Reviewerly/exHarmony huggingface dataset:
On The Stability of Polynomial Spectral Graph Filters

Spectral graph filters are a key component in state-of-the-art machine learning models used for graph-based learning, such as graph neural networks. For certain tasks stability of the spectral graph filters is important for learning suitable representations. Understanding the type of structural perturbation to which spectral graph filters are robust lets us reason as to when we may expect them to be well suited to a learning task. In this work, we first prove that polynomial graph filters are stable with respect to the change in the normalised graph Laplacian matrix. We then show empirically that properties of a structural perturbation, specifically the relative locality of the edges removed in a binary graph, effect the change in the normalised graph Laplacian. Together, our results have implications on designing robust graph filters and representations under structural perturbation.
I walk, therefore I am: a multidimensional study on the influence of the locomotion method upon presence in virtual reality

Abstract A defining virtual reality (VR) metric is the sense of presence, a complex, multidimensional psychophysical construct that represents how intense is the sensation of actually being there, inside the virtual environment (VE), forgetting how technology mediates the experience. Our paper explores how locomotion influences presence, studying two different ways of artificial movement along the VE: walking-in-place (through head bobbing detection) and indirect walking (through touchpad). To evaluate that influence, a narrative-neutral maze was created, from where 41 participants (N=41) had to escape. Measuring presence is a controversial topic since there is not a single, objective measure but a wide range of metrics depending on the different theoretical basis. For this reason, we have used for the first time, representative metrics from all three traditional dimensions of presence: subjective presence (SP) (self-reported through questionnaires), behavioral presence (BP) (obtained from unconscious reactions while inside the VE), and physiological presence (PP) [usually measured using heart rate or electrodermal activity (EDA)]. SP was measured with the ITC-SOPI questionnaire, BP by collecting the participants’ reactions, and PP by using a bracelet that registered EDA. The results show two main findings: (i) There is no correlation between the different presence metrics. This opens the door to a simpler way of measuring presence in an objective, reliable way. (ii) There is no significant difference between the two locomotion techniques for any of the three metrics, which shows that the authenticity of VR does not rely on how you move within the VE.
Speaker Awareness for Speech Emotion Recognition

&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;&lt;span&gt;The idea of recognizing human emotion through speech (SER) has recently received considerable attention from the research community, mostly due to the current machine learning trend. Nevertheless, even the most successful methods are still rather lacking in terms of adaptation to specific speakers and scenarios, evidently reducing their performance when compared to humans. In this paper, we evaluate a largescale machine learning model for classification of emotional states. This model has been trained for speaker iden- tification but is instead used here as a front-end for extracting robust features from emotional speech. We aim to verify that SER improves when some speak- er&lt;/span&gt;&lt;span&gt;’&lt;/span&gt;&lt;span&gt;s emotional prosody cues are considered. Experiments using various state-of- the-art classifiers are carried out, using the Weka software, so as to evaluate the robustness of the extracted features. Considerable improvement is observed when comparing our results with other SER state-of-the-art techniques.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
Unsupervised Speaker Adaptation Using Attention-Based Speaker Memory for End-to-End ASR

We propose an unsupervised speaker adaptation method inspired by the neural Turing machine for end-to-end (E2E) automatic speech recognition (ASR). The proposed model contains a memory block that holds speaker i-vectors extracted from the training data and reads relevant i-vectors from the memory through an attention mechanism. The resulting memory vector (M-vector) is concatenated to the acoustic features or to the hidden layer activations of an E2E neural network model. The E2E ASR system is based on the joint connectionist temporal classification and attention-based encoder-decoder architecture. M-vector and i-vector results are compared for inserting them at different layers of the encoder neural network using the WSJ and TED-LIUM2 ASR benchmarks. We show that M-vectors, which do not require an auxiliary speaker embedding extraction system at test time, achieve similar word error rates (WERs) compared to i-vectors for single speaker utterances and significantly lower WERs for utterances in which there are speaker changes.
Superpixel Segmentation Via Convolutional Neural Networks with Regularized Information Maximization

We propose an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time. Our method generates superpixels via CNN from a single image without any labels by minimizing a proposed objective function for superpixel segmentation in inference time. There are three advantages to our method compared with many of existing methods: (i) leverages an image prior of CNN for superpixel segmentation, (ii) adaptively changes the number of superpixels according to the given images, and (iii) controls the property of superpixels by adding an auxiliary cost to the objective function. We verify the advantages of our method quantitatively and qualitatively on BSDS500 dataset.
On The Choice of Graph Neural Network Architectures

