper, we present an efficient privacy-preserving machine learning scheme for multiple data providers. The proposed scheme allows all participants in the system model to publicly verify the correctness of the encrypted data. Furthermore, a unidirectional proxy re-encryption (UPRE) scheme is employed to reduce the high computational costs along with multiple data providers. The cloud server embeds noise in the encrypted data, allowing the analytics to apply machine learning techniques and preserve the privacy of data providers’ information. The results and experiments tests demonstrate that the proposed scheme has the ability to reduce computational costs and communication overheads.
Dynamic Recursive Neural Network

This paper proposes the dynamic recursive neural network (DRNN), which simplifies the duplicated building blocks in deep neural network. Different from forwarding through different blocks sequentially in previous networks, we demonstrate that the DRNN can achieve better performance with fewer blocks by employing block recursively. We further add a gate structure to each block, which can adaptively decide the loop times of recursive blocks to reduce the computational cost. Since the recursive networks are hard to train, we propose the Loopy Variable Batch Normalization (LVBN) to stabilize the volatile gradient. Further, we improve the LVBN to correct statistical bias caused by the gate structure. Experiments show that the DRNN reduces the parameters and computational cost and while outperforms the original model in term of the accuracy consistently on CIFAR-10 and ImageNet-1k. Lastly we visualize and discuss the relation between image saliency and the number of loop time.
Handling concept drift via model reuse

In many real-world applications, data are often collected in the form of a stream, and thus the distribution usually changes in nature, which is referred to as concept drift in the literature. We propose a novel and effective approach to handle concept drift via model reuse, that is, reusing models trained on previous data to tackle the changes. Each model is associated with a weight representing its reusability towards current data, and the weight is adaptively adjusted according to the performance of the model. We provide both generalization and regret analysis to justify the superiority of our approach. Experimental results also validate its efficacy on both synthetic and real-world datasets.
Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating

This paper proposes a new fine-grained dynamic pruning technique for CNN inference, named channel gating, and presents an accelerator architecture that can effectively exploit the dynamic sparsity. Intuitively, channel gating identifies the regions in the feature map of each CNN layer that contribute less to the classification result and turns off a subset of channels for computing the activations in these less important regions. Unlike static network pruning, which removes redundant weights or neurons prior to inference, channel gating exploits dynamic sparsity specific to each input at run time and in a structured manner. To maximize compute savings while minimizing accuracy loss, channel gating learns the gating thresholds together with weights automatically through training. Experimental results show that the proposed approach can significantly speed up state-of-the-art networks with a marginal accuracy loss, and enable a trade-off between performance and accuracy. This paper also shows that channel gating can be supported with a small set of extensions to a CNN accelerator, and implements a prototype for quantized ResNet-18 models. The accelerator shows an average speedup of 2.3× for ImageNet when the theoretical FLOP reduction is 2.8×, indicating that the hardware can effectively exploit the dynamic sparsity exposed by channel gating.