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals. With the development of graph networks, this setup has become a de facto benchmark for a significant body of research. Interestingly, several works have recently shown that in this particular setting, graph neural networks do not perform much better than predefined low-pass filters followed by a linear classifier. However, when learning from little data in a high-dimensional space, it is not surprising that simple and heavily regularized methods are near-optimal. In this paper, we show empirically that in settings with fewer features and more training data, more complex graph networks significantly outperform simple models, and propose a few insights towards the proper choice of graph network architectures. We finally outline the importance of using sufficiently diverse benchmarks (including lower dimensional signals as well) when designing and studying new types of graph neural networks.
A Support Vector Regression-Based Integrated Navigation Method for Underwater Vehicles

When Doppler velocity log (DVL) works in a complex underwater environment, it has the possibility of malfunction at any time, which will affect the positioning accuracy of underwater integrated navigation system (INS). In this work, the INS/DVL integrated navigation system model is established to deal with DVL malfunctions, and the support vector regression (SVR) algorithm is used to establish the velocity regression prediction model of DVL. An optimized grid search-genetic algorithm is used to select the best parameters of SVR. Simulations are designed to compare the results of SVR prediction model and isolating DVL during DVL failure. The semi-physical experiment is carried out to verify the validity and applicability of DVL velocity prediction model. The experimental results show that the INS/DVL integrated navigation system with the proposed model based on SVR performs better than the original integrated navigation system during DVL malfunction.
A Semantic Matching Strategy for Very Large Knowledge Bases Integration

Over the last few decades, data has assumed a central role, becoming one of the most valuable items in society. The exponential increase of several dimensions of data, e.g. volume, velocity, variety, veracity, and value, has led the definition of novel methodologies and techniques to represent, manage, and analyse data. In this context, many efforts have been devoted in data reuse and integration processes based on the semantic web approach. According to this vision, people are encouraged to share their data using standard common formats to allow more accurate interconnection and integration processes. In this article, the authors propose an ontology matching framework using novel combinations of semantic matching techniques to find accurate mappings between formal ontologies schemas. Moreover, an upper-level ontology is used as a semantic bridge. An implementation of the proposed framework is able to retrieve, match, and align ontologies. The framework has been evaluated with the state-of-the-art ontologies in the domain of cultural heritage and its performances have been measured by means of standard measures.
Power Modeling for Video Streaming Applications on Mobile Devices

In this paper, we derive an accurate power model for video streaming which we condense to the essential components contributing the most to the overall power consumption. As a use case, we choose mobile devices on the receiver side performing video streaming in broadcasting or end-to-end scenarios. In modeling, we consider the complete video streaming toolchain, which mainly consists of data acquisition, video processing, display, and audio handling. We compose an overall power model with the help of models from the literature and propose a dedicated feature selection approach to reveal the most important factors related to power consumption. The resulting models achieve mean estimation errors below 7.61%. Results from feature selection indicate that the display brightness, the bitrate, and the frame rate have the highest impact on the power consumption.
Graph Auto-Encoder for Graph Signal Denoising

Signal denoising is an important problem with a vast literature. Recently, signal denoising on graphs has received a lot of attention due to the increasing use of graph-structured signals. However, well-established signal denoising methods do not generalize to graph signals with irregular structures, while existing graph denoising methods do not capture well the abstract representations inherent in the signals. To bridge this gap, we propose to use graph convolutional neural network with a Kron-reduction-based pooling operator for denoising on graphs. The proposed model can effectively capture the irregular data structure and learn the underlying representations in the signals, leading to improved performance over existing methods in experiments involving real-world traffic signals.
Deep Joint Source-Channel Coding of Images with Feedback

We consider wireless transmission of images in the presence of channel output feedback, by introducing an autoencoder-based deep joint source-channel coding (JSCC) scheme. We achieve impressive results in terms of the end-to-end reconstruction quality for fixed length transmission, and in terms of the average delay for variable length transmission. To the best of our knowledge, this is the first practical JSCC scheme that can fully exploit channel output feedback, demonstrating yet another setting in which modern machine learning techniques can enable the design of new and efficient communication methods that surpass the performance of traditional structured coding schemes.
Fast Switch-Based Load Balancer Considering Application Server States

Large-scale services are generally hosted on multiple application servers to scale out in today's data centers. Load balancers distribute users' requests across these servers. Software load balancer and switch-based load balancer are two typical classes of load balancers. However, most of the existing mechanisms either exhibit high processing latency at load balancers or likely lead to unbalanced requests distribution without considering the disparity of the application servers. In this paper, we study how the disparity of application servers significantly impacts the response time of requests. A fast switch-based Load Balancer considering Application Server states (LBAS) then is proposed to minimize the processing latency at both load balancers and application servers. The data plane of LBAS is well designed to store millions of connections in limited storage capacity without violating per-connection consistency. Besides, a partial dynamic weighting algorithm based on the Ridge Regression theory is designed and implemented to decrease the processing latency at application servers. We implement LBAS using the P4 programming language and conduct a series of extensive experiments to evaluate the performance. The results demonstrate that the proposed LBAS mechanism significantly reduces the response time of requests compared with Uniform random, Static weight, and Spotlight in various scenarios.
Data Dieting in GAN Training

We investigate training Generative Adversarial Networks, GANs, with less data. Subsets of the training dataset can express empirical sample diversity while reducing training resource requirements, e.g., time and memory. We ask how much data reduction impacts generator performance and gauge the additive value of generator ensembles. In addition to considering stand-alone GAN training and ensembles of generator models, we also consider reduced data training on an evolutionary GAN training framework named Redux-Lipizzaner. Redux-Lipizzaner makes GAN training more robust and accurate by exploiting overlapping neighborhood-based training on a spatial 2D grid. We conduct empirical experiments on Redux-Lipizzaner using the MNIST and CelebA data sets.
Security analysis and secure channel-free certificateless searchable public key authenticated encryption for a cloud-based Internet of things

With the rapid development of informatization, an increasing number of industries and organizations outsource their data to cloud servers, to avoid the cost of local data management and to share data. For example, industrial Internet of things systems and mobile healthcare systems rely on cloud computing's powerful data storage and processing capabilities to address the storage, provision, and maintenance of massive amounts of industrial and medical data. One of the major challenges facing cloud-based storage environments is how to ensure the confidentiality and security of outsourced sensitive data. To mitigate these issues, He et al. and Ma et al. have recently independently proposed two certificateless public key searchable encryption schemes. In this paper, we analyze the security of these two schemes and show that the reduction proof of He et al.'s CLPAEKS scheme is incorrect, and that Ma et al.'s CLPEKS scheme is not secure against keyword guessing attacks. We then propose a channel-free certificateless searchable public key authenticated encryption (dCLPAEKS) scheme and prove that it is secure against inside keyword guessing attacks under the enhanced security model. Compared with other certificateless public key searchable encryption schemes, this scheme has higher security and comparable efficiency.
Non-contact strain evaluation for miniature tensile specimens of neutron-irradiated F82H by digital image correlation

The small specimen tensile test is commonly used as the basic test to evaluate the irradiation effect on the strength of fusion structural materials after irradiation, and a lot of attempts were made to qualify the data obtained by small size specimen, but the specific attention on the evaluation of strain were not satisfactory given. This study therefore aims to propose a novel non-contact strain measurement technique, based on digital image correlation (DIC) method, for reduced-activation ferritic/martensitic (RAFM) steel, e.g., F82H, as the leading candidate structural material of fusion in-vessel components in Japan. A flat rectangular miniature specimen, e.g., SS-J3 type, was adopted to evaluate non-irradiated and neutron-irradiated tensile properties. In the proposed DIC, very tiny surface machining flaws were recognized by the DIC computation program and utilized to set several pairs of the gauge end points with a fixed gauge length. In case of the room-temperature test, it was clearly demonstrated that this procedure could provide more precise and reliable data compared with the previous approach of the single-scan measurement under floated gauge length. Superior linearity was achieved and recognized DIC points of concern were traceable during the entire period of the test. By applying this technique, superior high-dose stability of elastic properties of F82H was first demonstrated.
From Marker to Markerless in Augmented Reality

Our article discusses existing methods in marker-based and markerless augmented reality and their evolution over time. Markers are the most optimal solution to solve the problem of calculating the pose for augmented reality and do not require powerful devices. Its simplicity, robustness and efficiency have a great advantage; the objects to detect are provided to the application and require to be always visible by the sensor. On the other hand, markerless augmented reality detects objects or characteristic points of a scene without any prior knowledge of the environment. This mechanism is more difficult to implement because it implements algorithms that are expensive in terms of computation time. The appearance and improvement of new devices have made it possible to exploit this technology and to detach from the presence of a marker in a scene and emerge to markerless methods.
Embedded Night-Vision System for Pedestrian Detection

Assistive vision-based solutions for the driver extend the capabilities of human vision and support safe travel. Unfortunately, their widespread usage is generally limited to expensive cars. Interestingly, a high price is most likely a derivative of the costs incurred in the research instead of the value of hardware components. In the article we show that a mobile system for pedestrian detection in severe lighting conditions can be build using state of the art algorithms and widely available hardware. The proposed night-vision system for pedestrian detection processes thermal images using a proprietary ODROID XU4 microcomputer under Ubuntu MATE operating system. We applied a cascade object detector for the task of human silhouette detection in context of thermal imagery and contrasted the results with the state of the art deep learning approach. The experiments conducted prove effectiveness of the proposed solution.
Accelerating Distributed Deep Learning By Adaptive Gradient Quantization

To accelerate distributed deep learning, gradient quantization technique is widely used to reduce the communication cost. However, the existing quantization schemes suffer from either model accuracy degradation or low compression ratio (arisen from a redundant setting of quantization level or high overhead in determining the level). In this work, we propose a novel adaptive quantization scheme (AdaQS) to explore the balance between model accuracy and quantization level. AdaQS determines the quantization level automatically according to gradient's mean to standard deviation ratio (MSDR). Then, to reduce the quantization overhead, we employ a computationally-friendly way of moment estimation to calculate the MSDR. Finally, theoretical analysis of AdaQS's convergence is conducted for non-convex objectives. Experiments demonstrate that AdaQS performs excellently on very deep model GoogleNet with 2.55% accuracy improvement relative to vanilla SGD and achieves 1.8x end-to-end speedup on AlexNet in a distributed cluster with 4*4 GPUs.
Noniterative Sparse LS-SVM Based on Globally Representative Point Selection

A least squares support vector machine (LS-SVM) offers performance comparable to that of SVMs for classification and regression. The main limitation of LS-SVM is that it lacks sparsity compared with SVMs, making LS-SVM unsuitable for handling large-scale data due to computation and memory costs. To obtain sparse LS-SVM, several pruning methods based on an iterative strategy were recently proposed but did not consider the quantity constraint on the number of reserved support vectors, as widely used in real-life applications. In this article, a noniterative algorithm is proposed based on the selection of globally representative points (global-representation-based sparse least squares support vector machine, GRS-LSSVM) to improve the performance of sparse LS-SVM. For the first time, we present a model of sparse LS-SVM with a quantity constraint. In solving the optimal solution of the model, we find that using globally representative points to construct the reserved support vector set produces a better solution than other methods. We design an indicator based on point density and point dispersion to evaluate the global representation of points in feature space. Using the indicator, the top globally representative points are selected in one step from all points to construct the reserved support vector set of sparse LS-SVM. After obtaining the set, the decision hyperplane of sparse LS-SVM is directly computed using an algebraic formula. This algorithm only consumes O(N2) in computational complexity and O(N) in memory cost which makes it suitable for large-scale data sets. The experimental results show that the proposed algorithm has higher sparsity, greater stability, and lower computational complexity than the traditional iterative algorithms.
Code-Switched Speech Synthesis Using Bilingual Phonetic Posteriorgram with Only Monolingual Corpora

Synthesizing fluent code-switched (CS) speech with consistent voice using only monolingual corpora is still a challenging task, since language alternation seldom occurs during training and the speaker identity is directly correlated with language. In this paper, we present a bilingual phonetic posteriorgram (PPG) based CS speech synthesizer using only monolingual corpora. The bilingual PPG is used to bridge across speakers and languages, which is formed by stacking two monolingual PPGs extracted from two monolingual speaker-independent speech recognition systems. It is assumed that bilingual PPG can represent the articulation of speech sounds speaker-independently and captures accurate phonetic information of both languages in the same feature space. The proposed model first extracts bilingual PPGs from training data. Then an encoder- decoder based model is used to learn the relationship between input text and bilingual PPGs, and the bilingual PPGs are mapped to acoustic features using bidirectional long-short term memory based model conditioned on speaker embedding to control speaker identity. Experiments validate the effectiveness of the proposed model in terms of speech intelligibility, audio fidelity and speaker consistency of the generated code-switched speech.
A Regularized Attention Mechanism for Graph Attention Networks

Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes the attention mechanism to efficiently learn continuous vector representations for semi-supervised learning problems. In this paper, we perform a detailed analysis of GAT models, and present interesting insights into their behavior. In particular, we show that the models are vulnerable to heterogeneous rogue nodes and hence propose novel regularization strategies to improve the robustness of GAT models. Using benchmark datasets, we demonstrate performance improvements on semi-supervised learning, using the proposed robust variant of GAT.